Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach

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The present study introduces a robust Performance Measurement System (PMS) incorporating Network Data Envelopment Analysis (NDEA) to address efficiency challenges in multi-stage manufacturing systems. Methods The study employs a case study approach within the pharmaceutical industry to reveal the pragmatic application of NDEA, which serves as the primary analytical instrument for evaluating performance across diverse production stages. Focusing on the production processes of intravenous (IV) sets, the research aims to highlight how NDEA disaggregates interconnected processes and quantify efficiency measures to pinpoint sources of inefficiencies in particular production stages and actionable insights for operational improvement. The analysis also explores the trade-off between model complexity and discrimination power as the number of stages increases. Results First, the NDEA-based PMS provides insights to address specific process inefficiencies on the shop floor, providing strategic insights for process improvement. Second, despite its power in pinpointing the source of inefficiency, modelling a process-based PMS faces a challenge as increasing the number of stages in the model presents a trade-off between the accuracy and discrimination power of the NDEA model. Conclusions This study holds significance within the broader field of performance measurement and efficiency analysis by bridging theoretical modelling and practical implementation. It advances existing knowledge through the integration of NDEA into a process-based PMS, offering a novel analytical framework for multi-stage manufacturing systems. By examining the trade-off between model complexity and discrimination power, this research contributes new methodological insights and extends the applicability of NDEA in real-world industrial settings. The framework offers managers actionable guidance for optimizing multi-stage manufacturing operations and contributes novel insights into the methodological behaviour of NDEA. Ultimately, this work strengthens the linkage between performance measurement theory and industrial practice, positioning NDEA as a valuable tool for continuous improvement in manufacturing systems. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-710/v2", "name": "Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing:..." } } ] } Home Browse Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing:... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Hanoum S and Shubbak M. Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.12688/f1000research.166387.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Case Study Revised Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] Syarifa Hanoum https://orcid.org/0000-0001-8999-2429 1,2 , Mahmood Shubbak https://orcid.org/0000-0002-1400-2790 2 Syarifa Hanoum https://orcid.org/0000-0001-8999-2429 1,2 , Mahmood Shubbak https://orcid.org/0000-0002-1400-2790 2 PUBLISHED 04 Dec 2025 Author details Author details 1 Department of Business Management, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia 2 Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, Muscat Governorate, 123, Oman Syarifa Hanoum Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation Mahmood Shubbak Roles: Conceptualization, Validation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Manufacturing inefficiencies result in substantial financial losses for global industries. The present study introduces a robust Performance Measurement System (PMS) incorporating Network Data Envelopment Analysis (NDEA) to address efficiency challenges in multi-stage manufacturing systems. Methods The study employs a case study approach within the pharmaceutical industry to reveal the pragmatic application of NDEA, which serves as the primary analytical instrument for evaluating performance across diverse production stages. Focusing on the production processes of intravenous (IV) sets, the research aims to highlight how NDEA disaggregates interconnected processes and quantify efficiency measures to pinpoint sources of inefficiencies in particular production stages and actionable insights for operational improvement. The analysis also explores the trade-off between model complexity and discrimination power as the number of stages increases. Results First, the NDEA-based PMS provides insights to address specific process inefficiencies on the shop floor, providing strategic insights for process improvement. Second, despite its power in pinpointing the source of inefficiency, modelling a process-based PMS faces a challenge as increasing the number of stages in the model presents a trade-off between the accuracy and discrimination power of the NDEA model. Conclusions This study holds significance within the broader field of performance measurement and efficiency analysis by bridging theoretical modelling and practical implementation. It advances existing knowledge through the integration of NDEA into a process-based PMS, offering a novel analytical framework for multi-stage manufacturing systems. By examining the trade-off between model complexity and discrimination power, this research contributes new methodological insights and extends the applicability of NDEA in real-world industrial settings. The framework offers managers actionable guidance for optimizing multi-stage manufacturing operations and contributes novel insights into the methodological behaviour of NDEA. Ultimately, this work strengthens the linkage between performance measurement theory and industrial practice, positioning NDEA as a valuable tool for continuous improvement in manufacturing systems. READ ALL READ LESS Keywords Network Data Envelopment Analysis, NDEA, Performance Measurement, Efficiency, Process Improvement, Decision-Making, Pharmaceutical Industry. Corresponding Author(s) Mahmood Shubbak ( [email protected] ) Close Corresponding author: Mahmood Shubbak Competing interests: No competing interests were disclosed. Grant information: This research received partial support from internal funding allocated in 2023 by the Department of Business Management at Institut Teknologi Sepuluh Nopember in Surabaya, Indonesia, particularly during the initial phase and fieldwork (case study research). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Hanoum S and Shubbak M. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Hanoum S and Shubbak M. Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.12688/f1000research.166387.2 ) First published: 18 Jul 2025, 14 :710 ( https://doi.org/10.12688/f1000research.166387.1 ) Latest published: 04 Dec 2025, 14 :710 ( https://doi.org/10.12688/f1000research.166387.2 ) Revised Amendments from Version 1 This revised version of the article incorporates substantial improvements in response to reviewer feedback, resulting in a more rigorous, transparent, and contextually grounded manuscript. The abstract has been strengthened with an explicit statement of the paper’s significance within the wider fields of performance measurement and efficiency analysis, clarifying the contribution of integrating Network DEA (NDEA) into a process-based performance measurement system (PMS). The introduction and literature review have been expanded with recent studies (including 2024–2025 publications) to ensure stronger engagement with current research, while citation formatting has been standardized for clarity and consistency. Major methodological clarifications have been added. The case study is now explicitly framed as an illustrative example designed to demonstrate the operational feasibility of an NDEA-based PMS rather than to support statistical generalization. Limitations related to sample size, statistical inference, and the unavailability of proprietary data are clearly acknowledged. The justification for the three-stage NDEA model has been strengthened by integrating explicit model selection criteria and insights from production engineers, ensuring closer alignment with real-world production flows. To enhance transparency, all inputs, outputs, and intermediate variables are now fully listed and described, and additional details have been added to contextualize the production environment, performance gaps, and the involvement of managerial stakeholders. The Results and Discussion section has been restructured to prevent overclaiming and to situate findings more clearly within existing literature, with conclusions now presented as preliminary and context-specific. Presentation quality has also been improved through comprehensive proofreading, standardized notation, and clearer figures and tables. Collectively, these revisions enhance the manuscript’s methodological rigour, contextual detail, and clarity while maintaining its core contribution: a practical framework for integrating NDEA into performance measurement in multi-stage manufacturing systems. This revised version of the article incorporates substantial improvements in response to reviewer feedback, resulting in a more rigorous, transparent, and contextually grounded manuscript. The abstract has been strengthened with an explicit statement of the paper’s significance within the wider fields of performance measurement and efficiency analysis, clarifying the contribution of integrating Network DEA (NDEA) into a process-based performance measurement system (PMS). The introduction and literature review have been expanded with recent studies (including 2024–2025 publications) to ensure stronger engagement with current research, while citation formatting has been standardized for clarity and consistency. Major methodological clarifications have been added. The case study is now explicitly framed as an illustrative example designed to demonstrate the operational feasibility of an NDEA-based PMS rather than to support statistical generalization. Limitations related to sample size, statistical inference, and the unavailability of proprietary data are clearly acknowledged. The justification for the three-stage NDEA model has been strengthened by integrating explicit model selection criteria and insights from production engineers, ensuring closer alignment with real-world production flows. To enhance transparency, all inputs, outputs, and intermediate variables are now fully listed and described, and additional details have been added to contextualize the production environment, performance gaps, and the involvement of managerial stakeholders. The Results and Discussion section has been restructured to prevent overclaiming and to situate findings more clearly within existing literature, with conclusions now presented as preliminary and context-specific. Presentation quality has also been improved through comprehensive proofreading, standardized notation, and clearer figures and tables. Collectively, these revisions enhance the manuscript’s methodological rigour, contextual detail, and clarity while maintaining its core contribution: a practical framework for integrating NDEA into performance measurement in multi-stage manufacturing systems. See the authors' detailed response to the review by Kassu Jilcha See the authors' detailed response to the review by pravin Ullagaddi See the authors' detailed response to the review by Rashed Ahmed READ REVIEWER RESPONSES Introduction In today’s rapidly evolving business landscape, the fierce competition for innovative products and services necessitates a focus on uniqueness to achieve success in both domestic and international markets ( Aziz et al., 2022 ; Handri et al., 2021 , Shubbak, 2018 ). As companies strive to differentiate themselves, they face the daunting reality that manufacturing inefficiencies drain billions of dollars from global industries annually. This financial burden underscores the pressing need for robust performance measurement tools, which are crucial for enhancing competitiveness and fostering the seeds of innovation necessary for future success ( Mio et al., 2022 ). Performance measurement is a vital process for organizations seeking to evaluate efficiency and effectiveness, aligning operational activities with strategic goals ( Dahinine et al., 2024 ; Melnyk et al., 2014 ; Neely et al., 2002 ; Pujotomo et al., 2022 ). Robust and flexible performance measurement tools are not just administrative necessities, they are strategic enablers that help organizations navigate financial burdens, enhance competitiveness, and foster the innovation and market dynamics needed for future success ( Calik, 2024 ; Xing et al., 2025 ; Xu & Zhu, 2024 ). In the manufacturing sector, where process efficiency is critical, Performance Measurement Systems (PMS) are pivotal in tracking, evaluating, and enhancing performance, to ensure the production process is in a cost-effective manner ( Hanoum, 2021 ; Hanoum & Islam, 2021 ; Rodríguez et al., 2024 ). Manufacturing firms should go above and beyond to achieve their strategic goals and increase their performance ( Azizi et al., 2025a , b ; Handri et al., 2021 ). Despite their recognized significance, current PMS frameworks frequently fail to deliver practical guidance for operationalizing performance indicators at the process level. Established models, such as the Balanced Scorecard ( Kaplan & Norton, 1996 ), the Baldrige Excellence Framework (BEF) ( Arif, 2007 ), and the European Foundation for Quality Management (EFQM) model ( Doeleman et al., 2014 ), highlight strategic alignment but often lack comprehensive methodologies for implementing performance measures in complex, multi-stage manufacturing contexts ( Neely et al., 2002 ; Van Looy & Shafagatova, 2016 ). The current PMS frameworks also lack guidance on how process performance measures are chosen and operationalized in practice. Recent studies have highlighted that most PMS frameworks remain focused on high-level, strategic, and financial indicators, offering limited capability to capture the dynamic interactions and process-level performance inherent in complex production systems ( Ma et al., 2025 ; Tavassoli, 2025 ; S. Wu et al., 2025 ; Ye et al., 2025 ). Without integrating process-based and data-driven perspectives, organizations struggle to translate strategic objectives into actionable operational improvements. These observations underscore the pressing need for refined analytical approaches capable of systematically evaluating multi-stage operations, identifying stage-specific inefficiencies, and providing granular insights to guide process optimization. This gap underscores the need for frameworks that align with strategic objectives and provide in-depth operational processes tailored to specific environments. Additionally, it underlines the need for advanced analytical tools that quantify efficiency and deliver detailed insights into specific process-related inefficiencies. Network Data Envelopment Analysis (NDEA) presents a robust framework by representing manufacturing operations as interlinked processes, thus capturing inputs and outputs across multiple stages of production. Unlike conventional Data Envelopment Analysis (DEA), which often treats production systems as monolithic entities or ‘black boxes’, NDEA disaggregates these systems. This enables a more granular examination of inefficiencies, allowing for the identification of targeted areas for enhancement ( Färe & Primont, 1984 ; Tone & Tsutsui, 2009 ). By integrating NDEA into a process-based PMS, organizations can bridge the gap between strategic intent and operational execution, offering a practical and analytically rigorous framework for multi-stage manufacturing environments. While existing research has demonstrated the utility of NDEA for benchmarking and efficiency evaluation, there remains a lack of studies applying NDEA within single enterprises to develop a process-based PMS ( Castelli et al., 2010 ; Kao, 2014 ; Rachmad et al., 2024 ). Additionally, the impact of increasing stages on the discrimination power of NDEA models remains underexplored. Addressing these gaps, this study investigates the following research questions: ▪ The practical application of NDEA in real-world settings faces numerous challenges ( J. S. Liu et al., 2013 ; Paradi & Sherman, 2014 ). To address this, the study asks: How can a NDEA-based PMS be practically implemented to improve multi-stage manufacturing processes? (RQ1). ▪ Several DEA studies have analysed the internal structure of Decision-Making Units (DMUs) and found that NDEA has a stronger discrimination power compared to classical DEA ( Castelli et al., 2010 ). This study aims to verify and expand on this theory by posing the question: How does the number of stages in a NDEA model affect its discrimination power? (RQ2). To answer these questions, this article presents a case study focused on the production line of a pharmaceutical company’s intravenous (IV) sets, exemplifying the intricacies of multi-stage manufacturing, featuring a combination of manual and automated processes. The case study is intentionally illustrative; the goal is to demonstrate the feasibility and practical utility of a NDEA-based PMS rather than to generate statistically generalizable results. Using a case study of intravenous (IV) set production, the research illustrates how NDEA can be operationalized to: • Provide a practical framework for routine efficiency monitoring and process improvement. • Identify stage-specific inefficiencies as the focus of process improvement. • Evaluate trade-offs between model granularity and discrimination power. By embedding model selection criteria, incorporating stage validation with production engineers, and accounting for undesirable outputs, the study provides a robust methodology for implementing NDEA in operational settings. In doing so, it bridges the theoretical development of network DEA with practical performance management needs in complex manufacturing environments. The structure of the article is organized as follows: the next section provides a literature review on NDEA, tracing its evolution from DEA to NDEA, discussing fundamental concepts, and detailing the selection of NDEA models. A section on methodology follows, presenting the stages of the research. The subsequent section presents a case study that illustrates the application of NDEA within an IV sets production line, in a pharmaceutical company. Following this, we explore the proposed practical NDEA-based performance management system (PMS) derived from the case study. The concluding sections summarize the key findings, draw conclusions, and make recommendations for future research. Literature review: Network data envelopment analysis From DEA to NDEA Data Envelopment Analysis (DEA) is a widely used methodology for evaluating efficiency across decision-making units (DMUs) based on inputs and outputs. However, its “black-box” approach often fails to capture the complexities of multi-stage systems where intermediate outputs are significant ( Färe & Primont, 1984 ; Seiford & Zhu, 1999 ). To address this limitation, Network Data Envelopment Analysis (NDEA) extends DEA by modelling the internal structure of DMUs, providing a more granular assessment of efficiency ( Tone & Tsutsui, 2009 ). NDEA offers unique advantages over classical DEA, including the ability to disaggregate multi-stage processes into sub-processes, enabling a detailed evaluation of inefficiencies ( Färe & Primont, 1984 ), and improve strategic decision-making by identifying performance bottlenecks across stages ( Kao, 2014 ). Table 1 summarizes key theoretical advancements of NDEA compared to DEA. Table 1. The key theoretical advancements of NDEA compared to DEA. Aspect DEA NDEA System Structure Treats DMUs as “black boxes” without internal process analysis. Models interconnected sub-processes within DMUs. Intermediate Outputs Does not account for intermediate products or services. Explicitly incorporates intermediate outputs between stages. Application Scope Primarily used for single-stage or static systems. Tailored for multi-stage, dynamic manufacturing systems. Recent literature demonstrates that the application of network-based efficiency measurement tools has expanded significantly across diverse industrial contexts, demonstrating its capability to assess multi-stage efficiency and support process-level decision-making. For example, Vogt et al. (2025) applied NDEA in a steel manufacturing company to benchmark efficiency in bar and profile rolling processes, identifying stage-specific inefficiencies and providing actionable insights for operational improvement. Additionally, NDEA has been integrated with machine learning to evaluate supply chain sustainability, demonstrating the effectiveness of hybrid approaches ( Koushki & Naghdehforoushha, 2025 ). Furthermore, Ahmadi et al. (2024) proposed an NDEA model capable of handling fuzzy data, offering a robust framework for measuring efficiency under uncertainty and complex production conditions. In essence, the shift from DEA to NDEA reflects a broader conceptual and practical progression: from measuring overall efficiency to understanding the dynamics of process-level performance and generating actionable insights that drive improvements. As industrial systems grow more complex and data-rich, NDEA and its adaptations will be crucial for informed, stage-specific decision-making. The basic concept of NDEA Färe and Primont (1984) initiated the exploration of the ‘black-box’ system of classical DEA and was followed by other scholars ( Seiford & Zhu, 1999 ; Wang et al., 1997 ). Figure 1 illustrates the interactions among inputs (x i ), outputs (y r ), and intermediate factors (z kh ) in two-stage manufacturing operations. S (k,h) represents the number of intermediate measures passing from the k th process to the h th process. Process 1 has input (x 1 ) and the output of process 1, which is called intermediate output (z 12 ), becomes the input in process 2. In addition to z 12 , process 2 has a supplementary input (x 2 ) that yields an output (y 2 ). Given that process 2 is the final process, y 2 is the final output of the manufacturing operations. Figure 1. Interaction among input, output, and intermediate factors in two-stage manufacturing operations. The figure illustrates the flow of desirable and undesirable outputs, including intermediate products (z12) between two processes, and the role of inputs (x1, x2) and final output (y2). While the intermediate and final products are characterized as desirable outputs, a manufacturing process often generates undesirable outputs including rejected products and waste. Figure 1 presents the undesirable outputs produced by both processes (y 1 UD and y 2 UD ). Given that desirable outputs are expected to be maximized, undesirable outputs must be minimized. Some authors have incorporated undesirable outputs in the DEA model ( Chung et al., 1997 ; Scheel, 2001 ). Selections of the NDEA models (a) Distance, orientation, and scale assumptions Researchers may choose between the radial and non-radial NDEA models. The radial approach works under the proportionality assumption in the changes of inputs or outputs. Because in manufacturing operations, production factors such as labour, materials, and capital may not be changed proportionally, we choose the network slacks-based measure (NSBM) that operates the efficiency measurement based on input excess and output shortfall ( Tone & Tsutsui, 2009 ). The choice of orientation between input and output depends on whether the decision-makers’ focus is on controlling inputs or outputs to improve efficiency. A non-oriented approach, as adopted here, avoids subjective prioritization and allows the model to identify which inputs or outputs require improvement purely based on data. Scale assumptions are also critical ( Zuniga-Gonzalez et al., 2025 ). While variable returns to scale (VRS) accommodates differing economies of scale among decision-making units, constant returns to scale (CRS) are appropriate when evaluating operations within a single plant where scale effects are minimal. In DEA literature, the combination of non-orientation, non-radial, and VRS modelling is often used to enhance the relevance of frontier efficiency studies ( Avkiran, 2011 ). VRS is often highlighted for accommodating the differing economies of scale among Decision-Making Units (DMUs), where scale is not constant in nature ( Zuniga-Gonzalez et al., 2025 ). However, this study evaluates a manufacturing line across various production periods where economies of scale are not a concern. Aparicio & Santín (2025) further refines this understanding through the concept of global scale efficiency, emphasizing that CRS assumptions are justified when scale variation is not structurally embedded in the production system. Given these factors, the choice of a non-oriented, non-radial, CRS-NSBM is methodologically justified for multi-stage manufacturing operations within a single plant. This model effectively captures non-proportional slack adjustments, accommodates both desirable and undesirable outputs, and eliminates scale distortions, thereby providing reliable and actionable insights into inefficiencies specific to each stage of production. (b) The presence of undesirable outputs NDEA models maximise the outputs of a system to achieve the system’s optimum efficiency. Manufacturing environments, however, may generate undesirable outputs such as production wastes, rejected products, and pollution. Therefore, NDEA, which incorporates undesirable outputs in its mathematical model, is considered. We follow ( W. B. Liu et al., 2010 ) by applying this undesirability phenomenon to the NSBM approach given by Tone and Tsutsui (2009) as presented in Model 1. The extended model is characterised as NSBM-non-oriented-CRS with undesirable outputs. (1) Min ρ o ∗ = ∑ k = 1 K W k [ 1 − 1 m k ( ∑ i = 1 m k s i k − x io k ) ] ∑ k = 1 K W k [ 1 + 1 R k D + R k UD ( ( ∑ r k D = 1 R k D S r k + D y ro k D ) + ( ∑ r k UD = 1 R k UD S r k + UD y ro k UD ) ) ] Subject to ∑ j = 1 n x ij k λ j k − S i k − ≤ x io k , i = 1 , … , m k , k = 1 , … , K ∑ j = 1 n y r k D j k D λ j k − S r k + D = y r k D o k D , r k D = 1 , … , R k D , k = 1 , … , K ∑ j = 1 n y r k UD j k UD λ j k − S r k + UD = y r k UD o k UD , r k UD = 1 , … , R k UD , k = 1 , … , K ∑ j = 1 n ∑ s ( k , h ) = 1 S ( k , h ) z s ( k , h ) j ( k , h ) λ j k = ∑ j = 1 n ∑ s ( k , h ) = 1 S ( k , h ) z s ( k , h ) j ( k , h ) λ j h , ∀ ( k , h ) , λ j k , S r k + ≥ 0 The study’s sample size would be n DMUs (j = 1, …, n), which consist of K sub-processes (k = 1, …, K). The objective ρ o ∗ characterises the non-oriented efficiency score of DMUo (the subscript “o” represents the DMU under analysis), while w k in this function is denoted as a subjective weight of the kth sub-process. Because the subjectivity is avoided in this study’s decision-making process, the weights are set as 1.00 for all sub-processes. If ρ o ∗ = 1 and all input and output slacks are equal to zero, then the DMUo is efficient. The method decomposes the overall efficiency into divisional/process efficiency ( ρ k ), given in model (2), where s i k − ∗ and s r k + ∗ are the optimal input-slack and output-slack for model (1). All nomenclatures of models (1) and (2) are described in Table 2 . (2) ρ k = 1 − 1 m k ( ∑ i = 1 m k s i k − ∗ x io k ) 1 + 1 r k ( ∑ r = 1 r k s r k + ∗ y ro k ) , k = 1 , … , K Table 2. The nomenclature of model 1. Subscript “ o ” is related to the DMU which is under observation ρ o ∗ = The overall non-oriented efficiency score of DMU o . w k = The weight of the k th process/division determined by decision-makers. x ij k = The i th input, i = 1, …, m k , which corresponds to the k th ( k = 1, …, K ) process of the j th DMU ( j = 1, …, n ). y rj k = The r th output, r = 1, …, r k, which corresponds to the k th ( k = 1, …, K ) process of the j th DMU ( j = 1, …, n ). S r k + = Amount of slack related to the r th output of the k th ( k = 1, …, K ) process. z s ( k , h ) j ( k , h ) = An intermediate factor from the k th process to the h th process ( k ≠ h and k , h = 1, …, K). λ j k = The intensity vector corresponding to the k th process of the j th DMU. n = Number of DMUs. K = Number of processes/divisions. m k = Number of inputs corresponding to the k th process. r k D = The subscript corresponding to desirable outputs of the divisions/processes. R k D = Number of desirable outputs corresponding to the k th process. r k UD = The subscript corresponding to undesirable outputs of the divisions/processes. R k UD = Number of undesirable outputs corresponding to the k th process. s ( k , h ) = The subscript of intermediate measure from the k th process to h th process. S ( k , h ) = Number of intermediate measures passing from the k th process to h th process. ρ k = The divisional/process efficiency of the k th process. Recent research has advanced the treatment of intermediate and undesirable outputs, refining NDEA models to better capture complex network structures, dual-role factors, and process interdependencies. For example, Lotfi et al. (2023) applied an NDEA model to assess both desirable and undesirable outputs in the wheat supply chain, demonstrating enhanced accuracy in identifying stage-specific inefficiencies. Ma et al. (2025) proposed a network slack-based measure incorporating dual-role factors and undesirable outputs to evaluate supply chain performance, highlighting the practical relevance of NDEA in complex production networks. Yang et al. (2024) further demonstrate the flexibility of NDEA by incorporating shared resources, negative data, and undesirable outputs in a multi-stage airline efficiency context, highlighting its capacity to model interdependencies realistically. This development allows decision-makers to simultaneously optimize performance while reducing waste or other negative byproducts, thereby providing a more nuanced and actionable understanding of operational efficiency. Essentially, NDEA’s ability to model undesirable outputs transforms efficiency assessment into a more realistic and strategically valuable instrument for complex production systems. Methodology The research begins with a comprehensive literature review to identify the most appropriate Non-Directional Efficiency Analysis (NDEA) model for multi-stage manufacturing. The review covered radial and non-radial approaches, constant and variable returns to scale (CRS/VRS), and input-, output-, and non-oriented structures. Building on this synthesis, we adopted the combined non-radial Non-SBM (NSBM) non-oriented CRS model as the analytical foundation for this study, as it is well-suited for capturing slacks, handling undesirable outputs, and modelling internal product flows within a single plant. The study uses a specific criterion for selecting the preferred network model: it should minimize the number of efficient DMUs while capturing key stages of production. This approach balances simplicity and adequacy, enabling systematic comparisons of network configurations rather than relying on qualitative judgments. This criterion ensures that the desired structure balances parsimony with adequacy, and provides a systematic basis for comparing alternative network decompositions rather than relying solely on qualitative judgment. To pinpoint the optimal number of stages for our pharmaceutical production line, we explored network configurations ranging from 2 to a comprehensive 4-stage model. This intricate design process was undertaken in close collaboration with both the production manager and the supervisory team, ensuring that each stage was thoughtfully validated. Together, we analysed the state groupings, carefully considering how integrating specific stages could optimise efficiency and streamline our operations. The discussions were rich and detailed, reflecting our commitment to creating a production network that not only meets our standards but also enhances the overall workflow. The next phase of the research involved an in-depth case study in which the selected NDEA model and three-stage structure were applied to a pharmaceutical manufacturing system. It is important to note that this case study serves as an illustrative example to demonstrate the practical implementation of the proposed NDEA-based PMS. Due to the single-case design, statistical validation or generalization of the results is not applied, and the findings primarily highlight methodological feasibility and operational insights rather than inferential conclusions. The case study application enabled us to identify the practical challenges of implementing NDEA within a performance measurement system (PMS), including data collection, stage decomposition, and the handling of undesirable outputs. The case study results informed a set of managerial recommendations, specifying sources of inefficiency and stage-specific improvement targets across the production line. Drawing from these findings, we developed a practical framework—referred to as the NDEA-based PMS—to guide managers in applying NDEA in multi-stage manufacturing settings. This framework synthesizes methodological rigor, empirical insights, and practitioner expertise, offering a structured approach to designing performance indicators, diagnosing inefficiencies, and supporting continuous process improvement within complex manufacturing environments. Applying NDEA to measure performance of a pharmaceutical production process: A case study This case study illustrates the transformation of manufacturing operations through the application of the NDEA model. It explores the practical implementation of NDEA to establish a multi-stage Performance Measurement System (PMS) within a pharmaceutical production environment. The study aims to achieve two main objectives: developing a practical, process-based performance measurement framework for single-enterprise, multi-stage systems, and examining the trade-off between NDEA model complexity and discrimination power. The case study is illustrative in nature, and due to the single-case design, statistical validation is not applied. The pharmaceutical company occupies approximately 60,000 square meters. It promotes four product groups: intravenous sets (IV Sets), IV Solutions, Therapeutic Drugs, and Clinical Nutrition. The company dominates the market with a 70% share in basic solutions and medicines, catering to both domestic and international markets. Its operations adhere to high regulatory standards and stringent quality requirements, making efficiency and waste minimization crucial for maintaining competitiveness. This study focuses on the production line for IV sets, medical devices used to deliver intravenous fluids or perform blood transfusions. The IV set line was chosen for this study because it represents one of the highest-revenue product lines. This strategic selection is based on several important reasons: ▪ Strategic Importance: IV sets represent one of the company’s highest-revenue product lines, accounting for nearly 30% of total sales. Optimal performance in this line directly impacts overall profitability. ▪ Operational Complexity: The production of IV sets involves combination of machining and manual processes, which present operational challenges for executives when managing performance fluctuations. ▪ Process Variability: The IV-set line frequently experiences performance fluctuations due to machine downtime, material inconsistencies, and manual assembly errors. These challenges underscore the need for a robust performance measurement framework to identify inefficiencies and guide process improvements. ▪ Scalability and Relevance: Insights from the IV-set line can be generalized to other product lines within the company, as many share similar multi-stage production structures. The IV production line experiences significant performance fluctuations, with early analysis revealing concerning trends: PVC granulation waste rates of 30%-35%, indicating substantial material loss. Additionally, machine downtime is a persistent issue, accounting for 15% to 20% of scheduled production hours, disrupting workflow and hampering overall efficiency. Furthermore, the output from manual assembly processes shows significant variability, largely influenced by the skill and consistency of the operators involved. This mix of inefficiencies underscores the need for targeted improvements to boost productivity and reduce waste. The study examines a 12-month production period, chosen to capture operational variability across the full production cycle, including seasonal fluctuations in raw material supply, maintenance schedules, and staffing. The monthly aggregation aligns with the company’s internal reporting and corporate board review processes, providing both sufficient granularity and reliability for performance evaluation. Production engineers and line supervisors actively contributed to this study. Their participation included validating the NDEA model structure, confirming stage groupings, and reviewing the selected inputs, outputs, and intermediate variables for each production stage. While the NDEA analysis provided actionable insights and was shared with managers, it was not formally implemented in daily operational control during the study period. Nevertheless, preliminary discussions were initiated within the company to consider targeted improvements, such as reducing PVC granulation waste and optimizing machine schedules. These steps demonstrate the potential practical utility of the proposed NDEA-based PMS for guiding future process improvements. Process mapping IV-sets are produced across six interconnected workstations, as depicted in Figure 2 . These stages include: Figure 2. Process map of the intravenous (IV) set production process. This diagram outlines six interconnected workstations: PVC Granulation, Moulding Extruder, Moulding Injection, Manual Assembly, Automatic Assembly, and Final Assembly & Packaging. PVC Granulation (WS1): Conversion of raw Polyvinyl Chloride (PVC) resins into medical-grade granules. ▪ Moulding Extruder (WS2): Fabrication of sub-assembled tubes using the granulated PVC. ▪ Moulding Injection (WS3): Production of components such as drip chambers, spikes, and needle covers. ▪ Manual Assembly (WS4): Manual combination of sub-assembled tubes and additional components. ▪ Automatic Assembly (WS5): Automated assembly of finished IV-set components, including drip chambers. ▪ Final Assembly and Packaging (WS6): Integration of all components, final quality checks, and packaging. Each stage is interdependent, with outputs from one stage serving as inputs for the next. This multi-stage structure makes the IV-set line an ideal case for applying NDEA, which accounts for intermediate outputs and interconnected processes. NDEA variables and model specification In NDEA studies, model specification is crucial to list the performance assessment criteria. However, studies that have rationalized the essential variables for performance assessment are scarce ( Cook et al., 2014 ; Kishore et al., 2024 ). Once the process map is available, identifying inputs and outputs for each process becomes more straightforward when adopting the NDEA for manufacturing operations. The initial process involves consuming various PVC resins as raw materials to produce non-toxic medical-grade PVC granules. These granules serve as inputs for the subsequent moulding processes. In the second stage, moulding involves the addition of additional materials to create a range of components. The moulding extruder generates an assortment of sub-assembled tubes, whereas the moulding injection yields drip chamber components (e.g., the central part of drip chambers, spikes, needle covers, joints, and seals) for the following assembly process. Both PVC granulation and moulding processes require operators and machines. The assembly processes combine all the parts produced by the prior workstations. The assembly station comprises three sub-stations: automatic assembly, manual assembly, and final assembly. The automatic assembly combines components from moulding injection into the finished drip chamber unit. It is characterized as a one-man-one-machine workstation, where machine-hour or man-hour is used interchangeably. At the manual assembly workstation, sub-assembled tubes from the moulding extruder and additional components from suppliers are manually assembled, requiring only man-hours as input. The final assembly line involves a combination of machine and manual work, which is reflected in man-hours and machine-hours. Outputs from earlier stages, that serve as inputs to subsequent stages, are called intermediate factors. The last stage generates the final outputs. The ultimate product of this production line is the IV-Set, available in various sizes intended for the administration of nutrition, medication, and blood. In addition, the IV-Set production line yields waste or rejected outputs known as undesirable outputs. Another undesirable output refers to machine downtime that occurs in the PVC granulation, moulding extruder, and moulding injection workstations. Meanwhile, the assembling activities that utilize machines and equipment with lower breakdown risks render machine downtime - an insignificant factor in all assembly processes. A favourable DEA/NDEA model is typically characterized by its discrimination power. The discrimination power of a DEA model can be compromised when a massive number of inputs and outputs are used, mainly because a particular number of DMUs is considered efficient in specific scenarios ( Cook et al., 2014 ). As a rule of thumb, the number of DMUs should be at least twice the total number of inputs and outputs. Adhering to this guideline minimizes the correlation between variables and DEA/NDEA outputs, thereby enhancing the discriminating power of the model. According to Castelli et al. (2010) , discrimination power is higher in the NDEA model, compared to the classical model. In certain cases, it is possible for most or even all DMUs to be deemed inefficient. However, the idea that adding more stages to the NDEA model improves discrimination power is inconclusive. The existing literature does not provide clear guidance on how to divide a system into multi-stage and interconnected sub-systems, nor does it specify the optimal number of stages needed for particular manufacturing operations. To bridge these gaps, this paper outlines five scenarios aimed at identifying the most appropriate NDEA model for the IV-Set production system (refer to Figure 3 ). The nomenclature related to Figure 3 can be found in Table 3 . Figure 3. Interaction of inputs, intermediate factors, and outputs in five NDEA models. The five subfigures represent different decomposition levels of the production system: (A) classical DEA, (B) two-stage NDEA, (C) three-stage NDEA, (D) four-stage NDEA, and (E) six-stage NDEA. Table 3. The NDEA data corresponding to Figure 3 . Variables Definitions Unit of measures x 1 j lk Raw materials corresponding to the k th process of the j th DMU. Kilograms (kgs) x 2 j lk Man-hour corresponding to the k th process of the j th DMU. Hours (hrs) x 3 j lk Machine-hour corresponding to the k th process of the j th DMU. hrs x 4 j lk Additional components corresponding to the k th process of the j th DMU. Pieces (pcs) y 1 j lkUD Rejected outputs (undesirable output) corresponding to the k th process of the j th DMU. kgs y 2 j lkUD Machine downtime (undesirable output) corresponding to the k th process of the j th DMU. hrs z 1 j l ( k , h ) Intermediate outputs from the k th process to the h th process ( k ≠ h and k , h = 1, …, K). kgs z 2 j l ( k , h ) Intermediate outputs from the k th process to the h th process ( k ≠ h and k , h = 1, …, K). pcs y 1 j lkD Final output (desirable output) corresponding to the k th process of the j th DMU. pcs L Numerator index corresponding to the NDEA model/scenario under evaluation ( l = 1, …, 5). Referring to Figure 3 , Model (1) illustrates the classical DEA, where the production line is a ‘black-box’ system and the intermediate factors are dismissed. Model (2) shows the simplest form of NDEA (two-stage NDEA), where the manufacturing process of IV-Set is divided into production and assembly stages. In model (2), the intermediate factors flow between the production and the assembly stages. Model (3) expands on Model (2) by dissecting the production stage into PVC granulation and moulding after considering the intermediate factors and undesirable outputs that flow between the three stages. Unlike Model (2), Model (3) treats the additional materials required for moulding as a separate variable. Model (4) segregates the production system into four stages based on the responsibilities of the four supervisors in the IV-Set department. Each supervisor oversees one of the following four units: PVC granulation, moulding, assembly, and final assembly. Model (5) incorporates the six workstations outlined in the process map. It looks into the internal structure of all processes, which consist of the largest number of stages and NDEA variables. The technical correctness of each model was validated by conducting a comprehensive review by the research team and production supervisors. The focus of this validation is to ascertain that there was no significant process change throughout the one-year study period to maintain the high acceptability of the models. Data collection and model selection The following sub-section details the data source, the selection of five models, and the decision support system facilitated by NDEA for performance evaluation and process improvement. The data from 12 months, including inputs, outputs, and intermediate variables, were extracted from the company. The one-year monthly production period served as the DMUs to meet the requirements of the annual review conducted by the corporate board. The NDEA efficiency scores were computed for each production month, hence treating the IV-Set production system as a one-, two-, three-, four-, or six-stage production process. The objective is to identify a model that aligns with NDEA requirements and serves as a decision support system for process improvement. The models must exhibit acceptable discrimination power and provide accurate insights for process improvement. The basic descriptive statistics shown at the bottom of Table 4 demonstrate how effectively the models differentiated among the DMUs’ efficiency levels. Table 4. Efficiency scores for NDEA model scenarios. NO DMU 1-stage 2-stage 3-stage 4-stage 6-stage 1 01_23 1 1 1 1 1 2 02_23 0.376 0.526 0.518 0.571 1 3 03_23 0.173 0.228 0.290 0.289 0.292 4 04_23 0.136 1 1 1 1 5 05_23 0.180 0.324 0.313 0.325 0.343 6 06_23 0.383 0.411 0.443 0.459 0.418 7 07_23 1 1 1 1 1 8 08_23 1 1 1 1 1 9 09_23 0.590 0.693 0.642 0.613 1 10 10_23 1 1 1 1 1 11 11_23 1 1 1 1 1 12 12_23 0.323 0.343 0.469 1 1 Average efficiency score 0.597 0.710 0.723 0.771 0.838 Least efficiency score 0.136 0.228 0.290 0.289 0.292 Standard deviation 0.347 0.306 0.294 0.291 0.295 Number of efficient DMUs 5 6 6 7 9 Apart from assessing whether an increased number of stages in NDEA can enhance discrimination power, examining the five models determined the most suitable model for the IV-Set production line. The discrimination power of NDEA was assessed using the following statistics: average efficiency score, minimum efficiency score, number of efficient DMUs, and standard deviation, with lower values indicating greater discrimination power. The descriptive statistics revealed that Model (1) performed best in distinguishing the efficiency scores throughout the production period. However, selecting a single-stage DEA model is unsuitable for a process-based PMS that emphasizes internal processes. This approach provided insufficient information to uncover the specific stages and production network that demands improvements. Model (5) was the favoured model for process enhancement because it offered detailed insights by breaking down the production line into more processes than the other models. However, more stages in an NDEA model introduce additional variables that may affect discrimination power. Based on Table 3 , Model (5) exhibited the lowest discrimination power, as indicated by the highest average, the lowest efficiency values, the smallest standard deviation, and the highest number of efficient DMUs. Upon comparing Models (1), (2), and (3), the statistical results disclosed that Model (3) had lower discrimination power than Models (1) and (2) for two reasons. First, Model (3) recorded a higher average efficiency score and the lowest efficiency score when compared to Models (1) and (2). Second, Model (3) had a smaller standard deviation value than the other two. Nonetheless, the oversimplification inherent in the single- and two-stage Models (1) and (2) limited their efficacy in comprehending the production system. After considering the trade-offs, Model (3) was selected as the preferred model for several reasons. Given its moderate number of stages, Model (3) strikes a balance between the discrimination power required by NDEA and the necessary details for process improvement purposes. From the stance of PMS, Model (3) aligns with the parsimony principle, which emphasizes data collection and processing without excessive cost and time implications. Efficiency scores and peer groups The NDEA non-parametric method measures efficiency by assessing each criterion measure (weighted output/input) and constructing an envelopment frontier across all measures to ascertain that the observed data points lie on or below the frontier. The three-stage NDEA model ( Figure 3C ) was deployed in this case study. Scores were computed based on the 12-month production period. The efficiency scores (see Table 5 ) revealed that 50% of the production period fell below the production frontier. Table 5 presents the inefficient production months and their respective peer groups. A peer group refers to the efficient months with the most similar circumstances to each inefficient month concerning the input and output sets. For example, the peer group of production period 02_23 includes 07_23 and 11_23. Table 5. The efficiency score of the three-stage NDEA-based PMS. Work-stations Efficient DMUs Inefficient DMUs 01_23 04_23 07_23 08_23 10_23 11_23 02_23 03_23 05_23 06_23 09_23 12_23 Overall 1 1 1 1 1 1 0.518 0.290 0.313 0.443 0.642 0.469 PVC 1 1 1 1 1 1 0.475 0.332 0.327 0.512 0.782 0.599 Moulding 1 1 1 1 1 1 0.629 0.340 0.332 0.628 0.735 0.443 Assembly 1 1 1 1 1 1 0.665 0.395 0.471 0.394 0.612 0.674 Peer groups (efficient months) 07_23 11_23 07_23 07_23 07_23 11_23 07_23 11_23 07_23 Descriptive statistics of the production system Average efficiency score Least efficiency score Standard Deviation Overall 0.723 0.290 0.303 PVC 0.752 0.327 0.284 Moulding 0.759 0.332 0.277 Assembly 0.768 0.394 0.258 The classical DEA displayed the sources of inefficiency through input and output variables. Taking a step further, the NDEA method evaluated each stage along the network to determine the production process with the most significant impact on the overall efficiency of the manufacturing operations. More insights were captured from the NDEA results, particularly by examining the descriptive statistics (see bottom of Table 4 ). The PVC granulation stage recorded the lowest efficiency score, which solidified its status as the most inefficient stage and a prominent contributor to the overall inefficiency of the IV-Set manufacturing line. The standard deviation of its efficiency score was the largest, translating into considerable performance fluctuations over the studied production year. On the contrary, the assembly stage displayed the highest average efficiency score and minimal performance variability, further confirming its pivotal role in bolstering the overall production efficiency. Process improvement Referring to the efficiency scores, the NDEA produced slacks for each variable in the model to signify the shortfall of outputs or the excess of inputs that rendered a DMU inefficient. Besides, the NDEA offered improvement targets for each production factor (i.e., material, man-hour, and machine-hour) and output (i.e., machine downtime, rejected outputs, and good products). Improvement can manifest as a decrease in inputs and undesirable outputs or an increase in desirable outputs. For each category in Figure 4 , the first, second, and third bars represent inputs consumed or outputs generated at the PVC granulation, moulding, and assembly workstations. The fourth bar depicts the average potential improvement required for each category. Figure 4. Percentage of potential improvement for the production process. This bar chart visualizes potential reductions in input materials, man-hours, machine-hours, and undesirable outputs, as well as the necessary increase in good outputs to achieve optimal efficiency. Figure 4 illustrates the imperative need for the company to minimize input materials, man-hours, and machine-hours by 45.08%, 38.16%, and 44.28%, respectively. Moreover, a reduction of 45.83% for machine downtime and 29.51% for rejected outputs/waste appears to be crucial. To achieve 100% efficiency for the entire production system, a comprehensive approach involving cutbacks in all input factors and undesirable outputs, along with an increment of 31.28% in good outputs, is essential. The proposed framework of NDEA-based PMS Managers find the NDEA-based PMS to be effective in analysing the performance fluctuations of production factors and outputs in each production process. This sheds light on the impact of such fluctuations on the overall performance of the production line. Both the production manager and supervisors concurred that the NDEA model comprehensively addressed the essential measures related to medical device manufacturing operations. The model facilitated identifying inefficient processes and pinpointed the production factors or outputs that required enhancement. Decisions associated with process enhancement typically fall in the purview of the manufacturing head department or production line managers and supervisors. A post-study meeting with the company’s executive board emphasized the need for a generic framework to extend DEA applications in manufacturing. A generalized framework is essential for managers applying similar techniques across production lines or manufacturing companies. Aligning with PMS principles, the proposed framework consists of three main phases: design, implementation, and review (see Figure 5 ). Figure 5. Proposed NDEA-based PMS framework. The diagram outlines three main phases: Design (process mapping and model selection), Implementation (data collection and scoring), and Review (adjustments and benchmarking). In the design phase, the initiation involves process mapping, outlining the systematic flow of the manufacturing process from raw materials to finished products ( Lindsay et al., 2003 ). A process map visually portrays the systematic flow of sub-processes from start to finish ( Wilson, 2004 ). According to ( Sinclair & Zairi, 1995a , 1995b ) performance measures are classified into inputs, processes, and outputs. The NDEA-based PMS defines input and output factors for each subprocess involved in the production line, enabling efficiency evaluation, benchmarking, and process improvement. Regular assessments of the production system’s performance—daily, weekly, or monthly—are crucial during the implementation phase. Data collection precedes the calculation of efficiency scores using NDEA software. The model outcomes provide performance scores for each DMU, identifying top performers. For underperforming DMUs, the NDEA model pinpoints sub-processes causing inefficiencies. In the review phase, two scenarios arise: for major modifications affecting the entire manufacturing process, the cycle resets to the start of the framework. For minor process changes, revisiting the last two stages—implementation and review—is sufficient. Minor alterations involve reviewing improvement targets and benchmarking against peer groups to align with the best performers. Results and discussion 1. Efficiency scores and peer groups The study assessed the IV-set production line’s performance using five NDEA models, focusing on a three-stage model for practical insights. Due to its single-case design, formal statistical validation was not conducted. Efficiency scores showed that 50% of production months (DMUs) were inefficient, providing guidance for process improvement. Inefficient DMUs were benchmarked against efficient peers, such as comparing production months 02_23, 07_23, and 11_23 to target reductions in material waste, machine downtime, and operator-related issues. This highlights NDEA’s ability to identify specific inefficiencies, unlike classical DEA, which treats the production line as a “black box” ( Lotfi et al., 2023 ; Tone & Tsutsui, 2009 ). 2. Stage-specific insights The stage-level analysis uncovered substantial variation in efficiency across the production line, revealing the following patterns: • PVC Granulation (WS1): The least efficient stage, with the largest performance variability. Inefficiencies were driven primarily by raw material quality, machine downtime, and operator variability. • Moulding (WS2–3): Moderate efficiency with intermediate variability; inefficiencies were related to subassembly quality and coordination issues. • Assembly (WS4–6): Highest average efficiency and minimal variability, indicating a stabilizing effect on overall production. These insights provide practical diagnostic guidance for managers, emphasizing that improving WS1 would significantly enhance overall production efficiency. This finding aligns with prior manufacturing studies, which often show early-stage processes dominate total inefficiency ( Castelli et al., 2010 ; Ma et al., 2025 ). 3. Trade-offs between stage granularity and discrimination power A comprehensive comparison of all five NDEA formulations highlights an inherent methodological tension in network modelling. As models incorporate greater stage granularity to more accurately reflect the complexity of real production systems, they also risk introducing dimensionality challenges, redundancy across variables, and sensitivity to data variability. These factors can ultimately weaken a model’s ability to discriminate between efficient and inefficient DMUs. Conversely, more consolidated network structures may enhance discrimination but at the cost of losing important process-level insights. The results of this study show a clear trade-off between retaining detailed system representation and maintaining analytical robustness: • Six-stage NDEA: Provided detailed process-level insights but classified a large share of DMUs as efficient, limiting its ability to differentiate performance. • Three-stage NDEA: Achieved the most balanced performance, preserving essential granularity while still enabling meaningful differentiation across DMUs. • One- and two-stage NDEA: Oversimplified the production process, missing important intermediate transformations. These outcomes demonstrate that network granularity increases the variable count while the DMU sample size remains fixed, thereby inflating degrees of freedom and compressing the efficiency frontier, thereby reducing discrimination ( Akbarian, 2021 ; Asanimoghadam et al., 2022 ). Correlated stages or redundant intermediate outputs can further dilute discrimination, causing DMUs to appear efficient and making it harder to distinguish true performance differences ( Amini et al., 2021 ). Model orientation, distance form, and inclusion of undesirable outputs also affect frontier shape and efficiency interpretation ( Tone & Tsutsui, 2009 ; Zuniga-Gonzalez et al., 2025 ). 4. Process improvement targets The NDEA model generated slacks for each input and output, translating inefficiency into actionable targets: Material consumption: Reduce by 45.08%, Man-hours: Reduce by 38.16%, Machine-hours: Reduce by 44.28%, Machine downtime: Reduce by 45.83%, Rejected outputs/waste: Reduce by 29.51%, Desirable outputs: Increase by 31.28%. These stage-specific targets allow managers to prioritize improvements, such as optimizing machine schedules, enhancing PVC granulation quality, and improving operator training in WS1. This highlights NDEA’s value in decision support and continuous monitoring, which classical DEA alone cannot provide. 5. Practical implications The findings of this study offer several actionable insights that can guide decision-makers, practitioners, and industry stakeholders in improving performance and refining management practices. These implications are outlined as follows: • Strategic stage selection : Strategic stage selection is crucial, with managers advised to balance stage design to capture key interdependencies without compromising discrimination power and group processes with minimal variability. • Targeted improvement : By adopting the NDEA-based PMS, managers can focus on the most inefficient stages and their sources of inefficiency. For example, in the IV-set production line, optimizing machine schedules and improving raw material quality in WS1 could significantly enhance overall efficiency. • Cross-industry applicability : The proposed NDEA-based PMS framework provide a potential applicability to other industries with complex, multi-stage production systems, such as other industries with complex, multi-stage production systems, like automotive or electronics, to identify bottlenecks and improve throughput. • Data-driven decision-making : The NDEA-based PMS allows data-driven decision-making. Hence , regularly updating efficiency scores and monitoring peer groups can help managers identify emerging inefficiencies and adapt processes accordingly. 6. Theoretical contributions By integrating methodological insights with practical modelling considerations, this study deepens and refines the theoretical understanding of performance measurement in multi-stage production systems. The key theoretical contributions are as follows: • Development of Practical Framework: The study contributes to the performance measurement literature by presenting of a practical framework for implementing NDEA-based PMS in manufacturing settings. This framework provides structured guidance for managers on stage selection and process consolidation to enhance discrimination power without sacrificing granularity. It emphasizes the importance of benchmarking and continuous monitoring to drive process improvements and efficiency gains. • Discrimination Power of NDEA Models: The study also contribute to the DEA/NDEA literature, by revealing that while increasing the number of stages provides greater granularity and insights into specific processes, it reduces the discrimination power of the model. The six-stage model, for instance, classified a disproportionately high number of Decision-Making Units (DMUs) as efficient, limiting its utility for pinpointing inefficiencies. In contrast, the three-stage model offered a balance between granularity and discrimination power, making it the most practical choice for performance evaluation. We reconcile this apparent conflict by highlighting several interlocking explanations consistent with the literature: ○ Dimensionality and sample size effects : Increasing network granularity raises the number of variables while keeping the DMU sample size fixed, potentially inflating degrees of freedom and compressing the frontier, thus reducing discriminatory power. This issue is well-documented in DEA studies as model complexity increases relative to sample size ( Akbarian, 2021 ; Asanimoghadam et al., 2022 ). ○ Correlation and redundancy among stages : When stages are highly correlated or intermediate products offer little unique information, adding more stages can dilute discrimination despite increasing parameterization, as decomposition is effective only when sub-processes provide distinct insights ( Cantor & Poh, 2020 ). ○ Orientation, distance form, and scale choices : Using a non-oriented, non-radial CRS NSBM and considering undesirable outputs alters the frontier’s shape compared to radial models, affecting inefficiency dimensions and potentially leading to divergent discrimination outcomes ( Tone & Tsutsui, 2009 ; Zuniga-Gonzalez et al., 2025 ). ○ Data quality and noise amplification : More complex network models can exacerbate measurement errors, reducing the ability to differentiate performance levels. Although managing undesirable data can help, it does not completely resolve this issue ( Lotfi et al., 2023 ; J. Wu et al., 2017 ). Conclusion This paper contributes to the manufacturing PMS research domain in several ways. First, it initiates the integration of NDEA into PMS for a manufacturing process, thereby developing a practical framework termed “NDEA-based PMS”. Second, the case study investigating the application of NDEA in a pharmaceutical production line shed light on the intricacies of the shop floor by modelling performance indicators for a multi-stage production line and highlighting the relevance of NDEA for manufacturing performance measurement and process improvement. The practical framework proposed from the insights of the case study, has answered RQ1, expanding the application of NDEA, highlighting its ability to decompose production stages and providing insightful information for strategic decision-making. Beyond this methodological contribution, the study yields two substantive empirical findings that merit careful comparison with existing literature. First, the application of a non-oriented, non-radial NSBM (CRS) that explicitly accounts for desirable and undesirable outputs produced robust stage-level efficiency estimates and practical diagnostic information consistent with prior work showing the value of network and slack-based approaches for multi-stage and environmentally-sensitive settings ( Lotfi et al., 2023 ; Ma et al., 2025 ; Tone & Tsutsui, 2009 ). Second, and more unexpectedly, our comparison across five NDEA formulations indicates that increasing the number of explicitly modelled stages reduced the discrimination power of efficiency scores in this case study (RQ2). This result contradicts claims in some prior contributions that NDEA generally increases discrimination relative to classical DEA ( Castelli et al., 2010 ). The inconsistency in discrimination outcomes across NDEA formulations can be attributed to several factors. As network granularity increases, more variables are introduced while DMUs remain constant, inflating degrees of freedom and compressing the efficiency frontier, which reduces discriminatory power. Highly correlated stages add parameters without improving differentiation. Model choices, such as non-oriented CRS NSBM structures and including undesirable outputs, also alter the efficiency frontier and influence inefficiency identification. Furthermore, increased model complexity can heighten data noise and measurement errors, affecting efficiency accuracy. Despite its contributions, this study is not without limitations. The proposed NDEA-based PMS framework, developed and applied within a single pharmaceutical production line, requires further validation before it can be confidently generalized to other manufacturing contexts, especially given the substantial variation in industrial environments and process configurations. The empirical analysis is based on a relatively small dataset—12 months of observations representing 12 DMUs—which is adequate for illustrative purposes but insufficient for formal statistical validation or hypothesis testing. Moreover, data availability and quality constraints, combined with assumptions of homogeneity within production units and the use of static, linear modelling structures, may oversimplify the dynamic and heterogeneous nature of real operational systems. Confidentiality restrictions further limited the level of detail that could be disclosed regarding actual inputs and outputs, reducing transparency and full replicability. Addressing these limitations in future research would strengthen the robustness of the findings and support broader applicability of the proposed framework. Future research should focus on external validation of the NDEA-based PMS framework across diverse manufacturing contexts and industries to enhance generalizability. Researchers may implement various methods to investigate the weights and relationships between performance measures. Exploring the dynamics of the manufacturing system over time is a promising avenue. Dynamic NDEA models may capture changes in efficiency, process interactions, and improvement targets to offer a more comprehensive view of the evolving manufacturing landscape. In conclusion, this research bridges a significant gap in the literature by integrating NDEA into a practical PMS framework, addressing the unique challenges of multi-stage manufacturing systems. The model’s capacity to grant detailed visibility and flexibility qualifies it as a transformative tool for researchers and practitioners. Future research is advised to persist in aligning operational practice with strategic intent and continue to serve the NDEA-based PMS framework as a portal to long-term competitiveness and efficiency in the complicated and globalized manufacturing landscape. Ethics statement This study did not involve human participants, human tissue, or personally identifiable information. The analysis was conducted using anonymized internal production data obtained from a private manufacturing firm under a confidentiality agreement. As such, the study falls outside the scope of research involving human subjects as defined by the Declaration of Helsinki, and formal approval by an Institutional Review Board (IRB) or ethics committee was not required. Nevertheless, the research protocol and data use were reviewed and approved by the Research Ethics Committee of the author’s institution to ensure compliance with institutional ethical standards and data governance requirements. No permit or reference number was issued, as the study did not involve human subjects or data requiring formal ethical clearance. Data availability statement The dataset used in this study comprises confidential internal production data obtained from a private manufacturing firm. Due to the sensitive nature of the data and the confidentiality agreement in place, the dataset cannot be made publicly available. Sharing the data would risk disclosing proprietary information and commercially sensitive operational details. This research involved no human subjects, and therefore did not require formal Institutional Review Board (IRB) approval. 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 18 Jul 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Business Management, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia 2 Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, Muscat Governorate, 123, Oman Syarifa Hanoum Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation Mahmood Shubbak Roles: Conceptualization, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This research received partial support from internal funding allocated in 2023 by the Department of Business Management at Institut Teknologi Sepuluh Nopember in Surabaya, Indonesia, particularly during the initial phase and fieldwork (case study research). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 04 Dec 2025, 14:710 https://doi.org/10.12688/f1000research.166387.2 version 1 Published: 18 Jul 2025, 14:710 https://doi.org/10.12688/f1000research.166387.1 Copyright © 2025 Hanoum S and Shubbak M. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Hanoum S and Shubbak M. Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.12688/f1000research.166387.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 04 Dec 2025 Revised Views 0 Cite How to cite this report: Issa HM. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.192568.r472794 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v2#referee-response-472794 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 21 Apr 2026 Hayder Mohammed Issa , University of Garmian, Kalar, Iraq Approved VIEWS 0 https://doi.org/10.5256/f1000research.192568.r472794 The manuscript demonstrates significant academic and practical value by employing a non-oriented, CRS-NSBM Network Data Envelopment Analysis to disaggregate the traditional "black-box" manufacturing model, thereby providing actionable, stage-specific insights into the production of intravenous sets. This approach is particularly ... Continue reading READ ALL The manuscript demonstrates significant academic and practical value by employing a non-oriented, CRS-NSBM Network Data Envelopment Analysis to disaggregate the traditional "black-box" manufacturing model, thereby providing actionable, stage-specific insights into the production of intravenous sets. This approach is particularly commendable for its integration of undesirable outputs, such as machine downtime and rejected products, which allows for a more realistic appraisal of operational inefficiencies, specifically identifying PVC granulation as the most volatile and inefficient stage. While the research effectively navigates the inherent trade-off between model granularity and discrimination power—rightly favoring the three-stage model for its balanced analytical robustness—the work could be further strengthened by addressing the constraints of the 12-month sample size, perhaps through the future consideration of higher-frequency weekly data to enhance statistical discrimination. Furthermore, acknowledging how the substantial potential improvements identified, such as the proposed 45% reduction in material consumption, would interface with the stringent validated state and regulatory requirements typical of pharmaceutical environments would offer a more nuanced perspective for practitioners. Notwithstanding these minor, optional considerations for future refinement, the paper’s methodological rigor and its successful bridging of performance measurement theory with industrial application make it a reasonable contribution to the field, and I recommend its acceptance as it stands. Is the background of the case’s history and progression described in sufficient detail? Yes Is the work clearly and accurately presented and does it cite the current literature? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Is the case presented with sufficient detail to be useful for teaching or other practitioners? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Process Engineering, Pharmaceutical Processing, Process Design & Development. Process Simulation & Modelling I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Issa HM. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.192568.r472794 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v2#referee-response-472794 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Illendula DS. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.192568.r469679 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v2#referee-response-469679 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 14 Apr 2026 Dr. Santhosh Illendula , Vijaya College of Pharmacy, Hayathnagar, Telangana, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.192568.r469679 The author has described case study report satisfactory Author has well explained the study with Interaction of inputs, intermediate factors, and outputs in five NDEA models, Interaction among input, output, and intermediate factors in two-stage manufacturing operations. NDEA optimizes multi-stage ... Continue reading READ ALL The author has described case study report satisfactory Author has well explained the study with Interaction of inputs, intermediate factors, and outputs in five NDEA models, Interaction among input, output, and intermediate factors in two-stage manufacturing operations. NDEA optimizes multi-stage pharmaceutical manufacturing by "opening the black box" of production to analyze, measure, and improve individual stages (e.g., synthesis, formulation, packaging) rather than just the final output. It identifies specific inefficiency sources, handles intermediate product transfers, enables better resource allocation, and supports data-driven decisions. These case study report has been satisfactory and accepted further process, Thank you Is the background of the case’s history and progression described in sufficient detail? Yes Is the work clearly and accurately presented and does it cite the current literature? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Is the case presented with sufficient detail to be useful for teaching or other practitioners? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Department of Pharmaceutical Analysis ( Pharmacy) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Illendula DS. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.192568.r469679 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v2#referee-response-469679 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Jilcha K. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.192568.r438579 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v2#referee-response-438579 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 26 Dec 2025 Kassu Jilcha , College of Technology and Built Environment, Addis Ababa, Addis Ababa, Ethiopia Approved VIEWS 0 https://doi.org/10.5256/f1000research.192568.r438579 Dears, The paper now has shown major improvement and if the case is needed, please try ... Continue reading READ ALL Dears, The paper now has shown major improvement and if the case is needed, please try to restructure the paper as Introduction, Literature review (alone standing title), Methodology, Result and Discussion and Conclusion. Regards Competing Interests: No competing interests were disclosed. Reviewer Expertise: Innovation and industrial engineering areas I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Jilcha K. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.192568.r438579 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v2#referee-response-438579 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 18 Jul 2025 Views 0 Cite How to cite this report: Ahmed R. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.183368.r405838 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v1#referee-response-405838 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 24 Sep 2025 Rashed Ahmed , North South University, Dhaka, Dhaka Division, Bangladesh Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.183368.r405838 Summary of the Article The manuscript presents a case study applying Network Data Envelopment Analysis (NDEA) to a pharmaceutical manufacturing line (intravenous (IV) sets) to build a process-based Performance Measurement System (PMS). The authors review DEA and ... Continue reading READ ALL Summary of the Article The manuscript presents a case study applying Network Data Envelopment Analysis (NDEA) to a pharmaceutical manufacturing line (intravenous (IV) sets) to build a process-based Performance Measurement System (PMS). The authors review DEA and NDEA theory and then describe the IV-set production process across six workstations. They collect 12 months of production data and compare five modeling scenarios (treating the process as 1 to 6 stages). Efficiency scores (DMU per month) are computed for each model; descriptive statistics show how many months are classified as “efficient.” From this analysis, the authors select a three-stage model (combining certain processes) as the preferred balance of discrimination power and detail. Using that model, they identify that the PVC granulation stage (Stage 1) is the least efficient (highest waste and downtime), whereas the final assembly stage is the most efficient. The NDEA yields slacks (shortfalls) indicating that large reductions in inputs (materials, labor, machine-hours) and undesirable outputs (rejects, downtime) would be needed to reach full efficiency. Based on these insights, the authors propose a practical NDEA-based PMS framework: it guides managers on how to select stages, benchmark against peer production months, monitor efficiency scores over time, and iteratively refine processes. The paper concludes that this framework “integrates NDEA into a practical PMS” and highlights its potential for guiding multi-stage manufacturing improvements and strategic decisions. The manuscript provides basic context about the pharmaceutical company (product lines, market share, regulatory environment) and maps the IV-set production stages. However, it lacks depth on the operational history or baseline performance issues motivating the study (e.g. why inefficiencies arose, any past improvements). Additional details (e.g. initial performance metrics, company’s maturity or prior interventions) would strengthen the case context.The overall narrative is understandable, and the paper cites many relevant sources (including recent 2022–2025 studies). However, the literature review omits several modern developments (e.g. recent advances in NDEA, bootstrap DEA, Industry 4.0 performance measurement) and some citations are outdated. The writing and notation have inconsistencies (e.g. merged words, unclear figures, table references), which occasionally impede clarity. Improved copy-editing, figure/table clarity, and inclusion of newer references would greatly enhance accuracy and readability.The study uses a non-parametric efficiency analysis (NDEA) without traditional statistical hypothesis tests or confidence intervals. The interpretation relies on descriptive statistics of efficiency scores. While NDEA itself is appropriate for the research question, the manuscript does not perform any statistical validation (e.g. bootstrap confidence intervals or tests) to support claims about differences between models or stages. Adding formal statistical validation methods would strengthen the analysis.The data are proprietary and require an NDA for access. The paper states that data are confidential, so independent researchers cannot verify the results. For reproducibility, the authors should provide (or detail) sufficient data summaries, synthetic datasets, or clear data processing steps. At minimum, comprehensive summary statistics or sample data should be supplied.The core findings (e.g. the 3-stage model’s balance between detail and discrimination, PVC granulation as the least efficient stage) align with the presented results. However, some claims exceed what the data show. For example, the assertion that this framework “bridges a significant gap” or that conclusions “contradict classical DEA” are overstated given the single-case analysis. The conclusions generalize broadly (to other industries or strategic decision-making) without validation. Toning down these claims and clearly linking conclusions to specific results would make them more defensible.The production process is well-mapped and described, and key efficiency scores are reported. Yet the case lacks certain practical details (e.g. actual input/output quantities, exact improvement actions taken, implementation challenges) that would benefit practitioners. For teaching purposes, more numerical examples or step-by-step illustrations of the NDEA application (beyond abstract descriptions) would improve usefulness. Comments to the Authors Thank you for addressing an important industrial problem by applying NDEA to pharmaceutical manufacturing. The case study is interesting, and the idea of integrating NDEA into a performance system is promising. However, the manuscript in its present form has multiple issues that must be addressed to make the study scientifically sound and useful. Below are the main concerns and suggestions: Sample Size and DEA Validity: You analyze only 12 DMUs (months) with many inputs and outputs (including intermediate flows). Standard DEA practice requires much larger samples (often 2–3× the total number of variables) to obtain reliable efficiency scores. With so few observations, nearly half the months are “efficient” by default. This undermines your conclusions about discrimination power. Suggestions: If more data are available (e.g. additional years or multiple production lines), include them to boost DMU count. Alternatively, simplify the model by reducing variables (e.g. combine some similar inputs or outputs) so that the 12 observations are sufficient. Clearly acknowledge this limitation in the text as a pilot study with limited generalizability. Avoid making broad claims based on this small sample. Statistical Validation: The current comparisons between models (1-stage vs 3-stage, etc.) are based solely on descriptive statistics (means, standard deviations). There is no measure of uncertainty. Suggestions: Use DEA bootstrap methods to compute confidence intervals for efficiency scores and test differences between models. Report p-values or other statistical tests (e.g. Friedman test) to support claims that one model discriminates better than another. Add sensitivity analysis: show how results change with small data perturbations or alternate variable selections. Model Selection Justification: The choice of the three-stage model is based on a qualitative “parsimony” argument, but this is not rigorous. Suggestions: Define explicit criteria for selecting the preferred model (for instance, minimize the number of efficient DMUs while still capturing all key processes). Consider formal model selection metrics (analogous to AIC, BIC) or cross-validation if possible. Explain why three stages reflect the real production better than two or four stages. Consult with production engineers to validate the stage grouping (the reader should see why combining certain stages makes sense). Data Transparency: The data are proprietary, but for scientific reporting, we need some level of transparency. Suggestions: Provide summary statistics (means, ranges) for each input and output variable. If possible, anonymize and share data (even if synthetic with a similar structure) as supplementary material. At a minimum, explicitly list all inputs/outputs by stage (not just symbolic names in Table 3). Briefly describe how each was measured. Case and Context Details: The narrative lacks detail on the real-world context and how the study was motivated or used. Suggestions: Quantify the performance gaps that prompted this study (e.g. known waste rates, downtime percentages prior to analysis). Explain why the 12-month period was chosen (seasonality issues? data availability?). Describe how managers or operators participated: was the NDEA analysis actually implemented or tested in operations? If any follow-up actions were taken (process changes, investments) as a result of this analysis, briefly mention them. Literature Review and References: Some relevant recent work appears to be missing. Suggestions: Include citations for modern NDEA applications and improvements (e.g. dynamic/network DEA, bootstrap DEA methods, DEA in healthcare or manufacturing post-2020). Mention any case studies of NDEA in pharmaceuticals or medical devices if available. Update or clarify references that seem only tangentially related (e.g. if a study is on general DEA in banking, explain relevance). Presentation and Clarity: There are multiple formatting and clarity issues. Suggestions: Carefully proofread: fix typos and spacing (e.g. “therewas,” “developingNDEA-based,” etc.). Standardize notation: ensure all variables (x, y, z) and subscripts are consistent and clearly defined (some appear cut or incomplete in tables/figures). Improve figures/tables: For example, Figure 3 (NDEA model diagrams) needs clear labels and captions; Table 5 should have a complete caption and readable formatting. In the text, clearly refer to all figures and tables at first mention and summarize their key points in words. Results Interpretation: The discussion should more directly connect to the literature and avoid overclaiming. Suggestions: When stating that “this contradicts classical DEA,” clarify whether you ran a classical DEA or are inferring from others’ claims. If you did classical DEA (1-stage model), present those results side-by-side. Frame findings as preliminary insights (“in this case, we found…”) rather than definitive rules. Compare your results with any similar studies (if available). Discuss whether, for instance, identifying the first stage as inefficient agrees with known industry benchmarks. Conclusions and Claims: Several claims go beyond the data. Suggestions: Tone down statements about generalizability (e.g. “applicable to automotive/electronics”). Instead, suggest these as potential extensions with future validation. Reiterate study limitations (sample size, single site, confidentiality constraints) explicitly in the conclusion. Clearly link each major conclusion back to specific results shown (for example, “Based on Table 4, models with more stages had higher average efficiency scores and more efficient DMUs, indicating lower discrimination”). By addressing the points above, the manuscript will be much stronger. In particular, focus on bolstering the methodological rigor (sample size issue, statistical validation) and ensuring claims are commensurate with what a single-case analysis can support. With these revisions, the paper will better serve practitioners interested in using NDEA for process improvement. Is the background of the case’s history and progression described in sufficient detail? Partly Is the work clearly and accurately presented and does it cite the current literature? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Partly Is the case presented with sufficient detail to be useful for teaching or other practitioners? Partly References 1. Pereira M, Dinis D, Ferreira D, Figueira J, et al.: A network Data Envelopment Analysis to estimate nations’ efficiency in the fight against SARS-CoV-2. Expert Systems with Applications . 2022; 210 . Publisher Full Text 2. Zubir M, Noor A, Mohd Rizal A, Harith A, et al.: Approach in inputs & outputs selection of Data Envelopment Analysis (DEA) efficiency measurement in hospitals: A systematic review. PLOS ONE . 2024; 19 (8). Publisher Full Text 3. Mitakos A, Mpogiatzidis P: Adapting Efficiency Analysis in Health Systems: A Scoping Review of Data Envelopment Analysis Applications During the COVID-19 Pandemic. Journal of Market Access & Health Policy . 2024; 12 (4): 306-316 Publisher Full Text 4. Kohl S, Schoenfelder J, Fügener A, Brunner J: The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science . 2019; 22 (2): 245-286 Publisher Full Text 5. Zhang T, Lu W, Tao H: Efficiency of health resource utilisation in primary-level maternal and child health hospitals in Shanxi Province, China: a bootstrapping data envelopment analysis and truncated regression approach. BMC Health Services Research . 2020; 20 (1). Publisher Full Text 6. Hou Y, Tao W, Hou S, Li W: Levels, trends, and determinants of effectiveness on the hierarchical medical system in China: Data envelopment analysis and bootstrapping truncated regression analysis. Frontiers in Public Health . 2022; 10 . Publisher Full Text 7. Pelone F, Kringos D, Romaniello A, Archibugi M, et al.: Primary Care Efficiency Measurement Using Data Envelopment Analysis: A Systematic Review. Journal of Medical Systems . 2015; 39 (1). Publisher Full Text 8. Tambare P, Meshram C, Lee C, Ramteke R, et al.: Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review. Sensors . 2021; 22 (1). Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: I am an operations researcher with applied expertise in Data Envelopment Analysis (including network and slacks-based models), statistical validation of frontier methods (bootstrap inference), and the design and evaluation of Performance Measurement Systems in manufacturing environments. My applied experience includes process mapping, efficiency improvement, and operational decision support in regulated manufacturing (pharmaceuticals/medical devices). I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ahmed R. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.183368.r405838 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v1#referee-response-405838 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Dec 2025 Mahmood Shubbak , Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman 10 Dec 2025 Author Response RESPONSE TO REVIEWER #2: Rashed Ahmed Comments to the Authors Thank you for addressing an important industrial problem by applying NDEA to pharmaceutical manufacturing. The case study is interesting, and ... Continue reading RESPONSE TO REVIEWER #2: Rashed Ahmed Comments to the Authors Thank you for addressing an important industrial problem by applying NDEA to pharmaceutical manufacturing. The case study is interesting, and the idea of integrating NDEA into a performance system is promising. However, the manuscript in its present form has multiple issues that must be addressed to make the study scientifically sound and useful. Below are the main concerns and suggestions: 1- Sample Size and DEA Validity: You analyze only 12 DMUs (months) with many inputs and outputs (including intermediate flows). Standard DEA practice requires much larger samples (often 2–3× the total number of variables) to obtain reliable efficiency scores. With so few observations, nearly half the months are “efficient” by default. This undermines your conclusions about discrimination power. Suggestions: If more data are available (e.g. additional years or multiple production lines), include them to boost DMU count. Alternatively, simplify the model by reducing variables (e.g. combine some similar inputs or outputs) so that the 12 observations are sufficient. Clearly acknowledge this limitation in the text as a pilot study with limited generalizability. Avoid making broad claims based on this small sample. Response: We thank you for highlighting the critical issue of sample size and its implications for the validity and discrimination power of DEA/NDEA models. We acknowledge that the small sample size limits statistical generalizability. The case study is intended as an illustrative example of our proposed NDEA-based PMS framework. We clarified this in the Introduction, Methodology, and Conclusion. To mitigate potential overclaiming, we also emphasized the descriptive and exploratory nature of the results rather than statistical inference. This article works under category of case study paper, thus the case is intentionally designed as an illustrative example, or demonstrative application of our proposed NDEA-based PMS framework. To follow up your note, we have revised the final part of the introduction as follows: “To answer these questions, this article presents a case study focused on the production line of a pharmaceutical company's intravenous (IV) sets, exemplifying the intricacies of multi-stage manufacturing, featuring a combination of manual and automated processes. The case study is intentionally illustrative; the goal is to demonstrate the feasibility and practical utility of a NDEA-based PMS rather than to generate statistically generalizable results. Using a case study of intravenous (IV) set production, the research illustrates how NDEA can be operationalized to: Provide a practical framework for routine efficiency monitoring and process improvement. Identify stage-specific inefficiencies as the focus of process improvement. Evaluate trade-offs between model granularity and discrimination power; By embedding model selection criteria, incorporating stage validation with production engineers, and accounting for undesirable outputs, the study provides a robust methodology for implementing NDEA in operational settings. In doing so, it bridges the theoretical development of network DEA with practical performance management needs in complex manufacturing environments.” 2- Statistical Validation: The current comparisons between models (1-stage vs 3-stage, etc.) are based solely on descriptive statistics (means, standard deviations). There is no measure of uncertainty. Suggestions: Use DEA bootstrap methods to compute confidence intervals for efficiency scores and test differences between models. Report p-values or other statistical tests (e.g. Friedman test) to support claims that one model discriminates better than another. Add sensitivity analysis: show how results change with small data perturbations or alternate variable selections. Response: We thank you for raising the critical point regarding the statistical validation. Due to the limited DMU sample, formal bootstrap methods and statistical tests were not applied. Our study focuses on developing the practical NDEA-based PMS framework. To follow up your suggestion, we have clarified this limitation in the Methodology and Discussion. To address the reviewer’s concern, we have revised the manuscript to: In introduction: Explicitly state that the aim of the study is methodological framework development rather than statistical inference, in introduction, as addressed in point 1. In methodology: Explicit statement in the methodology that that statistical validation is not feasible with the available sample. In conclusion: Acknowledge that bootstrap DEA and statistical testing represent valuable extensions, but are not feasible given the illustrative nature and limited data availability of the case setting. Suggest these techniques as directions for future research, particularly when larger datasets are available to support inferential analysis and sensitivity testing. 3- Model Selection Justification: The choice of the three-stage model is based on a qualitative “parsimony” argument, but this is not rigorous. Suggestions: Define explicit criteria for selecting the preferred model (for instance, minimize the number of efficient DMUs while still capturing all key processes). Consider formal model selection metrics (analogous to AIC, BIC) or cross-validation if possible. Explain why three stages reflect the real production better than two or four stages. Consult with production engineers to validate the stage grouping (the reader should see why combining certain stages makes sense). Response: We thank you for highlighting the need for a more rigorous justification of the chosen three-stage model. We agree that a qualitative parsimony argument alone is insufficient and have revised the manuscript accordingly. In the revised version of our methodology : We have adopted an explicit criterion for model selection: the preferred model minimizes the number of efficient DMUs while capturing all key production processes, aligning with your suggestion. Additionally, we have strengthened the justification for the three-stage structure by incorporating consultation with production engineers from the participating pharmaceutical manufacturer. Their expert input confirmed that the three-stage grouping most accurately reflects the functional and operational flow of the IV-set production line. In particular, they indicated that: A two-stage model would oversimplify the process and obscure necessary intermediate transformations. A four-stage model would fragment stages that are tightly integrated in practice, offering limited additional managerial insight. 4- Data Transparency: The data are proprietary, but for scientific reporting, we need some level of transparency. Suggestions: Provide summary statistics (means, ranges) for each input and output variable. If possible, anonymize and share data (even if synthetic with a similar structure) as supplementary material. At a minimum, explicitly list all inputs/outputs by stage (not just symbolic names in Table 3). Briefly describe how each was measured. Response: We thank you for emphasizing the importance of data transparency in scientific reporting. While we fully agree with the need for sufficient clarity to ensure reproducibility, we regret that the raw operational data cannot be shared due to strict confidentiality agreements with the participating pharmaceutical manufacturer. These data contain commercially sensitive production information that cannot be disclosed publicly, even in anonymized or synthetic form, under the terms of our partnership. To address the reviewer’s concern while respecting these constraints, we have provided the manuscript to strengthen transparency in the following ways: Detailed variable listing: We have expanded the description of the variables by explicitly listing all inputs, outputs, and intermediate flows by stage. Measurement explanations: For each variable, we now provide a brief explanation of how it is measured in the production process to ensure clarity and interpretability for readers. Clarification of confidentiality limitations: We explicitly state in the text that raw or shareable datasets cannot be provided due to proprietary restrictions. These revisions ensure that the methodology and variable structure are fully transparent, allowing readers to understand and interpret the model even without access to the protected dataset. 5- Case and Context Details: The narrative lacks detail on the real-world context and how the study was motivated or used. Suggestions: Quantify the performance gaps that prompted this study (e.g. known waste rates, downtime percentages prior to analysis). Explain why the 12-month period was chosen (seasonality issues? data availability?). Describe how managers or operators participated: was the NDEA analysis actually implemented or tested in operations? If any follow-up actions were taken (process changes, investments) as a result of this analysis, briefly mention them. Response: We thank the reviewer for highlighting the need to provide more contextual details and practical motivation for the case study. We have revised the manuscript to clarify the real-world context, quantify performance gaps, and describe managerial participation. We have added these details in the Case Study section (Section 5) to provide readers with a clearer understanding of the real-world context, performance gaps, and managerial involvement. 6. Literature Review and References: Some relevant recent work appears to be missing. Suggestions: Include citations for modern NDEA applications and improvements (e.g. dynamic/network DEA, bootstrap DEA methods, DEA in healthcare or manufacturing post-2020). Mention any case studies of NDEA in pharmaceuticals or medical devices if available. Update or clarify references that seem only tangentially related (e.g. if a study is on general DEA in banking, explain relevance). Response: Thank you for your valuable feedback. To follow up, we revised our manuscript (in the introduction and literature review sections ) by incorporating citations of recent (post-2020) applications of network/decomposed DEA, including dynamic/network DEA, bootstrap methods, and hybrid approaches in manufacturing and supply chain contexts. This situates our work at the current frontier of efficiency measurement research and clarifies our choice of non-radial, non-oriented NSBM CRS over dynamic or bootstrap methods. Meanwhile, we reviewed literature on NDEA applications in pharmaceuticals and medical devices, highlighting this emerging area. When no prior studies were found, we noted the gap and positioned our case study as an early contribution. We also clarified non-manufacturing DEA references by adding sentences that explain their relevance, focusing on methodological insights and discrimination power. We added more relevant references, as in the earlier note, to enhance the coherence of the literature review. 7. Presentation and Clarity: There are multiple formatting and clarity issues. Suggestions: Carefully proofread: fix typos and spacing (e.g. “therewas,” “developingNDEA-based,” etc.). Standardize notation: ensure all variables (x, y, z) and subscripts are consistent and clearly defined (some appear cut or incomplete in tables/figures). Improve figures/tables: For example, Figure 3 (NDEA model diagrams) needs clear labels and captions; Table 5 should have a complete caption and readable formatting. In the text, clearly refer to all figures and tables at first mention and summarize their key points in words. Response: We appreciate the reviewer’s careful attention to presentation and clarity. In response, we have undertaken a thorough review of the manuscript and implemented the improvements through the entire manuscript. 8. Results Interpretation: The discussion should more directly connect to the literature and avoid overclaiming. Suggestions: When stating that “this contradicts classical DEA,” clarify whether you ran a classical DEA or are inferring from others’ claims. If you did classical DEA (1-stage model), present those results side-by-side. Frame findings as preliminary insights (“in this case, we found…”) rather than definitive rules. Compare your results with any similar studies (if available). Discuss whether, for instance, identifying the first stage as inefficient agrees with known industry benchmarks. Response: We thank the reviewer for highlighting the need for more careful interpretation of the results and closer linkage to the literature. In response, we have revised the entire structure of Results and Discussion. 9. Conclusions and Claims: Several claims go beyond the data. Suggestions: Tone down statements about generalizability (e.g. “applicable to automotive/electronics”). Instead, suggest these as potential extensions with future validation. Reiterate study limitations (sample size, single site, confidentiality constraints) explicitly in the conclusion. Clearly link each major conclusion back to specific results shown (for example, “Based on Table 4, models with more stages had higher average efficiency scores and more efficient DMUs, indicating lower discrimination”). Response: We appreciate the reviewer for highlighting the importance of moderating claims and clarifying the limitations of our study. We have revised the Conclusion section accordingly. Specifically, we have: Softened statements regarding the generalizability of the results in both the results and discussion sections. Clearly reiterated the study's limitations, including sample size, being conducted at a single site, and confidentiality constraints, in the limitations section of the conclusion . RESPONSE TO REVIEWER #2: Rashed Ahmed Comments to the Authors Thank you for addressing an important industrial problem by applying NDEA to pharmaceutical manufacturing. The case study is interesting, and the idea of integrating NDEA into a performance system is promising. However, the manuscript in its present form has multiple issues that must be addressed to make the study scientifically sound and useful. Below are the main concerns and suggestions: 1- Sample Size and DEA Validity: You analyze only 12 DMUs (months) with many inputs and outputs (including intermediate flows). Standard DEA practice requires much larger samples (often 2–3× the total number of variables) to obtain reliable efficiency scores. With so few observations, nearly half the months are “efficient” by default. This undermines your conclusions about discrimination power. Suggestions: If more data are available (e.g. additional years or multiple production lines), include them to boost DMU count. Alternatively, simplify the model by reducing variables (e.g. combine some similar inputs or outputs) so that the 12 observations are sufficient. Clearly acknowledge this limitation in the text as a pilot study with limited generalizability. Avoid making broad claims based on this small sample. Response: We thank you for highlighting the critical issue of sample size and its implications for the validity and discrimination power of DEA/NDEA models. We acknowledge that the small sample size limits statistical generalizability. The case study is intended as an illustrative example of our proposed NDEA-based PMS framework. We clarified this in the Introduction, Methodology, and Conclusion. To mitigate potential overclaiming, we also emphasized the descriptive and exploratory nature of the results rather than statistical inference. This article works under category of case study paper, thus the case is intentionally designed as an illustrative example, or demonstrative application of our proposed NDEA-based PMS framework. To follow up your note, we have revised the final part of the introduction as follows: “To answer these questions, this article presents a case study focused on the production line of a pharmaceutical company's intravenous (IV) sets, exemplifying the intricacies of multi-stage manufacturing, featuring a combination of manual and automated processes. The case study is intentionally illustrative; the goal is to demonstrate the feasibility and practical utility of a NDEA-based PMS rather than to generate statistically generalizable results. Using a case study of intravenous (IV) set production, the research illustrates how NDEA can be operationalized to: Provide a practical framework for routine efficiency monitoring and process improvement. Identify stage-specific inefficiencies as the focus of process improvement. Evaluate trade-offs between model granularity and discrimination power; By embedding model selection criteria, incorporating stage validation with production engineers, and accounting for undesirable outputs, the study provides a robust methodology for implementing NDEA in operational settings. In doing so, it bridges the theoretical development of network DEA with practical performance management needs in complex manufacturing environments.” 2- Statistical Validation: The current comparisons between models (1-stage vs 3-stage, etc.) are based solely on descriptive statistics (means, standard deviations). There is no measure of uncertainty. Suggestions: Use DEA bootstrap methods to compute confidence intervals for efficiency scores and test differences between models. Report p-values or other statistical tests (e.g. Friedman test) to support claims that one model discriminates better than another. Add sensitivity analysis: show how results change with small data perturbations or alternate variable selections. Response: We thank you for raising the critical point regarding the statistical validation. Due to the limited DMU sample, formal bootstrap methods and statistical tests were not applied. Our study focuses on developing the practical NDEA-based PMS framework. To follow up your suggestion, we have clarified this limitation in the Methodology and Discussion. To address the reviewer’s concern, we have revised the manuscript to: In introduction: Explicitly state that the aim of the study is methodological framework development rather than statistical inference, in introduction, as addressed in point 1. In methodology: Explicit statement in the methodology that that statistical validation is not feasible with the available sample. In conclusion: Acknowledge that bootstrap DEA and statistical testing represent valuable extensions, but are not feasible given the illustrative nature and limited data availability of the case setting. Suggest these techniques as directions for future research, particularly when larger datasets are available to support inferential analysis and sensitivity testing. 3- Model Selection Justification: The choice of the three-stage model is based on a qualitative “parsimony” argument, but this is not rigorous. Suggestions: Define explicit criteria for selecting the preferred model (for instance, minimize the number of efficient DMUs while still capturing all key processes). Consider formal model selection metrics (analogous to AIC, BIC) or cross-validation if possible. Explain why three stages reflect the real production better than two or four stages. Consult with production engineers to validate the stage grouping (the reader should see why combining certain stages makes sense). Response: We thank you for highlighting the need for a more rigorous justification of the chosen three-stage model. We agree that a qualitative parsimony argument alone is insufficient and have revised the manuscript accordingly. In the revised version of our methodology : We have adopted an explicit criterion for model selection: the preferred model minimizes the number of efficient DMUs while capturing all key production processes, aligning with your suggestion. Additionally, we have strengthened the justification for the three-stage structure by incorporating consultation with production engineers from the participating pharmaceutical manufacturer. Their expert input confirmed that the three-stage grouping most accurately reflects the functional and operational flow of the IV-set production line. In particular, they indicated that: A two-stage model would oversimplify the process and obscure necessary intermediate transformations. A four-stage model would fragment stages that are tightly integrated in practice, offering limited additional managerial insight. 4- Data Transparency: The data are proprietary, but for scientific reporting, we need some level of transparency. Suggestions: Provide summary statistics (means, ranges) for each input and output variable. If possible, anonymize and share data (even if synthetic with a similar structure) as supplementary material. At a minimum, explicitly list all inputs/outputs by stage (not just symbolic names in Table 3). Briefly describe how each was measured. Response: We thank you for emphasizing the importance of data transparency in scientific reporting. While we fully agree with the need for sufficient clarity to ensure reproducibility, we regret that the raw operational data cannot be shared due to strict confidentiality agreements with the participating pharmaceutical manufacturer. These data contain commercially sensitive production information that cannot be disclosed publicly, even in anonymized or synthetic form, under the terms of our partnership. To address the reviewer’s concern while respecting these constraints, we have provided the manuscript to strengthen transparency in the following ways: Detailed variable listing: We have expanded the description of the variables by explicitly listing all inputs, outputs, and intermediate flows by stage. Measurement explanations: For each variable, we now provide a brief explanation of how it is measured in the production process to ensure clarity and interpretability for readers. Clarification of confidentiality limitations: We explicitly state in the text that raw or shareable datasets cannot be provided due to proprietary restrictions. These revisions ensure that the methodology and variable structure are fully transparent, allowing readers to understand and interpret the model even without access to the protected dataset. 5- Case and Context Details: The narrative lacks detail on the real-world context and how the study was motivated or used. Suggestions: Quantify the performance gaps that prompted this study (e.g. known waste rates, downtime percentages prior to analysis). Explain why the 12-month period was chosen (seasonality issues? data availability?). Describe how managers or operators participated: was the NDEA analysis actually implemented or tested in operations? If any follow-up actions were taken (process changes, investments) as a result of this analysis, briefly mention them. Response: We thank the reviewer for highlighting the need to provide more contextual details and practical motivation for the case study. We have revised the manuscript to clarify the real-world context, quantify performance gaps, and describe managerial participation. We have added these details in the Case Study section (Section 5) to provide readers with a clearer understanding of the real-world context, performance gaps, and managerial involvement. 6. Literature Review and References: Some relevant recent work appears to be missing. Suggestions: Include citations for modern NDEA applications and improvements (e.g. dynamic/network DEA, bootstrap DEA methods, DEA in healthcare or manufacturing post-2020). Mention any case studies of NDEA in pharmaceuticals or medical devices if available. Update or clarify references that seem only tangentially related (e.g. if a study is on general DEA in banking, explain relevance). Response: Thank you for your valuable feedback. To follow up, we revised our manuscript (in the introduction and literature review sections ) by incorporating citations of recent (post-2020) applications of network/decomposed DEA, including dynamic/network DEA, bootstrap methods, and hybrid approaches in manufacturing and supply chain contexts. This situates our work at the current frontier of efficiency measurement research and clarifies our choice of non-radial, non-oriented NSBM CRS over dynamic or bootstrap methods. Meanwhile, we reviewed literature on NDEA applications in pharmaceuticals and medical devices, highlighting this emerging area. When no prior studies were found, we noted the gap and positioned our case study as an early contribution. We also clarified non-manufacturing DEA references by adding sentences that explain their relevance, focusing on methodological insights and discrimination power. We added more relevant references, as in the earlier note, to enhance the coherence of the literature review. 7. Presentation and Clarity: There are multiple formatting and clarity issues. Suggestions: Carefully proofread: fix typos and spacing (e.g. “therewas,” “developingNDEA-based,” etc.). Standardize notation: ensure all variables (x, y, z) and subscripts are consistent and clearly defined (some appear cut or incomplete in tables/figures). Improve figures/tables: For example, Figure 3 (NDEA model diagrams) needs clear labels and captions; Table 5 should have a complete caption and readable formatting. In the text, clearly refer to all figures and tables at first mention and summarize their key points in words. Response: We appreciate the reviewer’s careful attention to presentation and clarity. In response, we have undertaken a thorough review of the manuscript and implemented the improvements through the entire manuscript. 8. Results Interpretation: The discussion should more directly connect to the literature and avoid overclaiming. Suggestions: When stating that “this contradicts classical DEA,” clarify whether you ran a classical DEA or are inferring from others’ claims. If you did classical DEA (1-stage model), present those results side-by-side. Frame findings as preliminary insights (“in this case, we found…”) rather than definitive rules. Compare your results with any similar studies (if available). Discuss whether, for instance, identifying the first stage as inefficient agrees with known industry benchmarks. Response: We thank the reviewer for highlighting the need for more careful interpretation of the results and closer linkage to the literature. In response, we have revised the entire structure of Results and Discussion. 9. Conclusions and Claims: Several claims go beyond the data. Suggestions: Tone down statements about generalizability (e.g. “applicable to automotive/electronics”). Instead, suggest these as potential extensions with future validation. Reiterate study limitations (sample size, single site, confidentiality constraints) explicitly in the conclusion. Clearly link each major conclusion back to specific results shown (for example, “Based on Table 4, models with more stages had higher average efficiency scores and more efficient DMUs, indicating lower discrimination”). Response: We appreciate the reviewer for highlighting the importance of moderating claims and clarifying the limitations of our study. We have revised the Conclusion section accordingly. Specifically, we have: Softened statements regarding the generalizability of the results in both the results and discussion sections. Clearly reiterated the study's limitations, including sample size, being conducted at a single site, and confidentiality constraints, in the limitations section of the conclusion . Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 14 Mar 2026 Mahmood Shubbak , Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman 14 Mar 2026 Author Response We have addressed your comments in the revised version of our manuscript. Please check it. Competing Interests: No competing interests were disclosed. We have addressed your comments in the revised version of our manuscript. Please check it. We have addressed your comments in the revised version of our manuscript. Please check it. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Dec 2025 Mahmood Shubbak , Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman 10 Dec 2025 Author Response RESPONSE TO REVIEWER #2: Rashed Ahmed Comments to the Authors Thank you for addressing an important industrial problem by applying NDEA to pharmaceutical manufacturing. The case study is interesting, and ... Continue reading RESPONSE TO REVIEWER #2: Rashed Ahmed Comments to the Authors Thank you for addressing an important industrial problem by applying NDEA to pharmaceutical manufacturing. The case study is interesting, and the idea of integrating NDEA into a performance system is promising. However, the manuscript in its present form has multiple issues that must be addressed to make the study scientifically sound and useful. Below are the main concerns and suggestions: 1- Sample Size and DEA Validity: You analyze only 12 DMUs (months) with many inputs and outputs (including intermediate flows). Standard DEA practice requires much larger samples (often 2–3× the total number of variables) to obtain reliable efficiency scores. With so few observations, nearly half the months are “efficient” by default. This undermines your conclusions about discrimination power. Suggestions: If more data are available (e.g. additional years or multiple production lines), include them to boost DMU count. Alternatively, simplify the model by reducing variables (e.g. combine some similar inputs or outputs) so that the 12 observations are sufficient. Clearly acknowledge this limitation in the text as a pilot study with limited generalizability. Avoid making broad claims based on this small sample. Response: We thank you for highlighting the critical issue of sample size and its implications for the validity and discrimination power of DEA/NDEA models. We acknowledge that the small sample size limits statistical generalizability. The case study is intended as an illustrative example of our proposed NDEA-based PMS framework. We clarified this in the Introduction, Methodology, and Conclusion. To mitigate potential overclaiming, we also emphasized the descriptive and exploratory nature of the results rather than statistical inference. This article works under category of case study paper, thus the case is intentionally designed as an illustrative example, or demonstrative application of our proposed NDEA-based PMS framework. To follow up your note, we have revised the final part of the introduction as follows: “To answer these questions, this article presents a case study focused on the production line of a pharmaceutical company's intravenous (IV) sets, exemplifying the intricacies of multi-stage manufacturing, featuring a combination of manual and automated processes. The case study is intentionally illustrative; the goal is to demonstrate the feasibility and practical utility of a NDEA-based PMS rather than to generate statistically generalizable results. Using a case study of intravenous (IV) set production, the research illustrates how NDEA can be operationalized to: Provide a practical framework for routine efficiency monitoring and process improvement. Identify stage-specific inefficiencies as the focus of process improvement. Evaluate trade-offs between model granularity and discrimination power; By embedding model selection criteria, incorporating stage validation with production engineers, and accounting for undesirable outputs, the study provides a robust methodology for implementing NDEA in operational settings. In doing so, it bridges the theoretical development of network DEA with practical performance management needs in complex manufacturing environments.” 2- Statistical Validation: The current comparisons between models (1-stage vs 3-stage, etc.) are based solely on descriptive statistics (means, standard deviations). There is no measure of uncertainty. Suggestions: Use DEA bootstrap methods to compute confidence intervals for efficiency scores and test differences between models. Report p-values or other statistical tests (e.g. Friedman test) to support claims that one model discriminates better than another. Add sensitivity analysis: show how results change with small data perturbations or alternate variable selections. Response: We thank you for raising the critical point regarding the statistical validation. Due to the limited DMU sample, formal bootstrap methods and statistical tests were not applied. Our study focuses on developing the practical NDEA-based PMS framework. To follow up your suggestion, we have clarified this limitation in the Methodology and Discussion. To address the reviewer’s concern, we have revised the manuscript to: In introduction: Explicitly state that the aim of the study is methodological framework development rather than statistical inference, in introduction, as addressed in point 1. In methodology: Explicit statement in the methodology that that statistical validation is not feasible with the available sample. In conclusion: Acknowledge that bootstrap DEA and statistical testing represent valuable extensions, but are not feasible given the illustrative nature and limited data availability of the case setting. Suggest these techniques as directions for future research, particularly when larger datasets are available to support inferential analysis and sensitivity testing. 3- Model Selection Justification: The choice of the three-stage model is based on a qualitative “parsimony” argument, but this is not rigorous. Suggestions: Define explicit criteria for selecting the preferred model (for instance, minimize the number of efficient DMUs while still capturing all key processes). Consider formal model selection metrics (analogous to AIC, BIC) or cross-validation if possible. Explain why three stages reflect the real production better than two or four stages. Consult with production engineers to validate the stage grouping (the reader should see why combining certain stages makes sense). Response: We thank you for highlighting the need for a more rigorous justification of the chosen three-stage model. We agree that a qualitative parsimony argument alone is insufficient and have revised the manuscript accordingly. In the revised version of our methodology : We have adopted an explicit criterion for model selection: the preferred model minimizes the number of efficient DMUs while capturing all key production processes, aligning with your suggestion. Additionally, we have strengthened the justification for the three-stage structure by incorporating consultation with production engineers from the participating pharmaceutical manufacturer. Their expert input confirmed that the three-stage grouping most accurately reflects the functional and operational flow of the IV-set production line. In particular, they indicated that: A two-stage model would oversimplify the process and obscure necessary intermediate transformations. A four-stage model would fragment stages that are tightly integrated in practice, offering limited additional managerial insight. 4- Data Transparency: The data are proprietary, but for scientific reporting, we need some level of transparency. Suggestions: Provide summary statistics (means, ranges) for each input and output variable. If possible, anonymize and share data (even if synthetic with a similar structure) as supplementary material. At a minimum, explicitly list all inputs/outputs by stage (not just symbolic names in Table 3). Briefly describe how each was measured. Response: We thank you for emphasizing the importance of data transparency in scientific reporting. While we fully agree with the need for sufficient clarity to ensure reproducibility, we regret that the raw operational data cannot be shared due to strict confidentiality agreements with the participating pharmaceutical manufacturer. These data contain commercially sensitive production information that cannot be disclosed publicly, even in anonymized or synthetic form, under the terms of our partnership. To address the reviewer’s concern while respecting these constraints, we have provided the manuscript to strengthen transparency in the following ways: Detailed variable listing: We have expanded the description of the variables by explicitly listing all inputs, outputs, and intermediate flows by stage. Measurement explanations: For each variable, we now provide a brief explanation of how it is measured in the production process to ensure clarity and interpretability for readers. Clarification of confidentiality limitations: We explicitly state in the text that raw or shareable datasets cannot be provided due to proprietary restrictions. These revisions ensure that the methodology and variable structure are fully transparent, allowing readers to understand and interpret the model even without access to the protected dataset. 5- Case and Context Details: The narrative lacks detail on the real-world context and how the study was motivated or used. Suggestions: Quantify the performance gaps that prompted this study (e.g. known waste rates, downtime percentages prior to analysis). Explain why the 12-month period was chosen (seasonality issues? data availability?). Describe how managers or operators participated: was the NDEA analysis actually implemented or tested in operations? If any follow-up actions were taken (process changes, investments) as a result of this analysis, briefly mention them. Response: We thank the reviewer for highlighting the need to provide more contextual details and practical motivation for the case study. We have revised the manuscript to clarify the real-world context, quantify performance gaps, and describe managerial participation. We have added these details in the Case Study section (Section 5) to provide readers with a clearer understanding of the real-world context, performance gaps, and managerial involvement. 6. Literature Review and References: Some relevant recent work appears to be missing. Suggestions: Include citations for modern NDEA applications and improvements (e.g. dynamic/network DEA, bootstrap DEA methods, DEA in healthcare or manufacturing post-2020). Mention any case studies of NDEA in pharmaceuticals or medical devices if available. Update or clarify references that seem only tangentially related (e.g. if a study is on general DEA in banking, explain relevance). Response: Thank you for your valuable feedback. To follow up, we revised our manuscript (in the introduction and literature review sections ) by incorporating citations of recent (post-2020) applications of network/decomposed DEA, including dynamic/network DEA, bootstrap methods, and hybrid approaches in manufacturing and supply chain contexts. This situates our work at the current frontier of efficiency measurement research and clarifies our choice of non-radial, non-oriented NSBM CRS over dynamic or bootstrap methods. Meanwhile, we reviewed literature on NDEA applications in pharmaceuticals and medical devices, highlighting this emerging area. When no prior studies were found, we noted the gap and positioned our case study as an early contribution. We also clarified non-manufacturing DEA references by adding sentences that explain their relevance, focusing on methodological insights and discrimination power. We added more relevant references, as in the earlier note, to enhance the coherence of the literature review. 7. Presentation and Clarity: There are multiple formatting and clarity issues. Suggestions: Carefully proofread: fix typos and spacing (e.g. “therewas,” “developingNDEA-based,” etc.). Standardize notation: ensure all variables (x, y, z) and subscripts are consistent and clearly defined (some appear cut or incomplete in tables/figures). Improve figures/tables: For example, Figure 3 (NDEA model diagrams) needs clear labels and captions; Table 5 should have a complete caption and readable formatting. In the text, clearly refer to all figures and tables at first mention and summarize their key points in words. Response: We appreciate the reviewer’s careful attention to presentation and clarity. In response, we have undertaken a thorough review of the manuscript and implemented the improvements through the entire manuscript. 8. Results Interpretation: The discussion should more directly connect to the literature and avoid overclaiming. Suggestions: When stating that “this contradicts classical DEA,” clarify whether you ran a classical DEA or are inferring from others’ claims. If you did classical DEA (1-stage model), present those results side-by-side. Frame findings as preliminary insights (“in this case, we found…”) rather than definitive rules. Compare your results with any similar studies (if available). Discuss whether, for instance, identifying the first stage as inefficient agrees with known industry benchmarks. Response: We thank the reviewer for highlighting the need for more careful interpretation of the results and closer linkage to the literature. In response, we have revised the entire structure of Results and Discussion. 9. Conclusions and Claims: Several claims go beyond the data. Suggestions: Tone down statements about generalizability (e.g. “applicable to automotive/electronics”). Instead, suggest these as potential extensions with future validation. Reiterate study limitations (sample size, single site, confidentiality constraints) explicitly in the conclusion. Clearly link each major conclusion back to specific results shown (for example, “Based on Table 4, models with more stages had higher average efficiency scores and more efficient DMUs, indicating lower discrimination”). Response: We appreciate the reviewer for highlighting the importance of moderating claims and clarifying the limitations of our study. We have revised the Conclusion section accordingly. Specifically, we have: Softened statements regarding the generalizability of the results in both the results and discussion sections. Clearly reiterated the study's limitations, including sample size, being conducted at a single site, and confidentiality constraints, in the limitations section of the conclusion . RESPONSE TO REVIEWER #2: Rashed Ahmed Comments to the Authors Thank you for addressing an important industrial problem by applying NDEA to pharmaceutical manufacturing. The case study is interesting, and the idea of integrating NDEA into a performance system is promising. However, the manuscript in its present form has multiple issues that must be addressed to make the study scientifically sound and useful. Below are the main concerns and suggestions: 1- Sample Size and DEA Validity: You analyze only 12 DMUs (months) with many inputs and outputs (including intermediate flows). Standard DEA practice requires much larger samples (often 2–3× the total number of variables) to obtain reliable efficiency scores. With so few observations, nearly half the months are “efficient” by default. This undermines your conclusions about discrimination power. Suggestions: If more data are available (e.g. additional years or multiple production lines), include them to boost DMU count. Alternatively, simplify the model by reducing variables (e.g. combine some similar inputs or outputs) so that the 12 observations are sufficient. Clearly acknowledge this limitation in the text as a pilot study with limited generalizability. Avoid making broad claims based on this small sample. Response: We thank you for highlighting the critical issue of sample size and its implications for the validity and discrimination power of DEA/NDEA models. We acknowledge that the small sample size limits statistical generalizability. The case study is intended as an illustrative example of our proposed NDEA-based PMS framework. We clarified this in the Introduction, Methodology, and Conclusion. To mitigate potential overclaiming, we also emphasized the descriptive and exploratory nature of the results rather than statistical inference. This article works under category of case study paper, thus the case is intentionally designed as an illustrative example, or demonstrative application of our proposed NDEA-based PMS framework. To follow up your note, we have revised the final part of the introduction as follows: “To answer these questions, this article presents a case study focused on the production line of a pharmaceutical company's intravenous (IV) sets, exemplifying the intricacies of multi-stage manufacturing, featuring a combination of manual and automated processes. The case study is intentionally illustrative; the goal is to demonstrate the feasibility and practical utility of a NDEA-based PMS rather than to generate statistically generalizable results. Using a case study of intravenous (IV) set production, the research illustrates how NDEA can be operationalized to: Provide a practical framework for routine efficiency monitoring and process improvement. Identify stage-specific inefficiencies as the focus of process improvement. Evaluate trade-offs between model granularity and discrimination power; By embedding model selection criteria, incorporating stage validation with production engineers, and accounting for undesirable outputs, the study provides a robust methodology for implementing NDEA in operational settings. In doing so, it bridges the theoretical development of network DEA with practical performance management needs in complex manufacturing environments.” 2- Statistical Validation: The current comparisons between models (1-stage vs 3-stage, etc.) are based solely on descriptive statistics (means, standard deviations). There is no measure of uncertainty. Suggestions: Use DEA bootstrap methods to compute confidence intervals for efficiency scores and test differences between models. Report p-values or other statistical tests (e.g. Friedman test) to support claims that one model discriminates better than another. Add sensitivity analysis: show how results change with small data perturbations or alternate variable selections. Response: We thank you for raising the critical point regarding the statistical validation. Due to the limited DMU sample, formal bootstrap methods and statistical tests were not applied. Our study focuses on developing the practical NDEA-based PMS framework. To follow up your suggestion, we have clarified this limitation in the Methodology and Discussion. To address the reviewer’s concern, we have revised the manuscript to: In introduction: Explicitly state that the aim of the study is methodological framework development rather than statistical inference, in introduction, as addressed in point 1. In methodology: Explicit statement in the methodology that that statistical validation is not feasible with the available sample. In conclusion: Acknowledge that bootstrap DEA and statistical testing represent valuable extensions, but are not feasible given the illustrative nature and limited data availability of the case setting. Suggest these techniques as directions for future research, particularly when larger datasets are available to support inferential analysis and sensitivity testing. 3- Model Selection Justification: The choice of the three-stage model is based on a qualitative “parsimony” argument, but this is not rigorous. Suggestions: Define explicit criteria for selecting the preferred model (for instance, minimize the number of efficient DMUs while still capturing all key processes). Consider formal model selection metrics (analogous to AIC, BIC) or cross-validation if possible. Explain why three stages reflect the real production better than two or four stages. Consult with production engineers to validate the stage grouping (the reader should see why combining certain stages makes sense). Response: We thank you for highlighting the need for a more rigorous justification of the chosen three-stage model. We agree that a qualitative parsimony argument alone is insufficient and have revised the manuscript accordingly. In the revised version of our methodology : We have adopted an explicit criterion for model selection: the preferred model minimizes the number of efficient DMUs while capturing all key production processes, aligning with your suggestion. Additionally, we have strengthened the justification for the three-stage structure by incorporating consultation with production engineers from the participating pharmaceutical manufacturer. Their expert input confirmed that the three-stage grouping most accurately reflects the functional and operational flow of the IV-set production line. In particular, they indicated that: A two-stage model would oversimplify the process and obscure necessary intermediate transformations. A four-stage model would fragment stages that are tightly integrated in practice, offering limited additional managerial insight. 4- Data Transparency: The data are proprietary, but for scientific reporting, we need some level of transparency. Suggestions: Provide summary statistics (means, ranges) for each input and output variable. If possible, anonymize and share data (even if synthetic with a similar structure) as supplementary material. At a minimum, explicitly list all inputs/outputs by stage (not just symbolic names in Table 3). Briefly describe how each was measured. Response: We thank you for emphasizing the importance of data transparency in scientific reporting. While we fully agree with the need for sufficient clarity to ensure reproducibility, we regret that the raw operational data cannot be shared due to strict confidentiality agreements with the participating pharmaceutical manufacturer. These data contain commercially sensitive production information that cannot be disclosed publicly, even in anonymized or synthetic form, under the terms of our partnership. To address the reviewer’s concern while respecting these constraints, we have provided the manuscript to strengthen transparency in the following ways: Detailed variable listing: We have expanded the description of the variables by explicitly listing all inputs, outputs, and intermediate flows by stage. Measurement explanations: For each variable, we now provide a brief explanation of how it is measured in the production process to ensure clarity and interpretability for readers. Clarification of confidentiality limitations: We explicitly state in the text that raw or shareable datasets cannot be provided due to proprietary restrictions. These revisions ensure that the methodology and variable structure are fully transparent, allowing readers to understand and interpret the model even without access to the protected dataset. 5- Case and Context Details: The narrative lacks detail on the real-world context and how the study was motivated or used. Suggestions: Quantify the performance gaps that prompted this study (e.g. known waste rates, downtime percentages prior to analysis). Explain why the 12-month period was chosen (seasonality issues? data availability?). Describe how managers or operators participated: was the NDEA analysis actually implemented or tested in operations? If any follow-up actions were taken (process changes, investments) as a result of this analysis, briefly mention them. Response: We thank the reviewer for highlighting the need to provide more contextual details and practical motivation for the case study. We have revised the manuscript to clarify the real-world context, quantify performance gaps, and describe managerial participation. We have added these details in the Case Study section (Section 5) to provide readers with a clearer understanding of the real-world context, performance gaps, and managerial involvement. 6. Literature Review and References: Some relevant recent work appears to be missing. Suggestions: Include citations for modern NDEA applications and improvements (e.g. dynamic/network DEA, bootstrap DEA methods, DEA in healthcare or manufacturing post-2020). Mention any case studies of NDEA in pharmaceuticals or medical devices if available. Update or clarify references that seem only tangentially related (e.g. if a study is on general DEA in banking, explain relevance). Response: Thank you for your valuable feedback. To follow up, we revised our manuscript (in the introduction and literature review sections ) by incorporating citations of recent (post-2020) applications of network/decomposed DEA, including dynamic/network DEA, bootstrap methods, and hybrid approaches in manufacturing and supply chain contexts. This situates our work at the current frontier of efficiency measurement research and clarifies our choice of non-radial, non-oriented NSBM CRS over dynamic or bootstrap methods. Meanwhile, we reviewed literature on NDEA applications in pharmaceuticals and medical devices, highlighting this emerging area. When no prior studies were found, we noted the gap and positioned our case study as an early contribution. We also clarified non-manufacturing DEA references by adding sentences that explain their relevance, focusing on methodological insights and discrimination power. We added more relevant references, as in the earlier note, to enhance the coherence of the literature review. 7. Presentation and Clarity: There are multiple formatting and clarity issues. Suggestions: Carefully proofread: fix typos and spacing (e.g. “therewas,” “developingNDEA-based,” etc.). Standardize notation: ensure all variables (x, y, z) and subscripts are consistent and clearly defined (some appear cut or incomplete in tables/figures). Improve figures/tables: For example, Figure 3 (NDEA model diagrams) needs clear labels and captions; Table 5 should have a complete caption and readable formatting. In the text, clearly refer to all figures and tables at first mention and summarize their key points in words. Response: We appreciate the reviewer’s careful attention to presentation and clarity. In response, we have undertaken a thorough review of the manuscript and implemented the improvements through the entire manuscript. 8. Results Interpretation: The discussion should more directly connect to the literature and avoid overclaiming. Suggestions: When stating that “this contradicts classical DEA,” clarify whether you ran a classical DEA or are inferring from others’ claims. If you did classical DEA (1-stage model), present those results side-by-side. Frame findings as preliminary insights (“in this case, we found…”) rather than definitive rules. Compare your results with any similar studies (if available). Discuss whether, for instance, identifying the first stage as inefficient agrees with known industry benchmarks. Response: We thank the reviewer for highlighting the need for more careful interpretation of the results and closer linkage to the literature. In response, we have revised the entire structure of Results and Discussion. 9. Conclusions and Claims: Several claims go beyond the data. Suggestions: Tone down statements about generalizability (e.g. “applicable to automotive/electronics”). Instead, suggest these as potential extensions with future validation. Reiterate study limitations (sample size, single site, confidentiality constraints) explicitly in the conclusion. Clearly link each major conclusion back to specific results shown (for example, “Based on Table 4, models with more stages had higher average efficiency scores and more efficient DMUs, indicating lower discrimination”). Response: We appreciate the reviewer for highlighting the importance of moderating claims and clarifying the limitations of our study. We have revised the Conclusion section accordingly. Specifically, we have: Softened statements regarding the generalizability of the results in both the results and discussion sections. Clearly reiterated the study's limitations, including sample size, being conducted at a single site, and confidentiality constraints, in the limitations section of the conclusion . Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 14 Mar 2026 Mahmood Shubbak , Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman 14 Mar 2026 Author Response We have addressed your comments in the revised version of our manuscript. Please check it. Competing Interests: No competing interests were disclosed. We have addressed your comments in the revised version of our manuscript. Please check it. We have addressed your comments in the revised version of our manuscript. Please check it. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Jilcha K. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.183368.r405835 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v1#referee-response-405835 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 10 Sep 2025 Kassu Jilcha , College of Technology and Built Environment, Addis Ababa, Addis Ababa, Ethiopia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.183368.r405835 Abstract Enhancement: The abstract should incorporate a clear statement regarding the ordinality or significance of this paper within the broader field. It is essential to articulate how this research contributes to existing knowledge, highlighting its unique value and relevance. ... Continue reading READ ALL Abstract Enhancement: The abstract should incorporate a clear statement regarding the ordinality or significance of this paper within the broader field. It is essential to articulate how this research contributes to existing knowledge, highlighting its unique value and relevance. Introduction Citations: The introduction section requires the inclusion of citations from recent studies published in 2025. This will help position the current research within the most up-to-date context, demonstrating engagement with the latest developments in the field. Citation Style Improvement: The citation style currently utilized, such as (Färe & Primont, 1984), should be revised. It is recommended to remove the brackets around the authors’ names and to place the publication year within brackets. This change will enhance readability and align the citations with standard practices in academic writing. Literature Review Depth: The literature review appears to be somewhat superficial. It is important to enrich this section with references to recent studies to provide a more comprehensive overview of the current state of research. Incorporating more contemporary sources will strengthen the foundation of this paper. Results and Discussion Expansion: The results and discussion section lacks depth and requires a more detailed comparison of the findings with prior studies. A thorough analysis that juxtaposes current results with existing literature will provide a clearer understanding of the implications and significance of the research outcomes. Is the background of the case’s history and progression described in sufficient detail? Partly Is the work clearly and accurately presented and does it cite the current literature? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Is the case presented with sufficient detail to be useful for teaching or other practitioners? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Innovation and industrial engineering areas I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Jilcha K. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.183368.r405835 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v1#referee-response-405835 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Dec 2025 Mahmood Shubbak , Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman 10 Dec 2025 Author Response AUTHORS' RESPONSE TO REVIEWER #1: Kassu Jilcha Comment #1: Abstract Enhancement: The abstract should incorporate a clear statement regarding the ordinality or significance of this paper within the broader ... Continue reading AUTHORS' RESPONSE TO REVIEWER #1: Kassu Jilcha Comment #1: Abstract Enhancement: The abstract should incorporate a clear statement regarding the ordinality or significance of this paper within the broader field. It is essential to articulate how this research contributes to existing knowledge, highlighting its unique value and relevance. Response; We appreciate your feedback. The significance of the paper has been incorporated into the conclusion section of the abstract as follows: “This study holds significance within the broader field of performance measurement and efficiency analysis by bridging theoretical modelling and practical implementation. It advances existing knowledge through the integration of NDEA into a process-based PMS, offering a novel analytical framework for multi-stage manufacturing systems. By examining the trade-off between model complexity and discrimination power, this research contributes new methodological insights and extends the applicability of NDEA in real-world industrial settings. The framework offers managers actionable guidance for optimizing multi-stage manufacturing operations and contributes novel insights into the methodological behaviour of NDEA. Ultimately, this work strengthens the linkage between performance measurement theory and industrial practice, positioning NDEA as a valuable tool for continuous improvement in manufacturing systems.” Comment #2: Introduction Citations: The introduction section requires the inclusion of citations from recent studies published in 2025. This will help position the current research within the most up-to-date context, demonstrating engagement with the latest developments in the field. Response: thank you for your feedback. We have added some updated citations, for example at the end of the first paragraph” Robust and flexible performance measurement tools are not just administrative necessities, they are strategic enablers that help organizations navigate financial burdens, enhance competitiveness, and foster the innovation and market dynamics needed for future success (Calik, 2024; Xing et al., 2025; Xu & Zhu, 2024).“ and also at the beginning of the third paragraph. Comment #3: Citation Style Improvement: The citation style currently utilized, such as (Färe & Primont, 1984),should be revised. It is recommended to remove the brackets around the authors’ names and to place the publication year within brackets. This change will enhance readability and align the citations with standard practices in academic writing. Response; Thank you for your correction. We have updated the citation style, specifically by removing the brackets around the author’s names at the beginning of sentences and placing the publication year within brackets. This adjustment is reflected in the literature review: “The Basic Concept of NDEA: Färe & Primont (1984) initiated the exploration of the 'black-box' system of classical DEA” Comment #4: Literature Review Depth: The literature review appears to be somewhat superficial. It is important to enrich this section with references to recent studies to provide a more comprehensive overview of the current state of research. Incorporating more contemporary sources will strengthen the foundation of this paper. Response: Thank you for this valuable comment. We agree that strengthening the depth and currency of the literature review will improve the overall foundation of the paper. In the revised manuscript, we have expanded the literature review to incorporate a broader range of recent and relevant studies, for example ”Recent research has advanced the treatment of intermediate and undesirable outputs, refining NDEA models to better capture complex network structures, dual-role factors, and process interdependencies. For example, Lotfi et al. (2023) applied an NDEA model to assess both desirable and undesirable outputs in the wheat supply chain, demonstrating enhanced accuracy in identifying stage-specific inefficiencies. Ma et al. (2025) proposed a network slack-based measure incorporating dual-role factors and undesirable outputs to evaluate supply chain performance, highlighting the practical relevance of NDEA in complex production networks. (Yang et al., 2024) further demonstrate the flexibility of NDEA by incorporating shared resources, negative data, and undesirable outputs in a multi-stage airline efficiency context, highlighting its capacity to model interdependencies realistically. This development allows decision-makers to simultaneously optimize performance while reducing waste or other negative byproducts, thereby providing a more nuanced and actionable understanding of operational efficiency. Essentially, NDEA’s ability to model undesirable outputs transforms efficiency assessment into a more realistic and strategically valuable instrument for complex production systems. These revisions provide a more comprehensive overview of the current research landscape and better contextualize the contribution of our work. We appreciate your guidance, which has helped us enhance the rigour and relevance of this section. AUTHORS' RESPONSE TO REVIEWER #1: Kassu Jilcha Comment #1: Abstract Enhancement: The abstract should incorporate a clear statement regarding the ordinality or significance of this paper within the broader field. It is essential to articulate how this research contributes to existing knowledge, highlighting its unique value and relevance. Response; We appreciate your feedback. The significance of the paper has been incorporated into the conclusion section of the abstract as follows: “This study holds significance within the broader field of performance measurement and efficiency analysis by bridging theoretical modelling and practical implementation. It advances existing knowledge through the integration of NDEA into a process-based PMS, offering a novel analytical framework for multi-stage manufacturing systems. By examining the trade-off between model complexity and discrimination power, this research contributes new methodological insights and extends the applicability of NDEA in real-world industrial settings. The framework offers managers actionable guidance for optimizing multi-stage manufacturing operations and contributes novel insights into the methodological behaviour of NDEA. Ultimately, this work strengthens the linkage between performance measurement theory and industrial practice, positioning NDEA as a valuable tool for continuous improvement in manufacturing systems.” Comment #2: Introduction Citations: The introduction section requires the inclusion of citations from recent studies published in 2025. This will help position the current research within the most up-to-date context, demonstrating engagement with the latest developments in the field. Response: thank you for your feedback. We have added some updated citations, for example at the end of the first paragraph” Robust and flexible performance measurement tools are not just administrative necessities, they are strategic enablers that help organizations navigate financial burdens, enhance competitiveness, and foster the innovation and market dynamics needed for future success (Calik, 2024; Xing et al., 2025; Xu & Zhu, 2024).“ and also at the beginning of the third paragraph. Comment #3: Citation Style Improvement: The citation style currently utilized, such as (Färe & Primont, 1984),should be revised. It is recommended to remove the brackets around the authors’ names and to place the publication year within brackets. This change will enhance readability and align the citations with standard practices in academic writing. Response; Thank you for your correction. We have updated the citation style, specifically by removing the brackets around the author’s names at the beginning of sentences and placing the publication year within brackets. This adjustment is reflected in the literature review: “The Basic Concept of NDEA: Färe & Primont (1984) initiated the exploration of the 'black-box' system of classical DEA” Comment #4: Literature Review Depth: The literature review appears to be somewhat superficial. It is important to enrich this section with references to recent studies to provide a more comprehensive overview of the current state of research. Incorporating more contemporary sources will strengthen the foundation of this paper. Response: Thank you for this valuable comment. We agree that strengthening the depth and currency of the literature review will improve the overall foundation of the paper. In the revised manuscript, we have expanded the literature review to incorporate a broader range of recent and relevant studies, for example ”Recent research has advanced the treatment of intermediate and undesirable outputs, refining NDEA models to better capture complex network structures, dual-role factors, and process interdependencies. For example, Lotfi et al. (2023) applied an NDEA model to assess both desirable and undesirable outputs in the wheat supply chain, demonstrating enhanced accuracy in identifying stage-specific inefficiencies. Ma et al. (2025) proposed a network slack-based measure incorporating dual-role factors and undesirable outputs to evaluate supply chain performance, highlighting the practical relevance of NDEA in complex production networks. (Yang et al., 2024) further demonstrate the flexibility of NDEA by incorporating shared resources, negative data, and undesirable outputs in a multi-stage airline efficiency context, highlighting its capacity to model interdependencies realistically. This development allows decision-makers to simultaneously optimize performance while reducing waste or other negative byproducts, thereby providing a more nuanced and actionable understanding of operational efficiency. Essentially, NDEA’s ability to model undesirable outputs transforms efficiency assessment into a more realistic and strategically valuable instrument for complex production systems. These revisions provide a more comprehensive overview of the current research landscape and better contextualize the contribution of our work. We appreciate your guidance, which has helped us enhance the rigour and relevance of this section. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Dec 2025 Mahmood Shubbak , Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman 10 Dec 2025 Author Response AUTHORS' RESPONSE TO REVIEWER #1: Kassu Jilcha Comment #1: Abstract Enhancement: The abstract should incorporate a clear statement regarding the ordinality or significance of this paper within the broader ... Continue reading AUTHORS' RESPONSE TO REVIEWER #1: Kassu Jilcha Comment #1: Abstract Enhancement: The abstract should incorporate a clear statement regarding the ordinality or significance of this paper within the broader field. It is essential to articulate how this research contributes to existing knowledge, highlighting its unique value and relevance. Response; We appreciate your feedback. The significance of the paper has been incorporated into the conclusion section of the abstract as follows: “This study holds significance within the broader field of performance measurement and efficiency analysis by bridging theoretical modelling and practical implementation. It advances existing knowledge through the integration of NDEA into a process-based PMS, offering a novel analytical framework for multi-stage manufacturing systems. By examining the trade-off between model complexity and discrimination power, this research contributes new methodological insights and extends the applicability of NDEA in real-world industrial settings. The framework offers managers actionable guidance for optimizing multi-stage manufacturing operations and contributes novel insights into the methodological behaviour of NDEA. Ultimately, this work strengthens the linkage between performance measurement theory and industrial practice, positioning NDEA as a valuable tool for continuous improvement in manufacturing systems.” Comment #2: Introduction Citations: The introduction section requires the inclusion of citations from recent studies published in 2025. This will help position the current research within the most up-to-date context, demonstrating engagement with the latest developments in the field. Response: thank you for your feedback. We have added some updated citations, for example at the end of the first paragraph” Robust and flexible performance measurement tools are not just administrative necessities, they are strategic enablers that help organizations navigate financial burdens, enhance competitiveness, and foster the innovation and market dynamics needed for future success (Calik, 2024; Xing et al., 2025; Xu & Zhu, 2024).“ and also at the beginning of the third paragraph. Comment #3: Citation Style Improvement: The citation style currently utilized, such as (Färe & Primont, 1984),should be revised. It is recommended to remove the brackets around the authors’ names and to place the publication year within brackets. This change will enhance readability and align the citations with standard practices in academic writing. Response; Thank you for your correction. We have updated the citation style, specifically by removing the brackets around the author’s names at the beginning of sentences and placing the publication year within brackets. This adjustment is reflected in the literature review: “The Basic Concept of NDEA: Färe & Primont (1984) initiated the exploration of the 'black-box' system of classical DEA” Comment #4: Literature Review Depth: The literature review appears to be somewhat superficial. It is important to enrich this section with references to recent studies to provide a more comprehensive overview of the current state of research. Incorporating more contemporary sources will strengthen the foundation of this paper. Response: Thank you for this valuable comment. We agree that strengthening the depth and currency of the literature review will improve the overall foundation of the paper. In the revised manuscript, we have expanded the literature review to incorporate a broader range of recent and relevant studies, for example ”Recent research has advanced the treatment of intermediate and undesirable outputs, refining NDEA models to better capture complex network structures, dual-role factors, and process interdependencies. For example, Lotfi et al. (2023) applied an NDEA model to assess both desirable and undesirable outputs in the wheat supply chain, demonstrating enhanced accuracy in identifying stage-specific inefficiencies. Ma et al. (2025) proposed a network slack-based measure incorporating dual-role factors and undesirable outputs to evaluate supply chain performance, highlighting the practical relevance of NDEA in complex production networks. (Yang et al., 2024) further demonstrate the flexibility of NDEA by incorporating shared resources, negative data, and undesirable outputs in a multi-stage airline efficiency context, highlighting its capacity to model interdependencies realistically. This development allows decision-makers to simultaneously optimize performance while reducing waste or other negative byproducts, thereby providing a more nuanced and actionable understanding of operational efficiency. Essentially, NDEA’s ability to model undesirable outputs transforms efficiency assessment into a more realistic and strategically valuable instrument for complex production systems. These revisions provide a more comprehensive overview of the current research landscape and better contextualize the contribution of our work. We appreciate your guidance, which has helped us enhance the rigour and relevance of this section. AUTHORS' RESPONSE TO REVIEWER #1: Kassu Jilcha Comment #1: Abstract Enhancement: The abstract should incorporate a clear statement regarding the ordinality or significance of this paper within the broader field. It is essential to articulate how this research contributes to existing knowledge, highlighting its unique value and relevance. Response; We appreciate your feedback. The significance of the paper has been incorporated into the conclusion section of the abstract as follows: “This study holds significance within the broader field of performance measurement and efficiency analysis by bridging theoretical modelling and practical implementation. It advances existing knowledge through the integration of NDEA into a process-based PMS, offering a novel analytical framework for multi-stage manufacturing systems. By examining the trade-off between model complexity and discrimination power, this research contributes new methodological insights and extends the applicability of NDEA in real-world industrial settings. The framework offers managers actionable guidance for optimizing multi-stage manufacturing operations and contributes novel insights into the methodological behaviour of NDEA. Ultimately, this work strengthens the linkage between performance measurement theory and industrial practice, positioning NDEA as a valuable tool for continuous improvement in manufacturing systems.” Comment #2: Introduction Citations: The introduction section requires the inclusion of citations from recent studies published in 2025. This will help position the current research within the most up-to-date context, demonstrating engagement with the latest developments in the field. Response: thank you for your feedback. We have added some updated citations, for example at the end of the first paragraph” Robust and flexible performance measurement tools are not just administrative necessities, they are strategic enablers that help organizations navigate financial burdens, enhance competitiveness, and foster the innovation and market dynamics needed for future success (Calik, 2024; Xing et al., 2025; Xu & Zhu, 2024).“ and also at the beginning of the third paragraph. Comment #3: Citation Style Improvement: The citation style currently utilized, such as (Färe & Primont, 1984),should be revised. It is recommended to remove the brackets around the authors’ names and to place the publication year within brackets. This change will enhance readability and align the citations with standard practices in academic writing. Response; Thank you for your correction. We have updated the citation style, specifically by removing the brackets around the author’s names at the beginning of sentences and placing the publication year within brackets. This adjustment is reflected in the literature review: “The Basic Concept of NDEA: Färe & Primont (1984) initiated the exploration of the 'black-box' system of classical DEA” Comment #4: Literature Review Depth: The literature review appears to be somewhat superficial. It is important to enrich this section with references to recent studies to provide a more comprehensive overview of the current state of research. Incorporating more contemporary sources will strengthen the foundation of this paper. Response: Thank you for this valuable comment. We agree that strengthening the depth and currency of the literature review will improve the overall foundation of the paper. In the revised manuscript, we have expanded the literature review to incorporate a broader range of recent and relevant studies, for example ”Recent research has advanced the treatment of intermediate and undesirable outputs, refining NDEA models to better capture complex network structures, dual-role factors, and process interdependencies. For example, Lotfi et al. (2023) applied an NDEA model to assess both desirable and undesirable outputs in the wheat supply chain, demonstrating enhanced accuracy in identifying stage-specific inefficiencies. Ma et al. (2025) proposed a network slack-based measure incorporating dual-role factors and undesirable outputs to evaluate supply chain performance, highlighting the practical relevance of NDEA in complex production networks. (Yang et al., 2024) further demonstrate the flexibility of NDEA by incorporating shared resources, negative data, and undesirable outputs in a multi-stage airline efficiency context, highlighting its capacity to model interdependencies realistically. This development allows decision-makers to simultaneously optimize performance while reducing waste or other negative byproducts, thereby providing a more nuanced and actionable understanding of operational efficiency. Essentially, NDEA’s ability to model undesirable outputs transforms efficiency assessment into a more realistic and strategically valuable instrument for complex production systems. These revisions provide a more comprehensive overview of the current research landscape and better contextualize the contribution of our work. We appreciate your guidance, which has helped us enhance the rigour and relevance of this section. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Ullagaddi p. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.183368.r405839 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v1#referee-response-405839 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Sep 2025 pravin Ullagaddi , University of the Cumberlands, Williamsburg, Kentucky, USA Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.183368.r405839 This manuscript presents a case study applying Network Data Envelopment Analysis (NDEA) to evaluate performance in pharmaceutical manufacturing, specifically focusing on intravenous (IV) set production. The authors analyze a single pharmaceutical company's production line across six interconnected workstations over ... Continue reading READ ALL This manuscript presents a case study applying Network Data Envelopment Analysis (NDEA) to evaluate performance in pharmaceutical manufacturing, specifically focusing on intravenous (IV) set production. The authors analyze a single pharmaceutical company's production line across six interconnected workstations over 12 monthly periods, comparing five different NDEA model configurations (1-stage through 6-stage). They conclude that a 3-stage model provides the optimal balance between analytical detail and discrimination power, and propose a generic framework for implementing NDEA-based Performance Measurement Systems (PMS) in manufacturing environments. The study identifies the PVC granulation stage as the primary source of inefficiency and recommends substantial reductions in inputs and undesirable outputs. The authors claim their framework contributes to manufacturing PMS literature and provides actionable guidance for multi-stage production systems. Critical Assessment Scientific Soundness Issues (Must Be Addressed) 1. Fundamental Sample Size Violation The most critical flaw is the use of only 12 Decision Making Units (DMUs) with models containing up to 15+ variables. This violates the basic DEA requirement that sample size should be at least twice the number of inputs plus outputs. This violation renders all efficiency scores and model comparisons unreliable. Required remediation: Expand the dataset to include at least 36 monthly observations, or Reduce model complexity to maximum 6 total variables, or Include multiple production lines/facilities to increase DMU count, or Acknowledge this as a pilot study with limited generalizability 2. Absence of Statistical Validation The paper presents efficiency scores and model comparisons without confidence intervals, significance tests, or uncertainty measures. Claims about discrimination power differences rely solely on descriptive statistics. Required remediation: Implement bootstrap methodology to generate confidence intervals for all efficiency scores Conduct statistical hypothesis tests for model comparison claims Add sensitivity analysis for variable selection and model specification Report p-values for claimed differences between models 3. Inadequate Model Selection Justification The selection of the 3-stage model as "optimal" lacks rigorous criteria. The authors cite parsimony and discrimination power but provide no statistical framework for this critical decision. Required remediation: Develop and apply formal model selection criteria (AIC, cross-validation, etc.) Test multiple variable combinations within each stage configuration Provide theoretical justification for why 3 stages best represent the production system Validate the stage structure with process engineers and production experts Literature and Presentation Issues 4. Outdated and Incomplete Literature Review The literature review misses significant recent developments in NDEA methodology and manufacturing performance measurement, relying heavily on foundational papers from the 1980s-2000s. Recommended improvements: Conduct systematic review of NDEA applications in manufacturing (2015-2024) Include recent advances in bootstrap DEA, dynamic NDEA, and two-stage models Cover pharmaceutical-specific performance measurement frameworks Address Industry 4.0 and digital manufacturing measurement approaches 5. Poor Presentation Quality Mathematical notation is inconsistent, figures are unclear, and writing quality impedes comprehension. Recommended improvements: Redesign Figure 3 with clear visual hierarchy and consistent labeling Standardize mathematical notation throughout Provide comprehensive copyediting for grammar and clarity Add detailed captions explaining all figure elements Data and Reproducibility Concerns 6. Complete Data Unavailability All source data is confidential and inaccessible, preventing independent verification or replication. Recommended approaches: Create synthetic datasets that preserve analytical relationships Provide detailed data generation procedures for replication Partner with other manufacturers to create multi-site validation At minimum, provide detailed descriptive statistics for all variables 7. Insufficient Case Context The background lacks detail necessary for understanding the organizational setting, problem severity, and implementation context. Required additions: Quantify baseline performance problems that motivated the study Describe the company's operational maturity and previous improvement initiatives Explain the 12-month study period selection and any external influences Document stakeholder engagement and validation processes Report actual implementation outcomes and organizational responses Methodological Limitations 8. Weak Theoretical Foundation The paper treats NDEA application as a primarily technical exercise without grounding in operations management theory or pharmaceutical manufacturing strategy. Recommended strengthening: Connect the analysis to manufacturing strategy frameworks Justify variable selection based on operations theory Link findings to broader pharmaceutical industry performance challenges Integrate regulatory compliance considerations into the analysis 9. Overstated Conclusions Claims about generalizability, framework novelty, and practical impact far exceed what the limited analysis supports. Required modifications: Restrict conclusions to the specific case studied Acknowledge the pilot nature of the study Remove claims about broad industry applicability without validation Present the framework as preliminary rather than proven Constructive Recommendations For Immediate Revision: Expand the empirical foundation by including additional time periods, production lines, or partner organizations to achieve adequate sample size Implement statistical rigor through bootstrap confidence intervals, hypothesis testing, and sensitivity analysis Strengthen case documentation with detailed organizational context, problem quantification, and implementation outcomes Moderate conclusions to reflect the study's limitations and preliminary nature For Long-term Research Development: Multi-site validation across different pharmaceutical manufacturers to test framework generalizability Longitudinal analysis tracking performance improvements over extended periods following NDEA implementation Comparative methodology study evaluating NDEA against alternative performance measurement approaches in manufacturing contexts Integration research examining how NDEA-based PMS interfaces with existing ERP/MES systems and organizational processes Verdict This manuscript addresses a relevant practical problem but suffers from fundamental methodological flaws that compromise its scientific validity. The sample size violation alone disqualifies the statistical analysis, while the absence of validation, weak theoretical foundation, and overstated conclusions further limit its contribution. Recommendation: Major revision is required with particular attention to the sample size issue, statistical validation, and conclusion moderation. The authors should consider repositioning this as a preliminary pilot study rather than a definitive framework development, with clear acknowledgment of limitations and need for further validation. The pharmaceutical manufacturing community would benefit from rigorous research in this area, but this work requires substantial strengthening to meet academic indexing standards and provide reliable guidance to practitioners. Is the background of the case’s history and progression described in sufficient detail? Partly Is the work clearly and accurately presented and does it cite the current literature? No If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No Is the case presented with sufficient detail to be useful for teaching or other practitioners? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Manufacturing Systems, Statistical analyses, Process Optimization, Pharmaceutical Manufacturing I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ullagaddi p. Reviewer Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.183368.r405839 ) The direct URL for this report is: https://f1000research.com/articles/14-710/v1#referee-response-405839 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 14 Mar 2026 Mahmood Shubbak , Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman 14 Mar 2026 Author Response We have revised our manuscript. Kindly check the new version. Also kindly note that its category is Case Study. Competing Interests: No competing interests were disclosed. We have revised our manuscript. Kindly check the new version. Also kindly note that its category is Case Study. We have revised our manuscript. Kindly check the new version. Also kindly note that its category is Case Study. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 14 Mar 2026 Mahmood Shubbak , Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman 14 Mar 2026 Author Response We have revised our manuscript. Kindly check the new version. Also kindly note that its category is Case Study. Competing Interests: No competing interests were disclosed. We have revised our manuscript. Kindly check the new version. Also kindly note that its category is Case Study. We have revised our manuscript. Kindly check the new version. Also kindly note that its category is Case Study. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 18 Jul 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 5 Version 2 (revision) 04 Dec 25 read read read Version 1 18 Jul 25 read read read pravin Ullagaddi , University of the Cumberlands, Williamsburg, USA Kassu Jilcha , College of Technology and Built Environment, Addis Ababa, Ethiopia Rashed Ahmed , North South University, Dhaka, Bangladesh Dr. Santhosh Illendula , Vijaya College of Pharmacy, Hayathnagar, India Hayder Mohammed Issa , University of Garmian, Kalar, Iraq Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Issa H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 21 Apr 2026 | for Version 2 Hayder Mohammed Issa , University of Garmian, Kalar, Iraq 0 Views copyright © 2026 Issa H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript demonstrates significant academic and practical value by employing a non-oriented, CRS-NSBM Network Data Envelopment Analysis to disaggregate the traditional "black-box" manufacturing model, thereby providing actionable, stage-specific insights into the production of intravenous sets. This approach is particularly commendable for its integration of undesirable outputs, such as machine downtime and rejected products, which allows for a more realistic appraisal of operational inefficiencies, specifically identifying PVC granulation as the most volatile and inefficient stage. While the research effectively navigates the inherent trade-off between model granularity and discrimination power—rightly favoring the three-stage model for its balanced analytical robustness—the work could be further strengthened by addressing the constraints of the 12-month sample size, perhaps through the future consideration of higher-frequency weekly data to enhance statistical discrimination. Furthermore, acknowledging how the substantial potential improvements identified, such as the proposed 45% reduction in material consumption, would interface with the stringent validated state and regulatory requirements typical of pharmaceutical environments would offer a more nuanced perspective for practitioners. Notwithstanding these minor, optional considerations for future refinement, the paper’s methodological rigor and its successful bridging of performance measurement theory with industrial application make it a reasonable contribution to the field, and I recommend its acceptance as it stands. Is the background of the case’s history and progression described in sufficient detail? Yes Is the work clearly and accurately presented and does it cite the current literature? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Is the case presented with sufficient detail to be useful for teaching or other practitioners? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Process Engineering, Pharmaceutical Processing, Process Design & Development. Process Simulation & Modelling I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Issa HM. Peer Review Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.192568.r472794) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-710/v2#referee-response-472794 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Illendula D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 14 Apr 2026 | for Version 2 Dr. Santhosh Illendula , Vijaya College of Pharmacy, Hayathnagar, Telangana, India 0 Views copyright © 2026 Illendula D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The author has described case study report satisfactory Author has well explained the study with Interaction of inputs, intermediate factors, and outputs in five NDEA models, Interaction among input, output, and intermediate factors in two-stage manufacturing operations. NDEA optimizes multi-stage pharmaceutical manufacturing by "opening the black box" of production to analyze, measure, and improve individual stages (e.g., synthesis, formulation, packaging) rather than just the final output. It identifies specific inefficiency sources, handles intermediate product transfers, enables better resource allocation, and supports data-driven decisions. These case study report has been satisfactory and accepted further process, Thank you Is the background of the case’s history and progression described in sufficient detail? Yes Is the work clearly and accurately presented and does it cite the current literature? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Is the case presented with sufficient detail to be useful for teaching or other practitioners? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Department of Pharmaceutical Analysis ( Pharmacy) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Illendula DS. Peer Review Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.192568.r469679) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-710/v2#referee-response-469679 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Jilcha K. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 26 Dec 2025 | for Version 2 Kassu Jilcha , College of Technology and Built Environment, Addis Ababa, Addis Ababa, Ethiopia 0 Views copyright © 2025 Jilcha K. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dears, The paper now has shown major improvement and if the case is needed, please try to restructure the paper as Introduction, Literature review (alone standing title), Methodology, Result and Discussion and Conclusion. Regards Competing Interests No competing interests were disclosed. Reviewer Expertise Innovation and industrial engineering areas I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Jilcha K. Peer Review Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.192568.r438579) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-710/v2#referee-response-438579 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Ahmed R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 24 Sep 2025 | for Version 1 Rashed Ahmed , North South University, Dhaka, Dhaka Division, Bangladesh 0 Views copyright © 2025 Ahmed R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (2) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Summary of the Article The manuscript presents a case study applying Network Data Envelopment Analysis (NDEA) to a pharmaceutical manufacturing line (intravenous (IV) sets) to build a process-based Performance Measurement System (PMS). The authors review DEA and NDEA theory and then describe the IV-set production process across six workstations. They collect 12 months of production data and compare five modeling scenarios (treating the process as 1 to 6 stages). Efficiency scores (DMU per month) are computed for each model; descriptive statistics show how many months are classified as “efficient.” From this analysis, the authors select a three-stage model (combining certain processes) as the preferred balance of discrimination power and detail. Using that model, they identify that the PVC granulation stage (Stage 1) is the least efficient (highest waste and downtime), whereas the final assembly stage is the most efficient. The NDEA yields slacks (shortfalls) indicating that large reductions in inputs (materials, labor, machine-hours) and undesirable outputs (rejects, downtime) would be needed to reach full efficiency. Based on these insights, the authors propose a practical NDEA-based PMS framework: it guides managers on how to select stages, benchmark against peer production months, monitor efficiency scores over time, and iteratively refine processes. The paper concludes that this framework “integrates NDEA into a practical PMS” and highlights its potential for guiding multi-stage manufacturing improvements and strategic decisions. The manuscript provides basic context about the pharmaceutical company (product lines, market share, regulatory environment) and maps the IV-set production stages. However, it lacks depth on the operational history or baseline performance issues motivating the study (e.g. why inefficiencies arose, any past improvements). Additional details (e.g. initial performance metrics, company’s maturity or prior interventions) would strengthen the case context.The overall narrative is understandable, and the paper cites many relevant sources (including recent 2022–2025 studies). However, the literature review omits several modern developments (e.g. recent advances in NDEA, bootstrap DEA, Industry 4.0 performance measurement) and some citations are outdated. The writing and notation have inconsistencies (e.g. merged words, unclear figures, table references), which occasionally impede clarity. Improved copy-editing, figure/table clarity, and inclusion of newer references would greatly enhance accuracy and readability.The study uses a non-parametric efficiency analysis (NDEA) without traditional statistical hypothesis tests or confidence intervals. The interpretation relies on descriptive statistics of efficiency scores. While NDEA itself is appropriate for the research question, the manuscript does not perform any statistical validation (e.g. bootstrap confidence intervals or tests) to support claims about differences between models or stages. Adding formal statistical validation methods would strengthen the analysis.The data are proprietary and require an NDA for access. The paper states that data are confidential, so independent researchers cannot verify the results. For reproducibility, the authors should provide (or detail) sufficient data summaries, synthetic datasets, or clear data processing steps. At minimum, comprehensive summary statistics or sample data should be supplied.The core findings (e.g. the 3-stage model’s balance between detail and discrimination, PVC granulation as the least efficient stage) align with the presented results. However, some claims exceed what the data show. For example, the assertion that this framework “bridges a significant gap” or that conclusions “contradict classical DEA” are overstated given the single-case analysis. The conclusions generalize broadly (to other industries or strategic decision-making) without validation. Toning down these claims and clearly linking conclusions to specific results would make them more defensible.The production process is well-mapped and described, and key efficiency scores are reported. Yet the case lacks certain practical details (e.g. actual input/output quantities, exact improvement actions taken, implementation challenges) that would benefit practitioners. For teaching purposes, more numerical examples or step-by-step illustrations of the NDEA application (beyond abstract descriptions) would improve usefulness. Comments to the Authors Thank you for addressing an important industrial problem by applying NDEA to pharmaceutical manufacturing. The case study is interesting, and the idea of integrating NDEA into a performance system is promising. However, the manuscript in its present form has multiple issues that must be addressed to make the study scientifically sound and useful. Below are the main concerns and suggestions: Sample Size and DEA Validity: You analyze only 12 DMUs (months) with many inputs and outputs (including intermediate flows). Standard DEA practice requires much larger samples (often 2–3× the total number of variables) to obtain reliable efficiency scores. With so few observations, nearly half the months are “efficient” by default. This undermines your conclusions about discrimination power. Suggestions: If more data are available (e.g. additional years or multiple production lines), include them to boost DMU count. Alternatively, simplify the model by reducing variables (e.g. combine some similar inputs or outputs) so that the 12 observations are sufficient. Clearly acknowledge this limitation in the text as a pilot study with limited generalizability. Avoid making broad claims based on this small sample. Statistical Validation: The current comparisons between models (1-stage vs 3-stage, etc.) are based solely on descriptive statistics (means, standard deviations). There is no measure of uncertainty. Suggestions: Use DEA bootstrap methods to compute confidence intervals for efficiency scores and test differences between models. Report p-values or other statistical tests (e.g. Friedman test) to support claims that one model discriminates better than another. Add sensitivity analysis: show how results change with small data perturbations or alternate variable selections. Model Selection Justification: The choice of the three-stage model is based on a qualitative “parsimony” argument, but this is not rigorous. Suggestions: Define explicit criteria for selecting the preferred model (for instance, minimize the number of efficient DMUs while still capturing all key processes). Consider formal model selection metrics (analogous to AIC, BIC) or cross-validation if possible. Explain why three stages reflect the real production better than two or four stages. Consult with production engineers to validate the stage grouping (the reader should see why combining certain stages makes sense). Data Transparency: The data are proprietary, but for scientific reporting, we need some level of transparency. Suggestions: Provide summary statistics (means, ranges) for each input and output variable. If possible, anonymize and share data (even if synthetic with a similar structure) as supplementary material. At a minimum, explicitly list all inputs/outputs by stage (not just symbolic names in Table 3). Briefly describe how each was measured. Case and Context Details: The narrative lacks detail on the real-world context and how the study was motivated or used. Suggestions: Quantify the performance gaps that prompted this study (e.g. known waste rates, downtime percentages prior to analysis). Explain why the 12-month period was chosen (seasonality issues? data availability?). Describe how managers or operators participated: was the NDEA analysis actually implemented or tested in operations? If any follow-up actions were taken (process changes, investments) as a result of this analysis, briefly mention them. Literature Review and References: Some relevant recent work appears to be missing. Suggestions: Include citations for modern NDEA applications and improvements (e.g. dynamic/network DEA, bootstrap DEA methods, DEA in healthcare or manufacturing post-2020). Mention any case studies of NDEA in pharmaceuticals or medical devices if available. Update or clarify references that seem only tangentially related (e.g. if a study is on general DEA in banking, explain relevance). Presentation and Clarity: There are multiple formatting and clarity issues. Suggestions: Carefully proofread: fix typos and spacing (e.g. “therewas,” “developingNDEA-based,” etc.). Standardize notation: ensure all variables (x, y, z) and subscripts are consistent and clearly defined (some appear cut or incomplete in tables/figures). Improve figures/tables: For example, Figure 3 (NDEA model diagrams) needs clear labels and captions; Table 5 should have a complete caption and readable formatting. In the text, clearly refer to all figures and tables at first mention and summarize their key points in words. Results Interpretation: The discussion should more directly connect to the literature and avoid overclaiming. Suggestions: When stating that “this contradicts classical DEA,” clarify whether you ran a classical DEA or are inferring from others’ claims. If you did classical DEA (1-stage model), present those results side-by-side. Frame findings as preliminary insights (“in this case, we found…”) rather than definitive rules. Compare your results with any similar studies (if available). Discuss whether, for instance, identifying the first stage as inefficient agrees with known industry benchmarks. Conclusions and Claims: Several claims go beyond the data. Suggestions: Tone down statements about generalizability (e.g. “applicable to automotive/electronics”). Instead, suggest these as potential extensions with future validation. Reiterate study limitations (sample size, single site, confidentiality constraints) explicitly in the conclusion. Clearly link each major conclusion back to specific results shown (for example, “Based on Table 4, models with more stages had higher average efficiency scores and more efficient DMUs, indicating lower discrimination”). By addressing the points above, the manuscript will be much stronger. In particular, focus on bolstering the methodological rigor (sample size issue, statistical validation) and ensuring claims are commensurate with what a single-case analysis can support. With these revisions, the paper will better serve practitioners interested in using NDEA for process improvement. Is the background of the case’s history and progression described in sufficient detail? Partly Is the work clearly and accurately presented and does it cite the current literature? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Partly Is the case presented with sufficient detail to be useful for teaching or other practitioners? Partly References 1. Pereira M, Dinis D, Ferreira D, Figueira J, et al.: A network Data Envelopment Analysis to estimate nations’ efficiency in the fight against SARS-CoV-2. Expert Systems with Applications . 2022; 210 . Publisher Full Text 2. Zubir M, Noor A, Mohd Rizal A, Harith A, et al.: Approach in inputs & outputs selection of Data Envelopment Analysis (DEA) efficiency measurement in hospitals: A systematic review. PLOS ONE . 2024; 19 (8). Publisher Full Text 3. Mitakos A, Mpogiatzidis P: Adapting Efficiency Analysis in Health Systems: A Scoping Review of Data Envelopment Analysis Applications During the COVID-19 Pandemic. Journal of Market Access & Health Policy . 2024; 12 (4): 306-316 Publisher Full Text 4. Kohl S, Schoenfelder J, Fügener A, Brunner J: The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science . 2019; 22 (2): 245-286 Publisher Full Text 5. Zhang T, Lu W, Tao H: Efficiency of health resource utilisation in primary-level maternal and child health hospitals in Shanxi Province, China: a bootstrapping data envelopment analysis and truncated regression approach. BMC Health Services Research . 2020; 20 (1). Publisher Full Text 6. Hou Y, Tao W, Hou S, Li W: Levels, trends, and determinants of effectiveness on the hierarchical medical system in China: Data envelopment analysis and bootstrapping truncated regression analysis. Frontiers in Public Health . 2022; 10 . Publisher Full Text 7. Pelone F, Kringos D, Romaniello A, Archibugi M, et al.: Primary Care Efficiency Measurement Using Data Envelopment Analysis: A Systematic Review. Journal of Medical Systems . 2015; 39 (1). Publisher Full Text 8. Tambare P, Meshram C, Lee C, Ramteke R, et al.: Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review. Sensors . 2021; 22 (1). Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise I am an operations researcher with applied expertise in Data Envelopment Analysis (including network and slacks-based models), statistical validation of frontier methods (bootstrap inference), and the design and evaluation of Performance Measurement Systems in manufacturing environments. My applied experience includes process mapping, efficiency improvement, and operational decision support in regulated manufacturing (pharmaceuticals/medical devices). I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (2) Author Response 10 Dec 2025 Mahmood Shubbak, Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman RESPONSE TO REVIEWER #2: Rashed Ahmed Comments to the Authors Thank you for addressing an important industrial problem by applying NDEA to pharmaceutical manufacturing. The case study is interesting, and the idea of integrating NDEA into a performance system is promising. However, the manuscript in its present form has multiple issues that must be addressed to make the study scientifically sound and useful. Below are the main concerns and suggestions: 1- Sample Size and DEA Validity: You analyze only 12 DMUs (months) with many inputs and outputs (including intermediate flows). Standard DEA practice requires much larger samples (often 2–3× the total number of variables) to obtain reliable efficiency scores. With so few observations, nearly half the months are “efficient” by default. This undermines your conclusions about discrimination power. Suggestions: If more data are available (e.g. additional years or multiple production lines), include them to boost DMU count. Alternatively, simplify the model by reducing variables (e.g. combine some similar inputs or outputs) so that the 12 observations are sufficient. Clearly acknowledge this limitation in the text as a pilot study with limited generalizability. Avoid making broad claims based on this small sample. Response: We thank you for highlighting the critical issue of sample size and its implications for the validity and discrimination power of DEA/NDEA models. We acknowledge that the small sample size limits statistical generalizability. The case study is intended as an illustrative example of our proposed NDEA-based PMS framework. We clarified this in the Introduction, Methodology, and Conclusion. To mitigate potential overclaiming, we also emphasized the descriptive and exploratory nature of the results rather than statistical inference. This article works under category of case study paper, thus the case is intentionally designed as an illustrative example, or demonstrative application of our proposed NDEA-based PMS framework. To follow up your note, we have revised the final part of the introduction as follows: “To answer these questions, this article presents a case study focused on the production line of a pharmaceutical company's intravenous (IV) sets, exemplifying the intricacies of multi-stage manufacturing, featuring a combination of manual and automated processes. The case study is intentionally illustrative; the goal is to demonstrate the feasibility and practical utility of a NDEA-based PMS rather than to generate statistically generalizable results. Using a case study of intravenous (IV) set production, the research illustrates how NDEA can be operationalized to: Provide a practical framework for routine efficiency monitoring and process improvement. Identify stage-specific inefficiencies as the focus of process improvement. Evaluate trade-offs between model granularity and discrimination power; By embedding model selection criteria, incorporating stage validation with production engineers, and accounting for undesirable outputs, the study provides a robust methodology for implementing NDEA in operational settings. In doing so, it bridges the theoretical development of network DEA with practical performance management needs in complex manufacturing environments.” 2- Statistical Validation: The current comparisons between models (1-stage vs 3-stage, etc.) are based solely on descriptive statistics (means, standard deviations). There is no measure of uncertainty. Suggestions: Use DEA bootstrap methods to compute confidence intervals for efficiency scores and test differences between models. Report p-values or other statistical tests (e.g. Friedman test) to support claims that one model discriminates better than another. Add sensitivity analysis: show how results change with small data perturbations or alternate variable selections. Response: We thank you for raising the critical point regarding the statistical validation. Due to the limited DMU sample, formal bootstrap methods and statistical tests were not applied. Our study focuses on developing the practical NDEA-based PMS framework. To follow up your suggestion, we have clarified this limitation in the Methodology and Discussion. To address the reviewer’s concern, we have revised the manuscript to: In introduction: Explicitly state that the aim of the study is methodological framework development rather than statistical inference, in introduction, as addressed in point 1. In methodology: Explicit statement in the methodology that that statistical validation is not feasible with the available sample. In conclusion: Acknowledge that bootstrap DEA and statistical testing represent valuable extensions, but are not feasible given the illustrative nature and limited data availability of the case setting. Suggest these techniques as directions for future research, particularly when larger datasets are available to support inferential analysis and sensitivity testing. 3- Model Selection Justification: The choice of the three-stage model is based on a qualitative “parsimony” argument, but this is not rigorous. Suggestions: Define explicit criteria for selecting the preferred model (for instance, minimize the number of efficient DMUs while still capturing all key processes). Consider formal model selection metrics (analogous to AIC, BIC) or cross-validation if possible. Explain why three stages reflect the real production better than two or four stages. Consult with production engineers to validate the stage grouping (the reader should see why combining certain stages makes sense). Response: We thank you for highlighting the need for a more rigorous justification of the chosen three-stage model. We agree that a qualitative parsimony argument alone is insufficient and have revised the manuscript accordingly. In the revised version of our methodology : We have adopted an explicit criterion for model selection: the preferred model minimizes the number of efficient DMUs while capturing all key production processes, aligning with your suggestion. Additionally, we have strengthened the justification for the three-stage structure by incorporating consultation with production engineers from the participating pharmaceutical manufacturer. Their expert input confirmed that the three-stage grouping most accurately reflects the functional and operational flow of the IV-set production line. In particular, they indicated that: A two-stage model would oversimplify the process and obscure necessary intermediate transformations. A four-stage model would fragment stages that are tightly integrated in practice, offering limited additional managerial insight. 4- Data Transparency: The data are proprietary, but for scientific reporting, we need some level of transparency. Suggestions: Provide summary statistics (means, ranges) for each input and output variable. If possible, anonymize and share data (even if synthetic with a similar structure) as supplementary material. At a minimum, explicitly list all inputs/outputs by stage (not just symbolic names in Table 3). Briefly describe how each was measured. Response: We thank you for emphasizing the importance of data transparency in scientific reporting. While we fully agree with the need for sufficient clarity to ensure reproducibility, we regret that the raw operational data cannot be shared due to strict confidentiality agreements with the participating pharmaceutical manufacturer. These data contain commercially sensitive production information that cannot be disclosed publicly, even in anonymized or synthetic form, under the terms of our partnership. To address the reviewer’s concern while respecting these constraints, we have provided the manuscript to strengthen transparency in the following ways: Detailed variable listing: We have expanded the description of the variables by explicitly listing all inputs, outputs, and intermediate flows by stage. Measurement explanations: For each variable, we now provide a brief explanation of how it is measured in the production process to ensure clarity and interpretability for readers. Clarification of confidentiality limitations: We explicitly state in the text that raw or shareable datasets cannot be provided due to proprietary restrictions. These revisions ensure that the methodology and variable structure are fully transparent, allowing readers to understand and interpret the model even without access to the protected dataset. 5- Case and Context Details: The narrative lacks detail on the real-world context and how the study was motivated or used. Suggestions: Quantify the performance gaps that prompted this study (e.g. known waste rates, downtime percentages prior to analysis). Explain why the 12-month period was chosen (seasonality issues? data availability?). Describe how managers or operators participated: was the NDEA analysis actually implemented or tested in operations? If any follow-up actions were taken (process changes, investments) as a result of this analysis, briefly mention them. Response: We thank the reviewer for highlighting the need to provide more contextual details and practical motivation for the case study. We have revised the manuscript to clarify the real-world context, quantify performance gaps, and describe managerial participation. We have added these details in the Case Study section (Section 5) to provide readers with a clearer understanding of the real-world context, performance gaps, and managerial involvement. 6. Literature Review and References: Some relevant recent work appears to be missing. Suggestions: Include citations for modern NDEA applications and improvements (e.g. dynamic/network DEA, bootstrap DEA methods, DEA in healthcare or manufacturing post-2020). Mention any case studies of NDEA in pharmaceuticals or medical devices if available. Update or clarify references that seem only tangentially related (e.g. if a study is on general DEA in banking, explain relevance). Response: Thank you for your valuable feedback. To follow up, we revised our manuscript (in the introduction and literature review sections ) by incorporating citations of recent (post-2020) applications of network/decomposed DEA, including dynamic/network DEA, bootstrap methods, and hybrid approaches in manufacturing and supply chain contexts. This situates our work at the current frontier of efficiency measurement research and clarifies our choice of non-radial, non-oriented NSBM CRS over dynamic or bootstrap methods. Meanwhile, we reviewed literature on NDEA applications in pharmaceuticals and medical devices, highlighting this emerging area. When no prior studies were found, we noted the gap and positioned our case study as an early contribution. We also clarified non-manufacturing DEA references by adding sentences that explain their relevance, focusing on methodological insights and discrimination power. We added more relevant references, as in the earlier note, to enhance the coherence of the literature review. 7. Presentation and Clarity: There are multiple formatting and clarity issues. Suggestions: Carefully proofread: fix typos and spacing (e.g. “therewas,” “developingNDEA-based,” etc.). Standardize notation: ensure all variables (x, y, z) and subscripts are consistent and clearly defined (some appear cut or incomplete in tables/figures). Improve figures/tables: For example, Figure 3 (NDEA model diagrams) needs clear labels and captions; Table 5 should have a complete caption and readable formatting. In the text, clearly refer to all figures and tables at first mention and summarize their key points in words. Response: We appreciate the reviewer’s careful attention to presentation and clarity. In response, we have undertaken a thorough review of the manuscript and implemented the improvements through the entire manuscript. 8. Results Interpretation: The discussion should more directly connect to the literature and avoid overclaiming. Suggestions: When stating that “this contradicts classical DEA,” clarify whether you ran a classical DEA or are inferring from others’ claims. If you did classical DEA (1-stage model), present those results side-by-side. Frame findings as preliminary insights (“in this case, we found…”) rather than definitive rules. Compare your results with any similar studies (if available). Discuss whether, for instance, identifying the first stage as inefficient agrees with known industry benchmarks. Response: We thank the reviewer for highlighting the need for more careful interpretation of the results and closer linkage to the literature. In response, we have revised the entire structure of Results and Discussion. 9. Conclusions and Claims: Several claims go beyond the data. Suggestions: Tone down statements about generalizability (e.g. “applicable to automotive/electronics”). Instead, suggest these as potential extensions with future validation. Reiterate study limitations (sample size, single site, confidentiality constraints) explicitly in the conclusion. Clearly link each major conclusion back to specific results shown (for example, “Based on Table 4, models with more stages had higher average efficiency scores and more efficient DMUs, indicating lower discrimination”). Response: We appreciate the reviewer for highlighting the importance of moderating claims and clarifying the limitations of our study. We have revised the Conclusion section accordingly. Specifically, we have: Softened statements regarding the generalizability of the results in both the results and discussion sections. Clearly reiterated the study's limitations, including sample size, being conducted at a single site, and confidentiality constraints, in the limitations section of the conclusion . View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Author Response 14 Mar 2026 Mahmood Shubbak, Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman We have addressed your comments in the revised version of our manuscript. Please check it. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Ahmed R. Peer Review Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.183368.r405838) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-710/v1#referee-response-405838 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Jilcha K. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 Sep 2025 | for Version 1 Kassu Jilcha , College of Technology and Built Environment, Addis Ababa, Addis Ababa, Ethiopia 0 Views copyright © 2025 Jilcha K. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Abstract Enhancement: The abstract should incorporate a clear statement regarding the ordinality or significance of this paper within the broader field. It is essential to articulate how this research contributes to existing knowledge, highlighting its unique value and relevance. Introduction Citations: The introduction section requires the inclusion of citations from recent studies published in 2025. This will help position the current research within the most up-to-date context, demonstrating engagement with the latest developments in the field. Citation Style Improvement: The citation style currently utilized, such as (Färe & Primont, 1984), should be revised. It is recommended to remove the brackets around the authors’ names and to place the publication year within brackets. This change will enhance readability and align the citations with standard practices in academic writing. Literature Review Depth: The literature review appears to be somewhat superficial. It is important to enrich this section with references to recent studies to provide a more comprehensive overview of the current state of research. Incorporating more contemporary sources will strengthen the foundation of this paper. Results and Discussion Expansion: The results and discussion section lacks depth and requires a more detailed comparison of the findings with prior studies. A thorough analysis that juxtaposes current results with existing literature will provide a clearer understanding of the implications and significance of the research outcomes. Is the background of the case’s history and progression described in sufficient detail? Partly Is the work clearly and accurately presented and does it cite the current literature? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Is the case presented with sufficient detail to be useful for teaching or other practitioners? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Innovation and industrial engineering areas I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Dec 2025 Mahmood Shubbak, Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman AUTHORS' RESPONSE TO REVIEWER #1: Kassu Jilcha Comment #1: Abstract Enhancement: The abstract should incorporate a clear statement regarding the ordinality or significance of this paper within the broader field. It is essential to articulate how this research contributes to existing knowledge, highlighting its unique value and relevance. Response; We appreciate your feedback. The significance of the paper has been incorporated into the conclusion section of the abstract as follows: “This study holds significance within the broader field of performance measurement and efficiency analysis by bridging theoretical modelling and practical implementation. It advances existing knowledge through the integration of NDEA into a process-based PMS, offering a novel analytical framework for multi-stage manufacturing systems. By examining the trade-off between model complexity and discrimination power, this research contributes new methodological insights and extends the applicability of NDEA in real-world industrial settings. The framework offers managers actionable guidance for optimizing multi-stage manufacturing operations and contributes novel insights into the methodological behaviour of NDEA. Ultimately, this work strengthens the linkage between performance measurement theory and industrial practice, positioning NDEA as a valuable tool for continuous improvement in manufacturing systems.” Comment #2: Introduction Citations: The introduction section requires the inclusion of citations from recent studies published in 2025. This will help position the current research within the most up-to-date context, demonstrating engagement with the latest developments in the field. Response: thank you for your feedback. We have added some updated citations, for example at the end of the first paragraph” Robust and flexible performance measurement tools are not just administrative necessities, they are strategic enablers that help organizations navigate financial burdens, enhance competitiveness, and foster the innovation and market dynamics needed for future success (Calik, 2024; Xing et al., 2025; Xu & Zhu, 2024).“ and also at the beginning of the third paragraph. Comment #3: Citation Style Improvement: The citation style currently utilized, such as (Färe & Primont, 1984),should be revised. It is recommended to remove the brackets around the authors’ names and to place the publication year within brackets. This change will enhance readability and align the citations with standard practices in academic writing. Response; Thank you for your correction. We have updated the citation style, specifically by removing the brackets around the author’s names at the beginning of sentences and placing the publication year within brackets. This adjustment is reflected in the literature review: “The Basic Concept of NDEA: Färe & Primont (1984) initiated the exploration of the 'black-box' system of classical DEA” Comment #4: Literature Review Depth: The literature review appears to be somewhat superficial. It is important to enrich this section with references to recent studies to provide a more comprehensive overview of the current state of research. Incorporating more contemporary sources will strengthen the foundation of this paper. Response: Thank you for this valuable comment. We agree that strengthening the depth and currency of the literature review will improve the overall foundation of the paper. In the revised manuscript, we have expanded the literature review to incorporate a broader range of recent and relevant studies, for example ”Recent research has advanced the treatment of intermediate and undesirable outputs, refining NDEA models to better capture complex network structures, dual-role factors, and process interdependencies. For example, Lotfi et al. (2023) applied an NDEA model to assess both desirable and undesirable outputs in the wheat supply chain, demonstrating enhanced accuracy in identifying stage-specific inefficiencies. Ma et al. (2025) proposed a network slack-based measure incorporating dual-role factors and undesirable outputs to evaluate supply chain performance, highlighting the practical relevance of NDEA in complex production networks. (Yang et al., 2024) further demonstrate the flexibility of NDEA by incorporating shared resources, negative data, and undesirable outputs in a multi-stage airline efficiency context, highlighting its capacity to model interdependencies realistically. This development allows decision-makers to simultaneously optimize performance while reducing waste or other negative byproducts, thereby providing a more nuanced and actionable understanding of operational efficiency. Essentially, NDEA’s ability to model undesirable outputs transforms efficiency assessment into a more realistic and strategically valuable instrument for complex production systems. These revisions provide a more comprehensive overview of the current research landscape and better contextualize the contribution of our work. We appreciate your guidance, which has helped us enhance the rigour and relevance of this section. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Jilcha K. Peer Review Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.183368.r405835) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-710/v1#referee-response-405835 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Ullagaddi p. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Sep 2025 | for Version 1 pravin Ullagaddi , University of the Cumberlands, Williamsburg, Kentucky, USA 0 Views copyright © 2025 Ullagaddi p. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript presents a case study applying Network Data Envelopment Analysis (NDEA) to evaluate performance in pharmaceutical manufacturing, specifically focusing on intravenous (IV) set production. The authors analyze a single pharmaceutical company's production line across six interconnected workstations over 12 monthly periods, comparing five different NDEA model configurations (1-stage through 6-stage). They conclude that a 3-stage model provides the optimal balance between analytical detail and discrimination power, and propose a generic framework for implementing NDEA-based Performance Measurement Systems (PMS) in manufacturing environments. The study identifies the PVC granulation stage as the primary source of inefficiency and recommends substantial reductions in inputs and undesirable outputs. The authors claim their framework contributes to manufacturing PMS literature and provides actionable guidance for multi-stage production systems. Critical Assessment Scientific Soundness Issues (Must Be Addressed) 1. Fundamental Sample Size Violation The most critical flaw is the use of only 12 Decision Making Units (DMUs) with models containing up to 15+ variables. This violates the basic DEA requirement that sample size should be at least twice the number of inputs plus outputs. This violation renders all efficiency scores and model comparisons unreliable. Required remediation: Expand the dataset to include at least 36 monthly observations, or Reduce model complexity to maximum 6 total variables, or Include multiple production lines/facilities to increase DMU count, or Acknowledge this as a pilot study with limited generalizability 2. Absence of Statistical Validation The paper presents efficiency scores and model comparisons without confidence intervals, significance tests, or uncertainty measures. Claims about discrimination power differences rely solely on descriptive statistics. Required remediation: Implement bootstrap methodology to generate confidence intervals for all efficiency scores Conduct statistical hypothesis tests for model comparison claims Add sensitivity analysis for variable selection and model specification Report p-values for claimed differences between models 3. Inadequate Model Selection Justification The selection of the 3-stage model as "optimal" lacks rigorous criteria. The authors cite parsimony and discrimination power but provide no statistical framework for this critical decision. Required remediation: Develop and apply formal model selection criteria (AIC, cross-validation, etc.) Test multiple variable combinations within each stage configuration Provide theoretical justification for why 3 stages best represent the production system Validate the stage structure with process engineers and production experts Literature and Presentation Issues 4. Outdated and Incomplete Literature Review The literature review misses significant recent developments in NDEA methodology and manufacturing performance measurement, relying heavily on foundational papers from the 1980s-2000s. Recommended improvements: Conduct systematic review of NDEA applications in manufacturing (2015-2024) Include recent advances in bootstrap DEA, dynamic NDEA, and two-stage models Cover pharmaceutical-specific performance measurement frameworks Address Industry 4.0 and digital manufacturing measurement approaches 5. Poor Presentation Quality Mathematical notation is inconsistent, figures are unclear, and writing quality impedes comprehension. Recommended improvements: Redesign Figure 3 with clear visual hierarchy and consistent labeling Standardize mathematical notation throughout Provide comprehensive copyediting for grammar and clarity Add detailed captions explaining all figure elements Data and Reproducibility Concerns 6. Complete Data Unavailability All source data is confidential and inaccessible, preventing independent verification or replication. Recommended approaches: Create synthetic datasets that preserve analytical relationships Provide detailed data generation procedures for replication Partner with other manufacturers to create multi-site validation At minimum, provide detailed descriptive statistics for all variables 7. Insufficient Case Context The background lacks detail necessary for understanding the organizational setting, problem severity, and implementation context. Required additions: Quantify baseline performance problems that motivated the study Describe the company's operational maturity and previous improvement initiatives Explain the 12-month study period selection and any external influences Document stakeholder engagement and validation processes Report actual implementation outcomes and organizational responses Methodological Limitations 8. Weak Theoretical Foundation The paper treats NDEA application as a primarily technical exercise without grounding in operations management theory or pharmaceutical manufacturing strategy. Recommended strengthening: Connect the analysis to manufacturing strategy frameworks Justify variable selection based on operations theory Link findings to broader pharmaceutical industry performance challenges Integrate regulatory compliance considerations into the analysis 9. Overstated Conclusions Claims about generalizability, framework novelty, and practical impact far exceed what the limited analysis supports. Required modifications: Restrict conclusions to the specific case studied Acknowledge the pilot nature of the study Remove claims about broad industry applicability without validation Present the framework as preliminary rather than proven Constructive Recommendations For Immediate Revision: Expand the empirical foundation by including additional time periods, production lines, or partner organizations to achieve adequate sample size Implement statistical rigor through bootstrap confidence intervals, hypothesis testing, and sensitivity analysis Strengthen case documentation with detailed organizational context, problem quantification, and implementation outcomes Moderate conclusions to reflect the study's limitations and preliminary nature For Long-term Research Development: Multi-site validation across different pharmaceutical manufacturers to test framework generalizability Longitudinal analysis tracking performance improvements over extended periods following NDEA implementation Comparative methodology study evaluating NDEA against alternative performance measurement approaches in manufacturing contexts Integration research examining how NDEA-based PMS interfaces with existing ERP/MES systems and organizational processes Verdict This manuscript addresses a relevant practical problem but suffers from fundamental methodological flaws that compromise its scientific validity. The sample size violation alone disqualifies the statistical analysis, while the absence of validation, weak theoretical foundation, and overstated conclusions further limit its contribution. Recommendation: Major revision is required with particular attention to the sample size issue, statistical validation, and conclusion moderation. The authors should consider repositioning this as a preliminary pilot study rather than a definitive framework development, with clear acknowledgment of limitations and need for further validation. The pharmaceutical manufacturing community would benefit from rigorous research in this area, but this work requires substantial strengthening to meet academic indexing standards and provide reliable guidance to practitioners. Is the background of the case’s history and progression described in sufficient detail? Partly Is the work clearly and accurately presented and does it cite the current literature? No If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No Is the case presented with sufficient detail to be useful for teaching or other practitioners? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Manufacturing Systems, Statistical analyses, Process Optimization, Pharmaceutical Manufacturing I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 14 Mar 2026 Mahmood Shubbak, Department of Management, College of Economics and Political Science, Sultan Qaboos University, Muscat, 123, Oman We have revised our manuscript. Kindly check the new version. Also kindly note that its category is Case Study. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Ullagaddi p. Peer Review Report For: Enhancing Efficiency in Multi-Stage Pharmaceutical Manufacturing: A Process-Based Network DEA Approach [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :710 ( https://doi.org/10.5256/f1000research.183368.r405839) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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