Boosting Manufacturing Efficiency through Coopetition: A Quantitative Analysis

Boosting Manufacturing Efficiency through Coopetition: A Quantitative Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Boosting Manufacturing Efficiency through Coopetition: A Quantitative Analysis Agostinho da Silva, Antonio Cardoso This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4023819/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Small and Medium-sized Enterprises (SMEs) are crucial to Europe's economy, yet they face significant challenges within global supply chains, primarily due to efficiency deficits. Despite their important role in employment, innovation, and wealth generation, these enterprises struggle to compete on a global scale. Recognizing the potential of coopetition - strategic collaboration among competitors - as a means to enhance efficiency, this study explores its impact within the Portuguese Ornamental Stone industry, a critical sector for the national economy. Employing a case study methodology in an IoT-enabled coopetition network, it is documented a substantial improvement in manufacturing efficiency (38.41%), alongside increases in efficiency consistency (20.68%), labor productivity (61.59%), and raw material yield (31.65%). These findings not only validate the efficacy of coopetition in overcoming the efficiency challenges faced by SMEs but also highlight specific areas for further enhancement, such as labor productivity consistency. This research contributes to filling the empirical gap in understanding coopetition's role in SME efficiency and sets the stage for future inquiries into optimizing and expanding coopetition networks. It underscores the necessity for comprehensive strategies to implement coopetition practices, providing a blueprint for SMEs to thrive in the competitive landscape of global digital supply chains. Coopetition Networks S-D Logic SMEs Efficiency Value Cocreation Figures Figure 1 Figure 2 Figure 3 Figure 4 Article Highlights Coopetition boosts SME efficiency by 38.41%, enhancing global competitiveness. Significant gains in labor productivity (61.59%) and raw material yield (31.65%) observed. Study advocates for broader adoption and optimization of coopetition strategies in SMEs. 1. Introduction Small and Medium-sized Enterprises (SMEs) are often lauded as the cornerstone of Europe's economy, playing a pivotal role in employment, innovation, and wealth creation. They contribute more than 20% to the EU27's total value-added and employ upwards of 35 million individuals (Di Bella et al., 2023). In an era increasingly dominated by global and digital supply chains, the quest for manufacturing efficiency has never been more critical (Bayne et al., 2017). Amidst this landscape, collaboration among SMEs emerges as a vital strategy for bolstering efficiency and competitiveness on a global stage (Bicen et al., 2021) (Lindström & Polsa, 2016). Such collaborative efforts are imperative not only for ensuring competitively priced, high-quality products but also for enhancing their attractiveness to a worldwide customer base (Ho et al., 2020). At the heart of this efficiency drive is the concept of coopetition—a synergistic blend of cooperation and competition (Arora & Brintrup, 2021) (Bicen et al., 2021). This approach has garnered substantial research interest for its potential to harmonize competitive dynamics with collaborative synergies among business rivals (Bacon et al., 2020). However, despite its promise, navigating the path towards enhanced efficiency through coopetition is beset with challenges, both internal and external. A notable obstacle is the inherent competitive mindset among SME owners, which often stymies collaborative ventures, thereby hindering growth. This challenge is compounded by a general lack of cooperation, which, according to the Economist Intelligence Unit (2021), threatens the very survival of European SMEs by limiting their participation in coopetition—a strategy that could significantly aid their scaling efforts (Appian, 2021). As academic interest in coopetition continues to surge, a significant research gap is unmistakably apparent: a notable shortage of empirical studies dedicated to quantifying the efficiency gains achieved through collaboration among manufacturing competitors. This scarcity of quantitative evidence not only weakens the theoretical foundation of coopetition but also hinders SMEs from adopting it as an effective strategy to boost efficiency. This study seeks to bridge this gap by focusing on the Ornamental Stone (OS) industry in Portugal, a sector of paramount importance to the national economy. Employing a case study methodology, it aims to quantitatively evaluate the efficiency gains for SMEs as they evolve their practices towards establishing coopetition networks. Through a robust quantitative analysis, this research assesses the impact of coopetition networks on critical efficiency metrics, such as First Time Through and Material Yield, before and after the adoption of an IoT-enabled coopetition network. By selecting a fitting empirical context for this evaluation, the study endeavors to unravel the effects of coopetition practices on enhancing key performance indicators vital for the efficiency of OS SMEs. 2. Methodology The case study methodology emerges as particularly potent in scenarios where the distinction between the phenomenon under investigation and its context is not immediately clear (Hollweck, 2015). It offers a comprehensive framework for event examination, data collection, analysis, and the dissemination of findings. This approach allows researchers to achieve a nuanced understanding of the event in question, shedding light on why it unfolded as it did, and identifying areas that warrant further exploration in subsequent studies. Efficiency within an industrial setting is influenced by a complex interplay of factors, primarily linked to production operations, logistics, and various other business activities. Through the prism of production optimization and astute resource utilization, the canon of lean management literature advocates for the appraisal of efficiency via the lens of Key Performance Indicators (KPIs) such as First Time Through (FTT), Performance Efficiency (PE), Raw Material Yield (RME), and Labour Productivity (LP). These KPIs serve as the cornerstone for a methodical evaluation of efficiency. They are delineated as follows: (1) Process Optimization - analysing and enhancing KPIs such as FTT, PE, RMY, and LP empowers companies to refine their operational processes; (2) Resource Utilization - optimizing these facets lies in the judicious utilization of resources. Businesses can bolster resource efficacy by curtailing raw material wastage, optimizing labour productivity, and securing elevated performance and FTT rates. Anchored on these concepts, this research methodology quantitatively examines the KPIs suite to gauge coopetition networks' impact on operational efficiency. Through this analytical lens, the study endeavours to shed light on the nuanced dynamics of coopetition and their consequential impact on the operational efficiency of OS SMEs within the digital supply chain milieu. The Key Performance Indicator for First Time Through (KPI FTT ) quantifies the proportion of products or services that align with quality and compliance benchmarks without rework or adjustments. From an operational perspective, KPI FTT is intrinsically linked to efficiency as it diminishes waste, conserves time, and curtails the necessity for additional resource allocation. Elevated KPI FTT rates indicate highly efficient processes where the output aligns with quality expectations on the initial attempt, thus significantly enhancing overall operational efficiency (Appendix, Table 1. Eq.1). The Key Performance Indicator for Performance Efficiency (KPI PE ) refers to the rate at which work is completed or the output is produced compared to the expected standards or benchmarks. Performance metrics can include speed, accuracy, and the ability to meet production targets. Scientifically, there is a direct correlation between high performance and efficiency, as better performance usually means more work is done correctly in less time, with optimal use of resources (Appendix, Table 1. Eq.2) The Key Performance Indicator for Raw Material Yield (KPI RMY ) measures the efficiency of converting raw materials into finished goods. A higher yield indicates that less raw material is wasted or discarded during production, which directly indicates process efficiency. Improving raw material yield can be related to optimizing chemical reactions, reducing material losses in manufacturing, and enhancing the precision of manufacturing processes. This optimization reduces costs and improves the sustainability of operations. The KPI RMY is calculated by dividing the amount of finished product obtained from a given raw material by the theoretical amount expected based on the process design, expressed as a percentage (Appendix, Table 1. Eq.3). The Key Performance Indicator for Labor Productivity (KPI LP ) is a metric for gauging the number of goods and services generated for each hour of labour expended. It stands as an essential barometer of efficiency, offering insightful revelations into the efficacy of harnessing labour resources. From a scientific standpoint, the enhancement of labour productivity is achievable through implementing comprehensive training programs, integrating advanced technological solutions, and optimising work processes. Elevated levels of labour productivity signify a streamlined production process, where minimal time and reduced labour resources are requisite for generating a single output unit. This efficiency not only underscores the operational excellence of an organization but also reflects its capability to maximize output while minimizing input, thereby fostering a competitive edge in the dynamic landscape of the industry (Appendix, Table 1. Eq.4). These KPIs are intricately woven into operational efficiency, acting not in isolation but as interconnected elements that shape the efficiency landscape together. Enhancements in any single aspect can catalyze improvements in overall efficiency. However, the holistic and integrated approach to refining these KPIs yields the most substantial gains. This comprehensive strategy for efficiency enhancement underscores the synergistic potential of addressing multiple facets simultaneously. In a bid to bolster the insights garnered regarding potential efficiency improvements, this methodology introduces an additional metric designed to assess the degree of variability or dispersion throughout the evaluation period, denoted as KPI(σ) (Appendix, Eq.5). This novel indicator aims to provide a more nuanced understanding of efficiency trends, offering a measure of consistency or fluctuation in performance metrics over time. By evaluating the independent variables dispersion, it is possible to comprehend the stability and reliability of efficiency gains (positive or negative), further enriching the analysis. To address the impact of adopting innovative practices, service literature introduces the concept of Innovation Outcomes (IO) (Lusch & Nambisan, 2015), which tracks the evolution of KPIs as companies innovate. This approach quantifies the effects of innovations, such as entering coopetition networks or leveraging new technologies, on organizational efficiency. By measuring changes in KPIs, Innovation Outcomes provide insights into how innovations enhance operational metrics like productivity, material yield, and process efficiency. This framework highlights the benefits of innovation and its role in improving competitiveness and sustainability within the digital supply chain. Innovation Outcomes offer a clear mechanism to gauge the transformative impact of new practices on OS SMEs. The Innovation Outcomes are, therefore, an integral part of this methodology. IO FFT , IO PE , IO RMY , and IO PL represent the quantitative gains in first-time through performance, material yield, and labor productivity, respectively, facilitated by coopetition. Moreover, a metric IO(σ) for assessing the variability or consistency gain in innovation outcomes over the testing period is introduced, utilizing standard deviation to provide insights into how efficiency and operational performance evolve (Appendix, Table 2). 3. Advancing Coopetition Networks In Ornamental SMEs through IoT The conversation about the Internet of Things (IoT), a term introduced by Kevin Ashton in 1998, has significantly progressed, paving the way for the emergence of the Industrial Internet of Things (IIoT). This evolution of IoT into the industrial domain aims to improve manufacturing efficiencies by adopting smart, interconnected systems. The IIoT integrates diverse technologies, fostering innovative value creation and competitiveness in the manufacturing industry by utilizing IP-based networks to improve connectivity and communication between industrial components. Incorporating the SD-L perspective enhances this discussion by focusing on the mutual value creation between customers and businesses through the interaction of operant (skills and knowledge) and operand (physical) resources. This underscores the transformative potential of IoT in redefining industrial capabilities, enabling advanced control, coordination, and management of manufacturing environments. This synergy of the digital and physical realms promises significant innovation and efficiency improvements, though it also presents challenges in nurturing creativity and ensuring process flexibility. An effort within the Inovmineral4.0 R&D project was creating an innovative IIoT platform, Cockpit4.0+, designed to be used as coopetition enabler of coopetition networks among stone enterprises. Currently, a prototype, the Cockpit4.0+, backed by project funding and a unique development opportunity, serves as a foundation for gathering quantitative research data. This case study, set in the ornamental stone industry, examines the effect of coopetition networks on SMEs' operational efficiency through the lens of S-D logic (Vargo et al., 2023). It showcases the potential of the early-stage Cockpit4.0+ platform and leverages a favorable funding climate to investigate the efficiencies and dynamics that coopetition introduces to the OS SME landscape. Empirical Context Identifying a suitable context for empirical investigation is critical in a quantitative research methodology, as emphasized by Johnson & Onwuegbuzie (2004). The selection process for an appropriate sector and companies to conduct this study was primarily influenced by considerations of practicality, including the specific nature of data requirements and the associated costs of data acquisition and analysis. This pragmatic approach ensured the selection was feasible and relevant, facilitating the collection of meaningful data that accurately reflects the dynamics and outcomes of coopetition networks within the chosen context (Johnson & Onwuegbuzie, 2004). Selecting an appropriate field for empirical testing is paramount in a quantitative research approach. Given the inherent specificity and associated data provision and collection costs, the choice of sector and companies for this study was guided by convenience. Population and Sample The dataset provided by the Portuguese Association of the Mineral Resources Industry (ASSIMAGRA) in 2019, primarily consisting of SMEs, underscores the Ornamental Stone (OS) sector as a vital component of Portugal's economy. Contributing over 16,600 direct jobs and exporting to 116 countries, the OS sector is a significant source of private sector employment, especially in the country's interior regions. Despite facing challenges, the sector has shown consistent export growth, making Portugal the eighth largest exporter in the international trade of OS and ranking second globally in terms of international trade per capita. The introduction of the Cockpit4.0+ system, an artefact leveraging Industrial Internet of Things (IIoT) technology, represents a pioneering opportunity, especially with the availability of funding for participating companies. This initiative to develop and implement a coopetition technology-enabled network prototype has been strategically leveraged to collect quantitative data critical for this research. Coopetition technology-enabled networks facilitate digital connectivity among companies to promote collaborative production processes, with the Cockpit4.0+ system ensuring the integration of rival firms into a genuine coopetition network. For this study, three stone producers were selected to assess the benefits of the Cockpit4.0+ prototype, i.e., the advantages derived from the coopetition technology-enabled network. Initial data collection focused on the current state of operations, followed by a second phase of data gathering after the prototype's installation and network activation. Direct and informal approaches were made to the managing directors of these companies, who were cordially invited to participate in the research. A written agreement was proposed, specifying the confidentiality terms, including assurances not to disclose the names of the companies, their customers, employees, materials, resources, or competitors. The companies' managers granted the researcher access to confidential information, including analytical and accounting data, order records, and management and production systems. To ensure data collection's accuracy and confidentiality, the researcher closely monitored the operations, direct and daily recording and collecting quantitative data from digital machinery and databases. This approach guarantees the precision and privacy of the collected information in Stone Companies. Data Collection To explore and evaluate the shift from Current Best Practices (CB.P) to Coopetition Production Practices (CN.P), the study implemented a data collection strategy over two separate intervals, each lasting fifty-four days. This methodical approach was designed to provide a basis for comparing operational efficiencies between conventional practices and those influenced by coopetition. In line with confidentiality agreements, all collected data was carefully anonymized, involving three companies referred to as "A", "B", and "C" to protect the privacy and maintain the anonymity of the entities involved. Phase 1 - Current Best Practices (CB.P): The first phase spanned from April 17th, 2023, to June 10th, 2023. Data reflecting the companies' adherence to CB.P during this timeframe were meticulously gathered (Appendix, Table 3). This stage was characterized by conventional operational approaches, wherein each entity depended entirely on its internal resources to produce and deliver goods. This phase aimed to capture a baseline of operations before introducing coopetition practices, serving as a reference point for subsequent comparative analysis. Phase 2: Coopetition Production Practices (CN.P): Following the initial assessment period, the study transitioned into its second phase, focusing on Coopetition Production Practices (CN.P). This stage introduced a paradigm shift, encouraging companies to participate in a cooperative but competitive network. This innovative approach allowed for the utilization of shared technologies and resources, fostering a unique blend of collaboration and competition. The data for this phase were from September 9 th , 2023, to November 14 th , 2023, aligned to ensure consistency with the structured methodology adopted for the CN.P phase, facilitating a direct comparison of operational efficiencies between the traditional practices and those influenced by coopetition (Appendix, Table 4). Data Handling and Confidentiality: In adherence to the confidentiality agreement and to maintain the anonymity of the participating entities, data was exported to Excel files, following predetermined procedures for data collection, recording, and exportation. This ensures a consistent and secure approach to data management, allowing for a rigorous analysis while respecting the privacy and proprietary concerns of the involved companies. 4. Results and Key Performance Indicators (KPIs) The analysis utilized data collected under Current Best Practices, outlined in Appendix Table 3, alongside data from Coopetition Technology-Enabled Networks, as detailed in Appendix Table 4. This comprehensive dataset facilitated an evaluation of coopetition effects across ten key performance indicators (KPIs), with findings summarized in Appendix Table 5. The juxtaposition of these datasets provides a nuanced understanding of how coopetition influences manufacturing efficiency across multiple dimensions. The First Time Through (KPI FTT ) indicator provides insights into manufacturing entities' operational excellence and quality control mechanisms. Appendix Table 3 demonstrates that under CB.P, the companies achieved a daily average output of 369.9 parts, with 31.3 parts being rejected owing to defects or requiring further correction. Consequently, this results in a KPI FTT of 90.9% across the companies' production lines, as detailed in Appendix, Table 5. This indicates that, on average, 90.9% of the parts produced meet the quality standards on their first pass, underscoring a notable level of process efficiency and quality control characteristic of conventional operational practices. Appendix Table 4 reveals that under CN.P, the companies maintained a steady average output quality rate of 90.9%. There was a significant increase in average daily production to 454.4 parts under the coopetition model. This surge in output, alongside the consistent quality level as evidenced by the unchanged KPI FTT in Appendix, Table 5, suggests that adopting coopetition technology-enabled networks has significantly boosted the companies' production capacities without sacrificing quality. The Performance Efficiency Indicator (KPI PE ) is crucial in assessing a manufacturing unit's ability to fulfil orders efficiently. This metric measures how effectively resources are utilized to meet demand; the KPIPE offers valuable insights into manufacturing processes' operational efficiency and effectiveness. Under CB.P, as outlined in Appendix, Table 3, the data harvested from a range of factories indicates an average output of 338.3 shipped parts, alongside an average daily operational time of 13.3 hours across the companies. This variation in hours reflects diverse operational strategies, with some companies adhering to a single 8-hour shift and others implementing two shifts, culminating in 16 hours. Based on this information, the KPI PE for these companies was determined to be 25.0 sold parts per hour, as recorded in Appendix Table 4. Upon adopting CN.P, companies witnessed a substantial increase in productivity, as demonstrated by the average number of shipped parts to 415.8, detailed in Appendix, Table 3. This leap in output was realized without any changes to the pre-existing shift schedules, highlighting that integrating CN.P - whereby competitors cooperate on select production facets - played a crucial role in boosting company performance. Consequently, the KPI PE for these entities advanced to 32.30 shipped parts per hour, as recorded in Appendix, Table 4. This improvement, equating to an additional 7.30 parts per hour over the pre-CN.P, signifies a significant enhancement in performance efficiency. The Material Yield Indicator (KPI RMY ) assesses material usage efficiency within the production process. KPI RMY underscores the efficiency level at which raw materials are converted into customer-acceptable products under conventional operational practices. Under CB.P, data from Appendix Table 3 indicates that the companies utilized an average of 425.3 square meters (m 2 ) of raw materials daily. From this input, an average of 323.7 parts per day were produced that met customer acceptance criteria, resulting in a material yield rate of 0.752 pieces per m 2 of raw material. Consequently, this analysis yields a KPI RMY of 0.752 parts per m 2 , as detailed in Appendix Table 5. With the transition to CN.P, Appendix Table 3 shows that the daily average of raw materials processed increased to 437.1 m2. Remarkably, the customer acceptance rate for produced parts surged to 454.3 units. This marked improvement resulted in a significant uptick in the KPI RMY across the companies, which escalated to 0.956 pieces per m 2 of raw material utilized, as reported in Appendix Table 4. This increase underscores the enhanced efficiency in material usage under the Coopetition Network Practices, reflecting a substantial improvement in converting raw materials into customer-acceptable products. The Labor Productivity Indicator (KPI LP ) quantifies the output per worker involved, assessing the workforce's efficiency in generating quality products. Under CN.P, companies managed an average daily shipment of 338.5 parts, utilizing an average workforce of 49.9, as detailed in Appendix Table 3. In this operational setting, the Labor Productivity Indicator (KPILP) was determined to be 6.8 pieces per worker throughout an 8-hour workday, according to the data presented in Appendix Table 4. With the transition to CN.P, a significant enhancement in labour productivity was noted. Maintaining the same average workforce of 49.9 workers, the daily output of high-quality pieces rose to an average of 415.8, as indicated in Appendix Table 3. This improvement resulted in an increased average KPI LP of 8.7 high-quality pieces per worker for an 8-hour shift, detailed in Appendix Table 4. 5. Discussions and Innovation Outcomes (IOs) Innovation Outcomes (IOs) encapsulating the efficiency gains resulting from the adoption of CN.P - a blend of collaboration and competition facilitated by technology within networks of OS SMEs - were analyzed. These IOs underscore the concrete advantages of such collaborative competition, evidencing significant improvements in efficiency metrics. Appendix Table 6 provides a concise overview of these enhancements, detailing the measurable improvements and the associated reduction in variability for each IO, thus showcasing the practical benefits of coopetition networks. No quantitative improvement was detected when examining the First Time Through outcomes. This indicates that the shift to coopetition did not directly lead to an increased success rate of processes or tasks being executed correctly on their first attempt. Nonetheless, a significant enhancement in consistency, quantified at 28.18%, was noted. This consistency improvement suggests a notable decrease in variability, which may imply a potential error reduction or the necessity for rework over time, as depicted in Figure 1. In analysing the development of the Performance Efficiency Outcome across OS SMEs, adopting coopetition network practices, showcases an exceptional average increase of 60.38%. This notable rise highlights the concrete advantages that coopetition brings to organizational processes. This improvement can be attributed to the transition to coopetition networks, which promotes a synergistic combination of resources, expertise, and capabilities, thereby significantly enhancing the efficiency of order fulfilment and optimization of the production process. Such a shift augments overall productivity and encourages a more adaptable and responsive manufacturing landscape. This environment is well-suited to meet changing market demands and consumer expectations, as illustrated in Figure 2. The transition to Coopetition Network Practices has significantly improved Raw Material Yield, with a recorded quantitative gain of 31.65%. This increase signifies enhanced utilization of raw materials, leading to reduced waste and more efficient production processes. Additionally, the gain in consistency is notably substantial, at 50.33%, which suggests a considerable reduction in variability. This consistency ensures a more predictable and efficient use of materials, highlighting the effectiveness of Coopetition Network practices in optimizing resource utilization and minimizing waste in the production cycle. Labor Productivity has witnessed a remarkable quantitative gain of 61.59%, reflecting a significant enhancement in output or efficiency per labour unit. However, the minimal consistency gain of 0.59% indicates that while productivity has improved, achieving uniformity across various scenarios or over different periods remains a challenge that needs focused attention. This extraordinary surge in labour productivity highlights the advantages of adopting a coopetition framework, where the synergistic interplay of collaboration and competition drives productivity optimization. Companies tap into a collective pool of expertise, capabilities, and technological advancements through coopetition, fostering a more dynamic and efficient work environment. This strategic approach not only amplifies the productivity of individual workers but also considerably boosts the company's overall operational effectiveness and competitive positioning in the market, as detailed in Figure 3. Observing the evolution of the KPIs (Figure 4), it is clear that, except for the First Time Through, all other metrics show positive development when OS SMEs implement coopetition network practices. From these innovation outcomes, it is observed that the quantitative impact on manufacturing efficiency is 38.41% when companies transition from their current practices to coopetition practices within networks, further supported by a 20.68% increase in the consistency of efficiency (Appendix, Table 6). Conclusion and Future Research The adoption of Coopetition Practices within an IoT-enabled network involving a group of Portuguese Ornamental Stone SMEs has led to a remarkable improvement in manufacturing efficiency, with a recorded increase of 38.41%. This underscores the significant impact that strategic collaborative competition can have on enhancing operational performance in the SME sector. This substantial improvement underscores the effectiveness of collaborative competition in optimizing production processes and resource utilization. Alongside these quantitative gains in efficiency, there has been a notable increase in efficiency consistency, quantified at 20.68%. Moreover, the findings indicate improvements in labour productivity and raw material yield, with quantitative gains of 61.59% and 31.65%, respectively. These improvements highlight the successful optimization of key resources in the production process, leading to less waste and higher output per input unit. This suggests that coopetition boosts performance and makes efficiency gains more predictable and stable over time. While significant strides have been made in enhancing productivity and efficiency, the minimal consistency gain in labour productivity (0.59%) indicates areas that require further attention. Future efforts should ensure that productivity improvements are achieved and consistently maintained across various operational scenarios and scaled effectively. In summary, by demonstrating tangible benefits gains and consistency in Efficiency, the coopetition networks may boost OS SMEs in the global digital supply chains context. This study conclusively demonstrates the tangible benefits of coopetition networks, especially their significant role in enhancing efficiency and ensuring operational consistency for Portuguese Stone SMEs engaged in global digital supply chains. These networks are identified as a potent tool for augmenting productivity and facilitating operational scale-up. Looking ahead, it is imperative that future research delves deeper into the potential for optimizing and expanding coopetition networks. Such studies should investigate how these networks can be adapted to meet the evolving demands of various industries. Additionally, the creation of detailed guidelines for the effective implementation of coopetition strategies stands to provide businesses with crucial frameworks for adopting these forward-thinking operational models. Embarking on this path could be a decisive move towards securing enhanced competitiveness and fostering growth in the contemporary digital landscape. Declarations Authorship Contribution Statement: Agostinho da Silva - Conceptualization, original draft writing, review & editing; Antonio J. Marques Cardoso - Conceptualization, original draft writing, review & editing. Disclosure of interest statement : The authors declare no conflicts of interest to disclose. Declaration of funding statement: nothing to declare. Data available on request from the authors: The data that support the findings of this study are available from the corresponding author, Agostinho da Silva, upon request. References Appian. (2021). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4023819","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":278082315,"identity":"f3206cba-1c13-4575-8f19-03927af793d9","order_by":0,"name":"Agostinho da 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09:31:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4023819/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4023819/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52622370,"identity":"076dcc97-a2ae-43be-99e7-afc34c137834","added_by":"auto","created_at":"2024-03-13 17:12:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFirst Time Through KPI Consistency improvements under Coopetition Networks.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4023819/v1/2efd73295d74cd65abb847f4.jpg"},{"id":52622362,"identity":"08b7dd3e-f4c8-4eea-882d-fc90013a1fd8","added_by":"auto","created_at":"2024-03-13 17:12:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79156,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerformance Efficiency KPI trend under Coopetition Networks.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4023819/v1/672f2166d4f28ec1b6fd8048.jpg"},{"id":52622363,"identity":"ca6161cc-d13c-4515-8395-50b1af107ddb","added_by":"auto","created_at":"2024-03-13 17:12:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLabor Productivity Innovation Outcome under Coopetition Network Practices.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4023819/v1/31f32cdb70f2c623ac3e3252.jpg"},{"id":52622389,"identity":"9f4d2153-adcd-4abe-9f51-78a5c086f12a","added_by":"auto","created_at":"2024-03-13 17:12:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKPIs evolution when companies transition to coopetition practices within networks\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4023819/v1/652de23792554604b4cbcf3d.jpg"},{"id":52623704,"identity":"ac52483e-cb28-48e2-a8a4-96efdcf983f2","added_by":"auto","created_at":"2024-03-13 17:21:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":540213,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4023819/v1/124ce3af-f5c2-4b1c-b98d-5f34a8468ee2.pdf"},{"id":52622415,"identity":"68636404-b9e9-4b9a-8674-bdb825022a58","added_by":"auto","created_at":"2024-03-13 17:12:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21841,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4023819/v1/bb1b28ac01af85a8a615ff9f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Boosting Manufacturing Efficiency through Coopetition: A Quantitative Analysis","fulltext":[{"header":"Article Highlights","content":"\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eCoopetition boosts SME efficiency by 38.41%, enhancing global competitiveness.\u003c/li\u003e\n \u003cli\u003eSignificant gains in labor productivity (61.59%) and raw material yield (31.65%) observed.\u003c/li\u003e\n \u003cli\u003eStudy advocates for broader adoption and optimization of coopetition strategies in SMEs.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1.\tIntroduction","content":"\u003cp\u003eSmall and Medium-sized Enterprises (SMEs) are often lauded as the cornerstone of Europe\u0026apos;s economy, playing a pivotal role in employment, innovation, and wealth creation. They contribute more than 20% to the EU27\u0026apos;s total value-added and employ upwards of 35 million individuals\u0026nbsp;(Di Bella et al., 2023). In an era increasingly dominated by global and digital supply chains, the quest for manufacturing efficiency has never been more critical\u0026nbsp;(Bayne et al., 2017). Amidst this landscape, collaboration among SMEs emerges as a vital strategy for bolstering efficiency and competitiveness on a global stage\u0026nbsp;(Bicen et al., 2021)\u0026nbsp;(Lindstr\u0026ouml;m \u0026amp; Polsa, 2016). Such collaborative efforts are imperative not only for ensuring competitively priced, high-quality products but also for enhancing their attractiveness to a worldwide customer base\u0026nbsp;(Ho et al., 2020).\u003c/p\u003e\n\u003cp\u003eAt the heart of this efficiency drive is the concept of coopetition\u0026mdash;a synergistic blend of cooperation and competition\u0026nbsp;(Arora \u0026amp; Brintrup, 2021)\u0026nbsp;(Bicen et al., 2021). This approach has garnered substantial research interest for its potential to harmonize competitive dynamics with collaborative synergies among business rivals\u0026nbsp;(Bacon et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, despite its promise, navigating the path towards enhanced efficiency through coopetition is beset with challenges, both internal and external. A notable obstacle is the inherent competitive mindset among SME owners, which often stymies collaborative ventures, thereby hindering growth. This challenge is compounded by a general lack of cooperation, which, according to the Economist Intelligence Unit (2021), threatens the very survival of European SMEs by limiting their participation in coopetition\u0026mdash;a strategy that could significantly aid their scaling efforts\u0026nbsp;(Appian, 2021).\u003c/p\u003e\n\u003cp\u003eAs academic interest in coopetition continues to surge, a significant research gap is unmistakably apparent: a notable shortage of empirical studies dedicated to quantifying the efficiency gains achieved through collaboration among manufacturing competitors. This scarcity of quantitative evidence not only weakens the theoretical foundation of coopetition but also hinders SMEs from adopting it as an effective strategy to boost efficiency.\u003c/p\u003e\n\u003cp\u003eThis study seeks to bridge this gap by focusing on the Ornamental Stone (OS) industry in Portugal, a sector of paramount importance to the national economy. Employing a case study methodology, it aims to quantitatively evaluate the efficiency gains for SMEs as they evolve their practices towards establishing coopetition networks. Through a robust quantitative analysis, this research assesses the impact of coopetition networks on critical efficiency metrics, such as First Time Through and Material Yield, before and after the adoption of an IoT-enabled coopetition network. By selecting a fitting empirical context for this evaluation, the study endeavors to unravel the effects of coopetition practices on enhancing key performance indicators vital for the efficiency of OS SMEs.\u003c/p\u003e"},{"header":"2.\tMethodology","content":"\u003cp\u003eThe case study methodology emerges as particularly potent in scenarios where the distinction between the phenomenon under investigation and its context is not immediately clear\u0026nbsp;(Hollweck, 2015). It offers a comprehensive framework for event examination, data collection, analysis, and the dissemination of findings. This approach allows researchers to achieve a nuanced understanding of the event in question, shedding light on why it unfolded as it did, and identifying areas that warrant further exploration in subsequent studies.\u003c/p\u003e\n\u003cp\u003eEfficiency within an industrial setting is influenced by a complex interplay of factors, primarily linked to production operations, logistics, and various other business activities. Through the prism of production optimization and astute resource utilization, the canon of lean management literature advocates for the appraisal of efficiency via the lens of Key Performance Indicators (KPIs) such as First Time Through (FTT), Performance Efficiency (PE), Raw Material Yield (RME), and Labour Productivity (LP). These KPIs serve as the cornerstone for a methodical evaluation of efficiency. They are delineated as follows: (1) Process Optimization - analysing and enhancing KPIs such as FTT, PE, RMY, and LP empowers companies to refine their operational processes; (2) Resource Utilization - optimizing these facets lies in the judicious utilization of resources. Businesses can bolster resource efficacy by curtailing raw material wastage, optimizing labour productivity, and securing elevated performance and FTT rates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnchored on these concepts, this research methodology quantitatively examines the KPIs suite to gauge coopetition networks\u0026apos; impact on operational efficiency. Through this analytical lens, the study endeavours to shed light on the nuanced dynamics of coopetition and their consequential impact on the operational efficiency of\u0026nbsp;OS\u0026nbsp;SMEs within the digital supply chain milieu.\u003c/p\u003e\n\u003cp\u003eThe Key Performance Indicator for First Time Through (KPI\u003csub\u003eFTT\u003c/sub\u003e) quantifies the proportion of products or services that align with quality and compliance benchmarks without rework or adjustments. From an operational perspective, KPI\u003csub\u003eFTT\u003c/sub\u003e is intrinsically linked to efficiency as it diminishes waste, conserves time, and curtails the necessity for additional resource allocation. Elevated KPI\u003csub\u003eFTT\u003c/sub\u003e rates indicate highly efficient processes where the output aligns with quality expectations on the initial attempt, thus significantly enhancing overall operational efficiency\u0026nbsp;\u003cstrong\u003e(Appendix, Table 1. Eq.1).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Key Performance Indicator for Performance Efficiency (KPI\u003csub\u003ePE\u003c/sub\u003e) refers to the rate at which work is completed or the output is produced compared to the expected standards or benchmarks. Performance metrics can include speed, accuracy, and the ability to meet production targets. Scientifically, there is a direct correlation between high performance and efficiency, as better performance usually means more work is done correctly in less time, with optimal use of resources\u0026nbsp;\u003cstrong\u003e(Appendix, Table 1. Eq.2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Key Performance Indicator for Raw Material Yield (KPI\u003csub\u003eRMY\u003c/sub\u003e) measures the efficiency of converting raw materials into finished goods. A higher yield indicates that less raw material is wasted or discarded during production, which directly indicates process efficiency. Improving raw material yield can be related to optimizing chemical reactions, reducing material losses in manufacturing, and enhancing the precision of manufacturing processes. This optimization reduces costs and improves the sustainability of operations. The KPI\u003csub\u003eRMY\u003c/sub\u003e is calculated by dividing the amount of finished product obtained from a given raw material by the theoretical amount expected based on the process design, expressed as a percentage\u0026nbsp;\u003cstrong\u003e(Appendix, Table 1. Eq.3).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Key Performance Indicator for Labor Productivity (KPI\u003csub\u003eLP\u003c/sub\u003e) is a metric for gauging the number of goods and services generated for each hour of labour expended. It stands as an essential barometer of efficiency, offering insightful revelations into the efficacy of harnessing labour resources. From a scientific standpoint, the enhancement of labour productivity is achievable through implementing comprehensive training programs, integrating advanced technological solutions, and optimising work processes. Elevated levels of labour productivity signify a streamlined production process, where minimal time and reduced labour resources are requisite for generating a single output unit. This efficiency not only underscores the operational excellence of an organization but also reflects its capability to maximize output while minimizing input, thereby fostering a competitive edge in the dynamic landscape of the industry\u0026nbsp;\u003cstrong\u003e(Appendix, Table 1. Eq.4).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese KPIs are intricately woven into operational efficiency, acting not in isolation but as interconnected elements that shape the efficiency landscape together. Enhancements in any single aspect can catalyze improvements in overall efficiency. However, the holistic and integrated approach to refining these KPIs yields the most substantial gains. This comprehensive strategy for efficiency enhancement underscores the synergistic potential of addressing multiple facets simultaneously.\u003c/p\u003e\n\u003cp\u003eIn a bid to bolster the insights garnered regarding potential efficiency improvements, this methodology introduces an additional metric designed to assess the degree of variability or dispersion throughout the evaluation period, denoted as KPI(\u0026sigma;)\u0026nbsp;\u003cstrong\u003e(Appendix, Eq.5).\u003c/strong\u003e This novel indicator aims to provide a more nuanced understanding of efficiency trends, offering a measure of consistency or fluctuation in performance metrics over time. By evaluating the independent variables dispersion, it is possible to comprehend the stability and reliability of efficiency gains (positive or negative), further enriching the analysis.\u003c/p\u003e\n\u003cp\u003eTo address the impact of adopting innovative practices, service literature introduces the concept of Innovation Outcomes (IO)\u0026nbsp;(Lusch \u0026amp; Nambisan, 2015), which tracks the evolution of KPIs as companies innovate. This approach quantifies the effects of innovations, such as entering coopetition networks or leveraging new technologies, on organizational efficiency. By measuring changes in KPIs, Innovation Outcomes provide insights into how innovations enhance operational metrics like productivity, material yield, and process efficiency. This framework highlights the benefits of innovation and its role in improving competitiveness and sustainability within the digital supply chain. Innovation Outcomes offer a clear mechanism to gauge the transformative impact of new practices on OS SMEs.\u003c/p\u003e\n\u003cp\u003eThe Innovation Outcomes are, therefore, an integral part of this methodology. IO\u003csub\u003eFFT\u003c/sub\u003e, IO\u003csub\u003ePE\u003c/sub\u003e, IO\u003csub\u003eRMY\u003c/sub\u003e, and IO\u003csub\u003ePL\u003c/sub\u003e represent the quantitative gains in first-time through performance, material yield, and labor productivity, respectively, facilitated by coopetition. Moreover, a metric IO(\u0026sigma;) for assessing the variability or consistency gain in innovation outcomes over the testing period is introduced, utilizing standard deviation to provide insights into how efficiency and operational performance evolve (Appendix, Table 2).\u003c/p\u003e"},{"header":"3.\tAdvancing Coopetition Networks In Ornamental SMEs through IoT","content":"\u003cp\u003eThe conversation about the Internet of Things (IoT), a term introduced by Kevin Ashton in 1998, has significantly progressed, paving the way for the emergence of the Industrial Internet of Things (IIoT). This evolution of IoT into the industrial domain aims to improve manufacturing efficiencies by adopting smart, interconnected systems. The IIoT integrates diverse technologies, fostering innovative value creation and competitiveness in the manufacturing industry by utilizing IP-based networks to improve connectivity and communication between industrial components.\u003c/p\u003e\n\u003cp\u003eIncorporating the SD-L perspective enhances this discussion by focusing on the mutual value creation between customers and businesses through the interaction of operant (skills and knowledge) and operand (physical) resources. This underscores the transformative potential of IoT in redefining industrial capabilities, enabling advanced control, coordination, and management of manufacturing environments. This synergy of the digital and physical realms promises significant innovation and efficiency improvements, though it also presents challenges in nurturing creativity and ensuring process flexibility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn effort within the Inovmineral4.0 R\u0026amp;D project was creating an innovative IIoT platform, Cockpit4.0+, designed to be used as coopetition enabler of coopetition networks among stone enterprises. Currently, a prototype, the Cockpit4.0+, backed by project funding and a unique development opportunity, serves as a foundation for gathering quantitative research data. This case study, set in the ornamental stone industry, examines the effect of coopetition networks on SMEs\u0026apos; operational efficiency through the lens of S-D logic\u0026nbsp;(Vargo et al., 2023). It showcases the potential of the early-stage Cockpit4.0+ platform and leverages a favorable funding climate to investigate the efficiencies and dynamics that coopetition introduces to the OS SME landscape.\u003c/p\u003e\n\u003ch2\u003eEmpirical Context\u003c/h2\u003e\n\u003cp\u003eIdentifying a suitable context for empirical investigation is critical in a quantitative research methodology, as emphasized by Johnson \u0026amp; Onwuegbuzie (2004). The selection process for an appropriate sector and companies to conduct this study was primarily influenced by considerations of practicality, including the specific nature of data requirements and the associated costs of data acquisition and analysis. This pragmatic approach ensured the selection was feasible and relevant, facilitating the collection of meaningful data that accurately reflects the dynamics and outcomes of coopetition networks within the chosen context\u0026nbsp;(Johnson \u0026amp; Onwuegbuzie, 2004).\u0026nbsp;Selecting an appropriate field for empirical testing is paramount in a quantitative research approach. Given the inherent specificity and associated data provision and collection costs, the choice of sector and companies for this study was guided by convenience.\u003c/p\u003e\n\u003ch2\u003ePopulation and Sample\u003c/h2\u003e\n\u003cp\u003eThe dataset provided by the Portuguese Association of the Mineral Resources Industry (ASSIMAGRA) in 2019, primarily consisting of SMEs, underscores the Ornamental Stone (OS) sector as a vital component of Portugal\u0026apos;s economy. Contributing over 16,600 direct jobs and exporting to 116 countries, the OS sector is a significant source of private sector employment, especially in the country\u0026apos;s interior regions. Despite facing challenges, the sector has shown consistent export growth, making Portugal the eighth largest exporter in the international trade of OS and ranking second globally in terms of international trade per capita.\u003c/p\u003e\n\u003cp\u003eThe introduction of the Cockpit4.0+ system, an artefact leveraging Industrial Internet of Things (IIoT) technology, represents a pioneering opportunity, especially with the availability of funding for participating companies. This initiative to develop and implement a coopetition technology-enabled network prototype has been strategically leveraged to collect quantitative data critical for this research. Coopetition technology-enabled networks facilitate digital connectivity among companies to promote collaborative production processes, with the Cockpit4.0+ system ensuring the integration of rival firms into a genuine coopetition network.\u003c/p\u003e\n\u003cp\u003eFor this study, three stone producers were selected to assess the benefits of the Cockpit4.0+ prototype, i.e., the advantages derived from the coopetition technology-enabled network. Initial data collection focused on the current state of operations, followed by a second phase of data gathering after the prototype\u0026apos;s installation and network activation.\u003c/p\u003e\n\u003cp\u003eDirect and informal approaches were made to the managing directors of these companies, who were cordially invited to participate in the research. A written agreement was proposed, specifying the confidentiality terms, including assurances not to disclose the names of the companies, their customers, employees, materials, resources, or competitors. The companies\u0026apos; managers granted the researcher access to confidential information, including analytical and accounting data, order records, and management and production systems.\u003c/p\u003e\n\u003cp\u003eTo ensure data collection\u0026apos;s accuracy and confidentiality, the researcher closely monitored the operations, direct and daily recording and collecting quantitative data from digital machinery and databases. This approach guarantees the precision and privacy of the collected information in Stone Companies.\u003c/p\u003e\n\u003ch2\u003eData Collection\u003c/h2\u003e\n\u003cp\u003eTo explore and evaluate the shift from Current Best Practices (CB.P) to Coopetition Production Practices (CN.P), the study implemented a data collection strategy over two separate intervals, each lasting fifty-four days. This methodical approach was designed to provide a basis for comparing operational efficiencies between conventional practices and those influenced by coopetition. In line with confidentiality agreements, all collected data was carefully anonymized, involving three companies referred to as \u0026quot;A\u0026quot;, \u0026quot;B\u0026quot;, and \u0026quot;C\u0026quot; to protect the privacy and maintain the anonymity of the entities involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhase 1 - Current Best Practices (CB.P):\u003c/strong\u003e The first phase spanned from April 17th, 2023, to June 10th, 2023. Data reflecting the companies\u0026apos; adherence to CB.P during this timeframe were meticulously gathered \u003cstrong\u003e(Appendix, Table 3).\u003c/strong\u003e This stage was characterized by conventional operational approaches, wherein each entity depended entirely on its internal resources to produce and deliver goods. This phase aimed to capture a baseline of operations before introducing coopetition practices, serving as a reference point for subsequent comparative analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhase 2: Coopetition Production Practices (CN.P):\u003c/strong\u003e Following the initial assessment period, the study transitioned into its second phase, focusing on Coopetition Production Practices (CN.P). This stage introduced a paradigm shift, encouraging companies to participate in a cooperative but competitive network. This innovative approach allowed for the utilization of shared technologies and resources, fostering a unique blend of collaboration and competition. The data for this phase were from September 9\u003csup\u003eth\u003c/sup\u003e, 2023, to November 14\u003csup\u003eth\u003c/sup\u003e, 2023, aligned to ensure consistency with the structured methodology adopted for the CN.P phase, facilitating a direct comparison of operational efficiencies between the traditional practices and those influenced by coopetition \u003cstrong\u003e(Appendix, Table 4).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData Handling and Confidentiality: In adherence to the confidentiality agreement and to maintain the anonymity of the participating entities, data was exported to Excel files, following predetermined procedures for data collection, recording, and exportation. This ensures a consistent and secure approach to data management, allowing for a rigorous analysis while respecting the privacy and proprietary concerns of the involved companies.\u003c/p\u003e"},{"header":"4.\tResults and Key Performance Indicators (KPIs)","content":"\u003cp\u003eThe analysis utilized data collected under Current Best Practices, outlined in Appendix Table 3, alongside data from Coopetition Technology-Enabled Networks, as detailed in Appendix Table 4. This comprehensive dataset facilitated an evaluation of coopetition effects across ten key performance indicators (KPIs), with findings summarized in Appendix Table 5. The juxtaposition of these datasets provides a nuanced understanding of how coopetition influences manufacturing efficiency across multiple dimensions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe First Time Through (KPI\u003csub\u003eFTT\u003c/sub\u003e)\u003c/strong\u003e indicator provides insights into manufacturing entities\u0026apos; operational excellence and quality control mechanisms.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eAppendix Table 3 demonstrates that under CB.P, the companies achieved a daily average output of 369.9 parts, with 31.3 parts being rejected owing to defects or requiring further correction. Consequently, this results in a KPI\u003csub\u003eFTT\u003c/sub\u003e of 90.9% across the companies\u0026apos; production lines, as detailed in Appendix, Table 5. This indicates that, on average, 90.9% of the parts produced meet the quality standards on their first pass, underscoring a notable level of process efficiency and quality control characteristic of conventional operational practices.\u003c/li\u003e\n \u003cli\u003eAppendix Table 4 reveals that under CN.P, the companies maintained a steady average output quality rate of 90.9%. There was a significant increase in average daily production to 454.4 parts under the coopetition model. This surge in output, alongside the consistent quality level as evidenced by the unchanged KPI\u003csub\u003eFTT\u003c/sub\u003e in Appendix, Table 5, suggests that adopting coopetition technology-enabled networks has significantly boosted the companies\u0026apos; production capacities without sacrificing quality.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eThe Performance Efficiency Indicator (KPI\u003csub\u003ePE\u003c/sub\u003e)\u003c/strong\u003e is crucial in assessing a manufacturing unit\u0026apos;s ability to fulfil orders efficiently. This metric measures how effectively resources are utilized to meet demand; the KPIPE offers valuable insights into manufacturing processes\u0026apos; operational efficiency and effectiveness.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eUnder CB.P, as outlined in Appendix, Table 3, the data harvested from a range of factories indicates an average output of 338.3 shipped parts, alongside an average daily operational time of 13.3 hours across the companies. This variation in hours reflects diverse operational strategies, with some companies adhering to a single 8-hour shift and others implementing two shifts, culminating in 16 hours. Based on this information, the KPI\u003csub\u003ePE\u003c/sub\u003e for these companies was determined to be 25.0 sold parts per hour, as recorded in Appendix Table 4.\u003c/li\u003e\n \u003cli\u003eUpon adopting CN.P, companies witnessed a substantial increase in productivity, as demonstrated by the average number of shipped parts to 415.8, detailed in Appendix, Table 3. This leap in output was realized without any changes to the pre-existing shift schedules, highlighting that integrating CN.P - whereby competitors cooperate on select production facets - played a crucial role in boosting company performance. Consequently, the KPI\u003csub\u003ePE\u003c/sub\u003e for these entities advanced to 32.30 shipped parts per hour, as recorded in Appendix, Table 4. This improvement, equating to an additional 7.30 parts per hour over the pre-CN.P, signifies a significant enhancement in performance efficiency.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eThe Material Yield Indicator (KPI\u003csub\u003eRMY\u003c/sub\u003e)\u003c/strong\u003e assesses material usage efficiency within the production process. \u003cstrong\u003eKPI\u003csub\u003eRMY\u003c/sub\u003e\u003c/strong\u003e underscores the efficiency level at which raw materials are converted into customer-acceptable products under conventional operational practices.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eUnder CB.P, data from Appendix Table 3 indicates that the companies utilized an average of 425.3 square meters (m\u003csup\u003e2\u003c/sup\u003e) of raw materials daily. From this input, an average of 323.7 parts per day were produced that met customer acceptance criteria, resulting in a material yield rate of 0.752 pieces per m\u003csub\u003e2\u003c/sub\u003e of raw material. Consequently, this analysis yields a KPI\u003csub\u003eRMY\u003c/sub\u003e of 0.752 parts per m\u003csub\u003e2\u003c/sub\u003e, as detailed in Appendix Table 5.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWith the transition to CN.P, Appendix Table 3 shows that the daily average of raw materials processed increased to 437.1 m2. Remarkably, the customer acceptance rate for produced parts surged to 454.3 units. This marked improvement resulted in a significant uptick in the KPI\u003csub\u003eRMY\u003c/sub\u003e across the companies, which escalated to 0.956 pieces per m\u003csup\u003e2\u003c/sup\u003e of raw material utilized, as reported in Appendix Table 4. This increase underscores the enhanced efficiency in material usage under the Coopetition Network Practices, reflecting a substantial improvement in converting raw materials into customer-acceptable products.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eThe Labor Productivity Indicator (KPI\u003csub\u003eLP\u003c/sub\u003e)\u003c/strong\u003e quantifies the output per worker involved, assessing the workforce\u0026apos;s efficiency in generating quality products.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eUnder CN.P, companies managed an average daily shipment of 338.5 parts, utilizing an average workforce of 49.9, as detailed in Appendix Table 3. In this operational setting, the Labor Productivity Indicator (KPILP) was determined to be 6.8 pieces per worker throughout an 8-hour workday, according to the data presented in Appendix Table 4.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWith the transition to CN.P, a significant enhancement in labour productivity was noted. Maintaining the same average workforce of 49.9 workers, the daily output of high-quality pieces rose to an average of 415.8, as indicated in Appendix Table 3. This improvement resulted in an increased average KPI\u003csub\u003eLP\u003c/sub\u003e of 8.7 high-quality pieces per worker for an 8-hour shift, detailed in Appendix Table 4.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"5.\tDiscussions and Innovation Outcomes (IOs)","content":"\u003cp\u003eInnovation Outcomes (IOs) encapsulating the efficiency gains resulting from the adoption of CN.P - a blend of collaboration and competition facilitated by technology within networks of OS SMEs - were analyzed. These IOs underscore the concrete advantages of such collaborative competition, evidencing significant improvements in efficiency metrics. Appendix Table 6 provides a concise overview of these enhancements, detailing the measurable improvements and the associated reduction in variability for each IO, thus showcasing the practical benefits of coopetition networks.\u003c/p\u003e\n\u003cp\u003eNo quantitative improvement was detected when examining the First Time Through outcomes. This indicates that the shift to coopetition did not directly lead to an increased success rate of processes or tasks being executed correctly on their first attempt. Nonetheless, a significant enhancement in consistency, quantified at 28.18%, was noted. This consistency improvement suggests a notable decrease in variability, which may imply a potential error reduction or the necessity for rework over time, as depicted in Figure 1.\u003c/p\u003e\n\u003cp\u003eIn analysing the development of the Performance Efficiency Outcome across OS SMEs, adopting coopetition network practices, showcases an exceptional average increase of 60.38%. This notable rise highlights the concrete advantages that coopetition brings to organizational processes. This improvement can be attributed to the transition to coopetition networks, which promotes a synergistic combination of resources, expertise, and capabilities, thereby significantly enhancing the efficiency of order fulfilment and optimization of the production process. Such a shift augments overall productivity and encourages a more adaptable and responsive manufacturing landscape. This environment is well-suited to meet changing market demands and consumer expectations, as illustrated in Figure 2.\u003c/p\u003e\n\u003cp\u003eThe transition to Coopetition Network Practices has significantly improved Raw Material Yield, with a recorded quantitative gain of 31.65%. This increase signifies enhanced utilization of raw materials, leading to reduced waste and more efficient production processes. Additionally, the gain in consistency is notably substantial, at 50.33%, which suggests a considerable reduction in variability. This consistency ensures a more predictable and efficient use of materials, highlighting the effectiveness of Coopetition Network practices in optimizing resource utilization and minimizing waste in the production cycle.\u003c/p\u003e\n\u003cp\u003eLabor Productivity has witnessed a remarkable quantitative gain of 61.59%, reflecting a significant enhancement in output or efficiency per labour unit. However, the minimal consistency gain of 0.59% indicates that while productivity has improved, achieving uniformity across various scenarios or over different periods remains a challenge that needs focused attention. This extraordinary surge in labour productivity highlights the advantages of adopting a coopetition framework, where the synergistic interplay of collaboration and competition drives productivity optimization. Companies tap into a collective pool of expertise, capabilities, and technological advancements through coopetition, fostering a more dynamic and efficient work environment. This strategic approach not only amplifies the productivity of individual workers but also considerably boosts the company\u0026apos;s overall operational effectiveness and competitive positioning in the market, as detailed in Figure 3.\u003c/p\u003e\n\u003cp\u003eObserving the evolution of the KPIs (Figure 4), it is clear that, except for the First Time Through, all other metrics show positive development when OS SMEs implement coopetition network practices.\u003c/p\u003e\n\u003cp\u003eFrom these innovation outcomes, it is observed that the quantitative impact on manufacturing efficiency is 38.41% when companies transition from their current practices to coopetition practices within networks, further supported by a 20.68% increase in the consistency of efficiency (Appendix, Table 6).\u003c/p\u003e"},{"header":"Conclusion and Future Research","content":"\u003cp\u003eThe adoption of Coopetition Practices within an IoT-enabled network involving a group of Portuguese Ornamental Stone SMEs has led to a remarkable improvement in manufacturing efficiency, with a recorded increase of 38.41%. This underscores the significant impact that strategic collaborative competition can have on enhancing operational performance in the SME sector. This substantial improvement underscores the effectiveness of collaborative competition in optimizing production processes and resource utilization. Alongside these quantitative gains in efficiency, there has been a notable increase in efficiency consistency, quantified at 20.68%. Moreover, the findings indicate improvements in labour productivity and raw material yield, with quantitative gains of 61.59% and 31.65%, respectively. These improvements highlight the successful optimization of key resources in the production process, leading to less waste and higher output per input unit. This suggests that coopetition boosts performance and makes efficiency gains more predictable and stable over time.\u003c/p\u003e\n\u003cp\u003eWhile significant strides have been made in enhancing productivity and efficiency, the minimal consistency gain in labour productivity (0.59%) indicates areas that require further attention. Future efforts should ensure that productivity improvements are achieved and consistently maintained across various operational scenarios and scaled effectively. In summary, by demonstrating tangible benefits gains and consistency in Efficiency, the coopetition networks may boost OS SMEs in the global digital supply chains context.\u003c/p\u003e\n\u003cp\u003eThis study conclusively demonstrates the tangible benefits of coopetition networks, especially their significant role in enhancing efficiency and ensuring operational consistency for Portuguese Stone SMEs engaged in global digital supply chains. These networks are identified as a potent tool for augmenting productivity and facilitating operational scale-up.\u003c/p\u003e\n\u003cp\u003eLooking ahead, it is imperative that future research delves deeper into the potential for optimizing and expanding coopetition networks. Such studies should investigate how these networks can be adapted to meet the evolving demands of various industries. Additionally, the creation of detailed guidelines for the effective implementation of coopetition strategies stands to provide businesses with crucial frameworks for adopting these forward-thinking operational models. Embarking on this path could be a decisive move towards securing enhanced competitiveness and fostering growth in the contemporary digital landscape.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship Contribution Statement:\u003c/strong\u003e Agostinho da Silva - Conceptualization, original draft writing, review \u0026amp; editing; Antonio J. Marques Cardoso - Conceptualization, original draft writing, review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of interest statement\u003c/strong\u003e: The authors declare no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of funding statement:\u003c/strong\u003e nothing to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData available on request from the authors:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author, Agostinho da Silva, upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAppian. (2021). Poor collaboration holds back European businesses digital ambitions. 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Service-dominant logic as a unifying theoretical framework for the re-institutionalization of the marketing discipline. \u003cem\u003eJournal of Business Research\u003c/em\u003e, \u003cem\u003e164\u003c/em\u003e, 113965. https://doi.org/10.1016/j.jbusres.2023.113965\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Coopetition, Networks, S-D Logic, SMEs, Efficiency, Value Cocreation","lastPublishedDoi":"10.21203/rs.3.rs-4023819/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4023819/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Small and Medium-sized Enterprises (SMEs) are crucial to Europe's economy, yet they face significant challenges within global supply chains, primarily due to efficiency deficits. Despite their important role in employment, innovation, and wealth generation, these enterprises struggle to compete on a global scale. Recognizing the potential of coopetition - strategic collaboration among competitors - as a means to enhance efficiency, this study explores its impact within the Portuguese Ornamental Stone industry, a critical sector for the national economy. Employing a case study methodology in an IoT-enabled coopetition network, it is documented a substantial improvement in manufacturing efficiency (38.41%), alongside increases in efficiency consistency (20.68%), labor productivity (61.59%), and raw material yield (31.65%). These findings not only validate the efficacy of coopetition in overcoming the efficiency challenges faced by SMEs but also highlight specific areas for further enhancement, such as labor productivity consistency. This research contributes to filling the empirical gap in understanding coopetition's role in SME efficiency and sets the stage for future inquiries into optimizing and expanding coopetition networks. It underscores the necessity for comprehensive strategies to implement coopetition practices, providing a blueprint for SMEs to thrive in the competitive landscape of global digital supply chains.","manuscriptTitle":"Boosting Manufacturing Efficiency through Coopetition: A Quantitative Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 17:11:26","doi":"10.21203/rs.3.rs-4023819/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec88d61f-f891-47b4-9310-9f23e7419797","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-13T17:12:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 17:11:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4023819","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4023819","identity":"rs-4023819","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}