Enhancing Supply Chain Management in Small-Medium Enterprises through Business Intelligence: A Conceptual Model Approach

Enhancing Supply Chain Management in Small-Medium Enterprises through Business Intelligence: A Conceptual Model Approach | 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 Research Article Enhancing Supply Chain Management in Small-Medium Enterprises through Business Intelligence: A Conceptual Model Approach mohammad Taghi Sadeghi, Ibaa Al hasan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4249032/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 In the contemporary business milieu, small and medium enterprises (SMEs) encounter distinct challenges in navigating the complexities of supply chain management (SCM) owing to resource constraints and operational limitations. This study proposes a conceptual model to bolster SCM efficiency within SMEs through the strategic application of business intelligence (BI). Adopting a qualitative and exploratory approach, the research delves into the experiences and insights of experts, managers, and information technology specialists entrenched in the SME landscape. Utilizing judgmental sampling, data saturation was achieved through semi-structured interviews, which were subsequently analyzed via thematic analysis. The findings unveil a comprehensive framework comprising 98 initial themes distilled into 55 basic themes, 15 organizing themes, and 4 global themes encompassing supply network analytics, business performance analytics, optimal path management, and retention and loyalty analytics. By embracing BI and integrating the proposed conceptual model, SMEs are poised to unlock novel growth trajectories and fortify their competitive standing in the contemporary business ecosystem. Small and Medium Enterprises Supply Chain Management Business Intelligence Supply Network Analytics Business Performance Analytics Optimal Path Management Figures Figure 1 1 Introduction In today's dynamic and competitive business environment, effective supply chain management (SCM) is paramount for Small-Medium Enterprises (SMEs) to thrive. SMEs play a crucial role in the global economy, contributing significantly to employment, innovation, and economic growth [ 1 ]. However, they often face unique challenges in managing their supply chains, stemming from limited resources, budgets, and bargaining power with suppliers. These challenges can lead to inefficiencies, suboptimal decision-making, and increased costs, ultimately impacting their competitiveness in the marketplace [ 2 ]. In recent years, the advent of Business Intelligence (BI) technologies has provided SMEs with new opportunities to address these challenges and optimize their supply chain operations [ 3 ]. BI encompasses the processes, technologies, and tools for transforming raw data into meaningful and actionable insights to support decision-making. By leveraging BI, SMEs can gain better visibility into their supply chains, improve forecasting accuracy, optimize inventory levels, enhance supplier relationships, and ultimately, drive operational efficiency and customer satisfaction [ 4 ]. However, despite the potential benefits, there is limited research and practical guidance available on how SMEs can effectively design and implement BI solutions tailored to their specific needs for supply chain management. Existing literature predominantly focuses on large enterprises, overlooking the unique constraints and requirements of SMEs in the context of BI adoption for SCM [ 5 ]. This paper aims to address this gap by proposing a comprehensive conceptual model for leveraging BI in SCM within SMEs. The conceptual model will be designed to accommodate the distinct characteristics and challenges of SMEs, offering practical insights and recommendations for their BI journey in supply chain management. The research will involve a thorough review of existing literature on BI, SCM, and SMEs to identify key gaps and challenges. Subsequently, a tailored BI conceptual model will be developed and implemented, followed by an evaluation of its effectiveness in enhancing supply chain performance. By providing practical guidance and empirical evidence, this research seeks to contribute to the body of knowledge on BI and SCM in the context of SMEs, enabling SMEs to harness the power of BI technologies to overcome supply chain challenges, improve decision-making capabilities, and gain a competitive edge in the marketplace. 2 Literature Review 2.1 Business Intelligence (BI) Business Intelligence (BI) refers to the technologies, strategies, and practices used by organizations to analyze and transform raw data into actionable insights for informed decision-making. It involves the collection, integration, and analysis of data from various sources, such as internal systems, external databases, and third-party sources, to uncover patterns, trends, and relationships that can drive business growth and efficiency [ 6 ]. At its core, BI encompasses a range of processes, including data mining, querying, reporting, and visualization, aimed at extracting valuable insights from large datasets. These insights enable stakeholders at all levels of an organization to understand past performance, monitor current operations, and forecast future trends, thereby supporting strategic planning and informed decision-making [ 7 ]. The ultimate goal of BI is to empower organizations to gain a competitive advantage by leveraging data-driven insights to optimize processes, identify opportunities, mitigate risks, and enhance performance across all functional areas. By providing a comprehensive view of business operations and market dynamics, BI enables organizations to adapt quickly to changing conditions, capitalize on emerging opportunities, and drive sustainable growth and profitability [ 8 ]. 2.2 Supply Chain Management (SCM) Supply Chain Management (SCM) refers to the strategic planning, coordination, and optimization of all activities involved in the sourcing, procurement, production, and logistics of goods and services, from raw material acquisition to the delivery of finished products to end customers. It encompasses the management of the entire supply chain network, including suppliers, manufacturers, distributors, retailers, and customers, with the goal of maximizing efficiency, minimizing costs, and delivering value to customers [ 9 ]. At its core, supply chain management involves the seamless integration and coordination of various functions and processes, such as inventory management, demand forecasting, production planning, transportation, warehousing, and distribution. It emphasizes collaboration and information sharing among supply chain partners to ensure timely delivery, optimal resource utilization, and responsiveness to changing market demands [ 10 ]. The primary objectives of supply chain management are to enhance operational efficiency, improve customer service levels, reduce lead times, minimize inventory costs, and mitigate risks throughout the supply chain. By optimizing supply chain processes and leveraging technology and best practices, organizations can achieve competitive advantages such as increased agility, flexibility, and innovation, ultimately driving business growth and success in dynamic and competitive markets [ 11 ]. 2.3 Small-Medium Enterprise (SME) A Small-Medium Enterprise (SME) is a business entity characterized by its relatively small size, typically employing fewer employees and generating lower revenue compared to larger corporations. While definitions of SMEs may vary across countries and industries, they generally encompass businesses that operate on a smaller scale with limited financial and human resources. SMEs often play a crucial role in driving economic development, contributing to job creation, innovation, and wealth generation within local and regional economies [ 12 ]. They are known for their flexibility, adaptability, and ability to respond quickly to market changes, making them essential components of vibrant and dynamic business ecosystems. SMEs can include a wide range of businesses across various sectors, including retail, manufacturing, services, and technology [ 13 ]. They may range from family-owned enterprises and startups to small-scale manufacturing units and professional service providers. Despite their smaller size, SMEs can exhibit significant diversity in terms of their products, services, and business models. While they may face challenges such as limited access to capital, market competition, and regulatory constraints, SMEs often leverage their agility and innovation capabilities to carve out niche markets, foster local entrepreneurship, and contribute to overall economic growth and prosperity [ 14 ]. 2.4 The Role of BI in Enhancing SCM Business intelligence (BI) empowers organizations to optimize supply chain management (SCM) by harnessing data-driven insights to enhance decision-making processes and operational efficiency. BI tools enable organizations to analyze historical data, market trends, and performance metrics to generate accurate demand forecasts, optimize inventory levels, and streamline logistics operations [ 15 ]. By leveraging BI, organizations can monitor key performance indicators (KPIs) in real-time, identify inefficiencies, and proactively address supply chain challenges, such as supplier disruptions or inventory shortages. Additionally, BI facilitates strategic supplier management by evaluating supplier performance metrics and fostering collaborative relationships with reliable and responsive partners. Overall, BI plays a critical role in driving continuous improvement and resilience within supply chain operations, enabling organizations to adapt quickly to changing market dynamics and gain a competitive edge in the marketplace [ 16 ]. 2.5 Supply Chain Challenges Faced by SMEs Small- and medium-sized enterprises (SMEs) encounter several challenges in managing their supply chains, stemming from their limited resources, capabilities, and market position. One of the primary challenges faced by SMEs is the difficulty of forecasting demand accurately. Limited historical data, volatile market conditions, and rapidly changing customer preferences make it challenging for SMEs to predict demand patterns effectively. As a result, SMEs may struggle with overstocking, leading to inventory holding costs or stockouts, resulting in lost sales opportunities and dissatisfied customers [ 17 ]. This uncertainty in demand forecasting can have significant implications for supply chain efficiency and financial performance. Another challenge for SMEs is their limited bargaining power with suppliers. Unlike larger corporations, SMEs often lack the negotiating leverage needed to secure favorable pricing, payment terms, and lead times from suppliers. This limitation can result in higher procurement costs, longer lead times, and increased dependency on a limited pool of suppliers. Additionally, SMEs may face difficulties in managing relationships with multiple suppliers and ensuring consistent quality and reliability across the supply chain network [ 18 ]. Furthermore, SMEs often struggle with limited visibility and control over their supply chain processes. With fewer resources and capabilities for data analytics and technology adoption, SMEs may lack real-time insights into inventory levels, order statuses, and supplier performance. This lack of visibility can hinder their ability to make informed decisions, identify inefficiencies, and respond quickly to changes in market conditions or customer demand. To address these challenges, SMEs need to invest in technology solutions tailored to their needs, cultivate strategic partnerships with suppliers and customers, and prioritize continuous improvement and innovation in their supply chain management practices. Through proactive measures and strategic initiatives, SMEs can enhance their supply chain efficiency, resilience, and competitiveness in today's dynamic business environment [ 19 ]. 2.6 Implementing the BI Conceptual Model in SME Supply Chain Management Implementing a Business Intelligence (BI) conceptual model in Small-Medium Enterprises (SMEs) for Supply Chain Management (SCM) involves a strategic approach tailored to the specific needs and constraints of SMEs. The process begins with a thorough assessment of the current state of SCM processes, data infrastructure, and technological capabilities within the organization. Clear objectives and key performance indicators (KPIs) are then defined in collaboration with stakeholders to guide the implementation process [ 20 ]. Data collection and integration processes are established to gather information from various sources across the supply chain, ensuring data quality and consistency for accurate analysis. Subsequently, BI tools and technologies are carefully selected and customized to align with SCM requirements, enabling the development of dashboards, reports, and visualizations that provide actionable insights into key metrics. User training and adoption programs are essential to promote the utilization of BI tools and foster a culture of data-driven decision-making within the organization [ 21 ]. Pilot implementations are conducted to validate the effectiveness of the BI conceptual model in specific SCM areas, followed by iterative improvements and scaling across the supply chain network. Continuous monitoring, feedback gathering, and optimization efforts ensure ongoing alignment with organizational goals and objectives, ultimately enabling SMEs to enhance their SCM efficiency, performance, and competitiveness [ 22 ]. Implementing a BI conceptual model in SME SCM requires a systematic approach focused on data-driven decision-making and continuous improvement. By leveraging BI tools and technologies, SMEs can gain valuable insights into supply chain processes, identify inefficiencies, and make informed decisions to optimize operations and drive business growth. However, successful implementation hinges on effective data collection, integration, and analysis, as well as user training and adoption. Pilot implementations and iterative improvements allow SMEs to validate the effectiveness of the BI conceptual model and tailor it to their specific needs and challenges. Through continuous monitoring, feedback gathering, and optimization efforts, SMEs can unlock the full potential of BI in SCM, improving operational efficiency, reducing costs, and gaining a competitive edge in the marketplace [ 23 ]. 3 Research Method 3.1 Research Type This study seeks to develop a conceptual model of business intelligence tailored for small and medium-sized enterprises (SMEs) involved in supply chain management. It adopts a qualitative and applied approach to address real-world challenges, making it practical in purpose. Furthermore, the research employs a descriptive-analytical method to explore and analyze the dynamics of BI implementation within SMEs' supply chain operations. 3.1.1 General Research Strategy As the research utilized exploratory interviews for data collection and thematic analysis for data interpretation, the current research strategy is qualitative in nature. 3.1.2 Data Collection Methods In this study, the main objective of designing a conceptual model of business intelligence for supply chain management in small and medium-sized companies was pursued through participant interviews. These interviews, conducted with an exploratory and semi-structured approach, constitute a qualitative research tool. Qualitative research was chosen to facilitate an in-depth understanding of participants' perceptions and the social and cultural contexts surrounding the phenomenon under investigation. Unlike quantitative approaches, qualitative research allows for a more comprehensive exploration of less familiar phenomena and a deeper understanding of individuals' interpretations of those phenomena [ 24 ]. The interview protocol included questions covering the following topics: 1. How would you define the key concepts of Business Intelligence (BI) in the context of Supply Chain Management (SCM) for Small-Medium Enterprises (SMEs)? 2. In your experience, what challenges do SMEs commonly face when implementing Business Intelligence solutions for optimizing supply chain processes, and how can these challenges be addressed? 3. Can you discuss your approach to integrating data from various sources within the supply chain to create a cohesive BI model? What considerations are important in this process? 4. SMEs often have resource constraints. How would you tailor a Business Intelligence conceptual model to meet the specific needs and resource limitations of small and medium enterprises in the realm of supply chain management? 5. What, in your view, are the most crucial Key Performance Indicators (KPIs) that should be incorporated into a BI model for evaluating and improving supply chain efficiency in SMEs? 6. Given the sensitivity of supply chain data, how would you address data security and privacy concerns in the design of a Business Intelligence system for SMEs, and what measures would you recommend? 7. How do you ensure the scalability of a BI conceptual model for SMEs, and what strategies would you employ to future-proof the model against technological advancements and evolving business needs? 8. User adoption is critical for the success of any BI system. How would you approach the design of user-friendly interfaces and what strategies would you implement to ensure effective user training and adoption in SMEs? 3.1.3 Data Analysis Methods Given the significance of the research topic, thematic analysis was employed to analyze the interview data. Thematic analysis is a method used to identify, analyze, and articulate patterns (themes) within the data, organizing it in detail. Braun and Clarke [ 25 ] outline six stages of thematic analysis. The first stage involves familiarizing oneself with the data to grasp its depth and content. The second stage entails creating primary codes to capture interesting characteristics within the data, resulting in 98 initial codes from the interviews. Subsequently, the third stage focuses on identifying basic themes by categorizing and summarizing the codes, yielding 55 basic themes. The fourth stage involves organizing these themes, refining them, and forming higher-level themes to ensure their validity in relation to the dataset, resulting in 15 organizing themes. In the fifth stage, global themes are defined and named, with data for each main theme analyzed to determine its nature and contents, resulting in four basic themes. Finally, in the sixth stage, a report is prepared, with the researcher synthesizing the abstract basic themes and conducting final analysis and report writing in alignment with the research objectives and structures. Table 1 contains the concepts identified in the text of an interview sample, which are categorized in the form of basic themes. This has been done for each of the interviews. MAXQDA is a software program designed for qualitative data analysis, primarily used in academic and research settings. It allows researchers to analyze and interpret unstructured data such as interviews, focus groups, surveys, articles, and more. MAXQDA provides tools for coding, categorizing, and visualizing data, facilitating the process of extracting meaningful insights from qualitative data. Table 1 Themes identified in the text of an interview sample with MAXQDA software Interview statement Basic themes In the context of SMEs and SCM, Business Intelligence refers to the systematic use of data analytics tools and technologies to gather, process, and analyze information from various stages of the supply chain. This enables SMEs to make informed decisions, optimize processes, and enhance overall efficiency Use of data analytics tools and technologies My approach involves understanding the end-to-end supply chain processes, identifying relevant data sources, and implementing a data integration strategy. This includes utilizing Extract, Transform, Load (ETL) processes, ensuring data quality, and employing robust data warehousing solutions." Approach to Integrating Data Key Performance Indicators (KPIs) such as order fulfillment time, inventory turnover, and on-time delivery are crucial for SMEs. These indicators directly impact efficiency and can be used to identify areas for improvement in the supply chain." Fulfillment time, inventory turnover, and on-time delivery Data security is paramount. I would implement robust encryption methods, access controls, and regularly update security protocols. Additionally, ensuring compliance with data protection regulations and conducting employee training on data handling best practices are integral components of a secure BI system Addressing Data Security Scalability is achieved through a flexible architecture and modular design. Employing cloud-based solutions allows for seamless scalability. To future-proof the model, staying abreast of technological advancements, adopting industry standards, and building in adaptability to evolving business needs are crucial Ensuring Scalability User-friendly interfaces are designed with simplicity and intuitiveness in mind. To ensure effective user training and adoption, I would conduct comprehensive training sessions, provide user-friendly documentation, and encourage user feedback for continuous improvement. Engaging end-users in the design process fosters a sense of ownership and increases adoption rates. Approach to designing user-friendly interfaces 3.2 Population and Sample The research targets a group comprising experts, managers, and IT professionals specializing in small and medium businesses, each possessing a minimum of 5 years of practical experience in the field. Employing the snowball sampling method, a non-probability technique commonly used to identify experts, interviews were conducted with information technology specialists and experts based in Baghdad. This approach involves leveraging existing connections to identify additional participants, thereby expanding the pool of potential interviewees. This methodology aligns with the recommendation outlined in reference [83], advocating for interviews with a range of 5 to 25 individuals who possess direct experience with the phenomenon under investigation. In this research, using judgmental sampling method, the selected samples have been identified and the data has reached the saturation stage in the interview with the 11th person. 3.2.1 Validity and Reliability of Data Collection Tools In order to ensure the validity and reliability of the research results, several measures were implemented. Firstly, all interviews were meticulously recorded and conducted with the explicit permission of the participants, ensuring the accuracy and completeness of the data. Additionally, to further enhance validity and reliability, the constructed concepts derived from the coding process were reviewed and commented on by qualified experts possessing the necessary expertise and competence in the field. Furthermore, to minimize bias and enhance consistency, the interviews were recorded by an individual well-versed in the research literature. To calculate the reliability of the interviews, a method involving the calculation of agreement between two coders was employed. Table 2 presents the level of agreement and the corresponding reliability percentage, indicating the extent to which the coding process yielded consistent and dependable results. This meticulous approach to data collection, analysis, and validation contributes to the overall trustworthiness and credibility of the research findings. Table 2 Calculating reliability of interviews through agreement between two coders Interview Total number of codes Number of agreements Number of disagreements Reliability percentage 3 15 9 4 71.22 5 18 6 2 69 9 11 8 5 59.62 Total 44 23 11 66.61 In this study, validating the research findings was deemed crucial, and thus, the Kappa coefficient was employed to assess the reliability of the results. To calculate the Kappa coefficient, four interviews were randomly assigned to a colleague who was tasked with coding the text independently. The resulting data were then organized into a two-dimensional matrix, which included the coder and their assigned codes. Subsequently, the codes were compared with each other, and the Kappa coefficient was computed using statistical software, such as SPSS. The obtained Kappa coefficient value of 0.85 indicates a high level of agreement between the reviewers, affirming the quality and consistency of the coded data. This high level of agreement suggests that the indicators used in the study are reliable and that the coding process yielded consistent results. The validation of the research findings through the Kappa coefficient reinforces the credibility and robustness of the study's conclusions, providing assurance to stakeholders and enhancing the overall trustworthiness of the research outcomes. Table 3 shows the value of the Kappa index coefficient. Table 3 Kappa index coefficient Researcher Unseen Seen Colleague Unseen 10 2 Agreement size Kappa Value Approximate significance Seen 0 19 Valid data 0.85 0.000 Total 10 21 31 3.2.2 Steps to Perform and Apply the Method This research endeavors to introduce a conceptual model of business intelligence for supply chain management in small and medium-sized enterprises (SMEs). Initially, a comprehensive review of literature pertaining to the research domain was conducted. Subsequently, data collection was undertaken utilizing a qualitative approach with the aid of interview tools. Following data collection, the gathered data underwent analysis employing thematic analysis methodology. Finally, the conceptual model of the research was derived from the analyzed data. In order to ensure the validity and reliability of the data collection process, the agreement between two coders was calculated using a methodical approach. This served as a means to assess the consistency and accuracy of the data collection tool. Furthermore, the validation of the research findings was executed utilizing a Triangulation design [ 26 ], enhancing the credibility and robustness of the research outcomes. This meticulous approach to data collection, analysis, and validation contributes to the overall trustworthiness and reliability of the research findings. 4 Research Findings In this section, descriptive statistics and tables pertaining to sample characteristics are presented. Understanding the characteristics of the sample is crucial as it allows for an insight into the general demographics and attributes of the studied population, facilitating comparability and generalizability of findings for other researchers. Subsequently, utilizing the thematic analysis method, the findings from the interviews were coded and analyzed. This process involved identifying recurring themes, patterns, and insights within the interview data. Finally, the conceptual model of the research, derived from the analyzed data, is elaborated upon. This conceptual model serves to provide a comprehensive framework for understanding the dynamics of business intelligence in the context of supply chain management within small and medium-sized enterprises (SMEs). 4.1 Demographic Profile Recognizing the significance of examining the demographic profile of the research participants, an assessment of the community under study will be conducted. The specifications and characteristics of the interviewees will be comprehensively detailed in Table 4 , providing valuable insights into the composition of the participant group. Table 4 Profiles and characteristics of interviewees Code Gender Education Expertise Experience P1 Man Masters IT 7 P2 Man Masters business management 6 P3 Man Masters IT 7 P4 Man P.h. D IT 6 P5 Female Masters IT 8 P6 Man Masters IT 7 P7 Man P.h.D IT 6 P8 Female P.h.D IT 8 P9 Female Masters business management 7 P10 Man P.h. D IT 10 P11 Man Masters IT/ business management 9 4.2 Analysis of the Data from the Interview During this phase, the interview data underwent analysis utilizing the thematic analysis method, comprising six distinct stages. These stages encompassed familiarizing with the data, creating primary codes, identifying selective codes, developing sub-themes, defining and naming global themes, and ultimately preparing a comprehensive report summarizing the findings. 4.2.1 Getting to Know the Data and Creating Basic Codes During this initial stage of the coding process, the data and interviews are thoroughly read and re-read multiple times to gain familiarity with their content. This iterative process facilitates the creation of initial codes and themes derived directly from the data. Each interview's outcomes are identified as primary and fundamental codes, which are then organized and tabulated appropriately. These codes are subsequently presented as concepts and key points extracted from the interviews, laying the foundation for further analysis and interpretation (Table 5 ). Table 5 Codes extracted from interviews Basic Themes Participant Holding costs and carrying costs P9 Inventory Turnover Comparison of sourcing costs across suppliers Identification of cost-saving opportunities Use of data analytics tools and technologies Product or service profitability P2 Identify periods of significant growth or decline Customer Acquisition and Conversion Brand Awareness and Perception Sales forecasting for better inventory planning P8 Demand forecasting based on market trends Inventory visibility across the supply chain. Analysis of distribution network effectiveness Transportation costs and efficiency P3 Route optimization analysis Addressing data security Fulfillment time P10 Warehouse capacity utilization Ticket resolution times Customer satisfaction scores Repeat support issues analysis Return rates and reasons P7 Analysis of returned product conditions Warranty claims analysis Identifying opportunities for network optimization Pick-and-pack efficiency P11 Real-time tracking of shipments. Warranty cost analysis Customer feedback analysis P4 Online reviews sentiment analysis On-time delivery metrics On-time delivery performance Quality of materials or goods received Compliance with contractual agreements On-Time Delivery Communication and Responsiveness Flexibility and Adaptability P1 Cost and Pricing Stockout and overstock analysis Stockout Prevention Safety Stock Management Ordering and Reorder Point Optimization Lead Time Management Approach to Integrating Data P5 Cost breakdown by supplier Sales revenue analysis Sales growth trends Customer demographics and preferences Customer lifetime value Market segmentation for targeted marketing Customer acquisition cost P6 approach to Designing User-Friendly Interfaces Ensuring Scalability Customer Retention and Loyalty Lead Generation and Quality 4.2.2 Selective Coding and Creation of Sub-Themes During the selective coding stage, researchers categorize various codes into concepts and organize the coded data summary accordingly. This process involves bringing together extracted codes that share semantic and conceptual similarities, thereby generating new meanings and insights. Subsequently, in creating organizing themes, two key steps are undertaken: reviewing and refining the coded summaries, and shaping and validating these themes. At this stage, all codes are grouped into a single class, and through labeling, the organizing and inclusive themes within each class are explained. This meticulous process ensures that the final themes accurately capture the essence of the data and provide meaningful interpretations of the research findings (Table 6 ). Table 6 Selective coding and creation of sub-themes Basic Themes Organizer Themes Global Themes On-time delivery performance Supplier Performance Supply Network Analytics Quality of materials or goods received Compliance with contractual agreements On-Time Delivery Communication and Responsiveness Flexibility and Adaptability Cost and Pricing Stockout and overstock analysis Inventory Management Holding costs and carrying costs Inventory Turnover Stockout Prevention Safety Stock Management Ordering and Reorder Point Optimization Lead Time Management Approach to Integrating Data Cost breakdown by supplier Cost Analysis Comparison of sourcing costs across suppliers Identification of cost-saving opportunities Use of data analytics tools and technologies Sales revenue analysis Sales Performance Business Performance Analytics Sales growth trends Product or service profitability Identify periods of significant growth or decline Customer demographics and preferences Customer Segmentation Customer lifetime value approach to Designing User-Friendly Interfaces Market segmentation for targeted marketing Customer acquisition cost Marketing Effectiveness Customer Acquisition and Conversion Brand Awareness and Perception Customer Retention and Loyalty Lead Generation and Quality Ensuring Scalability Sales forecasting for better inventory planning Forecasting Demand forecasting based on market trends On-time delivery metrics Logistics Performance Optimal Path Management Transportation costs and efficiency Route optimization analysis Addressing data security Fulfillment time Warehouse Efficiency Warehouse capacity utilization Pick-and-pack efficiency Real-time tracking of shipments Supply Chain Visibility Inventory visibility across the supply chain Analysis of distribution network effectiveness Distribution Network Optimization Identifying opportunities for network optimization Ticket resolution times Customer Support Retention and Loyalty Analytics Customer satisfaction scores Repeat support issues analysis Return rates and reasons Product Returns Analysis of returned product conditions Warranty claims analysis Warranty Analytics Warranty cost analysis Customer feedback analysis Feedback and Reviews Online reviews sentiment analysis 4.2.3 Defining and Naming Themes During the selective coding stage, researchers categorize various codes into concepts and organize the coded data summary accordingly. This process involves bringing together extracted codes that share semantic and conceptual similarities, thereby generating new meanings and insights. Subsequently, in creating organizing themes, two key steps are undertaken: reviewing and refining the coded summaries, and shaping and validating these themes. At this stage, all codes are grouped into a single class, and through labeling, the organizing and inclusive themes within each class are explained. This meticulous process ensures that the final themes accurately capture the essence of the data and provide meaningful interpretations of the research findings. Table 7 shows the global themes. Table 7 Global themes Themes of the research model Frequency of base codes Supply Network Analytics 19 Business Performance Analytics 16 Optimal Path Management 11 Retention and Loyalty Analytics 9 Total number of codes 55 4.2.4 Research Conceptual Model In the realm of business intelligence (BI) tailored for supply chain management (SCM) in small and medium enterprises (SMEs), the conceptual model comprises several pivotal dimensions, each contributing significantly to the enhancement of operational efficacy and decision-making prowess. One of these critical dimensions is supply network analytics, which delves into the intricate analysis of supplier performance metrics, inventory dynamics, and logistical efficiencies. By scrutinizing these facets, SMEs can discern patterns, identify bottlenecks, and optimize the seamless flow of goods and information across the supply chain. Figure 1 shows the research conceptual model (constructed by the researcher), which has 4 main dimensions obtained from the research: supply network analytics, business performance analytics, optimal path management, and retention and loyalty analytics. Furthermore, business performance analytics emerges as another cornerstone dimension, pivoting on the meticulous evaluation of key performance indicators (KPIs) spanning sales revenue, profitability margins, and customer satisfaction indices. Through the lens of BI, SMEs can gain granular insights into the efficacy of their strategies and operations, pinpointing areas of excellence and avenues for improvement with precision. This analytical approach empowers SMEs to make informed, data-driven decisions, thereby steering their ventures toward sustainable growth and competitive advantage. In tandem, optimal path management assumes a pivotal role, focusing on the strategic optimization of transportation routes, warehousing protocols, and distribution channels. By harnessing BI tools and techniques, SMEs can dissect complex logistical networks, unearth inefficiencies, and devise strategies to streamline operations, thereby mitigating costs and bolstering agility. This dimension serves as a linchpin for SMEs seeking to navigate the intricate maze of supply chain intricacies with finesse and efficacy. Lastly, retention and loyalty analytics emerge as a cornerstone dimension, emphasizing the paramount importance of customer-centric strategies in fostering enduring relationships and bolstering brand loyalty. By leveraging BI insights into customer behavior, preferences, and engagement patterns, SMEs can tailor personalized marketing campaigns, refine product offerings, and enhance the overall customer experience. This holistic approach not only nurtures customer loyalty but also lays the groundwork for sustained growth and profitability in the fiercely competitive SME landscape. In summation, the integration of these dimensions within the BI conceptual model equips SMEs with a robust framework for navigating the complexities of modern supply chain dynamics. Armed with actionable insights and data-driven strategies, SMEs can forge ahead with confidence, resilience, and adaptability, charting a course toward sustained success and market leadership in today's dynamic business landscape. 4.2.5 Validation of the Research Conceptual Model In this research, the researcher employed the triangulation method to validate the proposed model. Triangulation involves utilizing multiple methods of data collection, incorporating diverse data sources, analysts, or theories to corroborate research findings, and mitigating biases that may arise from over-reliance on any single method, source, analyst, or theoretical basis. In this study, the triangulation process entailed several steps [ 26 ]. Initially, three participants from the research cohort reviewed the reports generated in the first stage, and their feedback was integrated into the selective coding process, allowing for adjustments by the team members. Subsequently, collaborating experts conducted a comprehensive review of the coding categories, providing corrections and insights through a peer review process. These corrections were then applied to the research findings. Finally, the revised proposals were returned to the initial three participants, who provided further corrections and refinements to the research model. Through this iterative process of triangulation, the validity and reliability of the research findings were enhanced, ensuring a robust and comprehensive conceptual model of Business Intelligence for Supply Chain Management in small and medium enterprises. 5 Discussion, Conclusions and Suggestions The principal aim of this study is to improve supply chain management (SCM) within small and medium enterprises (SMEs) through the application of business intelligence (BI). Consequently, the subsequent section will provide a broad summary of the research findings, accompanied by practical suggestions aligned with the research objective. These recommendations are intended to offer tangible insights into enhancing SCM practices in SMEs through the effective utilization of BI tools and methodologies. 5.1 Discussion In the discussion, our study emphasizes the pivotal role of business intelligence (BI) in elevating supply chain management (SCM) practices within small- and medium-sized enterprises (SMEs). Through our conceptual model approach, we elucidate the multifaceted dimensions of BI-enabled SCM, highlighting themes such as supply network analytics and business performance analytics. These findings underscore the potential for SMEs to harness BI tools and techniques to optimize supplier relationships, enhance inventory management, and improve overall operational efficiency. Our conceptual model offers SMEs a structured framework to conceptualize and implement BI solutions tailored to their SCM needs. By focusing on key dimensions and components, SMEs can strategically integrate BI into their SCM processes to drive informed decision-making and performance improvement. However, it's crucial to acknowledge the challenges SMEs may face, including resource constraints and technological barriers, which could hinder the effective implementation of BI solutions. Looking ahead, future research should explore the practical implications of implementing BI solutions in SMEs' SCM operations and investigate their impact on performance metrics. Additionally, further studies on emerging technologies like artificial intelligence and the Internet of Things could provide valuable insights into enhancing BI capabilities in SCM. Ultimately, by embracing BI and leveraging our conceptual model, SMEs can unlock new avenues for competitiveness and growth in the ever-evolving business landscape. 5.2 Conclusions In conclusion, our study underscores the transformative potential of business intelligence (BI) in enhancing supply chain management (SCM) practices within small- and medium-sized enterprises (SMEs). Through our conceptual model approach, we have delineated key dimensions and components of BI-enabled SCM, emphasizing themes such as supply network analytics and business performance analytics. Our findings highlight the strategic importance of BI in enabling SMEs to optimize supplier relationships, streamline inventory management, and enhance overall operational efficiency. By leveraging BI tools and techniques, SMEs can gain valuable insights into their supply chain operations, driving informed decision-making and performance improvement. Moving forward, it is essential for SMEs to prioritize the integration of BI solutions into their SCM processes. However, challenges such as resource constraints and technological barriers may hinder the effective implementation of BI initiatives. Therefore, SMEs must invest in training and capacity-building initiatives to ensure successful adoption and utilization of BI tools. Ultimately, by embracing BI and leveraging our conceptual model, SMEs can unlock new opportunities for competitiveness and growth in today's dynamic business landscape. As technology continues to evolve, further research is needed to explore the practical implications of BI in SMEs' SCM operations and to identify emerging trends and opportunities for innovation. Overall, our study contributes to the growing body of literature on BI-enabled SCM in SMEs, providing valuable insights and guidance for practitioners and researchers alike. 5.3 Suggestions Suggestions for Enhancing Supply Chain Management in Small-Medium Enterprises through Business Intelligence: 1. Invest in User-Friendly BI Tools: SMEs should prioritize the adoption of user-friendly BI tools that facilitate easy data analysis and visualization. This will empower SCM personnel to gain actionable insights and make informed decisions in real-time. 2. Foster a Data-Driven Culture: Promote a culture of data-driven decision-making within the organization by providing training and support to SCM teams on utilizing BI tools effectively. Encourage collaboration and knowledge-sharing to maximize the benefits of BI across departments. 3. Focus on Supplier Relationship Management: Utilize BI analytics to evaluate supplier performance, identify areas for improvement, and strengthen relationships with key suppliers. By analyzing supplier data, SMEs can negotiate better terms, reduce risks, and ensure timely delivery of goods and services. 4. Optimize Inventory Management: Leverage BI insights to optimize inventory levels, reduce stockouts, and minimize holding costs. Implement demand forecasting tools, analyze historical sales data, and monitor inventory turnover ratios to streamline inventory management processes. 5. Enhance Logistics Efficiency: Use BI analytics to optimize transportation routes, reduce transportation costs, and improve delivery performance. Analyze route optimization data, track transportation metrics, and implement real-time tracking systems to enhance logistics efficiency. 6. Prioritize Customer-Centric Strategies: Adopt a customer-centric approach to SCM by analyzing customer data and preferences using BI tools. Segment customers based on their needs and preferences, personalize marketing strategies, and enhance customer satisfaction and loyalty. 7. Continuously Monitor and Evaluate Performance: Establish KPIs and performance metrics to track the effectiveness of BI-enabled SCM initiatives. Continuously monitor performance, analyze trends, and adjust strategies as needed to achieve operational excellence and competitive advantage. 8. Stay updated on emerging technologies: Keep abreast of emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT) that can enhance BI capabilities in SCM. Explore opportunities to integrate these technologies into existing BI frameworks to drive innovation and efficiency. By implementing these suggestions, SMEs can harness the power of business intelligence to enhance their supply chain management practices, drive operational efficiency, and achieve sustainable growth in today's competitive business landscape. 5.4 Research Limitations 1. The study primarily focused on specific indicators influencing the conceptual model of supply chain management business intelligence, potentially overlooking other factors that could also play a role. Future research could explore a broader range of indicators to provide a more comprehensive understanding of the complexities within supply chain management. 2. The research encountered challenges related to word tension, which at times made it difficult to clearly distinguish between certain terms or concepts. This limitation may have impacted the precision and accuracy of data interpretation and analysis. Strategies such as refining the research methodology or employing additional linguistic analysis techniques could help mitigate this challenge in future studies. Declarations Conflict of interest the authors have no relevant financial or non-financial interests to disclose. References Bahukeling, TS, Suroso, AI, Buono, A, Nurhayati, P (2024) Digital Marketing Alliance on Small Medium Enterprises (SMES): A Systematic Literature Review. Journal Aplikasi Bisnis dan Manajemen (JABM) 10(1): 199-219 Wided, R (2024) The Role of Information Technology in Strengthening Strategic Flexibility and Organisational Resilience of Small Medium Enterprises Post COVID-19. Journal of Information & Knowledge Management 24:50-61 Mohammed, AB, Al-Okaily, M, Qasim, D, Al-Majali, MK (2024) Towards an understanding of business intelligence and analytics usage: evidence from the banking industry . International Journal of Information Management Data Insights 4(1): 100215 Kumar, S, Aithal, PS (2023) Tech-Business Analytics–A Review Based New Model to Improve the Performances of Various Industry Sectors . International Journal of Applied Engineering and Management Letters (IJAEML) 7(1): 67-91 Dey, PK, Chowdhury, S, Abadie, A, Vann Yaroson, E, Sarkar, S (2023) Artificial intelligence-driven supply chain resilience in Vietnamese manufacturing small-and medium-sized enterprises. International Journal of Production Research 1(2): 1-20 Bharadiya, JP (2023) A comparative study of business intelligence and artificial intelligence with big data analytics. American Journal of Artificial Intelligence 7(1): 10-24 Al-Okaily, A, Teoh, AP, Al-Okaily, M, Iranmanesh, M, Al-Betar, MA (2023) The efficiency measurement of business intelligence systems in the big data-driven economy: a multidimensional model. Information Discovery and Delivery 51(4): 404-416 Ahmad, H, Hanandeh, R, Alazzawi, F, Al-Daradkah, A, ElDmrat, A, Ghaith, Y, Darawsheh, S (2023) The effects of big data, artificial intelligence, and business intelligence on e-learning and business performance: Evidence from Jordanian telecommunication firms International. Journal of Data and Network Science 7(1): 35-40 Taj, S, Imran, AS, Kastrati, Z, Daudpota, SM, Memon, R A, Ahmed, J (2023) IoT-based supply chain management: A systematic literature review. Internet of Things 24, 100-122 Júnior, ELP, Morreira, MÂL, Gomes, C F S, dos Santos, M, de Araújo Costa, A P, Chagas, S D S S, Kojima, EH (2023) Supply Chain Management (SCM): an Analysis based on the CRITIC-GRA-3N Method in the selection of auto parts suppliers for an auto parts dealer in the city of Guaratinguetá . Procedia Computer Science 221, 402-409 Kadir, S, Shaikh, JM (2023) The effects of e-commerce businesses to small-medium enterprises: Media techniques and technology . In AIP Conference Proceedings 2643(1) AIP Publishing Ariani, DW (2023) Exploring Relationship of Job Satisfaction, Organizatonal Culture, and Employee Performance in Small Medium Enterprise International. Journal of Professional Business Review 8(2): 876-901 Permatasari, P, Gunawan, J (2023) Sustainability policies for small medium enterprises: WHO are the actors?. Cleaner and Responsible Consumption 9(1), 100-122 Aroba, OJ, Mnguni, S B (2023, January) An Enterprise Resource Planning (ERP) SAP implementation case study in South Africa small medium enterprise sectors In International Conference on Digital Technologies and Applications (pp 348-354) Cham: Springer Nature Switzerland Yan, Y, Zhang, H, Du, S, Ma, Y (2023) Bi-SCM: bidirectional spiking cortical model with adaptive unsharp masking for mammography image enhancement. Multimedia Tools and Applications 82(8): 12081-12098 Nag, A, Choudhary, N, Sinha, D, Sinha, A P, Mishra, S (2023) Predictive analytics-new business intelligence in SCM. International Journal of Value Chain Management 14(3): 325-345 Sahoo, PBB, Thakur, V (2023) Enhancing the performance of Indian micro, small and medium enterprises by implementing supply chain finance: challenges emerging from COVID-19 pandemic. Benchmarking International Journal 30(6): 2110-2138 Abdelfattah, F, Malik, M, Al Alawi, A M, Sallem, R, Ganguly, A (2023) Towards measuring SMEs performance amid the COVID-19 outbreak: exploring the impact of integrated supply chain drivers. Journal of Global Operations and Strategic Sourcing 16(2): 520-540 Ramakrishna, Y, Alzoubi, H, Indiran, L (2023) An empirical investigation of effect of sustainable and smart supply practices on improving the supply chain organizational performance in SMEs in India. Uncertain Supply Chain Management 11(3): 991-1000 AL-Shboul, MDA (2023) Better understanding of technology effects in adoption of predictive supply chain business analytics among SMEs: fresh insights from developing countries. Business Process Management Journal 29(1): 159-177 Khanuja, A, Jain, RK (2023) The conceptual framework on integrated flexibility: an evolution to data-driven supply chain management. The TQM Journal 35(1): 131-152 Van Nguyen, T, Pham, HT, Ha, HM, Tran, TTT (2024) An integrated model of supply chain quality management, Industry 35 and innovation to improve manufacturers' performance–a case study of Vietnam. International Journal of Logistics Research and Applications 27(2): 261-283 Donyavi, S, Flanagan, R, Assadi-Langroudi, A, Parisi, L (2024) Understanding the complexity of materials procurement in construction projects to build a conceptual framework influencing supply chain management of MSMEs. International Journal of Construction Management 24(2): 177-186 Jando, C, Dionne, F (2024) A call for qualitative research in Contextual Behavioral Science. Journal of Contextual Behavioral Science 10(2) :11-21 Braun, V, Clarke, V (2006) Using thematic analysis in psychology. Qualitative research in psychology 3(2): 77-101 Jack, EP, Raturi, AS (2006) Lessons learned from methodological triangulation in management research. Management research news 29(6): 345-357 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4249032","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290316250,"identity":"13a1ebb4-f9c4-4398-9e0b-50ec2ea74069","order_by":0,"name":"mohammad Taghi Sadeghi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYNACNgYGA2bmAwyMDRJQEQP8qqFa2BJI1cLAYwDUQoSTdOc3MH/4UWZjt52d5/Pnyh0W8gYHmB9+YCi4h1OL2TEGNsmec2nJO5t5t0mePSNhuOEAm7EEg0ExXi0MvG2Hkw0O825jbGyTYNxwgMEM6M4EfFqYP/5t+w/UwvP4I1CL/YYD7N8IaWGQ5m07YAfUwiAJ1JK44QAPIVsS26RlziUnGBxmM5NsPCORPPMwT7FEAj4thw8f/vimzM7e4PxhoMN21Nn2HW/f+OHDH9xaGKBxkdgAF2AGYnwaYMCeCDWjYBSMglEwUgEA6tlRyysJJVYAAAAASUVORK5CYII=","orcid":"","institution":"University of Qom","correspondingAuthor":true,"prefix":"","firstName":"mohammad","middleName":"Taghi","lastName":"Sadeghi","suffix":""},{"id":290316251,"identity":"e3e57d02-784c-43f2-8a87-5b02e35be0c7","order_by":1,"name":"Ibaa Al hasan","email":"","orcid":"","institution":"University of Qom","correspondingAuthor":false,"prefix":"","firstName":"Ibaa","middleName":"Al","lastName":"hasan","suffix":""}],"badges":[],"createdAt":"2024-04-10 19:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4249032/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4249032/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54756369,"identity":"4a277dcc-a2d5-4c66-b6eb-793d9b55e8b1","added_by":"auto","created_at":"2024-04-16 10:00:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch Conceptual Model (Researcher made)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4249032/v1/e6223328b3842cb131458940.png"},{"id":65895519,"identity":"1ffb3976-5617-4d55-9dd5-005754e48f85","added_by":"auto","created_at":"2024-10-04 06:23:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1096462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4249032/v1/35881d23-e665-4457-89be-ec98055b9132.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Supply Chain Management in Small-Medium Enterprises through Business Intelligence: A Conceptual Model Approach","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn today's dynamic and competitive business environment, effective supply chain management (SCM) is paramount for Small-Medium Enterprises (SMEs) to thrive. SMEs play a crucial role in the global economy, contributing significantly to employment, innovation, and economic growth [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, they often face unique challenges in managing their supply chains, stemming from limited resources, budgets, and bargaining power with suppliers. These challenges can lead to inefficiencies, suboptimal decision-making, and increased costs, ultimately impacting their competitiveness in the marketplace [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In recent years, the advent of Business Intelligence (BI) technologies has provided SMEs with new opportunities to address these challenges and optimize their supply chain operations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. BI encompasses the processes, technologies, and tools for transforming raw data into meaningful and actionable insights to support decision-making. By leveraging BI, SMEs can gain better visibility into their supply chains, improve forecasting accuracy, optimize inventory levels, enhance supplier relationships, and ultimately, drive operational efficiency and customer satisfaction [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, despite the potential benefits, there is limited research and practical guidance available on how SMEs can effectively design and implement BI solutions tailored to their specific needs for supply chain management. Existing literature predominantly focuses on large enterprises, overlooking the unique constraints and requirements of SMEs in the context of BI adoption for SCM [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis paper aims to address this gap by proposing a comprehensive conceptual model for leveraging BI in SCM within SMEs. The conceptual model will be designed to accommodate the distinct characteristics and challenges of SMEs, offering practical insights and recommendations for their BI journey in supply chain management. The research will involve a thorough review of existing literature on BI, SCM, and SMEs to identify key gaps and challenges. Subsequently, a tailored BI conceptual model will be developed and implemented, followed by an evaluation of its effectiveness in enhancing supply chain performance.\u003c/p\u003e \u003cp\u003eBy providing practical guidance and empirical evidence, this research seeks to contribute to the body of knowledge on BI and SCM in the context of SMEs, enabling SMEs to harness the power of BI technologies to overcome supply chain challenges, improve decision-making capabilities, and gain a competitive edge in the marketplace.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Business Intelligence (BI)\u003c/h2\u003e \u003cp\u003eBusiness Intelligence (BI) refers to the technologies, strategies, and practices used by organizations to analyze and transform raw data into actionable insights for informed decision-making. It involves the collection, integration, and analysis of data from various sources, such as internal systems, external databases, and third-party sources, to uncover patterns, trends, and relationships that can drive business growth and efficiency [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. At its core, BI encompasses a range of processes, including data mining, querying, reporting, and visualization, aimed at extracting valuable insights from large datasets. These insights enable stakeholders at all levels of an organization to understand past performance, monitor current operations, and forecast future trends, thereby supporting strategic planning and informed decision-making [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The ultimate goal of BI is to empower organizations to gain a competitive advantage by leveraging data-driven insights to optimize processes, identify opportunities, mitigate risks, and enhance performance across all functional areas. By providing a comprehensive view of business operations and market dynamics, BI enables organizations to adapt quickly to changing conditions, capitalize on emerging opportunities, and drive sustainable growth and profitability [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Supply Chain Management (SCM)\u003c/h2\u003e \u003cp\u003eSupply Chain Management (SCM) refers to the strategic planning, coordination, and optimization of all activities involved in the sourcing, procurement, production, and logistics of goods and services, from raw material acquisition to the delivery of finished products to end customers. It encompasses the management of the entire supply chain network, including suppliers, manufacturers, distributors, retailers, and customers, with the goal of maximizing efficiency, minimizing costs, and delivering value to customers [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. At its core, supply chain management involves the seamless integration and coordination of various functions and processes, such as inventory management, demand forecasting, production planning, transportation, warehousing, and distribution. It emphasizes collaboration and information sharing among supply chain partners to ensure timely delivery, optimal resource utilization, and responsiveness to changing market demands [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The primary objectives of supply chain management are to enhance operational efficiency, improve customer service levels, reduce lead times, minimize inventory costs, and mitigate risks throughout the supply chain. By optimizing supply chain processes and leveraging technology and best practices, organizations can achieve competitive advantages such as increased agility, flexibility, and innovation, ultimately driving business growth and success in dynamic and competitive markets [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Small-Medium Enterprise (SME)\u003c/h2\u003e \u003cp\u003eA Small-Medium Enterprise (SME) is a business entity characterized by its relatively small size, typically employing fewer employees and generating lower revenue compared to larger corporations. While definitions of SMEs may vary across countries and industries, they generally encompass businesses that operate on a smaller scale with limited financial and human resources. SMEs often play a crucial role in driving economic development, contributing to job creation, innovation, and wealth generation within local and regional economies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. They are known for their flexibility, adaptability, and ability to respond quickly to market changes, making them essential components of vibrant and dynamic business ecosystems. SMEs can include a wide range of businesses across various sectors, including retail, manufacturing, services, and technology [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. They may range from family-owned enterprises and startups to small-scale manufacturing units and professional service providers. Despite their smaller size, SMEs can exhibit significant diversity in terms of their products, services, and business models. While they may face challenges such as limited access to capital, market competition, and regulatory constraints, SMEs often leverage their agility and innovation capabilities to carve out niche markets, foster local entrepreneurship, and contribute to overall economic growth and prosperity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 The Role of BI in Enhancing SCM\u003c/h2\u003e \u003cp\u003eBusiness intelligence (BI) empowers organizations to optimize supply chain management (SCM) by harnessing data-driven insights to enhance decision-making processes and operational efficiency. BI tools enable organizations to analyze historical data, market trends, and performance metrics to generate accurate demand forecasts, optimize inventory levels, and streamline logistics operations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. By leveraging BI, organizations can monitor key performance indicators (KPIs) in real-time, identify inefficiencies, and proactively address supply chain challenges, such as supplier disruptions or inventory shortages. Additionally, BI facilitates strategic supplier management by evaluating supplier performance metrics and fostering collaborative relationships with reliable and responsive partners. Overall, BI plays a critical role in driving continuous improvement and resilience within supply chain operations, enabling organizations to adapt quickly to changing market dynamics and gain a competitive edge in the marketplace [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Supply Chain Challenges Faced by SMEs\u003c/h2\u003e \u003cp\u003eSmall- and medium-sized enterprises (SMEs) encounter several challenges in managing their supply chains, stemming from their limited resources, capabilities, and market position. One of the primary challenges faced by SMEs is the difficulty of forecasting demand accurately. Limited historical data, volatile market conditions, and rapidly changing customer preferences make it challenging for SMEs to predict demand patterns effectively. As a result, SMEs may struggle with overstocking, leading to inventory holding costs or stockouts, resulting in lost sales opportunities and dissatisfied customers [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This uncertainty in demand forecasting can have significant implications for supply chain efficiency and financial performance. Another challenge for SMEs is their limited bargaining power with suppliers. Unlike larger corporations, SMEs often lack the negotiating leverage needed to secure favorable pricing, payment terms, and lead times from suppliers. This limitation can result in higher procurement costs, longer lead times, and increased dependency on a limited pool of suppliers. Additionally, SMEs may face difficulties in managing relationships with multiple suppliers and ensuring consistent quality and reliability across the supply chain network [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, SMEs often struggle with limited visibility and control over their supply chain processes. With fewer resources and capabilities for data analytics and technology adoption, SMEs may lack real-time insights into inventory levels, order statuses, and supplier performance. This lack of visibility can hinder their ability to make informed decisions, identify inefficiencies, and respond quickly to changes in market conditions or customer demand. To address these challenges, SMEs need to invest in technology solutions tailored to their needs, cultivate strategic partnerships with suppliers and customers, and prioritize continuous improvement and innovation in their supply chain management practices. Through proactive measures and strategic initiatives, SMEs can enhance their supply chain efficiency, resilience, and competitiveness in today's dynamic business environment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Implementing the BI Conceptual Model in SME Supply Chain Management\u003c/h2\u003e \u003cp\u003eImplementing a Business Intelligence (BI) conceptual model in Small-Medium Enterprises (SMEs) for Supply Chain Management (SCM) involves a strategic approach tailored to the specific needs and constraints of SMEs. The process begins with a thorough assessment of the current state of SCM processes, data infrastructure, and technological capabilities within the organization. Clear objectives and key performance indicators (KPIs) are then defined in collaboration with stakeholders to guide the implementation process [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Data collection and integration processes are established to gather information from various sources across the supply chain, ensuring data quality and consistency for accurate analysis. Subsequently, BI tools and technologies are carefully selected and customized to align with SCM requirements, enabling the development of dashboards, reports, and visualizations that provide actionable insights into key metrics. User training and adoption programs are essential to promote the utilization of BI tools and foster a culture of data-driven decision-making within the organization [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Pilot implementations are conducted to validate the effectiveness of the BI conceptual model in specific SCM areas, followed by iterative improvements and scaling across the supply chain network. Continuous monitoring, feedback gathering, and optimization efforts ensure ongoing alignment with organizational goals and objectives, ultimately enabling SMEs to enhance their SCM efficiency, performance, and competitiveness [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImplementing a BI conceptual model in SME SCM requires a systematic approach focused on data-driven decision-making and continuous improvement. By leveraging BI tools and technologies, SMEs can gain valuable insights into supply chain processes, identify inefficiencies, and make informed decisions to optimize operations and drive business growth. However, successful implementation hinges on effective data collection, integration, and analysis, as well as user training and adoption. Pilot implementations and iterative improvements allow SMEs to validate the effectiveness of the BI conceptual model and tailor it to their specific needs and challenges. Through continuous monitoring, feedback gathering, and optimization efforts, SMEs can unlock the full potential of BI in SCM, improving operational efficiency, reducing costs, and gaining a competitive edge in the marketplace [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Research Method","content":"\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.1 Research Type\u003c/h2\u003e\n \u003cp\u003eThis study seeks to develop a conceptual model of business intelligence tailored for small and medium-sized enterprises (SMEs) involved in supply chain management. It adopts a qualitative and applied approach to address real-world challenges, making it practical in purpose. Furthermore, the research employs a descriptive-analytical method to explore and analyze the dynamics of BI implementation within SMEs\u0026apos; supply chain operations.\u003c/p\u003e\n \u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.1.1 General Research Strategy\u003c/h2\u003e\n \u003cp\u003eAs the research utilized exploratory interviews for data collection and thematic analysis for data interpretation, the current research strategy is qualitative in nature.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.1.2 Data Collection Methods\u003c/h2\u003e\n \u003cp\u003eIn this study, the main objective of designing a conceptual model of business intelligence for supply chain management in small and medium-sized companies was pursued through participant interviews. These interviews, conducted with an exploratory and semi-structured approach, constitute a qualitative research tool. Qualitative research was chosen to facilitate an in-depth understanding of participants\u0026apos; perceptions and the social and cultural contexts surrounding the phenomenon under investigation. Unlike quantitative approaches, qualitative research allows for a more comprehensive exploration of less familiar phenomena and a deeper understanding of individuals\u0026apos; interpretations of those phenomena [\u003cspan\u003e24\u003c/span\u003e]. The interview protocol included questions covering the following topics:\u003c/p\u003e\n \u003cp\u003e1. How would you define the key concepts of Business Intelligence (BI) in the context of Supply Chain Management (SCM) for Small-Medium Enterprises (SMEs)?\u003c/p\u003e\u0026nbsp;2. In your experience, what challenges do SMEs commonly face when implementing Business Intelligence solutions for optimizing supply chain processes, and how can these challenges be addressed? \u003cspan\u003e3. Can you discuss your approach to integrating data from various sources within the supply chain to create a cohesive BI model? What considerations are important in this process?\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e4. SMEs often have resource constraints. How would you tailor a Business Intelligence conceptual model to meet the specific needs and resource limitations of small and medium enterprises in the realm of supply chain management?\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e5. What, in your view, are the most crucial Key Performance Indicators (KPIs) that should be incorporated into a BI model for evaluating and improving supply chain efficiency in SMEs?\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e6. Given the sensitivity of supply chain data, how would you address data security and privacy concerns in the design of a Business Intelligence system for SMEs, and what measures would you recommend?\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e7. How do you ensure the scalability of a BI conceptual model for SMEs, and what strategies would you employ to future-proof the model against technological advancements and evolving business needs?\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e8. User adoption is critical for the success of any BI system. How would you approach the design of user-friendly interfaces and what strategies would you implement to ensure effective user training and adoption in SMEs?\u003cbr\u003e\u003c/span\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.1.3 Data Analysis Methods\u003c/h2\u003e\n \u003cp\u003eGiven the significance of the research topic, thematic analysis was employed to analyze the interview data. Thematic analysis is a method used to identify, analyze, and articulate patterns (themes) within the data, organizing it in detail. Braun and Clarke [\u003cspan\u003e25\u003c/span\u003e] outline six stages of thematic analysis. The first stage involves familiarizing oneself with the data to grasp its depth and content. The second stage entails creating primary codes to capture interesting characteristics within the data, resulting in 98 initial codes from the interviews. Subsequently, the third stage focuses on identifying basic themes by categorizing and summarizing the codes, yielding 55 basic themes. The fourth stage involves organizing these themes, refining them, and forming higher-level themes to ensure their validity in relation to the dataset, resulting in 15 organizing themes. In the fifth stage, global themes are defined and named, with data for each main theme analyzed to determine its nature and contents, resulting in four basic themes. Finally, in the sixth stage, a report is prepared, with the researcher synthesizing the abstract basic themes and conducting final analysis and report writing in alignment with the research objectives and structures.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan\u003e1\u003c/span\u003e contains the concepts identified in the text of an interview sample, which are categorized in the form of basic themes. This has been done for each of the interviews. MAXQDA is a software program designed for qualitative data analysis, primarily used in academic and research settings. It allows researchers to analyze and interpret unstructured data such as interviews, focus groups, surveys, articles, and more. MAXQDA provides tools for coding, categorizing, and visualizing data, facilitating the process of extracting meaningful insights from qualitative data.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThemes identified in the text of an interview sample with MAXQDA software\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eInterview statement\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eBasic themes\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIn the context of SMEs and SCM, Business Intelligence refers to the systematic use of data analytics tools and technologies to gather, process, and analyze information from various stages of the supply chain. This enables SMEs to make informed decisions, optimize processes, and enhance overall efficiency\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eUse of data analytics tools and technologies\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMy approach involves understanding the end-to-end supply chain processes, identifying relevant data sources, and implementing a data integration strategy. This includes utilizing Extract, Transform, Load (ETL) processes, ensuring data quality, and employing robust data warehousing solutions.\u0026quot;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eApproach to Integrating Data\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eKey Performance Indicators (KPIs) such as order fulfillment time, inventory turnover, and on-time delivery are crucial for SMEs. These indicators directly impact efficiency and can be used to identify areas for improvement in the supply chain.\u0026quot;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eFulfillment time, inventory turnover, and on-time delivery\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eData security is paramount. I would implement robust encryption methods, access controls, and regularly update security protocols. Additionally, ensuring compliance with data protection regulations and conducting employee training on data handling best practices are integral components of a secure BI system\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAddressing Data Security\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eScalability is achieved through a flexible architecture and modular design. Employing cloud-based solutions allows for seamless scalability. To future-proof the model, staying abreast of technological advancements, adopting industry standards, and building in adaptability to evolving business needs are crucial\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eEnsuring Scalability\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eUser-friendly interfaces are designed with simplicity and intuitiveness in mind. To ensure effective user training and adoption, I would conduct comprehensive training sessions, provide user-friendly documentation, and encourage user feedback for continuous improvement. Engaging end-users in the design process fosters a sense of ownership and increases adoption rates.\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eApproach to designing user-friendly interfaces\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cbr\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.2 Population and Sample\u003c/h2\u003e\n \u003cp\u003eThe research targets a group comprising experts, managers, and IT professionals specializing in small and medium businesses, each possessing a minimum of 5 years of practical experience in the field. Employing the snowball sampling method, a non-probability technique commonly used to identify experts, interviews were conducted with information technology specialists and experts based in Baghdad. This approach involves leveraging existing connections to identify additional participants, thereby expanding the pool of potential interviewees. This methodology aligns with the recommendation outlined in reference [83], advocating for interviews with a range of 5 to 25 individuals who possess direct experience with the phenomenon under investigation. In this research, using judgmental sampling method, the selected samples have been identified and the data has reached the saturation stage in the interview with the 11th person.\u003c/p\u003e\n \u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.2.1 Validity and Reliability of Data Collection Tools\u003c/h2\u003e\n \u003cp\u003eIn order to ensure the validity and reliability of the research results, several measures were implemented. Firstly, all interviews were meticulously recorded and conducted with the explicit permission of the participants, ensuring the accuracy and completeness of the data. Additionally, to further enhance validity and reliability, the constructed concepts derived from the coding process were reviewed and commented on by qualified experts possessing the necessary expertise and competence in the field. Furthermore, to minimize bias and enhance consistency, the interviews were recorded by an individual well-versed in the research literature. To calculate the reliability of the interviews, a method involving the calculation of agreement between two coders was employed. Table \u003cspan\u003e2\u003c/span\u003e presents the level of agreement and the corresponding reliability percentage, indicating the extent to which the coding process yielded consistent and dependable results. This meticulous approach to data collection, analysis, and validation contributes to the overall trustworthiness and credibility of the research findings.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCalculating reliability of interviews through agreement between two coders\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eInterview\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eTotal number of codes\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eNumber of agreements\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eNumber of disagreements\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eReliability percentage\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e15\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e9\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e4\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e71.22\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e18\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e6\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e69\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e9\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e11\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e8\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e59.62\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTotal\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e44\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e23\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e11\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e66.61\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cbr\u003e\n \u003cp\u003eIn this study, validating the research findings was deemed crucial, and thus, the Kappa coefficient was employed to assess the reliability of the results. To calculate the Kappa coefficient, four interviews were randomly assigned to a colleague who was tasked with coding the text independently. The resulting data were then organized into a two-dimensional matrix, which included the coder and their assigned codes. Subsequently, the codes were compared with each other, and the Kappa coefficient was computed using statistical software, such as SPSS.\u003c/p\u003e\n \u003cp\u003eThe obtained Kappa coefficient value of 0.85 indicates a high level of agreement between the reviewers, affirming the quality and consistency of the coded data. This high level of agreement suggests that the indicators used in the study are reliable and that the coding process yielded consistent results. The validation of the research findings through the Kappa coefficient reinforces the credibility and robustness of the study\u0026apos;s conclusions, providing assurance to stakeholders and enhancing the overall trustworthiness of the research outcomes. Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e shows the value of the Kappa index coefficient.\u003c/p\u003e\u003cbr\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eKappa index coefficient\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003eResearcher\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eUnseen\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSeen\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003eColleague\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eUnseen\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e10\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAgreement size\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eKappa\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eValue\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eApproximate significance\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSeen\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e19\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003eValid data\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.000\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003eTotal\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e10\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e21\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e31\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\u003cbr\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.2.2 Steps to Perform and Apply the Method\u003c/h2\u003e\n \u003cp\u003eThis research endeavors to introduce a conceptual model of business intelligence for supply chain management in small and medium-sized enterprises (SMEs). Initially, a comprehensive review of literature pertaining to the research domain was conducted. Subsequently, data collection was undertaken utilizing a qualitative approach with the aid of interview tools. Following data collection, the gathered data underwent analysis employing thematic analysis methodology. Finally, the conceptual model of the research was derived from the analyzed data. In order to ensure the validity and reliability of the data collection process, the agreement between two coders was calculated using a methodical approach. This served as a means to assess the consistency and accuracy of the data collection tool. Furthermore, the validation of the research findings was executed utilizing a Triangulation design [\u003cspan\u003e26\u003c/span\u003e], enhancing the credibility and robustness of the research outcomes. This meticulous approach to data collection, analysis, and validation contributes to the overall trustworthiness and reliability of the research findings.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Research Findings","content":"\u003cp\u003eIn this section, descriptive statistics and tables pertaining to sample characteristics are presented. Understanding the characteristics of the sample is crucial as it allows for an insight into the general demographics and attributes of the studied population, facilitating comparability and generalizability of findings for other researchers. Subsequently, utilizing the thematic analysis method, the findings from the interviews were coded and analyzed. This process involved identifying recurring themes, patterns, and insights within the interview data. Finally, the conceptual model of the research, derived from the analyzed data, is elaborated upon. This conceptual model serves to provide a comprehensive framework for understanding the dynamics of business intelligence in the context of supply chain management within small and medium-sized enterprises (SMEs).\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Demographic Profile\u003c/h2\u003e \u003cp\u003eRecognizing the significance of examining the demographic profile of the research participants, an assessment of the community under study will be conducted. The specifications and characteristics of the interviewees will be comprehensively detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, providing valuable insights into the composition of the participant group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProfiles and characteristics of interviewees\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpertise\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExperience\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ebusiness management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP.h. D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP.h.D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP.h.D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ebusiness management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP.h. D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT/ business management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Analysis of the Data from the Interview\u003c/h2\u003e \u003cp\u003eDuring this phase, the interview data underwent analysis utilizing the thematic analysis method, comprising six distinct stages. These stages encompassed familiarizing with the data, creating primary codes, identifying selective codes, developing sub-themes, defining and naming global themes, and ultimately preparing a comprehensive report summarizing the findings.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Getting to Know the Data and Creating Basic Codes\u003c/h2\u003e \u003cp\u003eDuring this initial stage of the coding process, the data and interviews are thoroughly read and re-read multiple times to gain familiarity with their content. This iterative process facilitates the creation of initial codes and themes derived directly from the data. Each interview's outcomes are identified as primary and fundamental codes, which are then organized and tabulated appropriately. These codes are subsequently presented as concepts and key points extracted from the interviews, laying the foundation for further analysis and interpretation (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCodes extracted from interviews\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic Themes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipant\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHolding costs and carrying costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInventory Turnover\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison of sourcing costs across suppliers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentification of cost-saving opportunities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of data analytics tools and technologies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduct or service profitability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentify periods of significant growth or decline\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer Acquisition and Conversion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrand Awareness and Perception\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSales forecasting for better inventory planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemand forecasting based on market trends\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInventory visibility across the supply chain.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis of distribution network effectiveness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation costs and efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoute optimization analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAddressing data security\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFulfillment time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eP10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarehouse capacity utilization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTicket resolution times\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer satisfaction scores\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeat support issues analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReturn rates and reasons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis of returned product conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarranty claims analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentifying opportunities for network optimization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePick-and-pack efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReal-time tracking of shipments.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarranty cost analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer feedback analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline reviews sentiment analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn-time delivery metrics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn-time delivery performance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of materials or goods received\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompliance with contractual agreements\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn-Time Delivery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunication and Responsiveness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlexibility and Adaptability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost and Pricing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStockout and overstock analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStockout Prevention\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSafety Stock Management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrdering and Reorder Point Optimization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLead Time Management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproach to Integrating Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost breakdown by supplier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSales revenue analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSales growth trends\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer demographics and preferences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer lifetime value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket segmentation for targeted marketing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer acquisition cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eapproach to Designing User-Friendly Interfaces\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnsuring Scalability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer Retention and Loyalty\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLead Generation and Quality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Selective Coding and Creation of Sub-Themes\u003c/h2\u003e \u003cp\u003eDuring the selective coding stage, researchers categorize various codes into concepts and organize the coded data summary accordingly. This process involves bringing together extracted codes that share semantic and conceptual similarities, thereby generating new meanings and insights. Subsequently, in creating organizing themes, two key steps are undertaken: reviewing and refining the coded summaries, and shaping and validating these themes. At this stage, all codes are grouped into a single class, and through labeling, the organizing and inclusive themes within each class are explained. This meticulous process ensures that the final themes accurately capture the essence of the data and provide meaningful interpretations of the research findings (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelective coding and creation of sub-themes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic Themes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganizer\u003c/p\u003e \u003cp\u003eThemes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlobal Themes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn-time delivery performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eSupplier Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"18\" rowspan=\"19\"\u003e \u003cp\u003eSupply Network Analytics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of materials or goods received\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompliance with contractual agreements\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn-Time Delivery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunication and Responsiveness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlexibility and Adaptability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost and Pricing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStockout and overstock analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eInventory Management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHolding costs and carrying costs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInventory Turnover\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStockout Prevention\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSafety Stock Management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrdering and Reorder Point Optimization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLead Time Management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproach to Integrating Data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost breakdown by supplier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCost Analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison of sourcing costs across suppliers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentification of cost-saving opportunities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of data analytics tools and technologies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSales revenue analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSales Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"15\" rowspan=\"16\"\u003e \u003cp\u003eBusiness Performance Analytics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSales growth trends\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduct or service profitability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentify periods of significant growth or decline\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer demographics and preferences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCustomer Segmentation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer lifetime value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eapproach to Designing User-Friendly Interfaces\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket segmentation for targeted marketing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer acquisition cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMarketing Effectiveness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer Acquisition and Conversion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrand Awareness and Perception\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer Retention and Loyalty\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLead Generation and Quality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnsuring Scalability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSales forecasting for better inventory planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eForecasting\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemand forecasting based on market trends\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn-time delivery metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLogistics Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eOptimal Path Management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation costs and efficiency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoute optimization analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAddressing data security\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFulfillment time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWarehouse Efficiency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarehouse capacity utilization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePick-and-pack efficiency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReal-time tracking of shipments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSupply Chain Visibility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInventory visibility across the supply chain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis of distribution network effectiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDistribution Network Optimization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentifying opportunities for network optimization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTicket resolution times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCustomer Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eRetention and Loyalty Analytics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer satisfaction scores\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeat support issues analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReturn rates and reasons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProduct Returns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis of returned product conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarranty claims analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWarranty Analytics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarranty cost analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer feedback analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFeedback and Reviews\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline reviews sentiment analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Defining and Naming Themes\u003c/h2\u003e \u003cp\u003eDuring the selective coding stage, researchers categorize various codes into concepts and organize the coded data summary accordingly. This process involves bringing together extracted codes that share semantic and conceptual similarities, thereby generating new meanings and insights. Subsequently, in creating organizing themes, two key steps are undertaken: reviewing and refining the coded summaries, and shaping and validating these themes. At this stage, all codes are grouped into a single class, and through labeling, the organizing and inclusive themes within each class are explained. This meticulous process ensures that the final themes accurately capture the essence of the data and provide meaningful interpretations of the research findings. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the global themes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGlobal themes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThemes of the research model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency of base codes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupply Network Analytics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness Performance Analytics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimal Path Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetention and Loyalty Analytics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of codes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.2.4 Research Conceptual Model\u003c/h2\u003e \u003cp\u003eIn the realm of business intelligence (BI) tailored for supply chain management (SCM) in small and medium enterprises (SMEs), the conceptual model comprises several pivotal dimensions, each contributing significantly to the enhancement of operational efficacy and decision-making prowess. One of these critical dimensions is supply network analytics, which delves into the intricate analysis of supplier performance metrics, inventory dynamics, and logistical efficiencies. By scrutinizing these facets, SMEs can discern patterns, identify bottlenecks, and optimize the seamless flow of goods and information across the supply chain. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the research conceptual model (constructed by the researcher), which has 4 main dimensions obtained from the research: supply network analytics, business performance analytics, optimal path management, and retention and loyalty analytics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, business performance analytics emerges as another cornerstone dimension, pivoting on the meticulous evaluation of key performance indicators (KPIs) spanning sales revenue, profitability margins, and customer satisfaction indices. Through the lens of BI, SMEs can gain granular insights into the efficacy of their strategies and operations, pinpointing areas of excellence and avenues for improvement with precision. This analytical approach empowers SMEs to make informed, data-driven decisions, thereby steering their ventures toward sustainable growth and competitive advantage.\u003c/p\u003e \u003cp\u003eIn tandem, optimal path management assumes a pivotal role, focusing on the strategic optimization of transportation routes, warehousing protocols, and distribution channels. By harnessing BI tools and techniques, SMEs can dissect complex logistical networks, unearth inefficiencies, and devise strategies to streamline operations, thereby mitigating costs and bolstering agility. This dimension serves as a linchpin for SMEs seeking to navigate the intricate maze of supply chain intricacies with finesse and efficacy.\u003c/p\u003e \u003cp\u003eLastly, retention and loyalty analytics emerge as a cornerstone dimension, emphasizing the paramount importance of customer-centric strategies in fostering enduring relationships and bolstering brand loyalty. By leveraging BI insights into customer behavior, preferences, and engagement patterns, SMEs can tailor personalized marketing campaigns, refine product offerings, and enhance the overall customer experience. This holistic approach not only nurtures customer loyalty but also lays the groundwork for sustained growth and profitability in the fiercely competitive SME landscape.\u003c/p\u003e \u003cp\u003eIn summation, the integration of these dimensions within the BI conceptual model equips SMEs with a robust framework for navigating the complexities of modern supply chain dynamics. Armed with actionable insights and data-driven strategies, SMEs can forge ahead with confidence, resilience, and adaptability, charting a course toward sustained success and market leadership in today's dynamic business landscape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.2.5 Validation of the Research Conceptual Model\u003c/h2\u003e \u003cp\u003eIn this research, the researcher employed the triangulation method to validate the proposed model. Triangulation involves utilizing multiple methods of data collection, incorporating diverse data sources, analysts, or theories to corroborate research findings, and mitigating biases that may arise from over-reliance on any single method, source, analyst, or theoretical basis. In this study, the triangulation process entailed several steps [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Initially, three participants from the research cohort reviewed the reports generated in the first stage, and their feedback was integrated into the selective coding process, allowing for adjustments by the team members. Subsequently, collaborating experts conducted a comprehensive review of the coding categories, providing corrections and insights through a peer review process. These corrections were then applied to the research findings. Finally, the revised proposals were returned to the initial three participants, who provided further corrections and refinements to the research model. Through this iterative process of triangulation, the validity and reliability of the research findings were enhanced, ensuring a robust and comprehensive conceptual model of Business Intelligence for Supply Chain Management in small and medium enterprises.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 Discussion, Conclusions and Suggestions","content":"\u003cp\u003eThe principal aim of this study is to improve supply chain management (SCM) within small and medium enterprises (SMEs) through the application of business intelligence (BI). Consequently, the subsequent section will provide a broad summary of the research findings, accompanied by practical suggestions aligned with the research objective. These recommendations are intended to offer tangible insights into enhancing SCM practices in SMEs through the effective utilization of BI tools and methodologies.\u003c/p\u003e\n\u003cdiv id=\"Sec26\"\u003e\n \u003ch2\u003e5.1 Discussion\u003c/h2\u003e\n \u003cp\u003eIn the discussion, our study emphasizes the pivotal role of business intelligence (BI) in elevating supply chain management (SCM) practices within small- and medium-sized enterprises (SMEs). Through our conceptual model approach, we elucidate the multifaceted dimensions of BI-enabled SCM, highlighting themes such as supply network analytics and business performance analytics. These findings underscore the potential for SMEs to harness BI tools and techniques to optimize supplier relationships, enhance inventory management, and improve overall operational efficiency.\u003c/p\u003e\n \u003cp\u003eOur conceptual model offers SMEs a structured framework to conceptualize and implement BI solutions tailored to their SCM needs. By focusing on key dimensions and components, SMEs can strategically integrate BI into their SCM processes to drive informed decision-making and performance improvement. However, it\u0026apos;s crucial to acknowledge the challenges SMEs may face, including resource constraints and technological barriers, which could hinder the effective implementation of BI solutions.\u003c/p\u003e\n \u003cp\u003eLooking ahead, future research should explore the practical implications of implementing BI solutions in SMEs\u0026apos; SCM operations and investigate their impact on performance metrics. Additionally, further studies on emerging technologies like artificial intelligence and the Internet of Things could provide valuable insights into enhancing BI capabilities in SCM. Ultimately, by embracing BI and leveraging our conceptual model, SMEs can unlock new avenues for competitiveness and growth in the ever-evolving business landscape.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec27\"\u003e\n \u003ch2\u003e5.2 Conclusions\u003c/h2\u003e\n \u003cp\u003eIn conclusion, our study underscores the transformative potential of business intelligence (BI) in enhancing supply chain management (SCM) practices within small- and medium-sized enterprises (SMEs). Through our conceptual model approach, we have delineated key dimensions and components of BI-enabled SCM, emphasizing themes such as supply network analytics and business performance analytics. Our findings highlight the strategic importance of BI in enabling SMEs to optimize supplier relationships, streamline inventory management, and enhance overall operational efficiency. By leveraging BI tools and techniques, SMEs can gain valuable insights into their supply chain operations, driving informed decision-making and performance improvement. Moving forward, it is essential for SMEs to prioritize the integration of BI solutions into their SCM processes. However, challenges such as resource constraints and technological barriers may hinder the effective implementation of BI initiatives. Therefore, SMEs must invest in training and capacity-building initiatives to ensure successful adoption and utilization of BI tools.\u003c/p\u003e\n \u003cp\u003eUltimately, by embracing BI and leveraging our conceptual model, SMEs can unlock new opportunities for competitiveness and growth in today\u0026apos;s dynamic business landscape. As technology continues to evolve, further research is needed to explore the practical implications of BI in SMEs\u0026apos; SCM operations and to identify emerging trends and opportunities for innovation. Overall, our study contributes to the growing body of literature on BI-enabled SCM in SMEs, providing valuable insights and guidance for practitioners and researchers alike.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\"\u003e\n \u003ch2\u003e5.3 Suggestions\u003c/h2\u003e\n \u003cp\u003eSuggestions for Enhancing Supply Chain Management in Small-Medium Enterprises through Business Intelligence:\u003c/p\u003e\n \u003cp\u003e1. Invest in User-Friendly BI Tools: SMEs should prioritize the adoption of user-friendly BI tools that facilitate easy data analysis and visualization. This will empower SCM personnel to gain actionable insights and make informed decisions in real-time.\u003c/p\u003e\u0026nbsp;\u003cp\u003e2. Foster a Data-Driven Culture: Promote a culture of data-driven decision-making within the organization by providing training and support to SCM teams on utilizing BI tools effectively. Encourage collaboration and knowledge-sharing to maximize the benefits of BI across departments. \u003cspan\u003e3. Focus on Supplier Relationship Management: Utilize BI analytics to evaluate supplier performance, identify areas for improvement, and strengthen relationships with key suppliers. By analyzing supplier data, SMEs can negotiate better terms, reduce risks, and ensure timely delivery of goods and services.\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e4. Optimize Inventory Management: Leverage BI insights to optimize inventory levels, reduce stockouts, and minimize holding costs. Implement demand forecasting tools, analyze historical sales data, and monitor inventory turnover ratios to streamline inventory management processes.\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e5. Enhance Logistics Efficiency: Use BI analytics to optimize transportation routes, reduce transportation costs, and improve delivery performance. Analyze route optimization data, track transportation metrics, and implement real-time tracking systems to enhance logistics efficiency.\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e6. Prioritize Customer-Centric Strategies: Adopt a customer-centric approach to SCM by analyzing customer data and preferences using BI tools. Segment customers based on their needs and preferences, personalize marketing strategies, and enhance customer satisfaction and loyalty.\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e7. Continuously Monitor and Evaluate Performance: Establish KPIs and performance metrics to track the effectiveness of BI-enabled SCM initiatives. Continuously monitor performance, analyze trends, and adjust strategies as needed to achieve operational excellence and competitive advantage.\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e8. Stay updated on emerging technologies: Keep abreast of emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT) that can enhance BI capabilities in SCM. Explore opportunities to integrate these technologies into existing BI frameworks to drive innovation and efficiency.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003eBy implementing these suggestions, SMEs can harness the power of business intelligence to enhance their supply chain management practices, drive operational efficiency, and achieve sustainable growth in today\u0026apos;s competitive business landscape.\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\"\u003e\n \u003ch2\u003e5.4 Research Limitations\u003c/h2\u003e\u003cbr\u003e\u003cspan\u003e\n \u003cp\u003e1. The study primarily focused on specific indicators influencing the conceptual model of supply chain management business intelligence, potentially overlooking other factors that could also play a role. Future research could explore a broader range of indicators to provide a more comprehensive understanding of the complexities within supply chain management.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e2. The research encountered challenges related to word tension, which at times made it difficult to clearly distinguish between certain terms or concepts. This limitation may have impacted the precision and accuracy of data interpretation and analysis. Strategies such as refining the research methodology or employing additional linguistic analysis techniques could help mitigate this challenge in future studies.\u003c/p\u003e\n \u003c/span\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e the authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBahukeling, TS, Suroso, AI, Buono, A, Nurhayati, P (2024) Digital Marketing Alliance on Small Medium Enterprises (SMES): A Systematic Literature Review. Journal Aplikasi Bisnis dan Manajemen (JABM) 10(1): 199-219\u003c/li\u003e\n\u003cli\u003eWided, R (2024) The Role of Information Technology in Strengthening Strategic Flexibility and Organisational Resilience of Small Medium Enterprises Post COVID-19. 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International Journal of Logistics Research and Applications 27(2): 261-283\u003c/li\u003e\n\u003cli\u003eDonyavi, S, Flanagan, R, Assadi-Langroudi, A, Parisi, L (2024) Understanding the complexity of materials procurement in construction projects to build a conceptual framework influencing supply chain management of MSMEs. International Journal of Construction Management 24(2): 177-186\u003c/li\u003e\n\u003cli\u003eJando, C, Dionne, F (2024) A call for qualitative research in Contextual Behavioral Science. Journal of Contextual Behavioral Science 10(2) :11-21\u003c/li\u003e\n\u003cli\u003eBraun, V, Clarke, V (2006) Using thematic analysis in psychology. Qualitative research in psychology 3(2): 77-101\u003c/li\u003e\n\u003cli\u003eJack, EP, Raturi, AS (2006) Lessons learned from methodological triangulation in management research. Management research news 29(6): 345-357\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":"Small and Medium Enterprises, Supply Chain Management, Business Intelligence, Supply Network Analytics, Business Performance Analytics, Optimal Path Management","lastPublishedDoi":"10.21203/rs.3.rs-4249032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4249032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the contemporary business milieu, small and medium enterprises (SMEs) encounter distinct challenges in navigating the complexities of supply chain management (SCM) owing to resource constraints and operational limitations. This study proposes a conceptual model to bolster SCM efficiency within SMEs through the strategic application of business intelligence (BI). Adopting a qualitative and exploratory approach, the research delves into the experiences and insights of experts, managers, and information technology specialists entrenched in the SME landscape. Utilizing judgmental sampling, data saturation was achieved through semi-structured interviews, which were subsequently analyzed via thematic analysis. The findings unveil a comprehensive framework comprising 98 initial themes distilled into 55 basic themes, 15 organizing themes, and 4 global themes encompassing supply network analytics, business performance analytics, optimal path management, and retention and loyalty analytics. By embracing BI and integrating the proposed conceptual model, SMEs are poised to unlock novel growth trajectories and fortify their competitive standing in the contemporary business ecosystem.\u003c/p\u003e","manuscriptTitle":"Enhancing Supply Chain Management in Small-Medium Enterprises through Business Intelligence: A Conceptual Model Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-16 10:00:04","doi":"10.21203/rs.3.rs-4249032/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":"334334ce-5399-4729-bbef-05209bffd98d","owner":[],"postedDate":"April 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-04T06:23:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-16 10:00:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4249032","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4249032","identity":"rs-4249032","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}