International Journal of Business Performance and Supply Chain Modelling (8 papers in press)

Regular Issues

• Impact of Supply Chain Information Sharing Capability on its Performance: Mediating Role of Agility and Adaptability  
  by Hemant Sharma 
  Abstract: Building strategic information sharing (SIS) capability among the supply chain's partner enterprises is crucial in the current volatile market environment to foster agility and adaptation, which in turn improve supply chain performance (SCP). The combined role of agility and adaptability and their effect on SCP are not well explored in the available literature. Given that the market is changing quickly and demands that supply chains be more responsive and adaptable, supply chain agility and adaptation seem to add more value and increase the efficacy of supply chains. This study suggests a model based on relational theory and resource-based view (RBV). A cross-sectional survey of 243 Indian auto component manufacturers was conducted to collect the data. Results depict that agility and adaptability mediate the SIS-SCP relationship fully and partially, respectively. The study also discusses the theoretical and managerial implications along with limitations and scope for future research.
  Keywords: strategic; information sharing; structural equation modelling; supply chain performance; agility; adaptability.
  DOI: 10.1504/IJBPSCM.2026.10074529
   
  
• Modelling of Performance Evaluation for Last Mile Delivery: Fuzzy AHP Approach  
  by Vivek Pathak, Dixit Garg, Ashish Agarwal 
  Abstract: With the rapid growth of retail and e-commerce, increasing urbanisation, evolving consumer behaviour, and a shared focus on sustainability, academic and industrial interest in last-mile logistics has surged significantly. Heightened global competition has further driven researchers and practitioners to design and implement innovative frameworks and models to enhance lastmile delivery. This paper highlights that the existing literature encompasses a wide range of factors and viewpoints, which can be broadly categorised into areas such as emerging technologies, operational optimisation, delivery chain structures, performance metrics, and geographical coverage. Among various multi-criteria decision-making (MCDM) approaches, this study employs the fuzzy analytical hierarchy process (FAHP), based on defined criteria and performance indicators. The research not only identifies key performance indicators (KPIs) within the downstream supply chain but also reveals inefficiencies and bottlenecks in the delivery flow. This technique is used to discover supply networks for warehouses. It is used to decide the comparative importance weightings or significance of various supply networks so that warehouses benefit most earnings and offer their customers great choice alternatives. The evaluation is based on three main criteria and 12 subcriteria, selected to assess and rank the factors influencing last-mile delivery performance. The findings underscore the critical importance of focusing on process management, environmental cost reduction, and customer satisfaction. This research paper is to evaluate and rank the factors influencing the performance of last mile practices also provided the industrialist and policy makers with sufficient information about the numerous factors influencing their last-mile performance.
  Keywords: Fuzzy analytical hierarchy process; Last mile; Performance indicators; Criterion; Radar analysis.
  DOI: 10.1504/IJBPSCM.2026.10074921
   
  
• Improving Primary Service Performance in a State Electricity Utility through Inventory Control and Process Redesign: a Discrete Event Simulation Study  
  by Siti Hanifah Sadiah, Meditya Wasesa 
  Abstract: This study explores the causes of performance deviations, improvement initiatives, and regulatory alignment in electricity primary service delivery. Using a mixed-methods case study of an Indonesian electricity utility, it combines qualitative diagnostics and quantitative modeling. Root cause analysis, control charts, Pareto analysis, and problem trees identify material unavailability
  Keywords: material optimization strategy; inventory control; logistics; root cause analysis; business process reengineering; electricity utilities; utilities sector supply chain; discrete-event simulation.
  DOI: 10.1504/IJBPSCM.2026.10074999
   
  
• Role of Inventories and Cash: Swedish Publicly Traded Manufacturing Companies in 2010-2023  
  by Olli-Pekka Hilmola, Per Hilletofth 
  Abstract: Since the Global Financial Crisis (GFC), the world experienced very low interest rates for more than a decade. This favored less efficient inventory management practices. The coronavirus era, along with the rise of military conflicts in recent years, has further affected prices, availability, deliveries, and sources of supplies. In this study, we analyze Swedish publicly traded manufacturing companies during this period. Inventory efficiencies decline gradually in the era of low interest rates and accelerate to do so since the dawn of the coronavirus crisis. Some improvements were detected in the last observation year. However, in terms of the absolute monetary amount and share of assets, inventories have substantially increased since 2021. Cash (liquid assets) held in assets has not changed much during the observation period, showing only a spike at the start of the coronavirus era. Companies seem to favor lower inventory efficiency systems without a higher emphasis on cash.
  Keywords: Inventory; cash; efficiency; analysis; Sweden.
  DOI: 10.1504/IJBPSCM.2026.10075018
   
  
• A Multi-Item Psychometric Scale for Assessing Sustainable Supply Chain Management Performance (SSCMP)  
  by Manori P. Kovilage  
  Abstract: The study developed a psychometric scale to measure sustainable supply chain management performance (SSCMP) using a mixed-methods approach and exploratory sequential design. Items were generated through expert interviews, focus groups, and literature review, then refined using exploratory factor analysis (EFA). The EFA identified SSCMP as a multidimensional construct with four dimensions financial, operational, environmental, and social and resulted in 18 measurement items. Confirmatory factor analysis (CFA) validated these dimensions and items. Financial indicators included lifecycle cost, net profit, return on investment (ROI), and inventory turnover; operational indicators included quality, flexibility, efficiency, customer satisfaction, and responsiveness. Environmental indicators focused on pollution control and resource usage, while social indicators addressed employee work quality and community well-being. The nomological validity of the SSCMP scale was confirmed, supporting the developed hypothesis. This study enhanced the SCM context by providing a validated SSCMP scale using established scale development procedures.
  Keywords: Sustainable Supply Chain Management Performance; Sustainable Development; Supply Chain Management; Sustainable Supply Chain Management.
  DOI: 10.1504/IJBPSCM.2026.10075019
   
  
• Supply Chain Mapping for Enhanced Visibility and Resilience: a Multi-Industry Comparative Study of Best Practices and Emerging Technologies  
  by Iskander Zouaghi, Samia Beldjoudi, Makaci Mourad 
  Abstract: Supply chain mapping is a strategic tool used to visualize and analyze the flow of materials, information, and processes across networks to enhance visibility and resilience. This study provides an integrated review of key mapping typologies, including Value Stream Mapping (VSM), Member-Based Mapping, Geographic-Based Mapping, Network Theory-Based Mapping, Simulation-Based Mapping, Closed-Loop Diagrams, Process-Based Mapping (BPMN), and Petri Nets Mapping. The research explores how these tools, when combined with technologies such as Digital Twins, Blockchain, and Artificial Intelligence, strengthen real-time monitoring, traceability, and adaptive risk management. A comparative analysis across the automotive, electronics, pharmaceutical, consumer goods, retail, food and beverage, and energy sectors reveals how mapping practices are adapted to industry-specific complexities. The findings demonstrate that combining advanced mapping methods with digital enablers is essential for developing transparent, agile, and resilient supply chains in an increasingly uncertain environment.
  Keywords: Supply Chain Mapping; Visibility; Resilience; Value Stream Mapping (VSM); Network Theory-Based Mapping; Geographic-Based Mapping; Member-Based Mapping; Simulation-Based Mapping; Closed-Loop Diagrams.
  DOI: 10.1504/IJBPSCM.2026.10075020
   
  
• Biomass and Bio-Energy Supply Chain for Sustainability: A Review from Triple Bottom Line Perspective and Future Research Directions  
  by Shantanu Trivedi, Saurav Negi 
  Abstract: The International Energy Agency projects that modern biomass could supply up to 10% of global primary energy demand by 2035. While hybrid solarbioenergy systems have supported progress toward several Sustainable Development Goals enhancing employment, health, and socio-economic development the implementation of large-scale programs remains slow due to perceived risks within the bio-resources supply chain. This study explores existing research and awareness on biomass and bioenergy supply chains and their sustainability implications through a triple bottom line lens. Using an exploratory research design, it applies a systematic literature review and content analysis to synthesise current knowledge and highlight emerging trends. Scopus-indexed studies were analysed to examine sustainability considerations in biomass refining, logistics, and supply chain operations. The review identifies three key thematic areas: logistical challenges and mitigation measures, sustainable supply chain practices, and future research pathways essential for advancing resilient and sustainable biowaste supply chain management.
  Keywords: Biomass Supply Chain; Bio-energy supply Chain; Sustainability; Triple Bottom Line; Sustainable supply chain.
  DOI: 10.1504/IJBPSCM.2026.10075032
   
  
• Challenges and Barriers to Adopting Artificial Intelligence for Demand Forecasting in the Luxury Industry: an Empirical Study  
  by Dhaou Ghoul, Jérôme Verny, Ouail Oulmakki, Anas Iftikhar 
  Abstract: This paper presents a field study on the application of Artificial Intelligence (AI), particularly Machine Learning, in the luxury supply chain (SC) sector for demand forecasting. This study aims to identify the challenges and barriers that luxury companies face in adopting AI-based tools for demand planning. This study analyses the difficulties of demand forecasting in the luxury sector, thus providing a better understanding of the complexity of utilizing AI tools in this industry. Additionally, it explores the potential of on-demand production as a solution to the challenges of sustainable development faced by the luxury sector. The methodology involved collecting data through qualitative interviews with demand planners in luxury companies. The findings shed light on the current state of demand planning in luxury companies and the challenges associated with adopting AI-based tools.
  Keywords: Demand forecasting; Machine Learning; Artificial Intelligence; supply chain; qualitative interviews.
  DOI: 10.1504/IJBPSCM.2026.10075051