International Journal of Business Performance and Supply Chain Modelling (15 papers in press)

Regular Issues

• 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
   
  
• Supplier Sustainability Evaluation: f-TOPSIS and Text Mining Approach  
  by Soumyanath Chatterjee, R.P. Mohanty, Sanjeev Kumar 
  Abstract: This paper introduces a novel approach to developing a generic Sustainable Supply Chain Management (SSCM) supplier evaluation framework that enables cross-system comparisons. Sustainability parameters were identified from the Database of Sustainability Indicators and Indices (DOSII) and analysed using text mining techniques to determine their relative importance. A fuzzy TOPSIS (f-TOPSIS) method was applied to evaluate supplier performance in the context of SSCM. The study demonstrates that a concise set of generic parameters, applicable across manufacturing-related supply chains, is sufficient for a comprehensive SSCM assessment. This research addresses the challenges often associated with the complexity and cost of implementing sustainable supply chains by offering a user-friendly and standardised evaluation tool. The proposed methodology highlights critical parameters, assigns relative weights, and utilises a simple linguistic framework for effective supplier assessment in SSCM.
  Keywords: Sustainability evaluation ; Supply Chain Management (SCM); Text Mining ; Fuzzy TOPSIS ; ISO:20400.
  DOI: 10.1504/IJBPSCM.2026.10075742
   
  
• Supply Chain Performance Enhancement using Digital Capabilities: An MCDM Approach for Barrier Analysis on Metaverse Deployment  
  by Kavitha Gurrala, Saradhi Gonela, Sharuti Choudhary, Prince Yeboah Asare 
  Abstract: The metaverse integrates digital and physical realms by utilizing technologies such as Virtual Reality (VR), Augmented Reality (AR), Blockchain, Artificial Intelligence (AI), and the Internet of Things (IoT), resulting in a 3D-enabled digital space holding significant potential for improving supply chain (SC) performance. Prior studies have investigated the impact of the merger of virtual and augmented settings on SC capabilities such as efficiency, resilience, sustainability, transparency, and safety, while some studies have only examined the implementation challenges. Nonetheless, a thorough identification of the barriers towards deploying metaverse technology (MT) within SC and an understanding of their interrelationships remains limited. This research seeks to fill this void by identifying an exhaustive set of MT deployment barriers within supply chains. It utilises ISM and MICMAC analysis to define contextual relationships, prioritise barriers, and assess their driving and dependence power. Additionally, the study offers strategic recommendations to address key barriers to facilitate MT enabled, multi-dimensional SC performance.
  Keywords: Barrier Analysis; Interpretive Structural Modelling (ISM); MCDM; Metaverse; MICMAC (Matrix Impact Cross Multiplication Applied to Classification); SC Performance.
  DOI: 10.1504/IJBPSCM.2026.10076208
   
  
• The Impact of Green Supply Chain Practices on Corporate Social Responsibility: A Quantitative Analysis  
  by Zhenghui Li, Thanuja Rathakrishnan, Sugganya Prem Krishnan Murty, Ge Wang, Qian Peng, Hui Zhang, Jianzhou Li 
  Abstract: With increasing environmental awareness, green supply chain (GSC) practices have attracted extensive attention from both academics and practitioners. This study aims to analyse the relationship between GSC practices and corporate social responsibility (CSR), with environmental performance as a mediator. Using a Likert-scale questionnaire, data were collected from 202 firms in Malaysias manufacturing, automotive, and textile sectors. Five GSC dimensions reverse logistics, waste management, policy regulation, green procurement, and technology adaptation were analysed quantitatively via SPSS. Results show that a significant positive link between GSC practices and CSR, with environmental performance playing a mediating role. The findings offer theoretical insights for scholars and practical guidance for enhancing CSR through improved environmental and supply chain strategies
  Keywords: Corporate Social Responsibility; Environmental Performance; Green Supply Chain; Sustainable Management Theory.
  DOI: 10.1504/IJBPSCM.2026.10076235
   
  
• The Impact of Enterprise Risk Management on Organisational Capabilities and Firm Performance: Evidence from Vietnam  
  by Vinh Quang Le, Nguyen Ngoc Long, Xuan Giang Pham, Thi Ngoc Mai Nguyen 
  Abstract: The current economic and political landscape presents a number of challenges for businesses. Effective risk management has become more critical than ever for businesses to survive. The aim of this study is to evaluate the influence of Enterprise Risk Management (ERM) on firm performance by investigating the role of organisational capabilities, including knowledge management, supply chain resilience, and technology adoption. The research was conducted in Vietnam, surveying 297 personnel in various businesses using the PLS-SEM method for analysis. The results show that embracing ERM improves organisational capabilities, which leads to competitiveness and higher financial and non-financial business performance. The research provides significant insights into how ERM can positively impact firm performance. It is suggested that ERM can help overcome the limitations imposed by hierarchical culture, thereby enhancing knowledge sharing among employees. Furthermore, the study emphasises the importance of further research on ERM and technology adoption in diverse business contexts.
  Keywords: Enterprise risk management; firm performance; competitive advantage; organisational capabilities; knowledge management; supply chain resilience; technology adoption.
  DOI: 10.1504/IJBPSCM.2026.10076313
   
  
• Circular Economy Enabled Business Model and Circular Supply Chain Framework for Manufacturing Organisations and Implications  
  by Mehul Patel, Akshay A. Pujara, Ravi Kant 
  Abstract: Globalized competitive markets and variable consumer demand needs transformation of business model from linear economy to circular economy. Circular Economy (CE) business model (BM) offers long-term dematerialized sustainable production-consumption system with reduction in waste, mitigation of environmental norms and has potential to meet consumer and market demands. Manufacturing organisations need a structured approach with radical and systemic innovative solutions to exploit and readily accessible CE enabled BM and circular supply chain (CSC) which is yet under the development stage. To address this gap, with series of meetings with four interested organisations and a panel of industrial experts, the outcome is presented here as a development of methodological framework of CE enabled BM and CSC in alliance with existing manufacturing organizations. Theoretical and managerial implications suggested. It is concluded that the adoption of CE strategies and practices, CE enablers through the developed framework are crucial for organisational managements and related SC partners to develop their differential business capabilities, maintain product circularity, optimize processes, and achieve dematerialisation, minimum waste, environmental management, and sustainability in competitive local and global markets.
  Keywords: Circular economy (CE); CE enabled business model (BM); circular supply chain (CSC); circular economy enablers (CEEs); circular production (CP).
  DOI: 10.1504/IJBPSCM.2026.10076355
   
  
• Operational Drivers of Seaport Service Quality: Disaggregating the ROPMIS Model in the Cargo Clearance Process  
  by Renger Kanani 
  Abstract: As competition between adjacent seaports intensifies due to ongoing infrastructure upgrading initiatives, service quality has emerged as an important differentiation strategy. Drawing on the resource-output-process-management-image-social responsibility (ROPMIS) model, this study investigated the drivers of seaport service quality in the Tanzanian context. Using structural equation modelling and data from 150 freight forwarders involved in clearing cargo at the Dar es Salaam seaport, the findings reveal that investment in cargo handling infrastructure and improved inter-institutional coordination enhance perceived seaport service quality, while the complexity of the cargo clearance process undermines service quality. Moreover, it is evident that the unreliability of information systems attenuates the benefits of inter-institutional coordination in improving service quality and accentuates the negative effect of process complexity. By disaggregating dimensions of the ROPMIS model and separating them from the evaluation of service quality, the study provides new insights into the drivers of service quality, underscoring the need to simultaneously upgrade cargo clearance infrastructure, simplify the clearance process and improve information system reliability to enhance seaport service quality.
  Keywords: perceived port service quality; Cargo handling infrastructure; inter-institutional coordination; cargo clearance process complexity; information system unreliability; ROPMIS Model.
  DOI: 10.1504/IJBPSCM.2026.10078470
   
  
• An Integrated Vendor Managed Inventory with Consignment Stock Model for Single- Vendor Single-Retailer Considering Imperfect Quality and Inspection Error  
  by Amanda Sofiana, Denny Andiya Nur Wibowo, Hasyim Asyari 
  Abstract: This study integrates Vendor Managed Inventory (VMI) and Consignment Stock (CS) in a two-echelon supply chain incorporating imperfect product quality and dual-type inspection errors (Type I and II) into joint cost optimization. A mathematical model was developed to minimize expected joint total costs by determining optimal shipment sizes and batch quantities. Using Wolfram Software, numerical results identified an optimal configuration of 9 lots of 153 units, resulting in a joint cost of $1199.85. Sensitivity analysis indicates the model is robust against holding cost variations but significantly influenced by ordering costs and Type II errors, which directly impact retailer profitability. By accounting for defective items and imperfect inspections, this research provides a practical framework to enhance vendor-retailer collaboration and inventory management efficiency.
  Keywords: Supply chain management; Vendor Managed Inventory; Consignment Stock; Inspection errors; Imperfect quality.
  DOI: 10.1504/IJBPSCM.2027.10079150
   
  
• Finding the Fastest Route via Rail for Indirect Journey  
  by Shah Rohan Rakesh, Pradeepmon T.G., Vinay Panicker 
  Abstract: Rail transportation serves as a vital mode of travel due to its efficiency, safety, and environmental sustainability. However, when direct trains are unavailable between the source and destination, passengers often face difficulties in identifying optimal inter-rail connections, leading to increased travel time and inconvenience. Existing studies on train routing primarily focus on direct connections or single-objective optimisation, leaving a research gap in addressing indirect journeys with multiple interchange stations under real operational constraints. This study addresses this gap by developing an optimised route selection model using the Dijkstra algorithm for the Indian Railways network, which comprises thousands of junctions with varying train frequencies and schedules. The model incorporates practical parameters such as multiple route options, train operating days, waiting times, and transfer possibilities. Furthermore, an enhanced version of the model is proposed that considers all node weights rather than only the least-weight paths, enabling improved accuracy in journey time estimation. The model is validated using real-world train schedule data from the Indian Railway Catering and Tourism Corporation (IRCTC). Results demonstrate that the proposed method effectively identifies optimal interchange stations and minimises total journey time. Compared to the basic algorithm, the improved model achieves up to 95% accuracy in providing equal or shorter travel times across test cases, establishing its potential as a decision-support tool for route planning in largescale railway networks.
  Keywords: Rail Transport; IRCTC; Dijkstra’s Algorithm; Minimum travel time.
  DOI: 10.1504/IJBPSCM.2026.10079218
   
  
• Utilising RFQ Analysis and the k-means Algorithm to Develop a Decision Support Model for Supply Chain Management in Retail  
  by Majdi Arrif 
  Abstract: This study introduces a novel RFQ-based decision support model for retail supply chain management, replacing the traditional monetary-based RFM (Recency, Frequency, Monetary) analysis with a quantity-driven RFQ (Recency, Frequency, Quantity) framework. The model addresses key gaps in retail analytics, including the limitations of monetary metrics in inflation-prone environments and the lack of product-level segmentation. By applying k-means clustering to both products and customers, the model offers a dual perspective that enhances decision-making in inventory management and marketing. Findings show that the RFQ model provides more stable and interpretable clusters, especially in price-volatile environments. The integrated analysis of product and customer clusters allows for more targeted resource allocation, improved stock management, and personalized marketing strategies. This paper contributes to the literature by extending the RFM framework and offering a comprehensive tool for optimizing supply chain and marketing decisions in dynamic retail contexts.
  Keywords: RFM analysis; k-means clustering; SCM; direct marketing.
  DOI: 10.1504/IJBPSCM.2027.10079399
   
  
• Managing Supply Chain Risks through Capacity Management Strategies and impact on Business Performance: a services industry Perspective  
  by Renu Rajani 
  Abstract: This study empirically examines how supply chain risks (SCRs), such as demand variability and capacity mismatches, interact with capacity management strategies (CMS) to affect service sector performance. Utilising structural equation modelling (SEM) to analyse data from 439 businesses across 10 Indian service industries, the author demonstrates that targeted CMS acts as a vital mediator to reverse the negative impact of SCRs on business outcomes. Crucially, the findings reveal that capacity planning information sharing and peak-period capacity augmentation serve as the most critical drivers for enhancing supply chain competitive performance, customer satisfaction, and financial results.
  Keywords: Supply chain; risk; capacity; demand; performance; services.
  DOI: 10.1504/IJBPSCM.2027.10080038
   
  
• A Contingency-Based Approach for Smart Supply Chain Strategy: An Integrated ISM-Fuzzy MICMAC Analysis  
  by Sumant Kumar Tewari  
  Abstract: This research investigates the key contingencies shaping supply chain strategy and their interdependencies to build a strategically responsive supply chain, with a focus on the Indian automobile industry. Through an extensive literature review, questionnaire survey, and expert interviews, we first identify and define critical supply chain drivers, then examine the challenges they pose and their strategic impact. Employing an integrated interpretive structural modelling (ISM) and fuzzyMICMAC analysis, we map and quantify relationships among these contingencies. Results reveal that Customer Service/Service Level and Financial Performance exhibit the highest dependency power, while Product Lifecycle Stage and Uniqueness demonstrate the strongest driving power. By visualising these critical contingencies in a directed graph and analysing their driving dependence dynamics, this study offers novel insights into complex strategic interactions. The findings equip practitioners and academics with a clear, datadriven framework for formulating smart supply chain strategies that align operational drivers with longterm objectives.
  Keywords: Contingency; Smart supply chain strategy; ISM-Fuzzy MICMAC; Automobile; Robust SC; Driving and Dependence Power.
  DOI: 10.1504/IJBPSCM.2027.10080042
   
  
• Unveiling Optimal Supply Chain Performance and Pricing Strategies in the Food Industry of Pakistan: a Qualitative Approach  
  by Ahsan Ali Ashraf, Fatima Rafique 
  Abstract: This paper examines how employees view organisational justice, leadership support, and the practices of the human resource department, and how these, in turn, affect their turnover intentions in Pakistani organisations. This paper employed a qualitative study design, where semi-structured interviews were carried out among persons employed in different organisations. Analysis of the data brought out the fact that the sense of fairness among employees, leadership support, and the conformity of HR practices have a heavy bearing on their affective commitment, satisfaction, and turnover intentions. The uniqueness of this study is that it investigated the issue of employee experiences within the context of the emerging economy, where qualitative studies on the topic are not common. Through the provision of narrative data, this study helped add meaning to turnover mechanisms and enriched the literature on organisational behaviour and human resource management by demonstrating the subjective meaning that employees give to the practices prevailing in their organisations.
  Keywords: Supply Chain Performance; Supply Chain Practices ; Pricing Strategies ; Food Industry.
  DOI: 10.1504/IJBPSCM.2027.10080366
   
  
• Optimising Intermodal and Multimodal Freight Transport between the USA and Pakistan: A Bi-Objective MILP Model for Cost and Energy Trade-offs  
  by Rizwan Shoukat, Xiaoqiang Zhang, Ayesha Saeed 
  Abstract: By juxtaposing multimodal and intermodal operations, this study aims to assist policymakers and supply chain managers in minimising the cost of logistics and improving business performance. The problem of minimising the cost of transporting freight from New York, the USA to Punjab, Pakistan is tackled using mixed-integer linear programming (MILP). We solved the optimisation problem by using a multiobjective genetic algorithm (MOGA) to identify Pareto front solutions for the cost trade-off between the two modes of transport. According to the findings, multimodal transportation (air, road, and rail) is 15% more cost-effective than intermodal transportation (air, road) when delivering less than a container unit. Additionally, this investigations breakdown analysis reveals that road freight is the second-highest means of transportation in terms of cost, trailing only air freight. In multimodal transportation, the share is air (74%), rail (1%), and road (25%). However, air transport accounts for 60% of intermodal transportation, while road transport accounts for 40%
  Keywords: supply chain managers; multimodal and intermodal; business performance; breakdown analysis; air freight.
  DOI: 10.1504/IJBPSCM.2026.10080405