International Journal of Advanced Operations Management (6 papers in press)

Regular Issues

• Optimised Inventory Allocation in Complex Multi-Tier Networks: Leveraging Sampling Techniques  
  by Atma Nand 
  Abstract: This paper introduces a data-driven, distribution-free framework for joint safety stock allocation and sizing in general acyclic multi-echelon networks. We formulate a two-stage mixed-integer stochastic programming model that integrates feature-driven demand learning via non-parametric kernel density estimation with multi-period service-level constraints. By exploiting the problems nested structure, we develop a Benders decomposition algorithm augmented with Pareto-optimal cuts and scenario reduction techniques, enabling scalable computation of base-stock policies under non-stationary demand. Our methodology eliminates parametric assumptions, directly leveraging historical demand trajectories and covariate data to approximate conditional demand distributions. Proposed Benders-based approach solves 38 benchmark supply chain networks with up to 1,000 nodes and 15 echelons within 2 hours using commercial solvers, outperforming monolithic MISP formulations by orders of magnitude. Empirical results confirm robustness to demand skewness, heteroscedasticity, and network density, achieving 12% 18% cost reductions over parametric baselines in non-Gaussian settings.
  Keywords: Multi-echelon inventory optimization; data-driven stochastic programming; Bender’s decomposition; nonparametric demand estimation; safety stock allocation; service level constraints.
  DOI: 10.1504/IJAOM.2026.10078841
   
  
• Optimisation of Inventory Decisions under Uncertainty Using a Fuzzy EOQ Model with Graded Mean Defuzzification  
  by Preety Poswal, Yogendra Rajoria, Deo Datta Aarya, Anand Chauhan 
  Abstract: At present, numerous organisations allow their businesses to flourish by effectively managing their inventory. Effective inventory management is vital for the optimal and streamlined functioning of an association. Excessive inventory levels can experience substantial carrying costs, thereby impacting the firm's overall financial performance. Balancing stock levels is essential to minimize these expenses. Many researchers are actively working to develop robust models for inventory control that can enhance decision-making. In today’s dynamic market, retailers and distributors face challenges in accurately predicting demand, highlighting the importance of advancing inventory management strategies. To tackle such uncertainties, fuzzy numbers are used to indicate the parameters of inventory. This research proposes the development of a Fuzzy economic order quantity (FEOQ) model and introduces an innovative defuzzification technique based on the Graded Mean Approach, which is more efficacious than its counterparts. Numerical examples have been presented for crisp set and fuzzy set conditions with computational results.
  Keywords: Fuzzy; Graded mean approach; Trapezoidal number; Pentagonal number; Sensitivity analysis.
  DOI: 10.1504/IJAOM.2026.10079340
   
  
• Decision Making via Pugh matrix with the Monte Carlo Weights  
  by Jiri Mazurek, Zuzana Neni?ková, Radomír Perzina 
  Abstract: Pugh Matrix Analysis (PMA) is a widely used multi-criteria decision making/aiding (MCDM/A) technique based on pairwise comparison of alternatives against a reference option. However, PMA relies on fixed criteria weights, which can produce unstable rankings under small weight variations. This paper introduces Pugh Matrix Analysis with Monte Carlo Weights (PMA-MCW), which uses Monte Carlo (MC) generated weight vectors to evaluate alternatives across the full weight space. The method provides mean Positive, Negative, and Overall scores and identifies dominant alternatives within specific weight regions, enabling systematic analysis of weight uncertainty and sensitivity. PMA-MCW is demonstrated on applications from space science and economics. Compared with traditional PMA, it reveals additional information, including the frequency of best rankings and the structure of dominance regions. The results show that PMA-MCW is a practical and computationally efficient extension supporting more robust decision making under uncertain criteria weights.
  Keywords: Criteria weights; Monte Carlo method; multi-criteria decision making; Pugh matrix; simulations.
  DOI: 10.1504/IJAOM.2026.10079554
   
  
• Integrating Big Data Analytics and Digital Twin Technologies to Improve the Key Performance Indicators for Robust Humanitarian Supply Chains and Emergency Preparedness in the Face of Natural Disasters  
  by Rachid Mharzi, Hassan Mharzi, Hamid Hssaine, Abdelmajid Elouadi 
  Abstract: This paper addresses supply chain (SC) challenges in Morocco arising from its unique geography and climate, focusing on SC essential disaster relief products (SCEDRP) amid earthquake disruptive events (EDEs). We develop a hybrid framework combining discrete-event simulation (DES), digital twin technology (DTT), and big data analytics (BDA) to assess vulnerabilities and enhance SC resilience. By integrating geospatial data (GIS), and machine learning, the adaptive DTT simulates infrastructure failures and optimizes resource allocation for proactive disaster management. Key performance indicators (KPIs) derived from DTT enable real-time monitoring and decision support. Sensitivity analyses reveal impacts of route closures and demand fluctuations, while AI-driven predictions improve lead times and recovery strategies. The proposed framework supports resilience-focused emergency management (RFEM) through predictive analytics, risk assessment, and stakeholder collaboration, offering a data-driven approach to strengthen humanitarian SCs in disaster-prone regions.
  Keywords: Key performance indicators; Resilient supply chain 4.0; Risk management 4.0; Emergency preparedness 4.0; Advanced decision making systems; Simulation; Modeling; Big data analyticts.
  DOI: 10.1504/IJAOM.2026.10080067
   
  
• Consumers' Intention to Use Virtual Fitting Rooms and Their Willingness to Pay More  
  by Zübeyir Çelik, Güzide Öncü Eroğlu Pektaş 
  Abstract: This study examines the factors influencing consumers' intention to use virtual fitting rooms and their willingness to pay more from the consumer perspective. Data were collected using "unrestricted self-selected surveys", an internet-based sampling method. Data were collected through an online survey from 230 volunteer participants who watched a video of a virtual fitting room. Statistical analyses indicate that perceived ease of use, perceived spatial presence, and perceived enjoyment positively affect perceived usefulness, which, in turn, significantly affects intention to use willingness to pay more. However, perceived information's effect on perceived usefulness is not significant. In contrast, desire has a moderating role in the effect of perceived usefulness on both intention to use and willingness to pay more. This study presents its findings on the role of desire in virtual fitting rooms, offering theoretical and practical insights. It also makes a valuable addition to literature on the adoption of technology.
  Keywords: Virtual Fitting Rooms; Consumer Perception; Intention to Use; Willingness to Pay More.
  DOI: 10.1504/IJAOM.2027.10080142
   
  
• Standardizing Operational Management terminology: Bridging gaps in Operational Excellence for the Manufacturing Sector  
  by Dimitrios Vlachopoulos, Sotirios Dimitriadis 
  Abstract: Terminological ambiguity remains a persistent yet underexplored challenge in the field of operations management (OM), particularly in manufacturing contexts. This study addresses the lack of definitional clarity by proposing a structured framework for standardising key terms commonly used in operational excellence (OpEx) discourse. Drawing from an extensive literature review and a comparative analysis of formal glossary sources (Encyclopedia of OM, ASCM Dictionary, ISO 9000:2015, PMBOK Guide, Lean Lexicon and the OECD Glossary of Statistical Terms), the study synthesises terminology into nine thematic clusters, including business operations, performance improvement, and operational excellence. The originality of the work lies in its bottom-up analytical method, starting from foundational terms and progressively building toward complex constructs. This constructivist approach allows for the identification of overlaps, inconsistencies, and conceptual gaps in both academic and industry usage. Key implications of the findings include enhanced consistency in communication, improved alignment of performance strategies, and the foundation for both academic research and industrial training. The study contributes theoretically by offering a taxonomy to support future conceptual development in OM and practically by offering a reference framework for professionals applying OpEx methodologies.
  Keywords: Operational excellence; operations management; manufacturing; business terminology; business glossary; taxonomy; performance management; performance measurement; business operational system.
  DOI: 10.1504/IJAOM.2027.10080698