
SPECIAL ISSUE: Decision-Oriented Analytical Modelling for Digital, Resilient and Sustainable Supply Chains in the Era of Industry 5.0
Introduction
In recent years, supply chains have become increasingly difficult to manage in any conventional sense. They are no longer stable, linear systems, but evolving networks shaped by data flows, disruptions, and shifting operational priorities. Under such conditions, decisions are rarely straightforward. Trade-offs between efficiency, resilience, and sustainability are not only unavoidable, but often poorly understood.
At the same time, the rapid diffusion of technologies associated with Industry 4.0—and more recently Industry 5.0—has added another layer of complexity. Tools such as artificial intelligence, digital twins, blockchain, and IoT systems promise better visibility and coordination, yet they also introduce new forms of uncertainty. In many cases, organisations have access to more data than before, but not necessarily better ways to use it when making decisions.
There is still a gap between how these methods are developed and how they are actually used in practice. A large part of the existing literature continues to focus on technologies or performance indicators in isolation. Much less attention is given to how different capabilities, system conditions, and external pressures come together when decisions need to be made in practice.
This Special Issue is intended to bring that aspect back into focus. The emphasis is not simply on modelling for its own sake, but on how analytical approaches are used when choices have to be made—whether at the level of system design, operational adjustment, or long-term planning.
Scope of the Special Issue
The issue is open to work that engages seriously with decision problems in supply chain settings. Submissions are expected to go beyond descriptive accounts or purely conceptual discussions.
Topics may include (but are not restricted to):
Decision models used in the design or reconfiguration of supply chain systems
Approaches that deal with conflicting objectives, especially where sustainability and resilience are involved
Simulation or optimisation work that reflects real decision constraints rather than idealised settings
Decision-support tools that make use of data, but also address uncertainty and imperfect information
Questions around technology adoption, particularly where the decision process itself is not straightforward
Ways of evaluating capabilities or system performance that actually inform subsequent actions
Work that shows how results can be interpreted and used in practice will be viewed more favourably than work that stops at the modelling stage.
Thematic Structure
Rather than treating topics as isolated domains, the Special Issue is organised around recurring decision contexts.
Theme 1: Capability-Based Decision Modelling under Industry 5.0
Modelling supply chain capabilities (agility, integration, visibility) as decision variables
Industry 5.0 technologies in supporting adaptive and human-centric decisions
Capability development and its role in strategic and operational decision-making
Theme 2: Resilience and Decision-Making under Uncertainty
Modelling disruption propagation and response decisions
Decision frameworks under uncertainty (stochastic, fuzzy, hybrid approaches)
Integration of resilience into decision-support systems
Theme 3: Sustainability and Multi-Objective Decision Analysis
Trade-off analysis between environmental, economic, and social objectives
Decision models for circular economy and green supply chains
Sustainability-driven performance evaluation and prioritisation
Theme 4: Digital Technologies and Decision-Support Systems
AI, blockchain, IoT, and digital twins in supporting supply chain decisions
Data-driven decision-making and real-time analytics
Digital platforms for coordination, transparency, and control
Proposed Timeline
Manuscript Submission Deadline: August 2026
Peer Review Completion: October 2026
Final Decision Notification: December 2026
Publication: Upon acceptance
Guest Editors
Dr. Sharfuddin Ahmed Khan
Industrial Systems Engineering, University of Regina, Canada
Email: Sharfuddin.Khan@uregina.ca
Dr. Syed Mehmood Hassan
Digital Engineering Management, Royal Holloway, University of London, UK
Email: Syed.Hasan@rhul.ac.uk
Dr. Golam Kabir
Industrial Systems Engineering, University of Regina, Canada
Department of Industrial and Systems Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia
Email: Sharfuddin.Khan@uregina.ca






