Supply Chain Analytics & Circular Economy

The Supply Chain Analytics and Circular Economy research group operates at the intersection of data-driven decision-making and sustainable resource management. The Supply Chain Analytics dimension focuses on leveraging advanced analytical techniques, optimization models, and data-driven tools to enhance supply chain efficiency, resilience, and responsiveness in increasingly complex and uncertain environments. It examines areas such as demand forecasting, inventory optimization, risk management, and digital supply chain transformation. The Circular Economy dimension emphasizes sustainable production and consumption systems by promoting resource efficiency, waste minimization, recycling, remanufacturing, and closed-loop supply chains. While each dimension maintains its distinct research focus, they are inherently interconnected: analytical insights enable better design and management of circular systems, while circular economy principles reshape supply chain structures and decision-making processes. This integrated approach allows the group to develop innovative, data-informed solutions that support both operational excellence and environmental sustainability. The group is committed to advancing knowledge through rigorous research that informs industry practices, public policy, and sustainable development strategies.

Objectives

Supply Chain Analytics
• To develop and apply advanced analytical models and data-driven techniques for improving supply chain efficiency, resilience, and decision-making under uncertainty.
• To investigate the role of digital technologies, including big data analytics and artificial intelligence, in transforming supply chain operations and enhancing visibility and coordination.

Circular Economy
• To explore strategies for transitioning from linear to circular supply chains through resource efficiency, waste reduction, recycling, and remanufacturing practices.
• To examine business models, policy frameworks, and organizational practices that support circular economy implementation across industries.

Integrated Perspective
• To analyze how supply chain analytics can enable and optimize circular economy practices, and how circularity principles influence supply chain design, performance, and sustainability outcomes.

Sustainable Development Goals (SDGs)
• SDG 8: Decent Work and Economic Growth – Enabling efficient, resilient, and sustainable supply chains that contribute to economic growth and employment.
• SDG 9: Industry, Innovation and Infrastructure – Enhancing supply chain innovation and digital transformation through advanced analytics and sustainable system design.
• SDG 12: Responsible Consumption and Production – Promoting efficient resource use, waste reduction, and sustainable production-consumption systems through circular economy practices.
• SDG 13: Climate Action – Supporting low-carbon and environmentally sustainable supply chain practices through circular approaches and optimized resource utilization.

Research Funding
• Project No. 16062, “Integrated Decision Support System for optimizing Public Procurement process in Pakistan”, worth Rs. 11156600/-, National Research Program for Universities (NRPU) Higher Education Commission (HEC).

Group Head
• Dr Afshan Naseem

Other Members

• Dr Asjad Shahzad
• Dr Syed Tasweer Hussain Shah
• Dr Leena Anum
• Dr Shujaat Ali
• Dr Sundus Younis

Research Collaborations
• University of the West of Scotland, United Kingdom
• Swinburn University & Torrens University, Australia
• United Arab Emirates University, UAE

Research Scholar Achievement
• PhD Scholar “Muzammil Mehmood” achieved the fully funded scholarship under International Research Support Initiative Program (IRSIP) by Higher Education Commission (HEC), Pakistan for his research work at Royal Melbourne Institute of Technology (Ranked 123 in the world QS 2024), Australia.
• PhD Scholar “Muhammad Shahzeb Khan” contributed as a research scholar under the HEC-funded Project No. 16062.
• PhD Scholar “Muhammad Ayyaz Khan” contributed as a research scholar under the HEC-funded Project No. 16062.

Postgraduate Research Theses

PhD Theses – Under supervision of Dr Afshan Naseem
•An Integrated Decision Support System for Bids Evaluation and Supplier Selection (BESS) in Public Procurement
•Optimize Pricing Strategy and Channel Selection for Dual Channel Closed-Loop Supply Chain in Trade-In Programs for New and Remanufactured Products
•Risk management strategies for hospitals using prescriptive modelingResilient supply chain networks under IoT: Modeling risks for Pharma 4.0
• Physical Internet in humanitarian supply chain: developing a resilient framework
• AI-Driven Optimization of Evacuation and Relief Logistics for Climate-Resilient and Sustainable Disaster Response
• Development of a Predictive Model for contractors’ evaluation in Public Procurement of construction work

PhD Theses – Under supervision of Dr Asjad Shahzad
• Hybrid AI and Multi-Criteria Decision-Making Framework for Supplier Selection in the Pharmaceutical Industry

PhD Theses – Under supervision of Dr Syed Tasweer Hussain Shah
• Developing a Model for Sustainable Supply Chain in the Medical Device Industry: A Pathway to Sustainable Healthcare

MS Theses – Under supervision of Dr Afshan Naseem
• Multi Criteria Decision Making for Pharmaceutical Cold Chain Planning
• Dynamic Ambulance Route Optimization using Real-Time Urban Traffic Data
• Risk Assessment of Warehouse Inventory Management Using an Integrated Cloud–DEMATEL Approach
• Leagile Supply Chain Management using Fuzzy SWARA-WASPAS and BWM approach
• Supplier Selection Model Based on Agility and Responsiveness Using an Integrated Approach of BWM-VIKOR
• A Holistic Risk Assessment Model for Supply Chain Vulnerabilities: Using ISM- MICMAC and CRITIC-DEMATEL Approach
• A Novel resilient and sustainable supplier selection model based on D-AHP, Fuzzy DEMATEL and Cloud model Theory
• Designing an AI-Driven Evaluation System for Public Procurement
• Optimizing Supplier Selection and Performance in the Oil and Gas Industry through a Hierarchical Fuzzy Inference System (HFIS) with Emphasis on Sustainability and HSE Criteria
• Green Supply Chain Learning Model for Sustainable Performance in Manufacturing Sector
• Green and Resilient Supplier Selection Model and Inventory Management under uncertainty for Cement Industry
• Optimization of Downstream Oil Supply Chain with respect to transport cost using Monte Carlo simulation to evaluate disruption scenarios
• Impact of Supplier Integration, Customer Integration and Internal Integration on Competitive Performance and Customer Satisfaction with Supply Chain Agility and Financial Supply Chain Management Practices
• Self-Industrialization versus Outsourcing: AHP-Based Model for Decision Process
• Service Level based Multiple Suppliers Selection with Suppliers Capacity Constraint
• Green Supplier Selection Model for Textile Industry

MS Thesis – Under supervision of Dr Asjad Shahzad

Optimizing the procurement of materials to reduce the time and cost in cable industry

MS Thesis – Under supervision of Dr Syed Tasweer Hussain Shah
• Relief optimization in the event of a disaster by using drones and trucks combination for supply of relief items in affected areas.

MS Theses – Under supervision of Dr Sundus Younis
• A Network-Driven Decision Support Framework for Optimizing Maternal and Preventive Healthcare Delivery in Pakistan Using SNA
• Evaluating the Sustainability of Electric Vehicles: An AHP Approach to Assessing the Future of Sustainable Mobility
• Design and Optimization of Public Healthcare Accessibility: A Data-Driven Analysis of Free Health Services in Pakistan’s Public Hospitals