AI-Driven Decision Analytics for Resilient and Sustainable Supply Chains

Topic Information

Dear Colleagues,

Global supply chains are undergoing profound transformations driven by increasing uncertainties, including geopolitical disruptions, climate change, public health emergencies, resource constraints, and rapidly evolving market demands. These challenges have highlighted the urgent need to develop supply chains that are not only efficient but also resilient, adaptive, and sustainable.

Meanwhile, rapid advances in artificial intelligence (AI), data analytics, and intelligent decision technologies have created new opportunities for transforming supply chain management and operations. AI-driven decision analytics, integrating machine learning, predictive intelligence, optimization techniques, digital twins, and intelligent decision-support systems, provides powerful capabilities for forecasting uncertainty, optimizing complex supply networks, enhancing operational resilience, and supporting sustainable development.

Despite significant progress, important research challenges remain regarding how AI technologies can be effectively integrated with supply chain theories, advanced analytical methods, and sustainability principles. Developing reliable, explainable, and actionable AI-driven decision frameworks requires interdisciplinary collaboration among researchers in artificial intelligence, operations research, industrial engineering, supply chain management, and sustainability science.

Under this emerging research direction, we sincerely invite internationally recognized scholars and research leaders to publish your original research or comprehensive review in relevant MDPI journals on the theme “AI-Driven Decision Analytics for Resilient and Sustainable Supply Chains”. Journals include Sustainability, Mathematics, Systems, Information, Applied Sciences, Analytics, and JTAER.

This Topic may focus on specific methodological innovations, theoretical developments, or application-oriented perspectives aligned with this broad research theme. We particularly welcome proposals that explore the integration of AI technologies with advanced decision analytics to address complex supply chain challenges under uncertainty.

Potential topics include, but are not limited to, the following:

• AI-driven supply chain forecasting, prediction, and risk analytics;
• Machine learning and deep learning approaches for intelligent decision-making;
• AI-enhanced robust, stochastic, and data-driven optimization;
• Digital twins and intelligent systems for supply chain resilience;
• Sustainable supply chain management enabled by AI analytics;
• Green logistics, carbon reduction, and circular economy strategies;
• Explainable AI and human–AI collaboration in operations and supply chain management.

This research direction has broad interdisciplinary significance and practical relevance across multiple sectors. In agriculture and food supply chains, AI-driven analytics can improve demand forecasting, production planning, climate-risk adaptation, and sustainable resource allocation. In city logistics and transportation systems, intelligent decision approaches can enhance routing optimization, inventory management, network resilience, and low-carbon operations. In healthcare supply chains, AI-based methods can support medical resource allocation, pharmaceutical supply management, emergency preparedness, and resilient healthcare delivery. Similar opportunities exist in manufacturing, energy systems, retail, and public infrastructure.

By bringing together leading scholars from diverse disciplines, these Topic Issues can provide an international platform for advancing theories, methodologies, and practical solutions for next-generation supply chains. We encourage experts with strong academic achievements and international research networks to lead Topic Issues that define emerging research frontiers and promote high-impact interdisciplinary collaboration.

As an academic initiative, this theme aims to foster global cooperation and accelerate the development of AI-driven decision analytics for building resilient and sustainable supply chains.

We sincerely look forward to collaborating with distinguished scholars worldwide to advance this important research agenda.

Prof. Dr. Kin Keung Lai
Prof. Dr. George Q. Huang
Prof. Dr. Yi-Jia Wang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Analytics analytics	-	4.3	2022	24.2 Days	CHF 1200	Submit
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Information information	4.3	8.2	2010	18.7 Days	CHF 1800	Submit
Journal of Theoretical and Applied Electronic Commerce Research jtaer	4.5	7.1	2006	20.9 Days	CHF 1400	Submit
Mathematics mathematics	2.3	5.4	2013	17.4 Days	CHF 2600	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit
Systems systems	3.8	5.4	2013	19.8 Days	CHF 2400	Submit

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