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Artificial Intelligence and Big Data Strategies for Sustainable and Resilient...
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Artificial Intelligence and Big Data Strategies for Sustainable and Resilient Supply Chain Management

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Supply Chain Management".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 5276

Special Issue Editor

Dr. Sebastian-Emanuel Stan

E-Mail Website
Guest Editor
Department of Management, “Nicolae Balcescu” Land Forces Academy, 550170 Sibiu, Romania
Interests: intangible assets; sustainability; ESG supply chains; digital transformation; AI in economic and management; bibliometric analysis

Special Issue Information

Dear Colleagues,

The rapid development of Artificial Intelligence (AI) and Big Data analytics is transforming supply chain management (SCM), offering new opportunities for efficiency, resilience, and sustainability. Global supply chains face increasing challenges due to disruptions, regulatory pressures, and the growing importance of ESG (Environmental, Social, and Governance) compliance. Leveraging AI-driven models, predictive analytics, and data-intensive strategies offers a pathway toward smarter, more adaptive, and sustainable supply chains.

This Special Issue explores the integration of AI-driven models, predictive analytics, and data-driven strategies into supply chains, with particular attention to ESG (Environmental, Social, and Governance) compliance, risk management, and digital transformation. We welcome both theoretical and applied research, including empirical studies, econometric analyses, simulation models, optimization approaches, and case-based investigations.

Topics of interest may include, but are not limited to, the following:

  • AI-enabled decision support systems, machine learning, and deep learning applications in procurement, logistics, and demand forecasting.
  • Big Data–driven risk analytics, blockchain, IoT, and digital twin solutions for resilient and transparent supply chains.
  • Econometric modeling and causal inference approaches for analyzing resilience, productivity, and ESG outcomes.
  • Quantitative evaluation of sustainability and climate policies in digitalized supply chains.
  • Productivity and efficiency measurement in AI-enabled operations and logistics networks.
  • Input–output and computable general equilibrium (CGE) models to assess economic, social, and environmental spillovers of digital SCM.
  • Cross-country and sectoral comparative studies on AI and Big Data adoption and their macroeconomic implications.
  • Bibliometric and scientometric analyses of innovation trends at the intersection of AI, Big Data, and sustainable SCM.

This Special Issue fits the scope of Systems by examining supply chains as complex socio-technical systems shaped by technological, environmental, and institutional dynamics. The integration of AI, Big Data, and digital tools reflects a systemic and holistic perspective on resilience, sustainability, and ESG compliance. By combining systems theory, econometric modeling, and interdisciplinary approaches, this Issue addresses operations, logistics, and digitalization in line with the journal’s focus on complex adaptive systems and socio-technical integration.

We look forward to receiving your contributions.

Dr. Sebastian-Emanuel Stan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence in supply chain management
  • big data analytics
  • sustainable supply chains
  • ESG-oriented management
  • digital transformation
  • smart logistics
  • resilience and risk management
  • econometric modeling
  • productivity and efficiency
  • decision support systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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27 pages, 1555 KB  
Article
Integrating AI and Big Data for Firm Resilience: The Mediating Roles of AI Capabilities and Supply Chain Agility
by Thamir Hamad Alaskar
Systems 2026, 14(5), 554; https://doi.org/10.3390/systems14050554 - 14 May 2026
Viewed by 331
Abstract
The integration of Artificial Intelligence (AI) and Big Data is increasingly associated with firms’ resilience in dynamic business environments. This study examines the relationships between AI–Big Data integration, AI capabilities, supply chain agility, and firm resilience, with particular attention paid to the mediating [...] Read more.
The integration of Artificial Intelligence (AI) and Big Data is increasingly associated with firms’ resilience in dynamic business environments. This study examines the relationships between AI–Big Data integration, AI capabilities, supply chain agility, and firm resilience, with particular attention paid to the mediating roles of AI capabilities and supply chain agility. Data were collected from 475 experts across firms in Saudi Arabia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that AI–Big Data integration is positively associated with AI capabilities and supply chain agility, both of which, in turn, significantly contribute to firm resilience. In addition, AI capabilities show a direct positive relationship with supply chain agility. The findings further confirm the mediating roles of AI capabilities and supply chain agility in strengthening organizational resilience. This study contributes to the Dynamic Capabilities View (DCV) and Knowledge-Based View (KBV) by empirically examining how integrated AI–Big Data relates to capability development and firm outcomes. The results also provide implications for managers seeking to align AI and Big Data initiatives with supply chain capabilities to support resilience in dynamic environments. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Strategies for Sustainable and Resilient Supply Chain Management)
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42 pages, 656 KB  
Article
Operational Resilience Under Carbon Constraints: A Socio-Technical Multi-Agentic Approach to Global Supply Chains
by Rashanjot Kaur, Triparna Kundu, Bhanu Sharma, Kathleen Marshall Park and Eugene Pinsky
Systems 2026, 14(4), 374; https://doi.org/10.3390/systems14040374 - 31 Mar 2026
Viewed by 654
Abstract
High-stakes logistics, defined as supply chains where delays, quality loss, or noncompliance have serious human, safety, financial, or geopolitical consequences, are a prominent case of a broader reality: global supply chains are safety-, cost-, and time-critical socio-technical systems where forecasting quality, vendor coordination, [...] Read more.
High-stakes logistics, defined as supply chains where delays, quality loss, or noncompliance have serious human, safety, financial, or geopolitical consequences, are a prominent case of a broader reality: global supply chains are safety-, cost-, and time-critical socio-technical systems where forecasting quality, vendor coordination, and operational decisions shape service levels and stakeholder welfare. At the same time, decarbonization pressures and the growing use of AI for planning and control introduce new risks and trade-offs across energy, computation, and physical logistics. We develop a multi-agent framework that models supply chain system-of-systems dynamics drawing on (1) supply chain decision functions (shipment planning, sourcing and vendor management), (2) national energy-transition conditions that determine grid carbon intensity, and (3) carbon-aware computation accounting for AI-enabled decision support. Methodologically, we combine predictive analytics, unsupervised segmentation, and a carbon-cost-of-intelligence layer in a scenario-based assessment of how national energy-transition profiles–from Norway to India–affect the intensity of AI compute carbon, meaning the carbon emissions generated by the hardware and data centers required to train and run AI models. We introduce the carbon-adjusted supply chain performance (CASP) metric that integrates physical transport carbon, cold-chain overhead where applicable, and AI compute carbon into a per-package-type performance measure. Our analysis yields three actionable outputs for systems engineering and environmental management: carbon, service, and cost trade-off frontiers; governance levers (sourcing portfolio rules, buffers, and compute policies); and system-level early-warning indicators for disruption amplification. This study implements a tool-augmented multi-agent system (orchestrator, risk, and sourcing agents) using AWS bedrock and strands agents, where LLM-based agents orchestrate deterministic analytical engines through structured tool interfaces with adaptive query generation. Theoretically, we extend previous systems-of-systems and sustainable supply chain findings by formalizing package-type-specific carbon–service frontiers and by embedding AI compute carbon into a socio-technical resilience framework. Practically, the CASP benchmark, governance lever analysis, and multi-agent implementation provide decision-makers with concrete tools to compare carriers, routes, and compute strategies across countries while making transparent the trade-offs between service reliability and total carbon. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Strategies for Sustainable and Resilient Supply Chain Management)
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22 pages, 583 KB  
Article
Seeing the Unseen: AI Assimilation and Supply–Demand Visibility for Effective Risk Management in Manufacturing Supply Chains
by Jiangmin Ding, Zhaoqi Li and Eon-Seong Lee
Systems 2026, 14(3), 300; https://doi.org/10.3390/systems14030300 - 12 Mar 2026
Viewed by 1267
Abstract
Artificial intelligence (AI) has become a strategic resource for enhancing supply chain resilience in environments characterized by growing uncertainty and complexity. Building on the resource-based view (RBV) and organizational information processing theory (OIPT), this study examines how AI assimilation as a firm-level strategic [...] Read more.
Artificial intelligence (AI) has become a strategic resource for enhancing supply chain resilience in environments characterized by growing uncertainty and complexity. Building on the resource-based view (RBV) and organizational information processing theory (OIPT), this study examines how AI assimilation as a firm-level strategic capability improves supply–demand visibility and strengthens supply chain risk management (SCRM). Using survey data collected from 129 manufacturing firms in China, the proposed research framework is tested through structural equation modeling. The results show that AI assimilation significantly enhances both supply–demand visibility and SCRM, with visibility playing a partial mediating role in translating AI-enabled capabilities into more effective risk control. These findings indicate that AI contributes to resilience not merely through technological deployment but through its integration into organizational processes that support information processing and coordination. From a managerial perspective, the study suggests that firms should approach AI as an ongoing strategic capability development process rather than a one-time technological investment. By embedding AI into core supply chain functions such as production planning, inventory management, and demand forecasting, firms can improve visibility, anticipate disruptions, and shift toward more proactive and resilient risk management practices. This study advances the literature by integrating RBV and OIPT to explain the strategic mechanisms through which AI assimilation enhances visibility in SCRM, providing empirical evidence from a manufacturing context. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Strategies for Sustainable and Resilient Supply Chain Management)
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38 pages, 916 KB  
Systematic Review
Integrating Business Intelligence and Operations Research for Sustainable Supply Chain Systems: A Systematic Review
by Rui Pedro Marques and Dorabella Santos
Systems 2025, 13(12), 1111; https://doi.org/10.3390/systems13121111 - 10 Dec 2025
Cited by 1 | Viewed by 2112
Abstract
This systematic review explores how business intelligence (BI) and operations research (OR) help organizations ensure sustainable practices in supply chain management (SCM). Drawing on 56 peer-reviewed studies, this review synthesizes how BI tools support sustainability by transforming large and complex datasets into actionable [...] Read more.
This systematic review explores how business intelligence (BI) and operations research (OR) help organizations ensure sustainable practices in supply chain management (SCM). Drawing on 56 peer-reviewed studies, this review synthesizes how BI tools support sustainability by transforming large and complex datasets into actionable insights, enhancing transparency, improving forecasting, optimizing production and inventory, reducing waste, and enabling circular economy practices. Complementarily, OR provides methodological rigor through optimization models, simulation, and multicriteria decision-making, enabling organizations to balance economic, environmental, and social objectives in supply chain design and operations. The findings reveal that BI and OR jointly contribute to 11 of the 17 United Nations Sustainable Development Goals (SDGs), demonstrating their strategic relevance for global sustainable development. This paper’s contribution is twofold: it consolidates fragmented academic research through an integrative framework clarifying how BI and OR reinforce sustainability within SCM, and it provides practitioners with evidence of how these tools can generate both operational efficiency and a competitive advantage while meeting environmental and social responsibilities. Future research should focus on bridging existing gaps in the literature and advancing the practical applications of these technologies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Strategies for Sustainable and Resilient Supply Chain Management)
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