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Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future...
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Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future Directions

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 5666

Special Issue Editor

Dr. Muhammad Noman Shafique

E-Mail Website
Guest Editor
Department of Environment and Planning, University of Aveiro, Aveiro, Portugal
Interests: supply chain management; logistics; digital business
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that this Special Issue of the MDPI journal Information is currently accepting articles on “Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future Directions”.

Reducing carbon footprints, managing resource scarcity, ensuring sustainable procurement, and minimizing waste are just a few of the major concerns regarding sustainable supply chains. With the global economy becoming more integrated and dynamic, artificial intelligence (AI) is becoming crucial in tackling these issues. AI-driven predictive analytics, real-time decision-making, and enhanced transparency are revolutionizing supply chain planning and management.

Even though supply chain management is progressively relying on AI, several important issues and unfulfilled research gaps remain to be considered. These include addressing ethical aspects, AI-driven decision-making, integrating AI into circular and multi-tiered supply chains, and ensuring accountability and transparency in AI applications. Additionally, the integration of AI with sustainability creates unique challenges, such as combining social, environmental, and economic objectives while supporting operational resilience and efficiency.

This Special Issue invites the submission of original research that can advance our understanding of AI’s contribution to sustainable supply chains. As existing research often overlooks this relationship, authors are highly encouraged to align their studies with the United Nation’s Sustainable Development Goals (SDGs).

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

  • AI and circular economy in supply chain management.
  • AI-enabled transparency and traceability in global supply chains.
  • Ethical considerations in AI applications for sustainability.
  • The role of AI in circular supply chains and reverse logistics.
  • AI-powered tools for monitoring and reducing carbon footprints.
  • Using AI to achieve economic, environmental, and social goals in supply chains.
  • Applying AI in industries such as manufacturing, logistics, retail, and agriculture.
  • The use of AI in risk management for sustainable supply chains.
  • Case studies on AI’s impact on supply chain resilience and sustainability.
  • Challenges and future directions for AI-driven sustainable supply chains.

We welcome the submission of theoretical, methodological, and applied research from academia, industry, and policymakers that focus on the topics mentioned above. Authors should demonstrate how their research contributes to and promotes the SDGs while providing practical implications from their findings. We especially encourage the inclusion of innovative frameworks or strategies that can leverage AI in the context of a circular economy and sustainable supply chain management.

Dr. Muhammad Noman Shafique
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. Information 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 1800 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 (AI) in supply chains
  • sustainable supply chain management practices
  • predictive analytics for supply chain optimization
  • supply chain transparency and traceability
  • supply chain resilience

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

Published Papers (4 papers)

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Research

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23 pages, 646 KB  
Article
Exploring the Perceived Impact of Smart City Dimensions on Supply Chain Management: A Case Study of a South African Municipality
by Alexander Bradley Samuels
Information 2026, 17(5), 450; https://doi.org/10.3390/info17050450 - 7 May 2026
Viewed by 276
Abstract
Municipalities in South Africa face increasing pressure to improve service delivery, operational efficiency, and sustainability amid growing urbanisation and governance challenges. The integration of smart city dimensions such as smart governance, mobility, and infrastructure offers a transformative approach to improve public sector supply [...] Read more.
Municipalities in South Africa face increasing pressure to improve service delivery, operational efficiency, and sustainability amid growing urbanisation and governance challenges. The integration of smart city dimensions such as smart governance, mobility, and infrastructure offers a transformative approach to improve public sector supply chain management. However, limited empirical research exists on how these dimensions are being applied in South African municipal contexts. This study aimed to evaluate the extent to which smart city dimensions are integrated into supply chain management practices within a South African municipality and to assess the impact of these initiatives on supply chain efficiency, transparency, and sustainability. A qualitative, exploratory case study design was employed. Twenty senior managers and key stakeholders from the supply chain department of the selected municipality were purposively sampled. Data were collected through semi-structured face-to-face interviews and analysed thematically using NVivo software. Lincoln and Guba’s trustworthiness framework guided the study’s rigour. The findings revealed partial and uneven integration of smart city dimensions, with notable developments in smart governance and mobility, but limited progress in areas such as infrastructure digitalisation and citizen-centric data platforms. Participants highlighted both innovation drivers and institutional barriers affecting the transition to smart-enabled supply chain practices. Smart city dimensions present significant potential to improve municipal supply chain management; however, effective integration requires structural alignment, digital investment, and organisational readiness. This study provides context-specific insights into the uneven and fragmented integration of smart city dimensions within municipal supply chain systems in a developing country context, emphasising the impact of institutional constraints, digital capability gaps, and governance misalignments on implementation outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future Directions)
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17 pages, 256 KB  
Article
Verifying SDG ESG Compliance in Manufacturing Industry Projects by Surveying Sponsors
by Kenneth David Strang and Narasimha Rao Vajjhala
Information 2026, 17(4), 311; https://doi.org/10.3390/info17040311 - 24 Mar 2026
Viewed by 526
Abstract
This study addresses a critical gap in the operationalization of sustainability frameworks at the project level by developing and validating an empirically grounded measurement instrument for assessing Environmental, Social, and Governance (ESG) compliance in manufacturing industry projects. While the United Nations Sustainable Development [...] Read more.
This study addresses a critical gap in the operationalization of sustainability frameworks at the project level by developing and validating an empirically grounded measurement instrument for assessing Environmental, Social, and Governance (ESG) compliance in manufacturing industry projects. While the United Nations Sustainable Development Goals (SDGs) articulate sustainability aspirations at the national and global level, and ESG frameworks capture organizational-level sustainability performance, no validated instrument exists for measuring ESG integration at the project level where sustainability commitments are ultimately operationalized. Drawing on the theoretical foundations of sustainable project management, stakeholder theory, and the ESG governance literature, the authors developed a 30-item survey instrument capturing six conceptual dimensions of ESG-aligned project performance. Data were collected from 2231 project sponsors and decision-makers in North American goods manufacturing firms classified under NAICS codes 31–33, which collectively encompass the entire manufacturing sector in North America. Through a sequential analytical approach employing principal component analysis (PCA) for initial item reduction, exploratory factor analysis (EFA) for dimensionality assessment, and structural equation modelling (SEM) for confirmatory validation, a parsimonious two-factor model emerged with excellent fit indices (CFI = 0.99, TLI = 0.98, RMSEA = 0.052, SRMR < 0.035). The first factor captures ESG planning activities undertaken during project initiation and planning phases, while the second factor represents ESG monitoring and controlling functions during project execution. The reduction from six theoretical dimensions to two empirical factors reflects lifecycle governance theory, where planning-phase governance and execution-phase control emerge as functionally distinct but correlated constructs. The validated instrument offers practical utility for project managers, organizational sustainability officers, and policy-makers seeking standardized benchmarks for ESG compliance at the operational project level. The validated instrument and complete survey are shared for replication and testing across different industries and countries. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future Directions)
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26 pages, 1886 KB  
Article
Path Planning with Adaptive Autonomy Based on an Improved A∗ Algorithm and Dynamic Programming for Mobile Robots
by Muhammad Aatif, Muhammad Zeeshan Baig, Umar Adeel and Ammar Rashid
Information 2025, 16(8), 700; https://doi.org/10.3390/info16080700 - 17 Aug 2025
Cited by 4 | Viewed by 2212
Abstract
Sustainable path-planning algorithms are essential for executing complex user-defined missions by mobile robots. Addressing various scenarios with a unified criterion during the design phase is often impractical due to the potential for unforeseen situations. Therefore, it is important to incorporate the concept of [...] Read more.
Sustainable path-planning algorithms are essential for executing complex user-defined missions by mobile robots. Addressing various scenarios with a unified criterion during the design phase is often impractical due to the potential for unforeseen situations. Therefore, it is important to incorporate the concept of adaptive autonomy for path planning. This approach allows the system to autonomously select the best path-planning strategy. The technique utilizes dynamic programming with an adaptive memory size, leveraging a cellular decomposition technique to divide the map into convex cells. The path is divided into three segments: the first segment connects the starting point to the center of the starting cell, the second segment connects the center of the goal cell to the goal point, and the third segment connects the center of the starting cell to the center of the goal cell. Since each cell is convex, internal path planning simply requires a straight line between two points within a cell. Path planning uses an improved A∗ (I-A∗) algorithm, which evaluates the feasibility of a direct path to the goal from the current position during execution. When a direct path is discovered, the algorithm promptly returns and saves it in memory. The memory size is proportional to the square of the total number of cells, and it stores paths between the centers of cells. By storing and reusing previously calculated paths, this method significantly reduces redundant computation and supports long-term sustainability in mobile robot deployments. The final phase of the path-planning process involves pruning, which eliminates unnecessary waypoints. This approach obviates the need for repetitive path planning across different scenarios thanks to its compact memory size. As a result, paths can be swiftly retrieved from memory when needed, enabling efficient and prompt navigation. Simulation results indicate that this algorithm consistently outperforms other algorithms in finding the shortest path quickly. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future Directions)
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Review

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37 pages, 570 KB  
Review
Autonomous Supply Chains: Integrating Artificial Intelligence, Digital Twins, and Predictive Analytics for Intelligent Decision Systems
by Mohammad Shamsuddoha, Honey Zimmerman, Tasnuba Nasir and Md Najmus Sakib
Information 2026, 17(4), 371; https://doi.org/10.3390/info17040371 - 15 Apr 2026
Viewed by 1587
Abstract
Autonomous supply chains (ASC) are the next generation of digitally empowered logistics and operations systems that can make adaptive, data-driven, and intelligent decisions. Innovations in artificial intelligence (AI), digital twins (DT), and predictive analytics (PA) are transforming traditional supply chains into integrated and [...] Read more.
Autonomous supply chains (ASC) are the next generation of digitally empowered logistics and operations systems that can make adaptive, data-driven, and intelligent decisions. Innovations in artificial intelligence (AI), digital twins (DT), and predictive analytics (PA) are transforming traditional supply chains into integrated and interactive networks to detect disruptions, simulate the future, and automatically modify operational decisions. This paper reviews the ASC mechanism and summarizes the increasing literature on the technologies and analytical capabilities available to support intelligent supply chain decision systems. A structured literature review was conducted using Scopus, Web of Science, and Google Scholar, resulting in 52 relevant studies after screening and eligibility assessment. The paper discusses the recent advances in AI-based forecasting, simulation environments using digital twins, data integration using the Internet of Things (IoT), and predictive analytics. These technologies can help an organization gain real-time visibility of the supply chain networks. They improve the precision of demand forecasting, optimize inventory and production planning, and dynamically coordinate logistics operations. Digital twins allow the development of virtual models of supply chain ecosystems, which could be used to test scenarios, analyze risks, and plan strategies. These capabilities combined can be used to create predictive and self-adaptive supply networks capable of being responsive to uncertainty and market volatility. Besides examining the technological foundations, the paper also tracks key challenges related to the move towards autonomous supply chains, such as data governance, system interoperability, cybersecurity risks, algorithm transparency, and the necessity of successful human-AI collaboration in decision-making. The synthesis leads to a multi-layered framework that integrates data acquisition, analytics, simulation, and execution for autonomous decision-making in supply chains. Future research directions in relation to resilient supply networks, intelligent automation, and adaptive supply chain ecosystems are also provided in the study. Through integrating existing information on the new forms of intelligent technology and how it can be incorporated into the supply chain systems, this review contributes to the literature on next-generation supply chains. It will also offer information to both researchers and practitioners aiming at designing autonomous as well as data-driven supply networks. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future Directions)
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