Artificial intelligence in supply chain decision-making: An environmental, social, and governance triggering and technological inhibiting protocol
Open accessAbstract
Purpose Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors. Design/methodology/approach Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain. Findings In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers. Research limitations/implications Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias. Originality/value The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.
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More than half of the reviewed studies report improvements in transparency, risk management, and decision consistency resulting from AI adoption.
“From a governance perspective, more than half of the reviewed studies report improvements in transparency, risk management, and decision consistency resulting from AI adoption.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Discussion with practical implications identifies enhanced transparency and reduced supply-chain risk as potential benefits of AI, and the Conclusions list supply-chain risk management as a governance theme. But the paper does not say that more than half of the reviewed studies reported improvements in those outcomes, nor does it establish improved decision consistency. It presents the benefits as aims or possibilities rather than documented results at the claimed frequency. (LLM, conf=0.88)
Experimental — accuracy not yet measuredEnergy and resource-efficiency improvements of around 5-15% are commonly reported in energy-intensive supply chains.
“Energy and resource-efficiency improvements of around 5-15% are also commonly reported in energy-intensive supply chains due to predictive maintenance and process optimization systems.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Manufacturing Environmental Sustainability section lists fuel-production efficiency and bioenergy supply optimization, and the Discussion with practical implications describes AI as helping make chemical and bioenergy supply chains more efficient. Neither passage reports 5–15% improvements or establishes that such gains are common; the searched passages also do not link them to predictive maintenance. (LLM, conf=0.80)
Experimental — accuracy not yet measuredAI measurably improves ESG performance in supply-chain decision-making by enhancing the scope, speed, and accuracy of managerial decisions.
“Hao and Demir [46] demonstrate, through a PRISMA-based systematic review of 605-629 peer-reviewed studies, that artificial intelligence measurably improves environmental, social, and governance (ESG) performance in supply-chain decision-making by enhancing the scope, speed, and accuracy of managerial decisions.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Methodology describes a PRISMA-based systematic review, and Proposition 1 in the Discussion says industries are leveraging AI to improve ESG performance. But the paper frames this as a proposition, not a measured improvement caused by greater decision scope, speed, and accuracy. The Methodology reports 1,394 potentially relevant articles and 73 initially identified as pertinent, not a review of 605–629 studies; page numbers were unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAI-driven supplier monitoring systems increase the detection of labor and safety-related non-compliance by 30-50%.
“In operational contexts, AI-supported monitoring and predictive maintenance contribute to 15-25% reductions in workplace incidents, while AI-driven supplier monitoring systems increase the detection of labor and safety-related non-compliance by 30-50%, enabling faster corrective actions and more effective social governance across supply chains.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Discussion with practical implications says AI can identify potential health and safety risks and help select suppliers that prioritize fair labour practices. It does not report a 30–50% increase in detecting labour or safety non-compliance, or establish that supplier monitoring enables faster corrective action. (LLM, conf=0.78)
Experimental — accuracy not yet measuredGovernance enhancements are often accompanied by operational cost reductions of around 5-12%.
“These governance enhancements are often accompanied by operational cost reductions of around 5-12%, particularly where AI supports lean practices and circular economy strategies.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The discussion (Section 4; page not provided) identifies cost reduction as a focus of AI-supported governance in retail supply chains. It does not report a 5–12% reduction or link that figure to lean practices or circular economy strategies. (LLM, conf=0.86)
Experimental — accuracy not yet measured
AI enables earlier identification of ESG-related risks, shortening response times by 20-40% compared to periodic manual audits.
“AI-based multi-criteria decision-support systems improve supplier ESG ranking accuracy by approximately 15-25% relative to expert-only assessments and enable earlier identification of ESG-related risks, shortening response times by 20-40% compared to periodic manual audits.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Discussion with practical implications says AI can help identify health and safety risks and provide real-time supply-chain performance data. It does not report earlier ESG-risk identification relative to periodic manual audits or a 20–40% reduction in response times; searches for that comparison and figure found no support. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI-enabled reductions in excess inventory and material waste are approximately 10-30%.
“In particular, demand forecasting, inventory optimization, and logistics routing models are associated with approximately 10-30% reductions in excess inventory and material waste, while AI-supported transport and production planning contributes to 8-20% reductions in emission-intensive activities.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Discussion with practical implications (page unavailable) says AI-assisted inventory management can reduce waste and inefficiencies by identifying stockouts and overstocking, and also discusses demand forecasting and route optimization. It gives no 10–30% estimate for reductions in excess inventory or material waste; searches for that figure and for quantified inventory or waste reductions found none. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI-supported transport and production planning contributes to 8-20% reductions in emission-intensive activities.
“In particular, demand forecasting, inventory optimization, and logistics routing models are associated with approximately 10-30% reductions in excess inventory and material waste, while AI-supported transport and production planning contributes to 8-20% reductions in emission-intensive activities.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Discussion with practical implications (section 4; page unavailable) says AI could help chemical and bioenergy supply chains pursue net-zero emissions through decisions about production and logistics. It does not report an observed 8–20% reduction in emission-intensive activities; the claim turns a potential benefit into a quantified result. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSocial ESG improvements are reported in roughly 35-45% of the analyzed studies.
“Social ESG improvements are reported less frequently but remain substantial, appearing in roughly 35-45% of the analyzed studies, especially in safety-critical and consumer-facing industries.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
Table 7 (industry-specific frequency; page unavailable) identifies social-responsibility themes in several industries, and the Discussion describes examples in fashion and petroleum. But Table 7 measures the share of thematic codes within each industry, not the share of analyzed studies reporting improvements. The paper does not report the claimed 35–45% figure. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe legitimacy of AI-enabled decision-making in sustainable business models is positively associated with the transparency, explainability, and governance of AI systems.
“The legitimacy of AI-enabled decision-making in sustainable business models is positively associated with the transparency, explainability, and governance of AI systems.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
Propositions 3 and 4 in Section 4 link governance and transparent, accountable AI decision-making to sustainable supply-chain practices. The paper does not establish a positive association with legitimacy or address explainability as a separate factor, and its scope is supply chains rather than sustainable business models generally. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAI enhances decision effectiveness in sustainable business models when AI systems are explicitly aligned with ESG objectives rather than purely economic performance metrics.
“AI enhances decision effectiveness in sustainable business models when AI systems are explicitly aligned with environmental, social, and governance (ESG) objectives rather than purely economic performance metrics.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Discussion (Proposition 2; page unavailable) says AI can optimize supply-chain decisions to advance environmental sustainability and regulatory compliance, and the Conclusions link decision-making to social well-being and sustainable supply chains. The paper does not establish that explicitly aligning AI with ESG objectives improves decision effectiveness compared with using purely economic metrics, nor does it make that claim across sustainable business models generally. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI improves the efficiency of sustainable business model decision-making when deployed as a decision-support system that complements, rather than substitutes, human managerial judgment.
“AI improves the efficiency of sustainable business model decision-making when deployed as a decision-support system that complements, rather than substitutes, human managerial judgment.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Discussion with practical implications (Proposition 3; page unavailable) says AI can assist sustainable supply-chain practices while enhancing efficiency, and calls for human–AI collaboration. Table 3 (indexed under Conclusions; page unavailable) describes intelligent decision-support systems as providing information and analysis. The paper does not establish the claimed efficiency improvement for sustainable business-model decisions or compare AI that complements managerial judgment with AI that substitutes for it. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI contributes to sustainable business model innovation when conceptualized as a dynamic capability that enables continuous adaptation to environmental and social complexity.
“AI contributes to sustainable business model innovation when conceptualized as a dynamic capability that enables continuous adaptation to environmental and social complexity.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
In §4, Proposition 1 links AI adoption to improved ESG performance and sustainable development, supporting the broad claim that AI can contribute to sustainability. The paper does not frame AI as a dynamic capability, or say that it drives sustainable business model innovation through continuous adaptation to environmental and social complexity. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAI-based multi-criteria decision-support systems improve supplier ESG ranking accuracy by approximately 15-25% relative to expert-only assessments.
“AI-based multi-criteria decision-support systems improve supplier ESG ranking accuracy by approximately 15-25% relative to expert-only assessments and enable earlier identification of ESG-related risks, shortening response times by 20-40% compared to periodic manual audits.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Conclusions (Table 8; page unavailable) identify sustainable supplier selection as a governance theme, but do not report supplier ESG ranking accuracy. Searches for “15–25%,” ranking accuracy, multi-criteria decision support, and expert-only assessments found no comparison supporting the claimed improvement. (LLM, conf=0.74)
Experimental — accuracy not yet measuredAI-supported monitoring and predictive maintenance contribute to 15-25% reductions in workplace incidents.
“In operational contexts, AI-supported monitoring and predictive maintenance contribute to 15-25% reductions in workplace incidents, while AI-driven supplier monitoring systems increase the detection of labor and safety-related non-compliance by 30-50%, enabling faster corrective actions and more effective social governance across supply chains.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
The Manufacturing Environmental Sustainability section mentions production monitoring, scheduling, and worker well-being, but does not link AI-supported monitoring or predictive maintenance to fewer workplace incidents. Searches for predictive maintenance, workplace injuries, and the 15–25% figure found no passage reporting that reduction; no page number was available in our copy. (LLM, conf=0.74)
Experimental — accuracy not yet measuredAI-based quality inspection and anomaly detection systems reduce product defect rates by approximately 20-40% compared to manual inspection.
“AI-based quality inspection and anomaly detection systems reduce product defect rates by approximately 20-40% compared to manual inspection processes, thereby improving product safety and consumer protection.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [46]
Section 3.1 reports percentages describing the reviewed articles, not changes in product defect rates. The manufacturing discussion mentions production monitoring but does not report an AI-versus-manual inspection comparison. Searches for defect, inspection, and the 20–40% figure found no passage supporting the attributed result. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Islam, M. M. Z., & Hasan, M. M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0216-2.ch005
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Chakraborty, A., & Rani, M. V. (2026) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2026.3671804
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Karunarathna, K., & Kavirathna, C. (2026) · International Research Conference on Smart Computing and Systems Engineering (SCSE) · doi:10.1109/scse70081.2026.11499934
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Khamisu, M. S., Khandelwal, N., & Paluri, R. (2026) · Discover Sustainability · doi:10.1007/s43621-026-03104-0
Rizwan, M., & Nkiko, C. M. (2026) · International Conference on AI Innovations and Industry (ICAIII) · doi:10.1109/icaiii69475.2026.11521224
Basumatary, R., & Hajoary, D. (2026) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom70271.2026.11526146
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Pescada, S. S. P. V., & Teixeira, F. J. C. E. S. N. (2026) · Journal of Modelling in Management · doi:10.1108/jm2-06-2025-0320
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- Authors
- Hao, X., & Demir, E.
- Affiliations
- Cardiff University (GB)
- Khalifa University of Science and Technology (AE)
- Venue
- Journal of Modelling in Management
- Publisher
- Emerald
- Published
- 2023-07-17
- Volume
- 19
- Issue
- 2
- Pages
- 605-629
- ISSN
- 1746-5664 (ISSN-L)
- OpenAlex
- W4384199462
- Cited by
- 92
- References
- 102
- Citations by year
- 2026: 33 · 2025: 37 · 2024: 21 · 2023: 1
- FWCI
- 16.95
- Type
- journal article
- Language
- en
- Open access
- green
- orca.cardiff.ac.uk
- Locations
- Journal of Modelling in Management (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion, cc-by-nc)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Strategy and Management › Sustainable Supply Chain Management
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- artificial intelligence adoption, supply chain management, supply chain decision-making, environmental, social and governance, AI adoption barriers, supply chain sustainability, supply chain risk management, circular economy, data security and privacy, ethical AI, human-AI decision-making, sustainable supplier selection
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