Global supply chains get sustainability playbook powered by AI and expert consensus

The study identifies a clear pattern across all four dimensions of sustainable supply chain performance. In the environmental category, emissions monitoring and reduction emerges as the top priority, reflecting the growing need to meet regulatory standards and decarbonization targets. Closely following are energy efficiency and sustainable product design, both of which emphasize intelligent resource use through automation and AI-supported design processes.

Global supply chains are under increasing pressure to deliver not just efficiency but resilience and responsibility. To address this crisis, companies are looking to digital transformation as a strategic route to resilient and sustainable supply chains. But with dozens of competing technologies and sustainability metrics in play, decision-makers face a critical question: Which digital enablers should be prioritized to align sustainability with performance?

A new study published in Sustainability, titled “Enhancing Sustainable Global Supply Chain Performance: A Multi-Criteria Decision-Making-Based Approach to Industry 4.0 and AI Integration,” addresses this challenge by introducing a Best–Worst Method (BWM) decision framework. Developed by researchers Dalia Štreimikienė, Ahmad Bathaei, and Justas Streimikis, the model evaluates and ranks 20 sustainability indicators across environmental, operational, strategic, and social dimensions, using expert judgments from 37 specialists in digital transformation and supply chain management.

Which sustainability indicators lead a digitally transformed supply chain?

The study identifies a clear pattern across all four dimensions of sustainable supply chain performance. In the environmental category, emissions monitoring and reduction emerges as the top priority, reflecting the growing need to meet regulatory standards and decarbonization targets. Closely following are energy efficiency and sustainable product design, both of which emphasize intelligent resource use through automation and AI-supported design processes. In contrast, waste-to-energy and circular economy integration were assigned lower weights, possibly due to higher implementation barriers or longer ROI cycles.

On the operational side, supply chain traceability ranks highest. This indicator points to the critical importance of visibility in modern logistics, enabling real-time product tracking via blockchain and IoT. Smart inventory management and predictive maintenance follow, highlighting AI’s role in reducing stockouts, improving equipment uptime, and minimizing operational waste. Surprisingly, logistics optimization and product lifecycle management receive lower weights, suggesting that while important, they may not deliver the same level of near-term sustainability impact under current digital conditions.

The strategic domain underscores supply chain resilience as the single most important factor, capturing the urgent need for adaptive capacity in the face of disruptions, from pandemics to geopolitical unrest. Risk management and supplier collaboration are also prioritized, while regulatory compliance and real-time decision-making rank lower, suggesting that while these functions are essential, they serve more as enablers than primary strategic levers.

Finally, in the social dimension, ethical sourcing is identified as the dominant concern, with a substantial weight of over 70%. This underscores the importance of transparency, labor rights, and environmental accountability in procurement practices. Human–robot collaboration and customer engagement follow but are viewed more as downstream benefits of socially conscious supply chain design.

How was the prioritization framework built?

The study employs the Best–Worst Method (BWM), a multi-criteria decision-making (MCDM) tool designed to elicit consistent, expert-weighted rankings with fewer pairwise comparisons than conventional models like AHP or TOPSIS. Through a structured survey, 37 international experts evaluated 20 sustainability indicators derived from current literature and refined through expert review. The BWM approach was selected for its ability to reduce cognitive load while ensuring robust consistency across expert responses—confirmed in the study by a 0.0 consistency ratio across all groups.

The expert panel, selected through purposive sampling, represented a cross-section of academia, consultancy, and industry, with expertise spanning manufacturing, logistics, digital transformation, and sustainability governance. Participants averaged 12.4 years of experience, ensuring judgments were rooted in both theory and real-world application.

Sensitivity analysis confirmed the model’s reliability. Even when input weights for key indicators like sustainable product design were perturbed, rankings of high-priority factors such as emissions monitoring and energy efficiency remained stable. This demonstrates the framework’s robustness and practical utility for guiding strategic decisions under uncertainty.

What do these rankings mean for supply chain strategy?

The study provides a clear and actionable roadmap for companies seeking to align digital investment with sustainability goals. For policymakers, the results highlight where regulatory support or public-private collaboration can yield the highest impact, particularly in areas like emissions tracking technologies, ethical sourcing systems, and resilient infrastructure.

For corporate strategists, the ranked indicators serve as a data-driven guide for prioritizing digital adoption. For instance, investing in AI-based traceability platforms and predictive maintenance tools can produce both environmental and economic dividends. Similarly, initiatives that improve supply chain resilience or support transparent procurement can help firms meet ESG reporting requirements while safeguarding brand trust and continuity.

Crucially, the findings emphasize that sustainability is not monolithic. Depending on the company’s sector, geography, or maturity level, the relative importance of indicators may vary. The BWM framework allows organizations to adapt the weighting process to their context, making it a customizable decision-support tool.

The study also contributes to academic and practical discourse by bridging the gap between digital transformation and sustainability in supply chains - two areas that have often been treated in isolation.