Study Links AI and Digital Tools to Supply Chain Sustainability

Researchers Rym Belgaroui and Kaies Samet have presented a comprehensive framework examining how the adoption of digital technologies influences sustainable supply chain management. Published in the journal Discover Artificial Intelligence in October 2026, the study investigates the complex interplay between Industry 4.0 technologies and sustainability performance. The authors argue that while digital tools offer significant opportunities for improving environmental and social outcomes, their effectiveness is not automatic. Instead, the benefits of these technologies depend heavily on specific organizational mechanisms and human capabilities. This research moves beyond a simple technology-centric view to explore the underlying processes that translate digital adoption into measurable sustainability gains.

The study focuses on three key digital technologies: the Internet of Things, artificial intelligence, and blockchain. Each of these technologies serves a distinct function within the supply chain ecosystem. IoT devices primarily enable the sensing and real-time collection of operational data, providing a continuous stream of information about physical processes. Blockchain technology strengthens traceability, verification, and information integrity across supply chain transactions, ensuring that records are secure and verifiable. Artificial intelligence complements these data-generating technologies by processing large and heterogeneous datasets, identifying patterns, and generating predictions that support optimization and decision-making.

Central to the proposed model is the concept of supply chain transparency. The researchers posit that digital technology adoption enhances sustainability performance indirectly by improving the availability, accessibility, traceability, and verifiability of supply chain information. This transparency acts as a mediating mechanism, allowing firms to monitor environmental impacts, verify ethical work conditions, and optimize resource utilization. By making information more visible and accountable, digital tools create the informational conditions necessary for effective sustainability-oriented decisions. The study suggests that the value of digitalization lies not just in operational efficiency, but in the ability to reveal and manage sustainability-related data across the entire supply chain.

However, the study highlights that technology alone is insufficient to guarantee improved sustainability outcomes. The presence of a skilled workforce is identified as a critical moderating factor that strengthens the relationship between digital transformation and sustainability performance. Employees must possess the digital, analytical, and managerial skills required to operate these technologies, interpret complex data, and translate insights into operational actions. Without adequate human capital, firms may struggle to leverage their digital investments effectively. The authors emphasize that a socio-technical perspective is essential, as the performance outcomes of digitalization result from the dynamic interplay between technological systems and the people who use them.

To test these hypotheses, the researchers conducted an empirical quantitative study based on survey data collected from 250 manufacturing firms. The sample was selected using a stratified sampling method to ensure diversity across multiple industry sectors and geographic regions. Data were gathered through an electronic questionnaire distributed to senior managers responsible for supply chain, operations, or sustainability functions. The questionnaire assessed the extent of digital technology adoption, organizational capabilities, and sustainability performance metrics. All dimensions were operationalized using 5-point Likert scales, modified from validated scales in prior research to suit the specific context of the study.

The data analysis revealed strong construct validity and high internal consistency across all measured variables. Factor analysis indicated that the scales explained more than 75% of the variance, and Cronbach’s alpha values ranged from 0.895 to 0.933, confirming the reliability of the instruments. Correlation analysis showed significant positive associations among the core research variables, providing initial support for the proposed relationships. Hierarchical multiple regression analyses were then employed to test the direct, mediating, and moderating effects specified in the conceptual framework. These statistical procedures allowed the researchers to isolate the specific contributions of digital adoption, transparency, and workforce skills to sustainability performance.

The results confirmed that digital technology adoption has a significant positive association with environmental sustainability performance. The broader digital technology adoption construct, which encompasses the overall use of Industry 4.0 technologies, reported a standardized coefficient of 0.615. When examining artificial intelligence adoption specifically, the study found a standardized coefficient of 0.545, with an adjusted R-squared of 0.488. These findings indicate that AI constitutes a meaningful component of the digital-sustainability relationship, particularly through its analytical and optimization capabilities. The study distinguishes between the broad infrastructure provided by IoT and blockchain and the specialized analytical power of AI, suggesting that both are necessary for a comprehensive digital transformation strategy.

Furthermore, the analysis provided evidence for the partial mediation effect of supply chain transparency. This finding suggests that while digital technologies offer direct operational benefits, a substantial portion of their value is realized by enabling greater visibility and accountability across the supply chain. The strong moderating effect of a skilled workforce also supported the hypothesis that human capital is a pivotal strategic variable. Firms with a highly skilled workforce are better positioned to exploit technological capabilities and integrate digital information into decision-making. Conversely, organizations with inadequate workforce capabilities may see diminished returns on their technology investments, highlighting the importance of parallel investments in training and recruitment.

The study acknowledges several limitations, primarily stemming from its cross-sectional design. While the survey provides empirical evidence for the proposed relationships, the single-point-in-time data collection limits the ability to establish temporal precedence and definitive causal relationships. The authors suggest that future research should employ longitudinal designs or complementary qualitative approaches to examine how these dynamics evolve over time. Additionally, the focus on manufacturing firms in specific geographic regions may limit the generalizability of the findings to other industries or contexts. Despite these limitations, the study offers valuable insights for managers and policymakers seeking to leverage digital technologies for sustainability objectives.

In conclusion, the research underscores that sustainable supply chain performance is not solely a function of technological adoption but a result of the interaction between digital tools, transparency mechanisms, and human capabilities. Managers are advised to adopt a holistic vision that explicitly targets sustainability improvements and to match their digital investments with parallel investments in human capital. By understanding the mechanisms through which digital transformation drives sustainability, firms can better navigate the challenges of the Industry 4.0 era and achieve long-term value creation while meeting their environmental and social responsibilities.

Subject of Research: Impact of AI and digital transformation on sustainable supply chain performance

Article Title: Artificial intelligence and digital transformation for sustainable supply chain performance

Article References: Belgaroui, R., & Samet, K. (2026). Artificial intelligence and digital transformation for sustainable supply chain performance. Discover Artificial Intelligence, 6(1), Article 1615. https://doi.org/10.1007/s44163-026-02524-1

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02524-1

Keywords: Artificial Intelligence, Supply Chain Management, Sustainability, Digital Transformation, Industry 4.0, Blockchain, Internet of Things, Workforce Skills, Manufacturing, Environmental Performance, Artificial, intelligence