How big data and predictive analytics boosts supply chain sustainability

The transformative role of big data in global supply chains has long been debated, but a new peer-reviewed study offers rare insights into how predictive analytics can directly reshape the way businesses manage resources, enhance resilience, and meet sustainability targets. The research puts employees’ perspectives at the center of the debate, highlighting the organizational and cultural shifts required to fully unlock the promise of digital supply chain technologies.

Published in SAGE Open under the title “Unveiling the Impact of Big Data and Predictive Analytics Adoption on Sustainable Supply Chain Management: An Employee-Centric Perspective,” the study systematically examines how data-driven innovation is being adopted in Vietnamese industries and the broader implications for economic, environmental, and social sustainability.

How big data and predictive analytics shape sustainable supply chains

The study begins by addressing a key question: how does adopting big data and predictive analytics (BDPA) enhance sustainability in supply chain management (SCM)? The authors conducted an in-depth survey with 226 employees across Northern and Southern Vietnam, applying a robust statistical approach to map the relationship between BDPA adoption and supply chain outcomes.

Findings reveal that BDPA is positively associated with all three pillars of sustainability, economic, social, and environmental performance. Firms that have embraced predictive analytics reported improved efficiency, stronger resilience to disruptions, and reduced operational waste. By enabling demand forecasting, risk anticipation, and inventory optimization, predictive tools allow businesses to cut costs while simultaneously lowering emissions and resource use.

From an environmental standpoint, BDPA empowers companies to monitor energy consumption, reduce material waste, and minimize carbon footprints. Economically, data-driven processes enhance competitiveness, productivity, and long-term profitability. Socially, predictive analytics can also foster fairer workplace practices and align firms with international standards for ethical supply chain management.

Crucially, the research situates these findings within the context of the United Nations’ Sustainable Development Goals. The authors argue that big data can accelerate progress on SDG 8 (Decent Work and Economic Growth), SDG 12 (Responsible Consumption and Production), and SDG 5 (Gender Equality) by supporting evidence-based decisions that balance growth with social equity.

What organizational factors enable successful adoption?

While the technical benefits of big data are widely recognized, this study asserts that successful adoption depends on organizational conditions. Using the Resource-Based View (RBV) and theories of organizational learning and culture, the authors identify key enablers that determine whether firms can translate data into sustainable outcomes.

The analysis shows that organizational culture, management skills, and learning capacity are critical in shaping adoption. Firms that foster a culture of innovation, provide management with digital skills, and create environments for continuous learning are more likely to reap the full benefits of BDPA. In contrast, firms that lack these internal drivers risk underutilizing technology and failing to align predictive tools with long-term sustainability goals.

The authors note that employee readiness is a decisive factor. Since the research is employee-centric, it underscores how workforce perceptions influence adoption. Employees who feel empowered, trained, and supported by leadership are more likely to integrate data analytics into daily practices, from production planning to supplier evaluation.

In addition, the study warns that misaligned adoption, where technologies are imposed without cultural or structural adjustments, can lead to inefficiencies and missed opportunities. Therefore, investments in human capital and cultural alignment are just as important as investments in technological infrastructure.

Why the Employee Perspective Matters in Global Supply Chains

One of the most novel contributions of this research is its focus on the employee viewpoint, a perspective often overlooked in discussions of digital supply chain transformation. By highlighting how workers perceive and interact with predictive analytics, the study shows that the human dimension is indispensable to sustainable technology adoption.

Employees are not just passive users of analytics tools - they are active participants who interpret data, make operational decisions, and adapt strategies in response to insights. As such, their experiences offer a unique lens on the success or failure of big data initiatives. This approach challenges the dominant top-down narrative of technology adoption, replacing it with a more balanced model that considers both organizational leadership and employee agency.

The study further stresses that firms should consider training and communication strategies that demystify predictive analytics for employees. Doing so not only reduces resistance to new technologies but also ensures that data insights are effectively translated into practice. In global supply chains, where disruptions can cascade across borders, employee-level adoption becomes essential to maintaining resilience and ensuring sustainability commitments are met.

Limitations and future directions

While the findings are compelling, the authors acknowledge the limitations of their work. The study is based on cross-sectional data from Vietnam, meaning its conclusions may not fully capture the dynamics of BDPA adoption in other regions or over time. To strengthen the evidence base, the authors recommend future research using longitudinal data, cross-country comparisons, and multi-role analyses to explore how adoption differs across industries and organizational hierarchies.

Another recommended direction is leadership-focused research. While the employee perspective provides invaluable insights, understanding how leadership strategies interact with BDPA adoption could yield a more complete picture of sustainable digital transformation.