Description: Recent research states that data analytics hold significant potential in improving environmental and social sustainability in supply chains [1][2]. However, besides statistics, literature is scant on how this potential is actually put in place. One the other hand, despite supply chains in some industries demonstrate severe social impacts [3], the social component of sustainability is even less explored.

Objectives: This research aims to link data analytics and social sustainability in supply chains. In particular, to uncover how data analytics help to identify social sustainability issues and identify the tools to do that.

Methodology: Conceptual framework development through literature review and desk research (Phase 1); Primary data collection with exemplar firms to evaluate and improve the model developed (Phase 2)

Number of students: 1-2

[1] Bag et al., 2020. “Big data analytics as an operational excellence approach to enhance sustainable supply chain performance”

[2] Dubey et al., 2017. “Can big data and predictive analytics improve social and environmental sustainability?”

[3] Sauer 2021. “The complementing role of sustainability standards in managing international and multi-tiered mineral supply chains”

[4] Marshal et al., 2015. “Environmental and social supply chain management sustainability practices: Construct development and measurement”