Data analytics for supply chain resilience: a multiple case study analysis

Abstract

Developing supply chain (SC) resilience through data analytics has emerged as an important area of research recently. However, the current literature offers a limited common understanding of the impact of data analytics enabling SC resilience across several phases of resilience development. Thus, this study aims to explore the role of data analytics in identifying potential supply chain disruptions, mitigating risks, and improving supply chain performance. We use a multiple-case study qualitative research approach to understand how firms successfully implement data analytics in their supply chain operations. The research data were collected through semi-structured interviews conducted with 7 supply chain experts from six different firms in Finland. The analysis includes investigating specific tools and techniques used, the data sources, and the types of data analyzed. In addition, this study explores the challenges firms face during the implementation of data analytics and how data analytics effectively builds supply chain resilience. The findings highlight that data analytics offers valuable insights into the supply chain and supports firms to proactively identify and mitigate risks. Furthermore, this study highlights the importance of data quality, data integration, and the need for new skills and capabilities in implementing data analytics in the supply chain. This study contributes to the emerging literature on data analytics’ role in developing supply chain resilience and offers insights into the challenges and opportunities associated with its practical implementation. This study offers several theoretical and practical implications for supply chain research and managers.