A combined AHP-DEMATEL model approach to build tech-enabled resilient supply chain

Purpose Supply networks rarely operate in a stable, steady state. Thus, businesses must carefully plan for unpredictable events to mitigate risks. Consequently, this research investigates how artificial intelligence (AI), machine learning (ML) and big data analytics (BDA) improve supply chain resilience. Design/methodology/approach This study utilizes the integrated analytic hierarchy process (AHP) – decision-making trial and evaluation laboratory (DEMATEL) as the resolution technique to achieve the objective. By undertaking semi-structured interviews with supply chain experts from the fast moving consumer goods (FMCG) industry, the authors gathered useful information for AHP-DEMATEL analysis. The results obtained are validated through a qualitative survey approach. Findings Sub-factors used in the study were extracted from the extensive literature review. The AHP method was employed to prioritize the factors and sub-factors wherein AI comes out to be the most prominent technology to bring resilience in the supply chain by improving efficiency, followed by demand forecasting. The DEMATEL method bifurcates the sub-factors into cause and effect. Originality/value This study adds to the supply chain domain by identifying sub-factors that can be better managed by particular technologies, i.e. AI, ML and BDA.