Artificial intelligence enabled supply chain resilience: insights from FMCG industry
Devnaad Singh et al.
What the paper says
Purpose The purpose of this study is to investigate and develop capabilities to make supply chains resilient using qualitative analysis of fast-moving consumer goods (FMCG) industry located in India. In particular, authors aim to propose a framework to make supply chains resilient by infusing artificial intelligence (AI). Design/methodology/approach The authors acquired supportive data by conducting semi-structured interviews with 25 FMCG supply chain professionals during 2023. Using open, axial and selective coding approaches, the authors mapped and discovered the themes that constitute the essential elements of AI-enabled supply chain resilience. Findings The research findings reveal that supply chain capabilities are useful for mitigating the disruptions impact when infused with AI. The authors’ analysis underscore four principal domains in which AI is poised to enhance the resilience of supply chains. This study delves into four key capabilities of interest, namely: Routing Optimization, Efficiency, Periodic Monitoring and Demand Forecasting. The result of this study is the proposed framework which shows the impact of different AI-powered capabilities on supply chain which builds resilient supply chains. Research limitations/implications Infusing AI to different supply chain capabilities appears to be a successful way for making FMCG supply chains resilient. Only the supply chain capabilities cannot overcome the impact of disruptions, but the use of AI helps professionals and policymakers to better respond to disruptions. Originality/value Few studies demonstrate the impact of advanced technology in building resilient supply chains. To the best of the authors’ knowledge, no earlier researcher has attempted to infuse AI into supply chain capabilities to make them resilient with empirical studies with the theoretical framework of Dynamic Capability View (DCV).
3 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.32 × 0.4 = 0.13 |
| M · momentum | 0.57 × 0.15 = 0.09 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.