Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis
Lai‐Wan Wonga, Garry Wei‐Han Tanb c d, Keng‐Boon Ooib c d e, Binshan Linf, Yogesh Kumar Dwivedig h i j✉
- aSchool of Computing and Data Science, Xiamen University Malaysia, Sepang, Malaysia
- bNanchang Institute of Technology, Jiangxi, People’s Republic of China
- cUCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia
- dNanchang Institute of Technology, Jiangxi, People's Republic of China
- eCollege of Management, Chang Jung Christian University, Tainan City, Taiwan
- fCollege of Business, Louisiana State University, Shreveport, LA, USA
- gDepartment of Management, Symbiosis Institute of Business Management, Pune &
- hSymbiosis International (Deemed University), Pune, Maharashtra, India
- iEmerging Markets Research Centre (EMaRC), School of Management, Swansea University – Bay Campus, Swansea, UK
- jDepartment of Management, Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed University), Pune, Maharashtra, India
- ✉Corresponding author
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Abstract
This study posits that the use of artificial intelligence (AI) enables supply chains (SCs) to dynamically react to volatile environments, and alleviate potentially costly decision-makings for small-medium enterprises (SMEs). Building on a resource-based view, this work examines the impact of AI on SC risk management for SMEs. A structural model comprising of AI-risk management capabilities, SC re-engineering capabilities and supply chain agility (SCA) was developed and tested based on data collected from executives, managers and senior managers of SMEs The main methodological approach used in this study is partial least squares-based structural equation modelling (PLS-SEM) and artificial neural network (ANN). The results identified the use of AI for risk management influences SC re-engineering capabilities and agility. Re-engineering capabilities further affect and mediate agility. PLS-SEM and ANN were compared and the results revealed consistency for models A and B. Current levels of demand uncertainties in the SC challenges managers in making complex trade-off decisions that require huge management resources in very limited time. With AI, it is possible to model various scenarios to answer crucial questions that archaic infrastructures are not able to. This study combines a multi-construct agility concept and identified non-linear relationships in the model.