An Improved AI-Powered Supply Chain Model with IoT Integration to Enhance Organizational Efficiency
Background: Supply chain management has undergone a revolution thanks to the incorporation of cutting-edge technology like artificial intelligence (AI) and the internet of things (IoT) in today's global business operations. A paucity of literature exists that comprehensively establishes the impact of AI-powered supply chain models with IoT integration that contributes to supply chain resilience and supply chain performance and how it brings about operational performance efficiency. Aim: This study is based on the technology organization environment framework and the resource base theory of the firm in the business domain that explored the impact of an AI-powered Supply chain model that with the integration of IoT and other factors brings about organization operational efficiency. Method: The study enhanced an AI supply chain model by addition of more factors; thus eight (8) factors were included, classified into external, instantaneous and temporal factors. The external factors are the input factors to the model, they are the independent variables, the instantaneous otherwise known as the mediating factors are time bounded factors with no delay while the temporal factors are the dependent variables, and they are also time-bounded factors with delay. Nodes was used to represent the factors and the casual relationship of the identified factors was represented in form of flow arrows to show its connectivity. This was how the conceptual model was derived. Results: The inclusion of the eight factors resulted in a robust AI-powered supply chain model, used to influence important decisions in business organizations.
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