Applications of AI Operations in the Management and Decision-Making of Supply Chain Performance

Artificial intelligence (AI) is becoming more pervasive in academic study, with repercussions for many disciplines, including business. Moreover, many companies have made progress in incorporating AI technology into their supply chain processes in an effort to achieve sustainability. Artificial intelligence will undoubtedly have far-reaching consequences beyond the factory floor. AI can reduce the negative environmental effects of supply chains and increase their sustainability by increasing operational efficiencies. For instance, by optimizing truckloads and delivery routes to minimize fuel consumption while transporting supplies, ML-trained models can assist enterprises in lowering their energy usage. AI is used by businesses to improve worker safety, estimate demand for particular parts and components, track inventory, manage warehouse capacity, optimize shipping and delivery, and help maintain the integrity of transaction records across international supply chains. In business parlance, this term encompasses any division that has a role in the creation, distribution, and promotion of a company's wares. This means that companies will need engineering business managers that are up-to-date on the technology trends that might affect their industry and workplace. Both of these domains may be affected by the proposed modifications. This research provides a framework that may be utilised by practitioners as decision-making tools, outlining steps to take before and after using AI strategies in engineering operations. In this study, we offer the framework. Throughout the design process, the challenges, barriers, and opportunities associated with AI deployment were considered.