The AI-Driven Supply Chain: Optimizing Engine Part Logistics For Maximum Efficiency

Abstract

The world operates at a pace that is always too fast and too much for its good. The industry supply chains are no exception to that trend. With the high speed often necessary for industry and parts manufacture, the logistics of parts delivery are a critical but often overlooked area for potential improvement. The manufacturing company in this case study is a large automobile engine plant, which has slowly evolved into a pretty complex manufacturing and delivery system with many different teams and systems involved. The focus here will be upon a specific sub-process: the delivery and movement of individual engine components between a system of on-site 'supermarkets'. The current process relies heavily on a few employees' knowledge and experience. It needs metrics and data, making it easier to measure efficiency. Many different part types have specific packaging and storage requirements, leading to mistakes and damage. AI tools are being considered to improve logistics operations, and OptQuest is being used to determine its suitability. This will be compared to established statistical methods