Supply Chain Optimization

Intelligent Decision-Making


The traditional approach to supply chain management attempts to forecast future demand for resources based on historical data. Supply chain managers then add a safety stock to these levels to prevent stockouts and delays in production. These safety levels can be anywhere from weeks of extra supply to twice normal demand depending on the variation in needs for the product. This inventory level supports the overall production plan including stock levels in individual locations and transportation plans to meet manufacturing needs. Moving and holding extra inventory is a significant expense for manufacturers who are constantly looking for ways to improve profitably.


AI based supply chain optimization can utilize a variety of factors including historical data, environmental data and recent trends to predict optimal resource needs at each stage of production. AI models can also be used to find anomalous behaviour in current resource utilization and pinpoint areas for further investigation by supply chain managers. According to McKinsey, 61% of manufacturing executives report decreased costs, and 53% report increased revenues as a direct result of introducing Ai in supply chain.

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