Mehdi Maasoumy: Stochastic Optimization of Inventory at Large-scale Supply Chains скачать в хорошем качестве

Mehdi Maasoumy: Stochastic Optimization of Inventory at Large-scale Supply Chains

Companies managing physical goods face growing supply chain challenges, given accelerated business cycles, stochasticity in supply and demand, and complex, interdependent, global supply chains. Enterprises often use inventory buffers – for raw materials, work-in-process goods, and finished products – as a way to help them manage service levels, and on-time and in full (OTIF) metrics. However, holding excess inventory often complicates business operations, and can lock up billions of dollars of capital that could be otherwise deployed. Most manufacturing companies have historically deployed advanced Manufacturing Resource Planning (MRP) software, however, most commercially available MRP solutions do not result in optimal solutions for modern enterprises. The C3 AI Inventory Optimization application fundamentally re-formulates and solves the supply chain and inventory problem as a constrained stochastic optimization problem. Our goal is to find the optimal re-order parameters that minimize inventory levels subject to a pre-defined service-level constraint and other operational constraints. The output of our approach can be the optimal placement of orders or the optimal inputs into an MRP system to ensure that the MRP heuristic then results in the right material levels and production operations. Using C3 AI Inventory Optimization, we have helped our customers achieve inventory reductions of 30-50 percent‚ saving hundreds of millions of dollars at major enterprises at a global scale.