Inventory management and cost reduction of supply chain processes using AI based time-series forecasting and ANN modeling

Abstract

Reducing waste, and therefore cost, within a supply chain can be a very challenging process due to the large number of variables involved. One of the biggest wastes that are usually present in a supply chain are unnecessarily high inventory costs and shortage costs which are caused by errors in demand forecasting. A high variance between the forecasted demand and the actual demand results in costs that could have been avoided. To eliminate that waste, we developed a model that utilizes artificial neural network to accurately forecast the demand. The model performs forecasting analysis based on multilayer feed-forward neural network with backpropagation. The use of machine learning can assist with the rapid changes in customer demand. Our holistic solution will minimize the supply/demand mismatch and its associated costs and consequently increase profit margins