Developing Accurate Predictive Model Using Computational Intelligence for Optimal Inventory Management

Abstract

People are all currently living in the world where data has changed how company think, act and plan. Data, if used correctly, might be able to become a company's sharpest weapon in fighting the competition with other companies. Inventory cost is one of the most burdening costs in the food and beverage industry with the items like degradable raw materials or fresh ingredients. If not managed correctly might become a waste causing loss to the company. Degraded ingredients also might lower the overall food quality which might result in unsatisfied customers. Managing inventory, however, is not as easy as it seems, especially with the traditional method. This paper focuses on development of accurate predictive model using computational intelligence for optimal inventory management with a case study of restaurant ingredient management. Several machine learning algorithms like linear regression, multi-layer perceptron, random tree, random forest, and model trees were utilized to build accurate predictive models from time series data of the restaurant inventory. With good prediction system using computational intelligence, the inventory cost and wasted ingredients can be significantly reduced, which this eventually maximizes the profit.