Improvement in the purchase of imported goods through machine learning models for intelligent decision making

Abstract

Demand planning related to making purchases of SKUs to maintain the SLA given by the company's strategy and thus avoid stock breaks has an important role in the operation of the supply chain and the company's operation. Demand forecasts based on qualitative methods and manual methods based on historical data obtained impact on production planning, consequently, the fulfillment of the products the customer requires. The objective of defining and implementing a purchasing recommender has been raised based on the machine learning model that more effectively adapts to variations in demand for products classified under an ABC model. Scikit-learn libraries are used to implement demand prediction models trained with historical product information. The result is a proposed prediction model with a better confidence level than the company's current prediction model.