Predictive algorithms for supply chain management: a comprehensive approach to forecasting delivery times and managing risk

Abstract

The article delves into the challenges of delivery time management in today's business landscape. The authors underscore the need for precise delivery time forecasts, a key factor in maintaining a competitive edge and meeting customer expectations. They outline various methods for estimating the time of a selected commodity based on historical data, and stress the necessity of modern tools that can adapt to the intricate web of factors influencing delivery times and facilitate swift responses to changes. The following article presents an innovative delivery time forecasting application that integrates advanced predictive algorithms with historical data, current data, and external factors affecting the delivery process. The application was developed to provide more accurate delivery time forecasts and optimize logistics processes. Through advanced technologies, it can consider even the most complex scenarios and changes, allowing companies to plan and manage their logistics operations more effectively.

Keywords

• classification models
• regression models
• supply chain
• forecasting delivery times
• xgboost
• random forest
• lightgbm