Work

Predictive models for inventory optimization: a machine learning application for demand forecasting at a construction supplies distributor

Year: 2026

Type: article

Abstract: Inventory management is a critical task for distribution companies, as forecasting errors can lead to excess inventory, higher storage costs, or stockouts. This practice & policy paper describes the development and deployment of a machine learning-based predictive model designed to optimize inventory management for a medium-sized Brazilian construction supplies distributor. Using the Amazon Forecast and Amazon SageMaker Canvas platforms, the study demonstrated how advanced demand forecasting techniques reduce stockouts and enhance operational efficiency. The research compared various predictive algorithms, assessing their performance with metrics such as RMSE, MAPE, MASE, and WAPE. The results showed that the implemented model achieved approximately 99.31% forecast accuracy, offering significant benefits like fewer stockouts, optimized inventory levels, and improved purchasing planning. This practice study provides technical and managerial recommendations for companies seeking to predict their inventory needs accurately, demonstrating the machine learning capabilities for Supply Chain Management.

Source: Future Business Journal

DOI: 10.1186/s43093-026-00807-8

Authors: Diocélio Dornela Goulart, Rodrigo Baroni de Carvalho, Mariana Almeida Henriques, Bárbara Kawany Gonçalves Nunes Carvalho

Institutions: Pontifícia Universidade Católica de Minas Gerais, Fundação Dom Cabral

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico