Applying machine learning (ML) in the supply chain: predict the quality defect percentage in delivery from supplier

Luiz Eduardo Simão; Andre Moraes dos Santos

doi:10.20397/2177-6652/2024.v24i1.2564

Authors

Luiz Eduardo Simão Universidade do Vale do Itajaí - Univali Programa de Mestrado profissional em Administração - PMPGIL https://orcid.org/0000-0002-6526-9348
Andre Moraes dos Santos Universidade do Vale do Itajaí - Univali Programa de Mestrado profissional em Administração - PMPGIL https://orcid.org/0000-0002-8605-5234

DOI:

https://doi.org/10.20397/2177-6652/2024.v24i1.2564

Keywords:

Machine learning, quality, defects, suppliers

Abstract

Study Objective: The objective of this article was to describe the process of developing and implementing an ML model in the supply chain to predict the percentage of defective parts prior to supplier delivery.

Methodology/Approach: The methodology used was based on an action research project using a case study approach that described the steps divided into five phases: acquiring data, preparing data, analyzing data, communicating results, and applying results. The machine learning model applied the supplier's performance data related to the supply chain between the years 2021 and 2022.

Originality/Relevance: Despite the growing interest in ML techniques by many companies, challenges remain in known situations and potential applications in building explainable business and decision models. Thus, there is little empirical evidence of the relationship between effective implementation of machine learning (ML) techniques and their real effect on supply chain performance.

Main Results: The results show that, by employing the proposed method, inspection volumes can be reduced by more than 30%, and therefore, economic advantages can be generated by reducing inspections on material receipt.

Theoretical/Methodological Contributions: The main contribution was to demonstrate how the application of ML models can have a positive impact on supplier management process performance. Additionally, the article also describes how to use ML algorithms without the need to write code. Thus, the article can be a possible reference for organizations wishing to use similar ML approaches in their supply chains and improve the quality levels of their suppliers' performance.

Author Biography

Luiz Eduardo Simão, Universidade do Vale do Itajaí - Univali Programa de Mestrado profissional em Administração - PMPGIL

Graduação em Engenharia Química pela FURB - Fundação Universidade Regional de Blumenau, Mestre em Engenharia de Produção e Sistemas pela UFSC - Universidade Federal de Santa Catarina e Doutor em Engenharia de Produção e Sistemas pela UFSC - Universidade Federal de Santa Catarina, com Doutorado Sanduíche no Fraunhofer Institute IML - Institute for Material Flow and Logistics,Alemanha. Ocupou cargos de gerencia na área de qualidade e produção em algumas empresas industriais. Foi consultor de empresas do SENAI SC, onde atuou na coordenação de projetos de melhoria na eficiência e eficácia em várias empresas industriais, nas áreas de planejamento e controle da produção, otimização e simulação de sistemas de produção e logística, gestão de estoques e implantação programas utilizando o pensamento enxuto (lean). Atualmente, é professor dos cursos de graduação em logística, cursos de MBA para Global Traders, MBA em Exportação e Importação, MBA em International Business, MBA em Gestão Internacional e Projetos Globais, MBA em Engenharia de Produção, Processo e Qualidade, nas disciplinas de gestão estratégica de operações, logística empresarial e Tecnologias em gestão da produção e operações, gestão da logística e cadeia de suprimentos. É professor titular do programa de mestrado profissional em administração - gestão, internacionalização e logística (PMPGIL) na Univali. Consultor da projetos de melhoria em sistemas de produção e logística pela i9 Consultoria & Treinamento.

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Applying machine learning (ML) in the supply chain: predict the quality defect percentage in delivery from supplier

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DOI:

Keywords:

Abstract

Author Biography

Luiz Eduardo Simão, Universidade do Vale do Itajaí - Univali Programa de Mestrado profissional em Administração - PMPGIL

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