Applying machine learning (ML) in the supply chain: predict the quality defect percentage in delivery from supplier
DOI:
https://doi.org/10.20397/2177-6652/2024.v24i1.2564Palabras clave:
Machine learning, quality, defects, suppliersResumen
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.
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