Manufacturing time estimation for offer pricing: A machine learning application in a French metallurgy industry

Abstract

- In today's market where many companies are competing for the same opportunities, a quick, accurate, and reliable estimation of the offers' prices is essential for the suppliers. This allows them to answer to more solicitations and to remain competitive. Many suppliers use the manufacturing time as key parameter to define their offers' prices. However, in Make-To-Order (MTO) and Engineer-To-Order (ETO) contexts, with the increasing number of demands, the huge diversity of the products and the manufacturing process complexity, estimating the manufacturing time is challenging. To face this issue, this article proposes a new approach for the manufacturing time estimation using machine learning. Its originality relies on the exploitation of structured tabular data, unstructured textual data and domain knowledge. It also proposes two strategies that allow to choose the best estimation model for each product family, while minimizing the number of estimation models and considering product families with fewer available data. All the proposals have been applied to a real industrial case in a French small metallurgy industry, and the results have shown their applicability and effectiveness. Among the plethora of machine learning techniques implemented, CatBoost, which is a member of the family of Gradient Boosted Decision Trees (GBDT) ensemble techniques, provided the best results.