Intelligent cost estimation by machine learning in supply management: A structured literature review

Abstract

In the automotive industry, cost estimation of components to be purchased plays an important role for price negotiations with suppliers and, therefore, for cost control within the supply chain. While traditional bottom-up cost estimation is a very time consuming and know-how intensive process, intelligent machine learning methods have the potential to significantly reduce the effort in the cost estimation process. In this paper, a literature review on intelligent cost estimation methods for parts to be procured in the manufacturing industry is carried out by text mining. Following the results of this literature review, building blocks for an intelligent cost estimation system are outlined that comprise cost estimation methods, dimensionality reduction methods, methods for multi-level cost estimation, and methods for interpreting the results of the cost analysis. Regarding cost estimation methods , Artificial Neural Networks and Support Vector Machines outperform established linear regression algorithms. Dimensionality reduction methods like Correlation Analysis or Principal Component Analysis are rarely studied . Nevertheless, they contribute a lot to the reduction of expensively provided input parameters for cost estimation. Methods for multi-level cost estimation, that support cost prediction of parts and assemblies following the construction plan of a vehicle, and methods for interpretation of intelligent cost analytics cannot be found at all in literature. Consequently, in this paper corresponding approaches are derived from the areas of Multitask Learning and Explainable Machine Learning. Finally, a combination of methods considered most suitable for predictive analytics to estimate procurement costs is presented.