Big Data and Machine Learning in Cost Estimation : A Case Study

This paper presents a case study on the applicability of machine learning and big data technology for product cost estimation, using data on the material cost data of passenger cars. The study provides contributions on six research aspects. First, we show what machine learning algorithms are appropriate when dealing with product cost estimation of highly complex products with more than 2,000 parts and hundreds of cost drivers. Second, our case study contributes to the literature by providing a novel approach to increase the predictive accuracy of cost estimates of subsequent product generations. Third, we show that the accuracy is up to 3.5 times higher when using big data compared to an intermediate size of data. Fourth, machine learning can outperform cost estimates from cost experts, or produce at least comparable results, even when dealing with highly complex products. Then, we add to the current literature by evaluating use cases, issues, and benefits of machine learning and big data from the perspective of cost experts. Specifically, the case study shows that machine learning can reliably select the most important cost drivers (fifth aspect) and calculate the average cost of cost drivers over thousands of product configurations (sixth aspect). However, cost experts must be knowledgeable about the product and remain careful when interpreting machine learning outcomes as they can yield misleading outcomes for some exceptional cases. In conclusion, machine learning and big data empirically proved to be able to generate additional values in many aspects for managing cost during the early phase of new product development