Application of machine learning techniques for cost estimation of engineer to order products

Abstract

Cost engineering capabilities are becoming increasingly important for the competitiveness of industrial firms, especially for engineer to order products (ETOPs). Despite this relevance, the literature on the use of advanced data-driven methodologies, such as machine learning (ML), for early cost estimation (CE) of ETOPs is quite sparse. Furthermore, ML has still seen little use in real industrial applications due to several challenges. Accordingly, the objective of this paper is threefold: (a) to develop a solid early CE approach for ETOPs, including feature selection; (b) to investigate the benefits of adopting ML for ETOPs’ CE; (c) to identify how ML can be introduced into real industrial context with little knowledge on ML. Long action research has been carried out with a large industrial company that produces Oil & Gas ETOPs. We observed how ML facilitates the exploration of the relationships between the choices of early design stages and the CE. ML algorithms also allowed to both capture the high variability of the data and test different combinations of cost drivers in very effective ways. The project resulted in an accurate CE framework with an iterative feature selection process and an approach for introducing ML into a real industrial context.

KEYWORDS:

• Machine learning
• cost estimation
• engineer to order
• complex industrial goods
• cost engineering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data not available due to legal restrictions.

Additional information

Notes on contributors

Mario Rapaccini

Mario Rapaccini, Ph.D., Mech. Eng. is a professor of Business Strategy and of Innovation Management at the School of Engineering, University of Florence (UNIFI, Italy). His research focuses on digital innovation and servitisation of manufacturing companies. He is a member of the Faculty Staff of Scuola Sant’Anna, of Polytechnic of Milan and of Pisa University. He is the Director of the ASAP Interuniversity Research Center, the most important community on servitisation. He collaborates with global companies such as Ricoh, Canon, Leonardo, Electrolux, Engie, Tim, General Electric, Baker Hughes, in research, education and consultancy initiatives.

Veronica Loew Cadonna

Veronica Loew Cadonna MsC in Mechanical Engineering. She graduated at the University of Florence (UNIFI, Italy). Her interests involve cost estimation, production data analytics and process automation. She is currently working as COO’s assistant in an electronic company.

Leonardo Leoni

Leonardo Leoni is a Ph.D. student in Smart Industry, a course held by the University of Florence (UNIFI, Italy), University of Pisa (UNIPI, Italy) and University of Siena (UNISI, Italy). Currently, he is studying topics related to operations management, safety, risk and reliability analysis of industrial equipment. His research areas of interest include maintenance, industrial safety and risk management and plant engineering.

Filippo De Carlo

Filippo De Carlo is Associate Professor of Industrial Systems at the Department of Industrial Engineering (DIEF) at UNIFI (Italy) from 2006. He has been and he is involved in several European and National Projects. He is the author and co-author of more than 50 publications in international congresses and journals. He is the Associated Editor of an international journal. His research topics include industrial plant engineering, maintenance, industrial safety, and risk and energy management.