Product platform configuration decision in NPD with uncertain demands and module options

ABSTRACT

Platform-based product development is a cost-efficient approach to satisfy wide range of customer preferences. One important problem is the product platform configuration, in which two types of platform configuration are widely used, either module selection or module integration. The platform configuration based on module selection provides a broader solution space of platform selection, while module integration facilitates product platform commonality to gain economic benefits. Up to date, most research focused on the platform configuration based on module selection with a given module set. In this paper, we propose a new model to determine the optimal platform configuration for an external product family, considering both module selection and integration. The demand for the external product family is uncertain and assumed to follow normal distribution. A hybrid methodology combining simulated annealing and variable neighbourhood search is proposed. A numerical examination is carried out for the purpose of evaluation. The results show that the proposed model can provide a practically good solution. Main contributions of this research are two folds, one is the balance point between module selection and module integration in the platform configuration and the other is that we take into account the operation cost of module acquisition to the platform configuration problem.

KEYWORDS:

• Product platform configuration
• module selection
• module integration
• platform commonality
• supply chain cost

Disclosure statement

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

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 71572104].

Notes on contributors

Ting Wang

Ting Wang is currently pursuing Ph.D. research at the Department of Industrial and Systems Engineering, Keio University, Japan. He received his M.S. degree in Management Science and Engineering from Shanghai University, China in 2017. His research interests include engineering chain management and supply chain management, specifically mass customisation, product platform configuration and product family design considering supply chain management.

Jian Wang

Jian Wang is currently a professor in the department of Management Science and Engineering at Shanghai University, China. He received his Doctoral degree in Industrial Engineering and Management from Tokyo Institute of Technology in Japan and his Master’s and Bachelor’s degrees in Industrial Engineering from Xi’an Jiaotong University in China. His research interest includes R&D management, quality management, and manufacturing management.

Guixiang Jin

Guixiang Jin is currently an assistant professor in the Department of Systems Design at Seikei University, Japan. She received Ph.D. degree from Engineering school of science and technology, Keio University, Japan and her master’s degree in Business Administration from Jousai University in Japan. Her research interests include supply chain management, demand forecasting, production scheduling and inventory control.

Hiroaki Matsukawa

Hiroaki Matsukawa is currently a professor in the Department of Industrial and Systems Engineering at Keio University, Japan. He received Ph.D. degree from Tokyo Institute of Technology, Japan. His research interests include supply chain management, logistics and smart manufacturing.