Affiliations: [a] Glorious Sun School of Business and Management, Donghua University, Shanghai, China
| [b] School ofInformation Technology and Management, University of International Business and Economics, Beijing, China
| [c] Departmentof Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA
Correspondence:
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Corresponding author. Yaodong Ni, School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China. E-mail: [email protected].
Abstract: The customer demands of various products bring a challenge for manufacturers. They have to design customized products while maintaining economies of scale and low costs. In this paper, to address this challenge, four approaches are argued to help companies find out the optimal solutions of products’ performance and the maximum profit: (i) only platform modularity without component sharing (ii) only component sharing without platform modularity, (iii) using both platform modularity and component sharing to develop products, or iv) the products are developed individually from a given unshared components set. A theoretical model is proposed and the most profitable approach is found to develop a whole new product family when uncertainty exists in the customer demand and economies of scale with pre-defined parameters. We find that, when consumers’ valuation is considered, the manufacturer may prefer to adopt platform or component sharing individually rather than combining them because the performance of high-end products using platform and component sharing strategies is worse than that using two strategies separately. If platform and component sharing are adopted, the high-end product is under designed, but the manufacturer can benefit from economies of scale. When economies of scale of the platform are greater than or equal to that of component sharing, the optimal performance level of low-end products under platform strategy is lower than that under component sharing strategy. Finally, the detailed numerical analysis provides support for the feasibility and effectiveness of the model.
Keywords: Platform, component sharing, uncertainty theory
