Optimal pricing strategies for Manufacturing-as-a Service platforms to ensure business sustainability

Chaudhuri, Atanu; Datta, Partha Priya; Fernandes, Kiran J.; Xiong, Yu

Abstract

Manufacturing as a Service (MaaS) is a service which delivers manufactured products by connecting its network of suppliers with its customers on a digital platform. The long term sustainability of the MaaS platforms depends on their pricing strategy and whether they can generate sufficient volume from customers and encourage suppliers to do business with them. The objective of the paper is to develop optimal prices which a MaaS platform can charge to maximize its own profit as well as joint profit for itself and its supplier. Game theoretic models based on Hotelling's model are developed to determine the prices. We consider scenarios when the platform charges and does not charge subscription fees. We also determine conditions for the supplier to join the platform. Our models are motivated by real problems faced by a MaaS platform and have been validated using data provided by the platform. The models, the conditions developed and the insights obtained by validating the models using real data provide guidance to MaaS platforms to improve their business sustainability. The paper thus shows that MaaS platforms, when successfully designed, with appropriate pricing models can create an effective and economically sustainable business ecosystem.

Citation

Chaudhuri, A., Datta, P. P., Fernandes, K. J., & Xiong, Y. (2021). Optimal pricing strategies for Manufacturing-as-a Service platforms to ensure business sustainability. International Journal of Production Economics, 234, Article 108065. https://doi.org/10.1016/j.ijpe.2021.108065

• Journal Article Type: Article
• Acceptance Date: Feb 8, 2021
• Online Publication Date: Feb 16, 2021
• Publication Date: 2021-04
• Deposit Date: Feb 19, 2021
• Publicly Available Date: Aug 16, 2022
• Journal: International Journal of Production Economics
• Print ISSN: 0925-5273
• Electronic ISSN: 1873-7579
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 234
• Article Number: 108065
• DOI: https://doi.org/10.1016/j.ijpe.2021.108065
• Public URL: https://durham-repository.worktribe.com/output/1252103