Modeling competition over product life cycles

Chan, Ka Ching and Mills, Terry M. (2015) Modeling competition over product life cycles. Asia-Pacific Journal of Operational Research, 32 (4). pp. 1-16. ISSN 0217-5959


This paper presents a mathematical model, linking the classical Markov models for brand switching and models for product life cycles, to forecast competition analysis and market share. This integrated model can be used to forecast market shares of all competitors, and their market shares, including customers retained, customers gained from market growth, and customers gained from competitors over the product life cycle. Such information provides forecasters with valuable insight about their market positions. The model is generic and can be applied to different types of products and services, under different types and patterns of product life cycle curves. A numerical example on a typical mobile telecommunication industry is used to illustrate the application of the proposed approach.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Restricted access to published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 08 May 2018 05:31
Last Modified: 03 Sep 2018 03:47
Uncontrolled Keywords: Markov chain; forecasting; product life cycle; market share; competition; brand switching
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
15 Commerce, Management, Tourism and Services > 1503 Business and Management > 150399 Business and Management not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970115 Expanding Knowledge in Commerce, Management, Tourism and Services
E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1142/S0217595915500219

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