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Competitive diffusion process of repurchased products in knowledgeable manufacturing

Author

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  • Yan, Hong-Sen
  • Ma, Kai-Ping

Abstract

This paper presents a diffusion model to explain the competitive diffusion of the repurchased products in knowledgeable manufacturing. The acute market competition accelerates the products' improvement, which requires that the manufacturing enterprises be highly capable of rapid reaction by means of knowledgeable manufacturing. To forecast the diffusion behavior effectively enables the realization of knowledgeable manufacturing system (KMS) which targets T (time), Q (quality), C (cost), S (service), and E (environment). Various diffusion models have emerged since Bass model was firstly proposed in 1969. A nonlinear model of the repurchased competitive products is proposed on the basis of the product diffusion analysis. By taking the frequently purchased products as example, the stability of the model is examined in light of the qualitative theory of differential equations and proved by the approximate linearization method. As the qualitative analysis reveals, between the two frequently purchased products competing in the same market, one undoubtedly occupies a fixed market share while the other may finally be eliminated from the market. A special case of the problem is that both products are one-time-purchased. With the corresponding model given, the qualitative analysis shows that either of the products occupies a market share, the size of which is determined by the product's competitive strength and the new product's time-to-market. A system dynamics model is then established and simulated by vensim. The result is consistent with that of the qualitative analysis.

Suggested Citation

  • Yan, Hong-Sen & Ma, Kai-Ping, 2011. "Competitive diffusion process of repurchased products in knowledgeable manufacturing," European Journal of Operational Research, Elsevier, vol. 208(3), pages 243-252, February.
  • Handle: RePEc:eee:ejores:v:208:y:2011:i:3:p:243-252
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Guidolin, Mariangela & Guseo, Renato, 2015. "Technological change in the U.S. music industry: Within-product, cross-product and churn effects between competing blockbusters," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 35-46.
    2. Amini, Mehdi & Wakolbinger, Tina & Racer, Michael & Nejad, Mohammad G., 2012. "Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach," European Journal of Operational Research, Elsevier, vol. 216(2), pages 301-311.
    3. Hao-Xiang Wang & Hong-Sen Yan, 2016. "An interoperable adaptive scheduling strategy for knowledgeable manufacturing based on SMGWQ-learning," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1085-1095, October.
    4. Guseo, Renato & Mortarino, Cinzia, 2012. "Sequential market entries and competition modelling in multi-innovation diffusions," European Journal of Operational Research, Elsevier, vol. 216(3), pages 658-667.
    5. Hong, Jungsik & Koo, Hoonyoung & Kim, Taegu, 2016. "Easy, reliable method for mid-term demand forecasting based on the Bass model: A hybrid approach of NLS and OLS," European Journal of Operational Research, Elsevier, vol. 248(2), pages 681-690.
    6. Hong-Sen Yan & Wen-Chao Li, 2017. "A multi-objective scheduling algorithm with self-evolutionary feature for job-shop-like knowledgeable manufacturing cell," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 337-351, February.
    7. Duan, Hongbo & Zhang, Gupeng & Wang, Shouyang & Fan, Ying, 2018. "Peer interaction and learning: Cross-country diffusion of solar photovoltaic technology," Journal of Business Research, Elsevier, vol. 89(C), pages 57-66.
    8. Shuping Li & Zhen Jin, 2013. "Global Dynamics Analysis of Homogeneous New Products Diffusion Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-6, November.
    9. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    10. Hou, Rui & Wu, Jiawen & Du, Helen S., 2017. "Customer social network affects marketing strategy: A simulation analysis based on competitive diffusion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 644-653.
    11. Shi, Xiaohui & Chumnumpan, Pattarin, 2019. "Modelling market dynamics of multi-brand and multi-generational products," European Journal of Operational Research, Elsevier, vol. 279(1), pages 199-210.

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