IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0299944.html
   My bibliography  Save this article

Modeling and simulating the multi-generation product sales, production and inventory system within the context of quality upgrades

Author

Listed:
  • Tan Bo
  • Kenan Yuan
  • Yirui Ge

Abstract

The rapid development of science and technology has led to an increasing number of high-tech enterprises offering new products through successive generations of product upgrades. This trend presents a new challenge for the sustainable operations of enterprises. Based on the Norton-Bass model, this study begins by constructing a multi-generation product diffusion model within a single enterprise in the context of a monopoly under the quality upgrade scenario. Subsequently, a supply model is established based on this foundation, and these two models are seamlessly integrated using product sales volume as an interface, culminating in a comprehensive sales-supply system. This study analyzes the effects of new-product pricing, quality levels, initial stock, and production capacity on the performance of this system. The system dynamics (SD) method was used to simulate and solve the system in the decentralized and centralized decision-making modes, and the two decision-making modes were compared and analyzed. The research reveals several key findings. i) Comprehensive decision optimization yields enhanced profitability through joint optimization calculation of the multi-generation product diffusion system and the supply adjustment system. ii) consumer price sensitivity significantly affects product quality upgrades and profits. A negative correlation exists between consumer price sensitivity and both factors. The upgrades of product quality should be carefully traded off with consideration of pricing and quality costs. iii) Maximizing profits by maintaining a certain order level of backlog or stock shortage is beneficial for overall enterprise profitability. Additionally, optimal production capacity has been identified as a crucial element in efficient operational inventory management. This study expands the multi-generation product diffusion operational theory and provides valuable theoretical support and decision-making foundations for the sustainable management of enterprises.

Suggested Citation

  • Tan Bo & Kenan Yuan & Yirui Ge, 2024. "Modeling and simulating the multi-generation product sales, production and inventory system within the context of quality upgrades," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-26, April.
  • Handle: RePEc:plo:pone00:0299944
    DOI: 10.1371/journal.pone.0299944
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0299944
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0299944&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0299944?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
    2. Shuhua Chang & Xinyu Wang & Zheng Wang, 2015. "Modeling and Computation of Transboundary Industrial Pollution with Emission Permits Trading by Stochastic Differential Game," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-29, September.
    3. Debanjan Mitra & Peter N. Golder, 2006. "How Does Objective Quality Affect Perceived Quality? Short-Term Effects, Long-Term Effects, and Asymmetries," Marketing Science, INFORMS, vol. 25(3), pages 230-247, 05-06.
    4. Zhiling Guo & Jianqing Chen, 2018. "Multigeneration Product Diffusion in the Presence of Strategic Consumers," Information Systems Research, INFORMS, vol. 29(1), pages 206-224, March.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Pascal Courty & Javad Nasiry, 2016. "Product Launches and Buying Frenzies: A Dynamic Perspective," Production and Operations Management, Production and Operations Management Society, vol. 25(1), pages 143-152, January.
    7. Sunil Kumar & Jayashankar M. Swaminathan, 2003. "Diffusion of Innovations Under Supply Constraints," Operations Research, INFORMS, vol. 51(6), pages 866-879, December.
    8. Koray Cosguner & P. B. (Seethu) Seetharaman, 2022. "Dynamic Pricing for New Products Using a Utility-Based Generalization of the Bass Diffusion Model," Management Science, INFORMS, vol. 68(3), pages 1904-1922, March.
    9. Islam, Towhidul & Fiebig, Denzil G, 2001. "Modelling the Development of Supply-Restricted Telecommunications Markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(4), pages 249-264, July.
    10. Tülin Erdem & Michael P. Keane & Baohong Sun, 2008. "A Dynamic Model of Brand Choice When Price and Advertising Signal Product Quality," Marketing Science, INFORMS, vol. 27(6), pages 1111-1125, 11-12.
    11. Zhengrui Jiang & Dipak C. Jain, 2012. "A Generalized Norton-Bass Model for Multigeneration Diffusion," Management Science, INFORMS, vol. 58(10), pages 1887-1897, October.
    12. Jiong Sun & Jinhong Xie & Tao Chen & Fei Li & Gao Wang, 2022. "Managing Reference‐Group Effects in Sequential Product Upgrades," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 442-456, February.
    13. Trichy V. Krishnan & Frank M. Bass & Dipak C. Jain, 1999. "Optimal Pricing Strategy for New Products," Management Science, INFORMS, vol. 45(12), pages 1650-1663, December.
    14. Özlem Bilginer & Feryal Erhun, 2015. "Production and Sales Planning in Capacitated New Product Introductions," Production and Operations Management, Production and Operations Management Society, vol. 24(1), pages 42-53, January.
    15. Jiang, Like & Chen, Haibo & Paschalidis, Evangelos, 2023. "Diffusion of connected and autonomous vehicles concerning mode choice, policy interventions and sustainability impacts: A system dynamics modelling study," Transport Policy, Elsevier, vol. 141(C), pages 274-290.
    16. Frank M. Bass & Anand Krishnamoorthy & Ashutosh Prasad & Suresh P. Sethi, 2005. "Generic and Brand Advertising Strategies in a Dynamic Duopoly," Marketing Science, INFORMS, vol. 24(4), pages 556-568, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bo Tan & Zhiguo Zhu & Pan Jiang & Xiening Wang, 2023. "Modeling Multi-Generation Product Diffusion in the Context of Dual-Brand Competition and Sustainable Improvement," Sustainability, MDPI, vol. 15(17), pages 1-22, August.
    2. Bayrak, Busra & Guray, Busra & Uzunlar, Nilsu & Nadar, Emre, 2024. "Diffusion control in closed-loop supply chains: Successive product generations," International Journal of Production Economics, Elsevier, vol. 268(C).
    3. Ghobadi, Somayeh Najafi- & Bagherinejad, Jafar & Taleizadeh, Ata Allah, 2021. "A two-generation new product model by considering forward-looking customers: Dynamic pricing and advertising optimization," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    4. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    5. Aslan Lotfi & Zhengrui Jiang & Ali Lotfi & Dipak C. Jain, 2023. "Estimating Life Cycle Sales of Technology Products with Frequent Repeat Purchases: A Fractional Calculus-Based Approach," Information Systems Research, INFORMS, vol. 34(2), pages 409-422, June.
    6. Ashkan Negahban & Jeffrey S. Smith, 2018. "A joint analysis of production and seeding strategies for new products: an agent-based simulation approach," Annals of Operations Research, Springer, vol. 268(1), pages 41-62, September.
    7. 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.
    8. Negahban, Ashkan & Smith, Jeffrey S., 2018. "Optimal production-sales policies and entry time for successive generations of new products," International Journal of Production Economics, Elsevier, vol. 199(C), pages 220-232.
    9. Shun-Chen Niu, 2006. "A Piecewise-Diffusion Model of New-Product Demands," Operations Research, INFORMS, vol. 54(4), pages 678-695, August.
    10. Samuel Sale, R. & Mesak, Hani I. & Inman, R. Anthony, 2017. "A dynamic marketing-operations interface model of new product updates," European Journal of Operational Research, Elsevier, vol. 257(1), pages 233-242.
    11. R. Mark Krankel & Izak Duenyas & Roman Kapuscinski, 2006. "Timing Successive Product Introductions with Demand Diffusion and Stochastic Technology Improvement," Manufacturing & Service Operations Management, INFORMS, vol. 8(2), pages 119-135, June.
    12. Lim, Hyungsoo & Jun, Duk Bin & Hamoudia, Mohsen, 2019. "A choice-based diffusion model for multi-generation and multi-country data," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 163-173.
    13. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2017. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Working Papers hal-01592958, HAL.
    14. Krishnan, Trichy V. & Feng, Shanfei & Jain, Dipak C., 2023. "Peak sales time prediction in new product sales: Can a product manager rely on it?," Journal of Business Research, Elsevier, vol. 165(C).
    15. Brito, Thiago Luis Felipe & Islam, Towhidul & Stettler, Marc & Mouette, Dominique & Meade, Nigel & Moutinho dos Santos, Edmilson, 2019. "Transitions between technological generations of alternative fuel vehicles in Brazil," Energy Policy, Elsevier, vol. 134(C).
    16. Leachman, Robert C. & Ding, Shengwei, 2007. "Integration of speed economics into decision-making for manufacturing management," International Journal of Production Economics, Elsevier, vol. 107(1), pages 39-55, May.
    17. Peters, Kay & Albers, Sönke & Kumar, V., 2008. "Is there more to international Diffusion than Culture? An investigation on the Role of Marketing and Industry Variables," EconStor Preprints 27678, ZBW - Leibniz Information Centre for Economics.
    18. Krishnamoorthy, Anand & Prasad, Ashutosh & Sethi, Suresh P., 2010. "Optimal pricing and advertising in a durable-good duopoly," European Journal of Operational Research, Elsevier, vol. 200(2), pages 486-497, January.
    19. Michelle M.H. Şeref & Janice E. Carrillo & Arda Yenipazarli, 2016. "Multi-generation pricing and timing decisions in new product development," International Journal of Production Research, Taylor & Francis Journals, vol. 54(7), pages 1919-1937, April.
    20. Hongmin Li, 2020. "Optimal Pricing Under Diffusion-Choice Models," Operations Research, INFORMS, vol. 68(1), pages 115-133, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0299944. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.