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A joint analysis of production and seeding strategies for new products: an agent-based simulation approach

Author

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  • Ashkan Negahban

    (The Pennsylvania State University)

  • Jeffrey S. Smith

    (Auburn University)

Abstract

The goal of this paper is to provide a joint analysis of marketing and production strategies for new products to find the optimal combination of seeding and inventory build-up policies. We propose and experiment with an agent-based simulation model of new technology diffusion to evaluate different seeding criteria, fraction of the market to seed, and inventory build-up policies under various social network structures, demand backlogging levels, and product categories. In contrast to previous findings (that are mainly based on the assumption of unlimited supply), we show that the seeding strategy that maximizes the adoption rate is not necessarily optimal in the presence of supply constraints. More importantly, we show that determining seeding and build-up policies sequentially may lead to suboptimal decisions and that the optimal combination of seeding and build-up policy varies for different product categories. We study different small-world and scale-free networks and illustrate how the distribution of long-range connections and influential nodes affect the adoption, demand backlogging, and lost sales dynamics as well as the overall profit. The important implications of the findings for diffusion research as well as marketing and operations management practice are also discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:268:y:2018:i:1:d:10.1007_s10479-016-2389-8
    DOI: 10.1007/s10479-016-2389-8
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    References listed on IDEAS

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    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. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    3. Nejad, Mohammad G. & Amini, Mehdi & Babakus, Emin, 2015. "Success Factors in Product Seeding: The Role of Homophily," Journal of Retailing, Elsevier, vol. 91(1), pages 68-88.
    4. Cowan, Robin & Jonard, Nicolas, 2004. "Network structure and the diffusion of knowledge," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1557-1575, June.
    5. Sunil Kumar & Jayashankar M. Swaminathan, 2003. "Diffusion of Innovations Under Supply Constraints," Operations Research, INFORMS, vol. 51(6), pages 866-879, December.
    6. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    7. Dipak Jain & Vijay Mahajan & Eitan Muller, 1991. "Innovation Diffusion in the Presence of Supply Restrictions," Marketing Science, INFORMS, vol. 10(1), pages 83-90.
    8. Vijay Mahajan & Eitan Muller & Roger A. Kerin, 1984. "Introduction Strategy for New Products with Positive and Negative Word-of-Mouth," Management Science, INFORMS, vol. 30(12), pages 1389-1404, December.
    9. Teck-Hua Ho & Sergei Savin & Christian Terwiesch, 2002. "Managing Demand and Sales Dynamics in New Product Diffusion Under Supply Constraint," Management Science, INFORMS, vol. 48(2), pages 187-206, February.
    10. 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.
    11. Amini, Mehdi & Li, Haitao, 2011. "Supply chain configuration for diffusion of new products: An integrated optimization approach," Omega, Elsevier, vol. 39(3), pages 313-322, June.
    12. 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.
    13. Wenjing Shen & Izak Duenyas & Roman Kapuscinski, 2014. "Optimal Pricing, Production, and Inventory for New Product Diffusion Under Supply Constraints," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 28-45, February.
    14. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    15. Goldenberg, Jacob & Libai, Barak & Muller, Eitan, 2010. "The chilling effects of network externalities," International Journal of Research in Marketing, Elsevier, vol. 27(1), pages 4-15.
    16. John Hauser & Gerard J. Tellis & Abbie Griffin, 2006. "Research on Innovation: A Review and Agenda for," Marketing Science, INFORMS, vol. 25(6), pages 687-717, 11-12.
    17. A. Negahban & J.S. Smith, 2016. "The effect of supply and demand uncertainties on the optimal production and sales plans for new products," International Journal of Production Research, Taylor & Francis Journals, vol. 54(13), pages 3852-3869, July.
    18. Cantamessa, Marco & Valentini, Carlo, 2000. "Planning and managing manufacturing capacity when demand is subject to diffusion effects," International Journal of Production Economics, Elsevier, vol. 66(3), pages 227-240, July.
    19. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    20. Sanjeev Swami & Pankaj Khairnar, 2006. "Optimal normative policies for marketing of products with limited availability," Annals of Operations Research, Springer, vol. 143(1), pages 107-121, March.
    21. Jager, Wander, 2007. "The four P's in social simulation, a perspective on how marketing could benefit from the use of social simulation," Journal of Business Research, Elsevier, vol. 60(8), pages 868-875, August.
    22. Delre, S.A. & Jager, W. & Bijmolt, T.H.A. & Janssen, M.A., 2007. "Targeting and timing promotional activities: An agent-based model for the takeoff of new products," Journal of Business Research, Elsevier, vol. 60(8), pages 826-835, August.
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