Skip to main content
SpringerLink
Log in

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

  • S.I.: BOM in Social Networks
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Amini, M., & Li, H. (2011). Supply chain configuration for diffusion of new products: An integrated optimization approach. Omega, 39(3), 313–322.

    Article  Google Scholar 

  • Amini, M., Wakolbinger, T., Racer, M., & Nejad, M. G. (2012). Alternative supply chain production-sales policies for new product diffusion: An agent-based modeling and simulation approach. European Journal of Operational Research, 216(2), 301–311.

    Article  Google Scholar 

  • Aytac, B., & Wu, S. D. (2010). Characterization of demand for short life-cycle technology products. Annals of Operations Research, 203(1), 255–277.

    Article  Google Scholar 

  • Balci, O. (1994). Validation, verification, and testing techniques throughout the life cycle of a simulation study. Annals of Operations Research, 53(1), 121–173.

    Article  Google Scholar 

  • Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.

  • Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215–227.

    Article  Google Scholar 

  • Bilginer, O., & Erhun, F. (2015). Production and sales planning in capacitated new product introductions. Production and Operations Management, 24(1), 42–53.

    Article  Google Scholar 

  • Cantamessa, M., & Valentini, C. (2000). Planning and managing manufacturing capacity when demand is subject to diffusion effects. International Journal of Production Economics, 66(3), 227–240.

    Article  Google Scholar 

  • Cowan, R., & Jonard, N. (2004). Network structure and the diffusion of knowledge. Journal of Economic Dynamics and Control, 28(8), 1557–1575.

    Article  Google Scholar 

  • Delre, S., Jager, W., Bijmolt, T., & Janssen, M. (2007). Targeting and timing promotional activities: An agent-based model for the takeoff of new products. Journal of Business Research, 60(8), 826–835.

    Article  Google Scholar 

  • Delre, S. A., Jager, W., Bijmolt, T. H. A., & Janssen, M. A. (2010). Will it spread or not? The effects of social influences and network topology on innovation diffusion. Journal of Product Innovation Management, 27(2), 267–282.

    Article  Google Scholar 

  • Garcia, R. (2005). Uses of agent-based modeling in innovation/new product development research. Journal of Product Innovation Management, 22(5), 380–398.

    Article  Google Scholar 

  • Goldenberg, J., Libai, B., & Muller, E. (2002). Riding the saddle: How cross-market communications can create a major slump in sales. Journal of Marketing, 66(2), 1–16.

    Article  Google Scholar 

  • Goldenberg, J., Libai, B., Moldovan, S., & Muller, E. (2007). The NPV of bad news. International Journal of Research in Marketing, 24(3), 186–200.

    Article  Google Scholar 

  • Goldenberg, J., Han, S., Lehmann, D. R., & Hong, J. W. (2009). The role of hubs in the adoption process. Journal of Marketing, 73(2), 1–13.

    Article  Google Scholar 

  • Goldenberg, J., Libai, B., & Muller, E. (2010). The chilling effects of network externalities. International Journal of Research in Marketing, 27(1), 4–15.

    Article  Google Scholar 

  • Guare, J. (1990). Six degrees of separation: A play. New York: Vintage Books.

    Google Scholar 

  • Haenlein, M., & Libai, B. (2013). Targeting revenue leaders for a new product. Journal of Marketing, 77(3), 65–80.

    Article  Google Scholar 

  • Hauser, J., Tellis, G. J., & Griffin, A. (2006). Research on innovation: A review and agenda for marketing science. Marketing Science, 25(6), 687–717.

    Article  Google Scholar 

  • Ho, T. H., Savin, S., & Terwiesch, C. (2002). Managing demand and sales dynamics in new product diffusion under supply constraint. Management Science, 48(2), 187–206.

    Article  Google Scholar 

  • Jager, W. (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, 60(8), 868–875.

    Article  Google Scholar 

  • Jain, D., Mahajan, V., & Muller, E. (1991). Innovation diffusion in the presence of supply restrictions. Marketing Science, 10(1), 83–90.

    Article  Google Scholar 

  • Kanniainen, J., Makinen, S., Piche, R., & Chakrabarti, A. (2011). Forecasting the diffusion of innovation: A stochastic Bass model with log-normal and mean-reverting error process. IEEE Transactions on Engineering Management, 58(2), 228–249.

    Article  Google Scholar 

  • Kiesling, E., Gunther, M., Stummer, C., & Wakolbinger, L. (2011). Agent-based simulation of innovation diffusion: A review. Central European Journal of Operations Research, 20(2), 183–230.

    Article  Google Scholar 

  • Kiss, C., & Bichler, M. (2008). Identification of influencers measuring influence in customer networks. Decision Support Systems, 46(1), 233–253.

    Article  Google Scholar 

  • Kumar, S., & Swaminathan, J. M. (2003). Diffusion of innovations under supply constraints. Operations Research, 51(6), 866–879.

    Article  Google Scholar 

  • Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007). The dynamics of viral marketing. ACM Transactions on the Web, 1(1), 228–237.

    Article  Google Scholar 

  • Libai, B., Muller, E., & Peres, R. (2005). The role of seeding in multi-market entry. International Journal of Research in Marketing, 22(4), 375–393.

    Article  Google Scholar 

  • Macal, C., & North, M. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162.

    Article  Google Scholar 

  • Mahajan, V., Muller, E., & Kerin, R. A. (1984). Introduction strategy for new products with positive and negative word-of-mouth. Management Science, 30(12), 1389–1404.

    Article  Google Scholar 

  • Milgram, S. (1967). The small-world problem. Psychology Today, 1(1), 60–67.

    Google Scholar 

  • Negahban, A., & Smith, J. S. (2014). Production-sales policies for new product diffusion under stochastic supply. In Proceedings of the sixth international conference on advances in system simulation(SIMUL’14) (pp. 197–202). Red Hook, NY: International Academy, Research, and IndustryAssociation (IARIA), Inc.

  • Negahban, A., & Smith, J. S. (2016). The effect of supply and demand uncertainties on the optimal production and sales plans for new products. International Journal of Production Research, 54(13), 3852–3869.

    Article  Google Scholar 

  • Negahban, A., & Yilmaz, L. (2014). Agent-based simulation applications in marketing research: An integrated review. Journal of Simulation, 8(2), 129–142.

    Article  Google Scholar 

  • Negahban, A., Yilmaz, L., & Nall, T. (2014). Managing production level in new product diffusion: An agent-based simulation approach. International Journal of Production Research, 52(17), 4950–4966.

    Article  Google Scholar 

  • Nejad, M. G., Amini, M., & Babakus, E. (2015a). Success factors in product seeding: The role of homophily. Journal of Retailing, 91(1), 68–88.

    Article  Google Scholar 

  • Nejad, M. G., Amini, M., & Sherrell, D. L. (2015). The profit impact of revenue heterogeneity and assortativity in the presence of negative word-of-mouth. International Journal of Research in Marketing. doi:10.1016/j.ijresmar.2015.11.005.

  • North, M. J., Collier, N. T., & Vos, J. R. (2006). Experiences creating three implementations of the repast agent modeling toolkit. ACM Transactions on Modeling and Computer Simulation, 16(1), 1–25.

    Article  Google Scholar 

  • Norton, J. A., & Bass, F. M. (1987). A diffusion theory model of adoption and substitution for successive generations of high-technology products. Management Science, 33(9), 1069–1086.

    Article  Google Scholar 

  • Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: A critical review and research directions. International Journal of Research in Marketing, 27(2), 91–106.

    Article  Google Scholar 

  • Rand, W., & Rust, R. T. (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing, 28(3), 181–193.

    Article  Google Scholar 

  • Rumbaugh, J., Jacobson, I., & Booch, G. (2004). The unified modeling language reference manual (2nd ed.). Boston, MA: Pearson Higher Education.

    Google Scholar 

  • Sargent, R. G. (2005). Verification and validation of simulation models. In Proceedings of the 37th winter simulation conference (pp. 130–143).

  • Shen, W., Duenyas, I., & Kapuscinski, R. (2014). Optimal pricing, production, and inventory for new product diffusion under supply constraints. Manufacturing and Service Operations Management, 16(1), 28–45.

    Article  Google Scholar 

  • Stonedahl, F., Rand, W., & Wilensky, U. (2010). Evolving viral marketing strategies. In Proceedings of the 12th annual conference on genetic and evolutionary computation (pp. 1195–1202).

  • Sultan, F., Farley, J. U., & Lehmann, D. R. (1990). A meta-analysis of applications of diffusion models. Journal of Marketing Research, 27(1), 70–77.

    Article  Google Scholar 

  • Swami, S., & Khairnar, P. J. (2006). Optimal normative policies for marketing of products with limited availability. Annals of Operations Research, 143(1), 107–121.

    Article  Google Scholar 

  • Twitter, Inc. (2016). Twitter top 100 most followed. http://www.twittercounter.com/pages/100/.

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  • Yenipazarli, A. (2014). A road map to new product success: Warranty, advertisement and price. Annals of Operations Research, 226(1), 669–694.

    Article  Google Scholar 

  • Zenobia, B., Weber, C., & Daim, T. (2009). Artificial markets: A review and assessment of a new venue for innovation research. Technovation, 29(5), 338–350.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Graduate Professional Studies, The Pennsylvania State University, Malvern, PA, 19355, USA

    Ashkan Negahban

  2. Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, 36849, USA

    Jeffrey S. Smith

Authors
  1. Ashkan Negahban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeffrey S. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashkan Negahban.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Negahban, A., Smith, J.S. A joint analysis of production and seeding strategies for new products: an agent-based simulation approach. Ann Oper Res 268, 41–62 (2018). https://doi.org/10.1007/s10479-016-2389-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-016-2389-8

Keywords

Search

Navigation