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

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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.

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Correspondence to Ashkan Negahban.

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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

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Keywords

  • Innovation diffusion
  • Seeding
  • Myopic and build-up policies
  • Social network
  • Agent-based modeling and simulation