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Alternative Supply Chain Production-Sales Policies for New Product Diffusion:\ud An Agent-Based Modeling and Simulation Approach

By Mehdi Amini, Tina Wakolbinger, Michael Racer and Mohammed G. Nejad


Applying Agent-Based Modeling and Simulation (ABMS) methodology, this paper\ud analyzes the impact of alternative production-sales policies on the diffusion of a new\ud product and the generated NPV of profit. The key features of the ABMS model, that\ud captures the marketplace as a complex adaptive system, are: (i) supply chain capacity is\ud constrained; (ii) consumers' new product adoption decisions are influenced by marketing\ud activities as well as positive and negative word of mouth (WOM) between consumers; (iii)\ud interactions among consumers taking place in the context of their social network are\ud captured at the individual level; and (iv) the new product adoption process is adaptive.\ud Conducting over 1 million simulation experiments, we determined the "best" productionsales\ud policies under various parameter combinations based on the NPV of profit generated\ud over the diffusion process. The key findings are as follows: (1) on average, the build-up\ud policy with delayed marketing is the preferred policy in the case of only positive WOM as\ud well as the case of positive and negative WOM. This policy provides the highest expected\ud NPV of profit on average and it also performs very smoothly with respect to changes in\ud build-up periods. (2) It is critical to consider the significant impact of negative word-of-mouth\ud on the outcomes of alternative production-sales policies. Neglecting the effect of\ud negative word-of-mouth can lead to poor policy recommendations, incorrect conclusions\ud concerning the impact of operational parameters on the policy choice, and suboptimal\ud choice of build-up periods. (authors' abstract

Topics: supply chain management / agent-based simulation / new product diffusion / word of mouth
Publisher: Elsevier
Year: 2012
DOI identifier: 10.1016/j.ejor.2011.07.040
OAI identifier:

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