The effect of supply and demand uncertainties on the optimal production and sales plans for new products

Ashkan Negahban, J. S. Smith

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

When introducing a new product, firms face a hierarchy of decisions at the strategic and operational levels including capacity sizing, time to market or starting sales, initial inventory required by the product’s release time and production management in response to changes in the demand (hereafter referred to as production-sales policies). The goal of this paper was to show the importance of considering both supply and demand uncertainties in the determination of the production-sales policy which has been overlooked in the existing literature. More specifically, we test two main hypotheses: (1) ignoring supply and demand uncertainties may lead to potentially incorrect decisions; and, (2) the decision could be different if risk is used as the primary performance measure instead of the commonly used expected (mean) profit. We perform extensive experimentation with a Monte Carlo simulation model of the stochastic supply-restricted new product diffusion and use different statistical procedures, namely, the Welch’s t-test and a nonparametric double-bootstrap method to compare the average and percentiles of the profit for different policies, respectively. The results indicate that the correctness of the two hypotheses depends on the diffusion speed, consumers’ backlogging behaviour, production capacity, price and variable production and inventory costs. The findings also have important implications for managers regarding market entry time, parameter estimation, production strategy and the implementation of the proposed model.

Original languageEnglish (US)
Pages (from-to)3852-3869
Number of pages18
JournalInternational Journal of Production Research
Volume54
Issue number13
DOIs
StatePublished - Jul 2 2016

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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