Efficient versus Responsive Supply Chain Choice: An Empirical Examination of Influential Factors

Abstract

Contemporary strategies in operations management suggest that successful firms align supply chain assets with product demand characteristics in order to exploit the profit potential of product lines fully. However, observation suggests that supply chain assets often are longer lived than product line decisions. This suggests that alignment between supply chain assets and demand characteristics is most likely to occur at the time of initial market entry. This article examines the association between product demand characteristics and the initial investment in a supply chain at the time of market entry. We characterize supply chains as responsive or efficient. A responsive supply chain is distinguished by short production lead‐times, low set‐up costs, and small batch sizes that allow the responsive firm to adapt quickly to market demand, but often at a higher unit cost. An efficient supply chain is distinguished by longer production lead‐times, high set‐up costs, and larger batch sizes that allow the efficient firm to produce at a low unit cost, but often at the expense of market responsiveness. We hypothesize that a firm's choice of responsive supply chain will be associated with lower industry growth rates, higher contribution margins, higher product variety, and higher demand or technological uncertainty. We further hypothesize that interactions among these variables either can reinforce or can temper the main effects. We report that lower industry growth rates are associated with responsive market entry, but this effect is offset if growth occurs during periods of high variety and high demand uncertainty. We report that higher contribution margins are associated with responsive market entry and that this effect is more pronounced when occurring with periods of high variety. Finally, we report that responsive market entry also is correlated positively with higher technological demand uncertainty. These results are found using data from the North American mountain bike industry.