Operational Decisions and Supply Chain Dynamics

While traditional research in operations management has focused on mathematical models for improving the flow of physical goods and information, this dissertation takes a quantitative empirical approach. With three distinct research projects it revisits central topics in the field and explores how operational decisions affect the performance of the firm and its trading partners. Chapter 2 examines the financial consequences that inventory leanness has on firm performance. We conduct an econometric analysis using 4,324 publicly traded U.S. manufacturing companies for the period 1980-2008. Using an instrumental variable fixed effects estimator we find a nonlinear relationship between inventory leanness and financial performance. However, we note that the maximum point of this inverted U-shaped relationship often lies at the extreme end of the investigated sample—suggesting a decreasing return from leanness rather than an optimal level. We show that the strength of this relationship is highly dependent on both industry and inventory type (raw materials, work in process and finished goods). The main novelty and direct implication of our findings is that most firms still have much potential to increase profitability by becoming leaner and they are unlikely to cross a threshold where profitability decreases with increased leanness. We display how much the average firm could gain by becoming leaner and show how this sensitivity changes by inventory component and industry. Finally, we highlight several new econometric aspects that we believe must be addressed when empirically investigating the inventory-performance link. The bullwhip effect is said to occur when demand variability is amplified from a downstream site (buyer) to an upstream site (supplier) in the supply chain. Chapter 3 contributes to the literature that empirically investigates the bullwhip effect by providing new evidence regarding its prevalence and magnitude. In contrast to previous work, we use a two-echelon approach, which allows us to observe varia- tions at both the upstream and the downstream sites. By drawing on a financial accounting standard regarding information disclosure about major customers, we are able to link 5,494 buyers-supplier dyads between 1976 and 2009. We merge this information with quarterly financial accounting data to form a sample of 14,933 buyer-supplier dyad observations. We correct for sample selection bias using propensity score matching and estimate the average bullwhip effect in our sample to 1.90 (i.e. 90% demand variability amplification between echelons). A significant bullwhip effect is found across industries (mining, manufacturing, wholesale and retail) and is supported by several robustness checks. We investigate and discuss how these results can be generalized beyond our sample. Chapter 4 explores knowledge spillovers in the supply chain. By building on the organizational learning literature we discuss how knowledge is diffused in the supply chain. Buyers-supplier relationship data is merged with financial accounting data, patent data and publication data to create a sample of 974 buyer-supplier dyad observations in the high tech industries. Using fixed effects estimation we show that a 1% increase in patent intensity at the buyers, on average leads to a 1% increase in patent intensity at the supplier. We find that the duration of the buyer-supplier relationship positively moderates this effect, but that the supplier’s sales dependency on the buyer has a negative moderating role. The technological proximity of the two firms, however, is not found to have a significant effect on spillovers. We discuss the implications of our findings for individual firms and theory.