What Can Be Learned from Classical Inventory Models? A Cross-Industry Exploratory Investigation
Sergey Rumyantsev,
Serguei Netessine
Citigroup Global Markets, Inc., 390 Greenwich Street, Fourth Floor, New York, New York 10013
The Wharton School, University of Pennsylvania, 3730 Walnut Street, Suite 500, Philadelphia, Pennsylvania 19104-6636
sergey.rumyantsev{at}citigroup.com
netessine{at}wharton.upenn.edu
Classical inventory models offer a variety of insights into the optimal way to manage inventories of individual products. However, top managers and industry analysts are often concerned with the aggregate macroscopic view of a firm's inventory rather than with the inventories of individual products. Given that classical inventory models often do not account for many practical considerations that a company's management faces (e.g., competition, industry dynamics, business cycles, the financial state of the company and of the economy, etc.) and that they are derived at the product level and not the firm level, can insights from these models be used to explain the inventory dynamics of entire companies? This exploratory study aims to address this issue using empirical data.
We analyze absolute and relative inventories using a quarterly data panel that contains 722 public U.S. companies for the period 1992–2002. We have chosen companies that are not widely diversified and whose business in large part relies on inventory management to concentrate on empirically testing hypotheses derived from a variety of classical inventory models (economic order quantity (EOQ), [Q, r], newsvendor, periodic review, etc.). We find empirical evidence that firms operating with more uncertain demand, longer lead times, and higher gross margins have larger inventories. Furthermore, larger companies appear to benefit from economies of scale and therefore have relatively less inventory than smaller companies. We obtain mixed evidence on the relationship between inventory levels and inventory holding costs. We also analyze the breakdown of data into eight segments—oil and gas, electronics, wholesale, retail, machinery, hardware, food, and chemicals—and find that, with a few notable exceptions, our hypotheses are supported within the segments as well. Overall, our results demonstrate that many of the predictions from classical inventory models extend beyond individual products to the aggregate firm level; hence, these models can help with high-level strategic choices in addition to tactical decisions.
Key Words: econometrics; panel data; regression analysis; inventory
History: Received: April 14, 2005;
accepted: February 2, 2007.
Copyright © 2007 by INFORMS.
