And Then There Were None

Wagner, Harvey M.

doi:10.1287/opre.50.1.217.17777

Cited by 43 publications

(29 citation statements)

References 30 publications

(7 reference statements)

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“…In an anniversary article, Wagner (2002) underscores the issues of aggregation and data availability. He points out that product-level theoretical modeling historically dominated the field of operations research, whereas industry-level data modeling was widely used in economics.…”

Section: Literature Reviewmentioning

confidence: 99%

What Can Be Learned from Classical Inventory Models? A Cross-Industry Exploratory Investigation

Rumyantsev

Netessine

2007

M&SOM

192

237

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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. Abstract: 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 and not at 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 US companies for the period 1992 to 2002. We have chosen compani...

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Section: Literature Reviewmentioning

confidence: 99%

What Can Be Learned from Classical Inventory Models? A Cross-Industry Exploratory Investigation

Rumyantsev

Netessine

2007

M&SOM

192

237

View full text Add to dashboard Cite

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“…For example, Little (2004) discusses several reasons why models are not used widely by managers and proposes the features of a decision calculus that make it more amenable to implementation. Wagner (2002) contends that automated replenishment rules are often not implemented because of data related challenges, and proposes that tests using real-life data should be used to determine the applicability of different replenishment rules.…”

Section: Literature Reviewmentioning

confidence: 99%

Ordering Behavior in Retail Stores and Implications for Automated Replenishment

et al. 2010

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Retail store managers may not follow order advices generated by an automated inventory replenishment system if their incentives differ from the cost minimization objective of the system or if they perceive the system to be suboptimal. We study the ordering behavior of retail store managers in a supermarket chain to characterize such deviations in ordering behavior and investigate their potential drivers. Using orders, shipments, and POS data for 19, 417 item-store combinations over 5 stores, we find that store managers systematically modify automated order advices by advancing orders from peak to non-peak days. We show that order advancement is explained significantly by hypothesized product characteristics, such as case-pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our results suggest that store managers add value. They improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. We test a heuristic procedure, based on our regression results, to modify order advices to mimic the behavior of store managers. Our method performs better than the store managers by achieving a more balanced handling workload with similar average days of inventory.

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“…One might expect the seemingly infinite stream of inventory theory related research to be a key resource for managers seeking to gain a competitive advantage through inventory management. However, some have suggested that managers who turn to inventory research may find it to be of little significance (Krautter 1999) or conclude that it has little to offer in terms of enhancing inventory practices (Wagner 2002). This has led many to suggest a gap exists between inventory theory and practice (Lenard and Roy 1995;Silver 1981; Wagner represent valuable research, input from practitioners is noticeably absent.…”

Section: Motivation For Researchmentioning

confidence: 99%

Critical challenges of inventory management in service parts supply: A Delphi study

2008

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The primary purpose of the study is to determine the critical challenges facing service parts managers and to help bridge the gap between research and practice. In order to accomplish this, a series of semi-structured interviews with 18 senior service parts managers from a variety of industries frequently dealing with service parts inventory issues was used as the basis of input for a web-based Delphi study. The results of the study include 18 commonly identified potential service parts inventory issues and ten key challenges for the service parts supply industry. The identified challenges should serve as a guide for both researchers seeking to contribute to the service parts body of knowledge and practitioners who are regularly faced with service parts inventory issues.

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And Then There Were None

Cited by 43 publications

References 30 publications

What Can Be Learned from Classical Inventory Models? A Cross-Industry Exploratory Investigation

What Can Be Learned from Classical Inventory Models? A Cross-Industry Exploratory Investigation

Ordering Behavior in Retail Stores and Implications for Automated Replenishment

Critical challenges of inventory management in service parts supply: A Delphi study

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