Inventory record inaccuracy: Causes and labor effects

Chuang, Howard Hao-Chun; Oliva, Rogelio

doi:10.1016/j.jom.2015.07.006

Cited by 51 publications

(44 citation statements)

References 64 publications

(18 reference statements)

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“…Because high employee turnover is often caused by working overtime, pressure, and fatigue, increasing staffing levels is an effective way to relieve workload and enhance service quality (Oliva and Sterman 2001). In addition to the well-known effect of labor on service quality, Ton (2009) finds that increasing the amount of labor leads to profit increases through labor effects on conformance quality and Chuang and Oliva (2015) find a positive impact of staffing levels and labor-mix on inventory data quality. Netessine et al (2010) examine the impact of labor planning and labor execution on store performance and find that matching store labor to traffic is associated with greater basket values.…”

Section: Related Literaturementioning

confidence: 99%

Traffic‐Based Labor Planning in Retail Stores

Chuang

Oliva

Perdikaki

2016

Production and Operations Management

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Staffing decisions are crucial for retailers since staffing levels affect store performance and labor‐related expenses constitute one of the largest components of retailers’ operating costs. With the goal of improving staffing decisions and store performance, we develop a labor‐planning framework using proprietary data from an apparel retail chain. First, we propose a sales response function based on labor adequacy (the labor to traffic ratio) that exhibits variable elasticity of substitution between traffic and labor. When compared to a frequently used function with constant elasticity of substitution, our proposed function exploits information content from data more effectively and better predicts sales under extreme labor/traffic conditions. We use the validated sales response function to develop a data‐driven staffing heuristic that incorporates the prediction loss function and uses past traffic to predict optimal labor. In counterfactual experimentation, we show that profits achieved by our heuristic are within 0.5% of the optimal (attainable if perfect traffic information was available) under stable traffic conditions, and within 2.5% of the optimal under extreme traffic variability. We conclude by discussing implications of our findings for researchers and practitioners.

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Section: Related Literaturementioning

confidence: 99%

Traffic‐Based Labor Planning in Retail Stores

Chuang

Oliva

Perdikaki

2016

Production and Operations Management

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“…The paper by Chuang and Oliva [18] continues previous related works on inventory inaccuracy and presents a two-stage model with a front shelf and a backroom. Each stage is prone to three types of errors.…”

Section: Backroom and Shelf Conceptmentioning

confidence: 86%

The Value of Inventory Accuracy in Supply Chain Management: Correlation Between Error Sources and Proactive Error Correction

Avrahami¹

2019

AJOMIS

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One of the key elements in supplay cahin management is accurate information. Decision makers are aware of inaccuracies in inventory levels and, therefore, routinely conduct inventory reviews to correct the discrepancies between IT records and actual inventory. Several studies have investigated error sources and the cumulative effect of errors on holding costs, shortage costs, order-up-to levels and time between inventory counts. In most works, the errors were independent of the demand, which is neither realistic nor accurate. Here we use familiar inventory errors and information scenarios already proposed in several previous papers. We offer a model that considers the correlation between inventory errors and demand. The effect of the relationship between the random variables is tested in the context of several different scenarios. Each scenario contains a different level of information about the underlying demand and inventory errors. We then analyze the effect of changes of the covariance on the cost and time between inventory counts in each scenario. Using these results we formulate the value of information and its dependence on the covariance. We use analytical methods to draw conclusions regarding single parameter set cases and a numerical full factorial study for average multiparameter cases. In both settings, we show that the value of information decreases as the covariance increases. Moreover, the reduction is more significant when the information scenario makes less assumptions. The same behavior is observed in stock review frequency. As covariance increases, the optimal number of periods between inventory reviews drops sharply. Finally, we propose several simple methods for proactive error correction. We show that without prior knowledge, these methods perform better than the basic information scenario. Using these results we are able to formulate recommendations for businesses with different profiles of correlation between demand, and demand and errors, e.g., automated warehouses with weak correlation compared to grocery stores.

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“…Wakeland et al () combined DOE with broad‐range sensitivity analyses to investigate a hybrid system dynamics–discrete event model that was based on a classic system dynamics software project model. Recently, Chuang and Oliva () used DOE to find the primary drivers of inventory record inaccuracy in their store inventory management model.…”

Section: Methodsmentioning

confidence: 99%