Erratum to ‘Inventory record inaccuracy: Causes and labor effects’

• Author: Howard Hao‐Chun Chuang,Rogelio Oliva
• DOI: http://doi.org/10.1016/j.jom.2016.01.002
• Published date: 01 March 2016
• Date: 01 March 2016

Erratum

Erratum to ‘Inventory record inaccuracy: Causes and labor effects’

Howard Hao-Chun Chuang

a

, Rogelio Oliva

b

,

*

a

College of Commerce, National Chengchi University, Taipei 11605, Taiwan, ROC

b

Mays Business School, Texas A&M University, College Station, TX 77843, USA

article info

Article history:

Available online 18 March 2016

Keywords:

Retail operations

Store execution

Inventory record inaccuracy

System dynamics

Design of experiments

Bayesian inference

Econometrics

abstract

Inventory record inaccuracy (IRI) is a pervasive problem in retailing and causes non-trivial profit loss. In

response to retailers’interest in identifying antecedents and consequences of IRI, we present a study that

comprises multiple modeling initiatives. We first develop a dynamic simulation model to compare and

contrast impacts of different operational errors in a continuous (Q,R) inventory system through a full-

factorial experimental design. While backroom and shelf shrinkage are found to be predominant

drivers of IRI, the other three errors related to recording and shelving have negligible impacts on IRI.

Next, we empirically assess the relationships between labor availability and IRI using longitudinal data

from five stores in a global retail chain. After deriving a robust measure of IRI through Bayesian

computation and estimating panel data models, we find strong evidence that full-time labor reduces IRI

whereas part-time labor fails to alleviate it. Further, we articulate the reinforcing relationships between

labor and IRI by formally assessing the gain of the feedback loop based on our empirical findings and

analyzing immediate, intermediate, and long-term impacts of IRI on labor availability. The feedback

modeling effort not only integrates findings from simulation and econometric analysis but also struc-

turally explores the impacts of current practices. We conclude by discussing implications of our findings

for practitioners and researchers.

©2015 Elsevier B.V. All rights reserved.

1. Introduction

Inventory record inaccuracy (IRI) refers to the discrepancy be-

tween physical and recorded inventory levels, and is a pervasive

problem in retailing. Kok and Shang (2014)conclude that IRI can be

attributed to shrinkage (e.g., spoilage and theft), transaction errors,

and misplacement. Because it is difficult to fully eliminate these

execution errors, IRI becomes a norm rather than an anomaly in the

retail sector. Kang and Gershwin (2005) report that inventory ac-

curacy in a global retailer is on average only 51%. DeHoratius and

Raman (2008) find 65% of the inventory records at a retail chain

to be inaccurate, and Oliva et al. (2015)observe that more than 60%

of SKUs in a European retail store have IRI. Most surprisingly, in a

retail store that had not even started operating, Raman et al. (2001)

found that the system had incorrect records for 29% of the items

and estimated that IRI reduces a company's total profits by 10%. At

the firm level, IRI can significantly distort aggregate book value of

inventory and business decisions. At the item level, IRI can delay

ordering decisions because most extant inventory models do not

differentiate between physical and system inventories. IRI also in-

terrupts shelf replenishment even when there is plenty of in-

ventory in the backroom. Consequently, retailerssuffer severe out-

of-stock (OOS) and significant economic loss.

To tackle IRI and associated OOS in retailing environments,

radio-frequency identification (RFID) has been deemed as a

promising solution (Heese, 2007; Lee and Ozer, 2007). However,

issues such as cost, ownership, and privacy/security hinder the full

implementation of RFID at the item-level (Kapoor et al., 2009). Even

when RFID becomes cheap enough to be fully adopted like bar-

coding, the fact that retail operations is a complicated issue

involving people, processes, and technology makes error-free op-

erations extremely difficult to achieve. In order for retailers to

enhance execution quality and data integrity, it is important for

managers to understand the causes of IRI and identify the policy

levers that they can use to reduce it.

While some empirical work has focused on product and store

attributes that affect IRI (e.g., DeHoratius and Raman, 2008), in this

work we explore the impact of store staffing levels and operational

performance on IRI. Our study comprises multiple modeling ini-

tiatives. First, grounded on empirical observations and field work,

DOIs of original article: http://dx.doi.org/10.1016/j.jom.2015.07.006,http://dx.

doi.org/10.1016/j.jom.2016.01.001.

*Corresponding author.

E-mail addresses: chuang@nccu.edu.tw (H.H.-C. Chuang), roliva@tamu.edu

(R. Oliva).

Contents lists available at ScienceDirect

Journal of Operations Management

journal homepage: www.elsevier.com/locate/jom

http://dx.doi.org/10.1016/j.jom.2016.01.002

0272-6963/©2015 Elsevier B.V. All rights reserved.

Journal of Operations Management 42-43 (2016) 96e110