RFID-Enabled Visibility and Retail Inventory RecordInaccuracy: Experiments in the Field

Bill C. HardgraveCollege of Business, Auburn University, 405 W. Magnolia Ave., Auburn, Alabama 36849, hardgrave@auburn.edu

John A. AloysiusSupply Chain Management Department, Sam M. Walton College of Business, University of Arkansas, Business Building 475d,

Fayetteville, Arkansas 72701, aloysius@uark.edu

Sandeep GoyalCollege of Business, University of Southern Indiana, 8600 University Blvd., Evansville, Indiana 47712, Sandeep@sandeepgoyal.com

A ccurate inventory records are key to effective store execution, affecting forecasting, ordering, and replenishment.Prior empirical research, however, shows that retailer inventory records are inherently inaccurate. Radio Fre-

quency Identification (RFID) enables visibility into the movement of inventories in the supply chain. Using two differ-ent field experiments, the current research investigates the effectiveness of this visibility in reducing retail storeinventory record inaccuracy (IRI). Study 1 used an interrupted time-series design and involved daily physical counts ofall products in one category in 13 stores (8 treatments and 5 controls) of a major global retailer over 23 weeks. Resultsindicate a significant decrease in IRI of approximately 26% due to RFID-enabled visibility. Using an untreated controlgroup design with pre-test and post-test, Study 2 expands the number of categories to five and the number of stores to62 (31 treatment and 31 control stores). Results show that the effectiveness of RFID in reducing IRI varies by category(ranging from no statistically significant improvement to 81%). Results also suggest that RFID ameliorates the effects ofknown determinants of IRI and provide the key insight that the technology is most effective for product categories char-acterized by these determinants.

Key words: supply chain management; inventory record inaccuracy (IRI); radio frequency identification (RFID); inventoryvisibility; field experimentHistory: Received: April 2009; Accepted: October 2011 by Vishal Gaur, Ananth Raman, Jayashankar M. Swaminathan, after2 revisions.

1. Introduction

Inventory record accuracy is an essential ingredientfor efficient and effective supply chain managementand store execution (DeHoratius and Raman 2008,Gaur et al. 2005, Heese 2007). Key functions such asforecasting, ordering, and in-store replenishment arebased on accurate inventory records. Most retailersrely upon automated ordering and replenishmentsystems or, at least, information from a system to pro-vide insight into what, when, and how much to order.For these systems to be effective, retailers must haverecords of their on-hand inventory. Unfortunately,“retailers are not very good at knowing how manyproducts they have in the stores” (Kang and Gersh-win 2005, p. 844). There is a discrepancy betweenretailer inventory records and actual inventory in thestore (DeHoratius et al. 2008). Thus, inventory recordaccuracy is often referred to the “missing link” in

retail execution (Heese 2007). Technology in the formof Radio Frequency Identification (RFID), however,provides unprecedented visibility (Delen et al. 2007,Whitaker et al. 2007) into movements of inventory inthe supply chain and, therefore, the potential toreduce inventory, save labor cost, and improve sup-ply chain coordination (Lee and Ozer 2007).Retailers as well as research studies recognize the

problem of inventory record inaccuracy (IRI; for areview, see Heese 2007). Studies have found thatretailers only have accurate inventory record infor-mation, typically known as perpetual inventory (PI,defined as the continuous system record of on-handstore inventory), on about 35% of their products(Raman et al. 2001). The implication is that orderingand replenishment decisions are based on informa-tion that is wrong more often than it is correct.Although it is recognized as a major obstacle to suc-cessful store execution, retailers have difficulty

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Vol. 22, No. 4, July–August 2013, pp. 843–856 DOI 10.1111/poms.12010ISSN 1059-1478|EISSN 1937-5956|13|2204|0843 © 2013 Production and Operations Management Society

determining when, how, and in what magnitude IRIoccurs (DeHoratius et al. 2008, Kang and Gershwin2005). Because of IRI, systems can order product thatis unnecessary or fail to order product that is needed(DeHoratius and Raman 2008). The net result is anestimated 10% reduction in profit due to IRI (Heese2007).To combat IRI, companies can increase the fre-

quency of physical counts, but this considerablyincreases labor costs and may not be effective (Millet1994) due to the sheer volume of differentiated prod-ucts stocked by retailers. In a busy consumer pack-aged goods store, serving a sizable population thatmay stock several variants of a product (e.g., cans ofsoup with similar labeling), the chances of humanerror are high. Maintaining additional safety stock isan option to guard against stockouts (Fisher et al.2000), but this measure increases inventory holdingcosts (Fisher and Rajaram 2000).Radio frequency identification can potentially help

companies automate the process of identifying andeliminating the source of errors (Bensoussan et al.2007, Kang and Gershwin 2005, McFarlane and Sheffi2003, Morey 1985). Delen et al. (2007) show howRFID can provide visibility into inventory move-ments from the receipt at the distribution center(DC) to intermediate and long-term storage at theDC, through the shipping process to the retail store,all the way to backroom and finally to the sales floor.Although most firms are aware of the technical spec-ifications of RFID, they want to know how this tech-nology will change their business processes (Gittlen2006, Hozak and Collier 2008). For example, Zipkin(2006) makes the point that the design and manage-ment of processes requires not just RFID, but alsocare, ingenuity, and wisdom. Very few companieswould implement a new technology such as RFIDbased on pure faith, but need value assessments,tests, or experiments (Dutta et al. 2007). In the pastseveral decades, barcode technology changed theway retailers and their suppliers do business, andRFID has the promise to be similarly transforma-tional for the retail industry.In this article, we demonstrate the utility of RFID in

retail store execution by showing that the technology-enabled visibility can be used to reduce IRI, which isvital for efficient store execution. We also examine theeffects of RFID on some known factors that influenceIRI. Recently, DeHoratius and Raman (2008) gave anunderstanding of the how and why of IRI by identify-ing seven determinants of IRI. We build upon theirstudy by investigating the extent to which RFID canameliorate the influence of these determinants.Finally, we look at the impact of RFID across multiplecategories of products. Consequently, the researchquestions driving this research are:

1. What is the magnitude of the impact of RFID-enabledvisibility on IRI?

2. Can RFID-enabled visibility ameliorate the effects ofknown predictors of IRI?

Despite the seemingly self-evident potential forRFID-enabled visibility to reduce IRI, due to the diffi-culty of rigorous study design in the field (Dutta et al.2007), very limited empirical research has demon-strated how RFID can help (Amini et al. 2007, Hard-grave et al. 2009). Until there are empirical estimatesof impact, many in the industry will continue to ques-tion its business value (Cachon and Fisher 2000, Cam-derelli 2008, Hozak and Collier 2008, McWilliams2007, Whitaker et al. 2007) and companies will beunwilling to adopt. The primary reason why it is diffi-cult to quantify the impact of the technology withouta controlled field experiment is that the operationalenvironment in which retailers operate is complex,with fallible human intervention, technological, bud-getary, and circumstantial constraints, and the impactof other contextual factors.The rest of this article is organized as follows: sec-

tion 2 sets the background and justification for thestudies; section 3 presents study 1; section 4 presentsstudy 2; and section 5 discusses the results and drawsimplications for future researchers and for firmsimplementing RFID.

2. Literature Review

Our work builds on prior research on RFID (e.g., De-len et al. 2007) and IRI (e.g., DeHoratius and Raman2008, Raman et al. 2001). Here, we discuss these twostreams (RFID and inventory record inaccuracy [IRI])that are key to our research.

2.1. RFID and VisibilityRadio frequency identification refers to a wireless sys-tem that relies on radio frequency waves to identifyobjects (Delen et al. 2007). This technology is oneexample of a family of auto-identification technolo-gies that also includes the ubiquitous barcode, but hasnumerous advantages relative to barcodes (for areview, see Barratt and Choi 2007).Radio frequency identification tagging may be

at the pallet level, the case level, or at the item level.Pallet-level tagging provides visibility primarily atthe receiving doors and is unlikely to give sufficientvisibility into in-store movement of goods so as toreduce IRI. Item-level tagging would result in a moregranular view of movements within the store but atthe current time is expensive to implement for low-priced products. Therefore, our research investigatescase-level tagging, which (as we will explain) pro-vides a sufficient view of in-store movements.

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record Inaccuracy844 Production and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society

Delen et al.’s (2007) case study illustrated the roleof RFID in the supply chain and showed preciselyhow RFID can enhance information visibility. Theirfocus was on the inventory flow from the DC to theretail store, which could be tracked by strategicallylocated read points at the DC receiving dock, by theconveyor belts in the DC, at the DC shipping dock,the store receiving dock, the backroom entrance to thesales floor, and by the box crusher. In the presentresearch, we define inventory visibility as the retail-er’s ability to determine the location of a unit of inven-tory at a given point in time (e.g., in the DC, in thebackroom of the store, on the sales floor, in the boxcrusher). Our purpose is to use this visibility to auto-mate updates to the PI record system and to demon-strate that the technology can improve systemaccuracy.

2.2. Inventory Record InaccuracyInventory record inaccuracy is defined as the absolutedifference between physical inventory and the infor-mation system inventory at any given time (see alsoDeHoratius and Raman 2008, Fleisch and Tellkamp2005). For example, Kang and Gershwin (2005) foundinventory record accuracy (exact match) to be about51% and only about 75% when relaxed to ± 5 units.Raman et al. (2001), in a study of 370,000 observationsacross a single retail chain, found 65% inaccuracy,20% of which differed by six or more units. Likewise,in a study of 166 items from 121 stores, Gruen andCorsten (2007) found IRI to be 55%. As insight intohow and why IRI occurs, DeHoratius and Raman(2008) list seven known determinants of IRI—itemcost (the retailer’s cost of an individual item), quantitysold (or sales velocity; number of units sold of an itemper year preceding the audit of that item), salesvolume (item cost 9 quantity sold), audit frequency(frequency of physical inventory audit), inventorydensity (the total number of units found in a retailer’sselling area), product variety (the number of differentmerchandise categories within a store), and thedistribution structure (whether it was shipped from aretailer-owned DC).

2.2.1. Overstated vs. Understated PI. There aretwo basic categories of IRI: overstated PI and under-stated PI. Gruen and Corsten (2007) indicated thatabout half of the time, PI is overstated (i.e., PI showsmore inventory than is actually in the store), andabout half the time PI is understated (i.e., PI showsless than what is in the store). Similarly, DeHoratiusand Raman (2008) found about 59% of the inaccuraterecords to be overstated vs. 41% understated. Foroverstated PI, the most serious and directly relatedproblem is out of stock—the system thinks it hasinventory on hand (i.e., phantom inventory), and thus

fails to order new inventory. For understated PI, themost pressing problem is excess inventory (i.e., hid-den inventory) because the system thinks it does nothave as much as it really does, thus ordering unneces-sary inventory. This unnecessary inventory poten-tially results in excess holding costs, excessivemarkdowns that impact margin, reduced turns, andbreakdowns in store execution-related errors (Ramanet al. 2001) such as out of stocks (Hardgrave et al.2008) due to inefficiencies created by extra inventory.

2.2.2. Trigger Events and the Use of RFID. Onthe basis of existing literature and interviews withmanagers from the retailer who participated in thestudy, we enumerate events that may trigger IRI.First, PI can be incorrectly manually adjusted byemployees. For example, when an employee believesthe product to be out of stock, PI may be mistakenlyset to zero when, in reality, product is in the back-room. Conversely, employees could think a case ofproduct is available in the store when it is not (if, forexample, they misidentify a product) and incorrectlyadjust PI upward. Thus, incorrect manual adjust-ments can create both under- and overstated PI.Second, products can be stolen, resulting in anoverstated PI condition. Third, damaged or spoiledproducts, when not recorded as such, result in over-stated PI. Fourth, returned products that should addinventory back to the system may not be accountedfor properly or are accounted for incorrectly (e.g.,showing a return of product A when, in fact, productB was returned), thus potentially creating under- oroverstated PI. Fifth, a store can receive mis-shipmentsfrom the distribution center, resulting in both over-and understated PI (overstated for products thatshould have been received, but were not; understatedfor products received that should not have beenreceived). Sixth, cashier error can cause both over-and understated PI inaccuracy. For example, if a cus-tomer is purchasing three items of product A andthree items of product B, but the cashier mistakenlyenters six items of product A, then the PI for productA will be understated by three units and the PI forproduct B will be overstated by three units. For moreinformation about triggers of inventory inaccuracy,see DeHoratius and Raman (2008), Raman et al.(2001), and Sahin and Dallery (2005).In this study, we are interested in RFID’s ability to

reduce IRI. With RFID, stores will know what caseshave been delivered to the store, taken to the salesfloor, or stocked in the backroom. At the store level,there are three RFID read points (see Figure 1): (i)receiving doors have read portals and capture readsfrom individual cases as they are unloaded from thetruck, (ii) the product moving to the sales floor areread by readers placed next to the doors going from

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record InaccuracyProduction and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society 845

the backroom to the sales floor and when the emptycartons return through the sales floor doors (anotherread is captured at this point), and (iii) placed into thebox crusher for disposal (the last read point).This visibility (of the location of a product—back-

room or sales floor) enables a much more accurateview of inventory. As product is sold, PI is updated(from the point of sale system). Coupled with theRFID-generated information of product location, thesystem has an indication of the accuracy of PI and cannow make decisions about what to stock. We illus-trate the logic used by the RFID system (hereinafter“RFID auto-adjust”) with the following two exam-ples: Suppose the system observes a PI count of 11 forProduct A and also shows an RFID read on a box(with a case pack size of 24) at the receiving door, butnone at the sales floor door (i.e., indicating the box isstill in the backroom). Thus, PI must be incorrectbecause at least 24 units are in the store and, subse-quently, RFID auto-adjust would modify the PI forProduct A. A second example: The system observesfor Product B a PI of 13, and the RFID reads indicate abox (with a case pack size of 12) had entered the back-room, but was not yet taken to the sales floor. RFIDauto-adjust would issue a picklist (instructing storepersonnel to restock the store shelf from the back-room) for this item, as it would surmise that only oneitem was on the sales floor (PI = 13 minus 12 in thebackroom). However, PI would appear to be accurateand RFID auto-adjust would not trigger a change.RFID auto-adjust made all determinations––whetherto adjust or not––automatically, with no human inter-vention. Overall, stores should have a better view ofwhich cases have been delivered to the store, taken tothe sales floor, or stocked in the backroom.To summarize, RFID is known to provide visi-

bility through the supply chain (Delen et al. 2007),visibility is presumed by analytical models toreduce inventory inaccuracy (Lee and Ozer 2007),and inventory inaccuracy has been shown toimpact profitability (Fleisch and Tellkamp 2005,

Lee and Ozer 2007, Meyer 1990). The currentresearch studies the hitherto unexplored linkagebetween RFID-enabled visibility and inventoryinaccuracy (Figure 2).

3. Study 1

3.1. Sample and ProcedureStudy 1 included 13 retail stores in a metropolitanarea, ranging from approximately 40,000 square feetto approximately 220,000 square feet. Eight of the 13stores were designated test stores, and the remainingfive stores served as control stores.1 All stores in thestudy were RFID-enabled. Control stores wereequipped with RFID equipment (readers), but datafrom the system were not captured. Only test storesused RFID auto-adjust to update their inventoryrecords. This approach, of RFID-enabling test andcontrol stores, ensured that the mere installation ofRFID equipment could not be a reason for anyimprovement in store execution (a placebo effect).The category selected for the study was aircare

products (air fresheners, candles, sprays, etc.) andwas selected by the retailer. All products in this cate-gory were shipped from the same distribution centerto all stores included in the study. The category wasphysically counted daily for 23 weeks by an indepen-dent firm specializing in counting inventory. Eachday, personnel from the independent firm followedthe same counting path (e.g., begin at bottom left shelfand work way to top right) between 4 P.M. and 8 P.M.to ensure consistency.After approximately 12 weeks of daily inventory

counts, all cases of products in the category beingshipped to all 13 stores were RFID tagged by the sup-pliers. We conducted this extended baseline countingperiod so that store personnel (in both test and controlstores) would be accustomed to the daily count, thusguarding against a change in behavior during thewindow of the experiment (Roth 2007 warns againstthis as a threat to external experimental validity).In the test stores in the treatment period, using a

system of logic based on the RFID reads,2 the systemmade adjustments to understated PI automaticallywhen triggered by the reads (as described earlier). Inthis particular field trial, the retailer was only inter-ested in studying the impact of RFID-enabled visibil-ity on understated PI as a means to reduce inventorycosts.

Backroom

Sales FloorSales

BoxReceiving Door Readers

Figure 1 Retail Store Read Points

+ - -

Research Gap

InventoryRecord

Inaccuracy

Profitability InventoryVisibility

RFID

Figure 2 Research Model

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record Inaccuracy846 Production and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society

In adjusting only one side of the problem (i.e.,understated PI), there is a danger of over-correctingone side and making the other side (i.e., overstatedPI) worse. In this case, an analysis of overstated PIfrom both the control and test stores suggests that thepre- vs. post-increase (0.38%) in test stores’ overstatedPI was not more than the pre- vs. post-increase(0.91%) in control stores’ overstated PI.

3.2. Study DesignTo investigate research question 1 (What is the magni-tude of the impact of RFID-enabled visibility on IRI?), weuse an interrupted time-series design that is “one ofthe most effective and powerful of all quasi-experi-mental designs” (Shadish et al. 2002, p. 159). This isespecially true when supplemented by design ele-ments such as the nonequivalent internal comparisongroup chosen to have maximum pre-test similarity tothe treatment group. Specifically, we use a within-store comparison of time series before and after theimplementation of the RFID auto-adjust. We performan additional test using a post-test only comparisonusing internal controls (Shadish et al. 2002) betweentest and control stores. Here, we conduct a between-store comparison of a set of (RFID-enabled) stores thatuse RFID to auto-adjust PI records and a set of (alsoRFID-enabled) control stores that do not use RFID toauto-adjust PI records.Consistent with prior studies (e.g., DeHoratius and

Raman 2008), IRI was measured as the absolute differ-ence between actual on-hand and PI (what the systemthinks is on hand). Herein, we are interested in themagnitude of the error (as indicated by the absolutedifference) and not merely whether the record wasaccurate. In addition to being consistent with otherstudies, case pack size and shelf quantity were consis-tent (on a stock keeping unit [SKU] basis) across allstores used in this study. Thus, the absolute difference(e.g., two units) was the same as the relative differ-ence because the case pack size was fixed across thestores for each product.

3.3. Results—Research Question 1: Impact of RFIDon IRITable 1 presents descriptive statistics and correlationsfor the stores in the experiment, pooled across thebaseline and treatment periods.

3.3.1. Comparison of IRI: Pre- and Post-Auto-adjust Implementation. We used a discontinuousgrowth model (see Bliese et al. 2007) in a linear mixedeffects model as specified in Bliese and Ployhart(2002) to test for the effect of the RFID auto-adjustwithin stores. Use of these methodologies allowed usto examine change in the IRI of a SKU over time bymodeling discontinuities (i.e., transitions) when thestores implemented RFID auto-adjust. Such changeover time cannot be modeled by linear models (Singerand Willett 2003) or even curvilinear models (Blieseet al. 2007). Further, repeated measures and the hier-archical nature of the data made it necessary for us touse linear-mixed effects instead of OLS regression,which assumes independence of observations. IRI ona given day is carried forward to the next day unlessthere is an adjustment. Also, IRI across SKUs in agiven store may not be independent due to executionpractices in that store.We centered our analysis around the actual date

when a given SKU was adjusted by RFID auto-adjust.Although the total duration of the study was23 weeks, a 40-day window around the adjustmentwas used at the SKU level to investigate the beforeand after effects of the RFID auto-adjust (20 daysbefore the adjustment and 20 days after the adjust-ment). Following this, we had a total sample of 2184observations. Although the 40-day window is some-what arbitrary, we wanted to choose a time periodlong enough to generate a pattern of inventory inac-curacy before a change and then observe the effectsafter the change. On the other hand, a very long timeperiod increases the likelihood that extraneous eventsmay be confounded with the effects of the experimen-tal manipulation (Roth 2007).

Table 1 Descriptive Statistics and Correlations

Variable Mean SD 1 2 3 4 5

1. Velocity 1.13 1.182. Item cost 1.72 0.76 �0.305**

3. Sales volume 21.78 20.26 0.650*** 0.125***

4. Variety 294.08 74.15 0.078*** 0.146*** 0.160***

5. Treatment 0.52 0.50 �0.038 0.001 �0.076** 0.059***

6. IRI 5.01 8.38 0.076*** �0.080*** 0.121*** 0.182*** 0.03

Velocity = Number of units sold per day; Item cost = Cost of an item; Sales volume = Item cost 9 velocity; Variety = Number of unique SKUs carried ina store; Treatment = Dummy variable coded 1 for RFID auto-adjust switched on, and 0 otherwise; IRI = |PI—actual count| if PI < actual count, and 0otherwise.***p < 0.001.**p < 0.01.

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record InaccuracyProduction and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society 847

Our discontinuous growth equation is representedby

IRIijk ¼ h0 þ STORE00k þ a�1PRE00k þ a�2TRANS00kþ a�3POST00k þ b�01ðVelocityjkÞþ b�02ðItem CostjkÞ þ b�03ðSales VolumejkÞþ c�001ðVarietykÞ þ eijk: ð1Þ

IRIijk is the inventory record inaccuracy in period i(i = 1,2,…,40), for SKU j (j = 1,2,…,337), in store k(k = 1,2,…,8). The fixed intercept parameter isdenoted h0, and the term STORE00k denotes the ran-dom main effect of store k. Here, three variables (i.e.,PRE00k, TRANS00k, POST00k) represent three differentphases—that is, the pre-test phase, the transitionphase, and the post-test phase. The pre-test phaserefers to the time periods before a SKU was adjusted,the transition phase refers to the time period whenthe SKU was adjusted by RFID auto-adjust, and thepost-test refers to the time periods after a SKU wasadjusted. The coding of these variables is presented inTable 2. The variables Velocityjk, Item Costjk, andSales_Volumejk are second (SKU) level variables.Finally, Varietyk is a third (store) level variable.For the pre-test phase, because POST = TRANS = 0,

the slope of the equation during the pre-test phase isrepresented by the coefficient of the PRE variable andEquation (1) can be presented as

IRIijk ¼ b0 þ b�1PRE00k: ð2Þ

With the transition dummy variable TRANS00k, wemodel a change in the intercept at day 21. After day21, Equation (1) is given by

IRIijk ¼ b0 þ b�1PRE00k þ b�2TRANS00k: ð3Þ

On the basis of the extant IRI literature (e.g.,DeHoratius and Raman 2008, Neely 1987) and theRFID literature (e.g., Delen et al. 2007), we expectedthat (i) IRI would increase over time, and therefore,

the coefficient of the variable PRE would be positive(test 1), and (ii) IRI would decrease immediately afterRFID auto-adjustment (research question 1). Thisdecrease can be tested by the variable TRANS suchthat if its value is negative and significant, it will indi-cate RFID auto-adjust decreased IRI (test 2). Althoughprior research suggests that IRI will increase overtime if left unchecked, with the activation of theRFID-enabled system we have no basis for assumingwhat will happen to PI after the adjustment is made(post-RFID).The results of the discontinuous growth linear

mixed effect model are presented in Table 3 andFigure 3. Consistent with the prior IRI literature, wefound that IRI was increasing before the RFID auto-adjustment (PRE = 0.138, p < 0.01; test 1). Moreimportantly, we found that IRI decreased during thetransition period (TRANS = �1.875, p < 0.001; test 2),as expected. To quantify the percentage improve-ment, we substituted the value of PRE and TRANS inEquations (2) and (3). On day 20 (before the imple-mentation of RFID auto-adjust), the IRI is 7.146. Onday 21 (after the implementation of RFID auto-adjust),the IRI is 5.271. This shows that the percentage

Table 2 Variable Coding

Pre Post Trans

1 0 02 0 0⋮ ⋮ ⋮20 0 021 1 022 2 1… … …39 19 140 20 1

PRE, Periods numbered consecutively for 40 day window around theadjustment; POST, Periods numbered 0 for 20 days before theadjustment, numbered consecutively after; TRANS, Numbered 0 beforethe adjustment, numbered 1 after.

Table 3 Results of Linear Mixed Effects

Variables Effect

(Intercept) 8.004***

Velocity �0.953**

Variety �0.003Item cost �0.040*

Sales volume 0.000PRE 0.138**

TRANS �1.875***

POST �0.345***

Velocity = Number of units sold per day; Item cost = Cost of an item;Sales volume = Item cost 9 velocity; Variety = Number of unique SKUscarried in a store; PRE: Periods numbered consecutively for 40 daywindow around the adjustment; POST: Periods numbered 0 for 20 daysbefore the adjustment, numbered consecutively; TRANS: Numbered 0before the adjustment, numbered 1 after.*p < 0.05.**p < 0.01.***p < 0.001.

Figure 3 Linear Growth Model for IRI

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record Inaccuracy848 Production and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society

reduction in IRI after the implementation of RFIDauto-adjust is about 26% (i.e., [7.146 – 5.271]/7.146).We did not have a basis to conjecture a positive, nega-tive, or zero slope in the post-adjustment period, butwe found that IRI continued to decrease after theintervention (POST = �0.345, p < 0.001).Recall that for the results presented in Table 3 and

Figure 3, we used a restricted data set consisting onlyof those SKUs that were RFID auto-adjusted. To sub-stantiate the robustness of our findings, we extendedour analysis to two additional supersets of SKUs: (i)the data set with all inaccurate SKUs (regardless ifthey were adjusted or not3), and (ii) the data set withall SKUs (accurate or inaccurate). Using these twomore inclusive, and ever broader, data sets, we foundthe same pattern of results, that is, for all inaccurateSKUs, TRANS = �1.37, p < 0.01 and for all SKUs,TRANS = �1.25, p < 0.01.

3.3.2. Comparison of Test vs. Control Stores. AsSKUs are grouped within stores, which are in turngrouped within treatment conditions, we used a lin-ear mixed effects model to analyze our data. Again,regression is not appropriate, as the assumption ofindependent observations fails. To test our conjecturethat RFID would decrease IRI we ran a hierarchicallinear mixed effects model including Velocity, Variety,Item Cost, and Sales Volume as fixed effects, since theyare known determinants of IRI (DeHoratius andRaman 2008) that we did not experimentally control:

IRIijk ¼ h0 þ STORE00k þ a�1PERIOD00k

þ b�01ðVelocityjkÞ þ b�02ðItem CostjkÞþ b�03ðSales VolumejkÞ þ c�001ðVarietykÞþ c�002ðTreatmentkÞ þ eijk:

IRIijk is the inventory record inaccuracy in period i(i = 1,2,…,40), for SKU j (j = 1,2,…,337), in store k(k = 1,2,…,8). The fixed intercept parameter isdenoted h0, and the term STORE00k denotes the ran-dom main effect of store k. The variable PERIOD00k

denotes the repeated measure of IRI for an individuallevel SKU and is a first level variable. The variablesVelocityjk, Item Costjk, and Sales_Volumejk are secondlevel (SKU) variables. Finally, Varietyk and Treatmentkare third level (store) variables. Table 4 shows thatthe level of IRI was significantly lower (�1.630,p < 0.01) for test stores than for control stores.

3.4. Results––Research Question 2: TheAmeliorating Effect of RFID on Predictors of IRIResearch question 2 asks: Can RFID-enabled visibilityameliorate the effects of known predictors of IRI? Of theseven known predictors found by DeHoratius andRaman (2008), we experimentally controlled for three

of the determinants (density, audit frequency, anddistribution method) and statistically controlled forthe remaining four (velocity, item cost, variety, andsales volume). To test the influence of RFID on thesepredictors, we created interaction terms by multiply-ing these predictors with the treatment dummy vari-able (0 = baseline period and 1 = treatment period).The results of the linear mixed effects model are pre-sented in Table 5.We found that the interaction terms with item cost

and variety were significant (p < 0.05). The interactionplots for these two variables are shown in Figure 4.For cost per item, consistent with DeHoratius andRaman (2008), we found that without RFID, as costper item increased, IRI decreased. With RFID, how-ever, cost per item did not have a significant effect onIRI (i.e., no difference between low and high costitems on IRI). For product variety, we did not find adifference between low variety and high variety inthe absence of RFID. However, we found that highvariety items exhibited less IRI, compared with lowvariety items, in an RFID-enabled environment.Although this is counter to DeHoratius and Raman(2008), who found variety to be positively related toIRI, it is most likely due to a confound with storeformat in our sample. As we were looking at a singlecategory (aircare), variance in variety was only foundbetween store types (large format vs. small format).As small format stores had smaller backrooms to storeinventory, RFID would be less helpful because of thesmaller backrooms; conversely, it would be more use-ful in larger stores with larger backrooms. Thus,although RFID was useful in both formats, it wasmore useful in the large stores.

3.5. Study 1 DiscussionStudy 1 investigated the effectiveness of RFID-enabled visibility in reducing retail store IRI. First, wecompared pre- and post-in-store IRI for the test stores.

Table 4 Linear Mixed Model for Comparison of Test vs. Control Stores

Variables Fixed effects

(Intercept) 5.654***

Velocity 2.356***

Variety 0.000Item cost 0.001Sales volume �0.002Test �1.630**

Period �0.008

Velocity = Number of units sold per day; Item cost = Cost of an item;Sales volume = Item cost 9 velocity; Variety = Number of unique SKUscarried in a store; Test: Dummy variable coded 1 for test stores and 0 forcontrol stores; Period: Day 1 starting when RFID auto-adjust was madeavailable in test store.***p < 0.001.**p < 0.01.

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record InaccuracyProduction and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society 849

The hierarchical discontinuous growth model showedthat RFID-enabled visibility results in a significantdecrease in IRI. One way to view this decrease is toexamine the shift in intercept pre- and post-auto-adjustment as illustrated earlier in Figure 3. Using thedata from Figure 3, the percentage decrease is 26%.Interestingly, we found that IRI continued to declineafter the auto-adjustment. An ad hoc analysis of thenumber of manual adjustments, a key cause of IRI,revealed a 41% decline in the number of manualadjustments after RFID auto-adjust was installed. Aswe observed from our discussion with the store man-agers, these (often incorrect) manual adjustments area key trigger for IRI. We believe this decline in man-ual adjustments, attributed to the system makingauto-adjustments, is likely to lead to fewer incorrectadjustments to PI (and, thus, one less source of error).Second, we conducted a between-store comparison

of the eight test and five control stores. The results ofthis comparison showed that RFID auto-adjust

decreased IRI as we found that the test stores had sig-nificantly lower IRI than the control stores.Overall, the discontinuous growth model from

study 1 provides evidence for the impact of case-levelRFID to reduce IRI. However, the use of a single cate-gory limits the generalizable insights that may bedrawn, and, therefore, we broaden the scope of thisresearch with Study 2.

4. Study 2

4.1. Sample and ProcedureStudy 2 included 62 stores representing various storeformats spread all across the United States from theretailer in study 1. Of the 62 stores, 31 were catego-rized as test stores and 31 were categorized as controlstores, matched by the retailer on a set of characteris-tics, such as annual sales.Unlike study 1, which used only a single category,

study 2 used five categories with 1268 unique SKU’s

Interaction Plot for RFID and Cost Interaction Plot for RFID and Variety

Low Cost High Cost

IRI

Without RFID With RFID

Low Variety High Variety

IRI

Without RFID With RFID

(a) (b)

Figure 4 Interaction Plots for Study 1

Table 5 Influence of RFID-enabled Visibility on Known Predictors of Inventory Inaccuracy

Variables Model 1 Model 2 Model 3 Model 4 Model 5

Intercept 8.794*** 8.961*** 8.578*** 8.509*** 8.632***

Treatment �2.385*** �1.932*** �1.964*** �1.606*** �1.899***

Item cost �0.003 �0.002 �0.003 �0.004 �0.005Velocity �0.858* �1.044** �0.571** �0.991** �1.186**

Variety �0.022 �0.025 �0.021 �0.021 0.023Sales volume 0.000 0.002 0.000 0.000 0.002Treatment 9 item cost 0.002***

Treatment 9 velocity �0.087Treatment 9 variety �0.157***

Treatment 9 sales volume �0.005

Velocity = Number of units sold per day; Item cost = Cost of an item; Sales volume = Item cost 9 velocity; Variety = Number of unique SKUs carried ina store; Treatment: Numbered 0 before the adjustment, numbered 1 after.*p < 0.05.**p < 0.01.***p < 0.001.

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record Inaccuracy850 Production and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society

for the experiment. These categories were aircareproducts (e.g., air fresheners, candles, sprays), floor-care (e.g., vacuums, carpet spot remover), formula(e.g., infant nutritional products, canned soy milk),ready to assemble furniture (e.g., computer cart, exec-utive chair), and quick cleaners (e.g., fabric cleaner,microfiber pads). These categories represented prod-ucts from across the store in a variety of sizes (frombaby formula to furniture), case pack sizes (from casepack sizes of 1–48), prices (from less than a dollar toseveral hundred dollars), and sales velocities (fromtens of units per day to one per week).All the SKUs in the five categories were physically

counted at two separate points in time. The first countwas 1 week before the implementation of the RFIDauto-adjust, and the second was 2 months after thefirst, approximately 7 weeks after the implementationof the RFID auto-adjust. This ensured that both physi-cal counts were conducted in the first week of therespective months. Physical counts were conductedby the same independent firm as study 1 and becauseof the large number of SKUs involved in this study,took approximately 5 days to complete.In contrast to study 1, where we only examined

understated IRI, we examined both understated andoverstated IRI in study 2 (using the same dependentvariable as study 1: absolute difference between actualon-hand inventory and PI). We operationalized mea-sures for factors known to influence IRI as follows:Sales velocity was the number of units of a SKU soldfor 2 months preceding the count across all stores; Itemcost was the cost of an item to the retailer; Sales volumewas the total dollar amount of a SKU sold for 2 monthspreceding the count across all stores; Variety was thetotal number of unique SKUs in a category; and Den-sity was the total number of units in a category dividedby linear feet of shelf space for that category.We used an untreated control group design with

pre-test and post-test (Shadish et al. 2002). We con-ducted a within-store comparison before and after theimplementation of RFID auto-adjust for both test andcontrol stores.

4.2. ResultsTable 6 presents descriptive statistics and correlationsfor the 62 stores in the experiment.

4.2.1. Study 2 Research Question 1: Impact ofRFID on IRI. The goal of study 2 was to examine theinfluence of RFID auto-adjust on IRI across categories.We conducted within-store comparisons for both teststores and control stores by categories, using linearmixed effects models where items were groupedwithin stores. Simplifying notation and showing fixedeffects only, IRI = b0 + b1 * Treatment, where Treat-ment was a dummy variable coded 1 for RFID auto-adjust switched on and 0 otherwise. The results forthe linear mixed effects models are presented inTable 7, along with the differences in the effect size ofthe treatment between test and control stores. Weassessed the statistical significance of these differ-ences by conducting a difference of differences test tosee if the pre- vs. post-differences in control storeswere significantly different from the pre- vs. post-dif-ferences in the test stores. Following the proceduresuggested by Cohen et al. (2003), we introduced aninteraction term in our linear mixed effects model.Therefore, IRI was now given by (again simplify-ing notation): IRI = b0 + b1 * Treatment + b2 * Group+ b3 * Treatment 9 Group, where Group was a dummyvariable coded 1 for test stores and 0 otherwise.The results of the linear mixed effects model show

that for all the categories except ready to assemblefurniture, pre- vs. post-decrease in the level of IRI wassignificantly higher for test stores than control stores.For ready to assemble furniture, we did not find anysignificant differences between test stores and controlstores.

4.2.2. Study 2 Research Question 2: TheAmeliorating Effect of RFID on Predictors ofIRI. We examined the moderating influence of RFID-enabled visibility on the DeHoratius and Raman(2008) predictors of IRI. With the inclusion of five gen-eral merchandise categories of products in study 2,

Table 6 Descriptive Statistics and Correlations

Variable Mean SD 1 2 3 4 5

1 IRI 3.16 11.382 Item cost 47.99 11.96 �0.049**

3 Variety 795.31 464.01 0.015** �0.198**

4 Velocity 52.4 184.95 0.400** �0.032** �0.037**

5 Sales volume 735.31 2786.83 0.201** 0.356** �0.177** 0.648**

6 Density 100.84 93.1 0.159** �0.217** 0.263** 0.170** �0.114**

Velocity = Quantity of item sold for 2 month preceding measurement; Item cost = Cost of an item to the retailer; Sales volume = Item cost X velocity;Variety = Total number of unique SKUs in a category; Density: Total number of units in a category divided by linear feet of shelf space for that category;IRI = |PI � actual count|***p < 0.001.**p < 0.01.

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record InaccuracyProduction and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society 851

we were able to examine the influence of RFID on theknown effects of product density, velocity, item cost,variety, and sales volume. To test the influence ofRFID on these predictors, we created interactionterms by multiplying these predictors with the treat-ment (dummy variable coded 1 for RFID auto-adjustswitched on and 0 otherwise). Table 8 shows the lin-ear mixed effects models.In the interaction plots in Figure 5a–c and e, consis-

tent with DeHoratius and Raman (2008), we find thatRFID appears to ameliorate the effect of inventorydensity, variety, sales volume, and velocity on IRI.The interaction plot (Figure 5d) suggests that withoutRFID, cost has no effect on IRI; with RFID, high costitems have higher IRI. This is counter to DeHoratiusand Raman (2008), who show that higher cost is nega-tively associated with IRI and counter to our ownfindings from study 1. We believe this is due to theprofile of product category attributes and we revisitthis issue in the next section.

4.2.3. Differences between Categories. Table 9presents average characteristics of different categoriesalong with the percentage change in the accuracyimprovement. We found that the accuracy improve-ment varied from 16% to 81% depending on the cate-

gory. It is important to note that the ready to assemblefurniture, a category with low sales velocity, highcost, and low density, was not influenced by RFID.More interestingly, we found that formula, a categorywith high sales velocity, low cost, high sales volume,and high density benefited the most with the presenceof RFID. Informal discussions with various retailerssuggest this category to be “high theft,” thus mostlikely experiencing high overstated PI and corre-sponding high IRI. This provides further evidencethat RFID is more effective in reducing inventoryinaccuracy in product categories that have highersales volume, higher dollar sales, greater SKU variety,and greater inventory density.Revisiting the ameliorating effect of RFID on cost,

as discussed, although the interaction was significant,neither the “without RFID” nor the “with RFID” rela-tionship was as expected. However, the explanationappears to be straightforward. The IRI for furniture—the highest cost category—was not positively affectedby RFID, perhaps due to its profile of other character-istics (low velocity and low density). This category,because of its high cost (more than double the cost ofthe next highest category and more than 20 times thelowest cost category), appears to have attenuated theeffect of cost in the overall interaction.

4.3. Study 2 DiscussionStudy 2 used a completely different sample of storesand was conducted more than a year after study 1.The analysis featured a cross-sectional snapshot ofdata before and after the intervention, unlike study 1,which examined the period before and after the inter-vention with some considerable detail. The consis-tency in the aircare category that found animprovement of 26% in study 1 compared with 30%for study 2 is some evidence for robustness of esti-mates. The slightly better improvement of 30% instudy 2 may be attributed to the inclusion of

Table 7 Effect Size for Treatment, Linear Mixed Effects Model

Category Control stores Test stores Difference

Floorcare �0.208* �0.899*** 0.691**

Aircare �1.099* �2.729*** 1.630***

Furniture �0.061 0.168 �0.229Formula 0.894 �2.004*** 2.898***

Quick cleaners 1.692** 1.319** 0.373**

Significance of difference assessed by interaction term of treatment (pre–post) and group (test–control).***p < 0.001.**p < 0.01.*p < 0.05.

Table 8 Influence of RFID-enabled Visibility on Known Predictors of Inventory Inaccuracy

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Intercept 1.759*** 1.833*** 1.167*** 1.651*** 0.606 1.221**

Treatment �1.977*** �2.177*** �0.806*** �1.682*** �0.840** �0.817**

Item cost 0.001 0.001 0.002 0.001 0.002 0.001Velocity 0.021*** 0.021*** 0.028*** 0.021*** 0.021*** 0.021***

Sales volume 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000***

Density 0.010*** 0.010*** 0.011*** 0.010*** 0.017*** 0.010***

Variety 0.000 0.000 0.000 0.000 0.000 0.001*

Treatment 9 cost 0.005*

Treatment 9 salesvol �0.014***

Treatment 9 velocity �0.0004***

Treatment 9 density �0.013***

Treatment 9 variety �0.001**

***p < 0.001.**p < 0.01.*p < 0.05.

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record Inaccuracy852 Production and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society

overstated in the IRI evaluation. We also found thatRFID ameliorated the influence of known predictorsof IRI. With the exception of cost, RFID reduced oreliminated the effect of the known predictors—den-sity, variety, velocity, sales volume—on IRI. This maybe the most important outcome of study 2, as it pro-vides additional insight into these predictors and theability to influence their effect on IRI. Moreover, wefound the influence of RFID on IRI varied by productcategory. The differences across categories reinforce

the results of the ameliorating effects of RFID on theknown predictors of IRI. For example, larger (i.e., lowdensity), low velocity items such as ready to assemblefurniture were not influenced by RFID, whereassmall, fast moving items (i.e., high density, highvelocity) such as formula were influenced the most.This categorical breakdown provides some predictiveinsight for both theory and practice as to what prod-uct categories are likely to show the greatest improve-ment with case-level tagging.

Interaction Plot for RFID and Density Interaction Plot for RFID and Variety

Interaction Plot for RFID and Velocity Interaction Plot for RFID and Cost

Low Density High Density

IRI

Without RFID With RFID

Low Variety High Variety

IRI

Without RFID With RFID

Low Velocity High Velocity

IRI

Without RFID With RFID

Low Cost High Cost

IRI

Without RFID With RFID

Interaction Plot for RFID and Sales Volume

Low Sales Vol High Sales Vol

IRI

Without RFID With RFID

(a) (b)

(c)

(e)

(d)

Figure 5 Interaction Plots for Study 2

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record InaccuracyProduction and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society 853

5. Discussion

In two separate field studies with a major retailer, weexamined two research questions: (i) What is the mag-nitude of the impact of RFID-enabled visibility onIRI? (ii) Can RFID-enabled visibility ameloriate theeffects of known predictors of IRI? Study 1 addressedquestion 1 in detail with some insight into question 2.Study 2 provided insight into the effects of RFID onthe known predictors of IRI and on the effects of cate-gory differences on the ability of RFID to reduce IRI.This research answers the call by prior research

(e.g., Dutta et al. 2007, Lee and Ozer 2007) to investi-gate the impact of RFID via empirical-based researchwith a well-designed sample and rigorous controls.Although it may be theoretically possible for a retailerto individually barcode scan every box entering thereceving door, going to the sales floor, returning fromthe sales floor, etc., the cost is prohibitive. It alsorequires diligence (i.e., perfect execution) by employ-ees (diligently scanning every box entering the back-room, leaving the backroom, going to the boxcrusher) that is not realistic. An examination of thecarefully constructed rationales for why DeHoratiusand Raman (2008) theorized their predictors wouldimpact IRI is informative. A broad generalizationwould characterize many of these rationales to bebased on the finite capacity of human agents to dealwith environmental and task complexity. Thus, it isunlikely that a solution based on human interventionwill solve the problem unless environmental and taskcomplexity are reduced. RFID provides an automatedzero human intervention solution to the problem.

5.1. Research Limitations and Future DirectionsIn this study, we investigated IRI across multiplestores from a single retailer. Thus, the results may notgeneralize across all retailers. However, as noted byDeHoratius and Raman (2008), the “advantages offield research within one organization include the useof detailed, firm-specific data and a deep understand-ing of the study context … [and] control for firm-spe-

cific factors that influence IRI” (p. 638). As this retaileris very large and successful, the results may havesuffered from a “ceiling” effect as compared to otherretailers; this retailer may already be very good. Inthis case, therefore, our estimates may be conserva-tive. The improvement brought about by RFID wasalso based on tagging cases only. With item-level tag-ging becoming the standard in the apparel industry,the impact of RFID may be greater. Therefore, futureresearch should investigate the influence of item-levelRFID on retail store execution.In study 1, IRI increased until RFID was used to

make an adjustment and resulted in a significantlylower inaccuracy point. We found inaccuracy to con-tinue to decline after the auto-adjustment. Althoughwe speculate that it may be due to fewer manualadjustments, which are a source of human error, wehave no firm evidence to confirm this linkage. A pro-cess analysis that identifies what caused the contin-ued decrease in IRI is an avenue for future research.It was not within the scope of our study to explicitly

quantify the return on investment (ROI) resultingfrom the reduction in IRI. However, typically, theretailer bears the brunt of the fixed costs (i.e., fromthe RFID readers installed in DCs, stores, etc.) and thesuppliers incur greater variable costs (i.e., the cost pertag). Camdereli and Swaminathan (2010) show thatthere is misalignment of incentives for the retailer andthe supplier to invest when there is a fixed cost ofinvestment. Further, reducing out of stocks is benefi-cial to the retailer because of store switching, but ben-eficial to the supplier because of brand switching(Corsten and Gruen 2004). An empirical investigationthat quantifies the benefits from RFID to retailers andto suppliers may be the subject of future research.

6. Conclusion

Inventory inaccuracy has plagued retailers for manyyears. Using two different field experiments that pro-vide ecological validity, this research investigatedthe impact of RFID-enabled perpetual inventory

Table 9 Characterization of Categories

Category Velocity Item cost Sales volume Variety Density % Improve

Floorcare 1.66 7.80 1.58 6.13 6.00 45.15%**

Aircare 9.44 1.00 1.00 9.36 56.00 29.56%***

Furniture 1.00 20.05 2.53 3.20 1.00 �60.64%Formula 13.16 4.04 6.46 2.35 32.50 81.60%***

Cleaners 8.30 2.37 2.41 1.00 18.00 16.86%**

Cell values (except % improve) have been re-scaled to protect information deemed confidential by the retailer. Cell values were re-scaled by dividing eachnumber in a column by the smallest number in the column and, thus, providing a relative value. For example, in the Item cost column, Furniture is 20.05times the value of Aircare (the lowest cost item).***p < 0.001.**p < 0.01.

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record Inaccuracy854 Production and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society

auto-adjustments on inventory record inaccuracy.Study 1 used a sample of 13 stores from a major retai-ler and daily counts (for 23 weeks) of 337 SKUs in asingle category (aircare products). Study 2 used amuch larger sample of 62 stores, five different catego-ries, and 1268 SKUs from the same retailer. We firstexamined the impact of RFID on IRI. We then investi-gated the ameliorating effects of RFID on known cau-sal predictors of inventory inaccuracy. Finally, wedrew inferences about the characteristics of productcategories for which RFID is effective in reducinginventory inaccuracy. Using one of the largest sam-ples of stores and SKUs of its kind with longitudinaldata across two separate experiments, our findingssuggest that the visibility provided by RFID is a viableintervention to reduce inventory inaccuracy. Theinsight that the product categories most likely to ben-efit from RFID may be characterized by predictors ofinventory inaccuracy is key. These results informfuture research as to ways in which IRI can be furtherreduced. For the practitioner, this is further evidencethat RFID can be used successfully, thus reducingexcess inventory and improving store execution.

Acknowledgments

The authors thank the special editors––Vishal Gaur, AnanthRaman, and Jayashankar M. Swaminathan––and two anon-ymous referees whose comments and suggestions consider-ably improved the article. The article also benefitted fromcomments from the audience at the POMS national meetingat San Diego and the Information Systems Colloquium atthe University of Arkansas. This research was supported inpart by the RFID Research Center at the University ofArkansas.

Notes

1We did not use a balanced design for two reasons: (i)Our primary test was the within-store comparison, andtherefore, it was more desirable to have a larger sample oftest stores, and (ii) the retailer wanted to observe moretest stores for their own internal process improvementpurposes.2The exact set of business rules used by the retailer inmaking a determination of whether to adjust PI are con-sidered proprietary and cannot be shared. In essence, thesystem used the visibility provided by the RFID readpoints to determine the location of product (backroom orsales floor), combined with the information from point ofsale, to determine the accuracy of PI, as prior exampleshave illustrated.3Recall that the retailer used the RFID data and a set ofdecision rules to determine whether to automaticallyadjust PI. There were several instances in which PI waswrong, but did not match the criteria as determined bythe retailer and was, thus, not adjusted. This extendeddata set included these records.

ReferencesAmini, M., R. F. Otondo, B. D. Janz, M. G. Pitts. 2007. Simulation

modeling and analysis: A collateral application and exposi-tion of RFID technology. Prod. Oper. Manag. 16(5): 586–598.

Barratt, M., T. Choi. 2007. Mandated RFID and institutionalresponses: Cases of decentralized business units. Prod. Oper.Manag. 16(5): 569–585.

Bensoussan, A., M. Cakanyildirim, S. P. Sethi. 2007. Economicevaluations of systems that expedite inventory information.Prod. Oper. Manag. 16(3): 360–368.

Bliese, P. D., D. McGurk, J. L. Thomas, T. J. Balkin, N. Wesensten.2007. Discontinuous growth modeling of adaptation to sleepsetting changes: Individual differences and age. Aviat. SpaceEnviron. Med. 78(5): 485–492.

Bliese, P. D., R. E. Ployhart. 2002. Growth modeling using randomcoefficient models: Model building, testing, and illustrations.Org. Res. Meth. 5(4): 362–386.

Cachon, G. P., M. Fisher. 2000. Supply chain inventory manage-ment and the value of shared information. Manage. Sci. 46(8):1032–1048.

Camderelli, A. 2008. Who is the winner of the RFID battle: Manu-facturer or retailer? POMS Chronicle 15(2): 18–19.

Camdereli, A. Z., J. M. Swaminathan. 2010. Misplaced inventoryand radio-frequency identification (RFID) technology: Infor-mation and coordination. Prod. Oper. Manag. 19(1): 1–18.

Cohen, P., J. Cohen, S. G. West, L. S. Aiken. 2003. Applied MultipleRegression/Correlation Analysis for the Behavioral Sciences, 2nded. Lawrence Erlbaum and Associates, Publishers, Hillsdale,NJ.

Corsten, T., D. Gruen. 2004. Stock-outs cause walkouts. Harv. Bus.Rev. 82(5): 26–28.

DeHoratius, N., A. J. Mersereau, L. Schrage. 2008. Retail inventorymanagement when records are inaccurate. Manuf. Serv. Oper.Manag. 10(2): 257–281.

DeHoratius, N., A. Raman. 2008. Inventory record inaccuracy: Anempirical analysis. Manage. Sci. 54(4): 627–641.

Delen, D., B. C. Hardgrave, R. Sharda. 2007. RFID for better sup-ply-chain management through enhanced information visibil-ity. Prod. Oper. Manag. 16(5): 613–624.

Dutta, A., H. L. Lee, S. Whang. 2007. RFID and operations man-agement: Technology, value, and incentives. Prod. Oper.Manag. 16(5): 646–655.

Fisher, M., K. Rajaram. 2000. Accurate retail testing of fashion mer-chandise:Methodology and application.Mark. Sci. 19(3): 266–278.

Fisher, M. L., A. Raman, A. S. McClelland. 2000. Rocket science retail-ing is almost here—Are you ready? Harv. Bus. Rev. 78(4): 115–124.

Fleisch, E., C. Tellkamp. 2005. Inventory inaccuracy and supplychain performance: A simulation study of a retail supplychain. Int. J. Prod. Econ. 95(3): 373–385.

Gaur, V., M. L. Fisher, A. Raman. 2005. An econometric analysisof inventory turnover performance in retail services. Manage.Sci. 51(2): 181–194.

Gittlen, S. 2006. The failure of RFID. Computerworld. Available athttp://www.computerworld.com/s/article/9001194/The_fai-lure_of_RFID (accessed date November 16, 2012).

Gruen, T. W., D. S. Corsten. 2007. A Comprehensive Guide to RetailOut-of-Stock Reduction in the Fast-Moving Consumer GoodsIndustry. Grocery Manufacturers of America. Available athttp://www.rw3.com/ufiles/file/Article5.pdf (accessed dateMarch 14, 2008)

Hardgrave, B. C., J. A. Aloysius, S. Goyal. 2009. Does RFIDimprove inventory accuracy? A preliminary analysis. Int. J.RF Technol. Res. Appl. 1(1): 44–56.

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record InaccuracyProduction and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society 855

Hardgrave, B. C., S. Langford, M. A. Waller, R. Miller. 2008. Mea-suring the impact of RFID on out of stocks at Wal-Mart. MISQuart. Exec. 7(4): 113–124.

Heese, H. S. 2007. Inventory record inaccuracy, double marginali-zation, and RFID adoption. Prod. Oper. Manag. 16(5): 542–553.

Hozak, K., D. A. Collier. 2008. RFID as an enabler of improvedmanufacturing performance. Dec. Sci. 39(4): 859–881.

Kang, Y., S. B. Gershwin. 2005. Information inaccuracy in inventorysystems: Stock loss and stock out. IIE Trans. 37(9): 843–859.

Lee, H., O. Ozer. 2007. Unlocking the value of RFID. Prod. Oper.Manag. 16(1): 40–64.

McFarlane, D., Y. Sheffi. 2003. The impact of automatic identificationon supply chain operations. Int. J. Logist. Manag. 14(1): 1–17.

McWilliams, G. (2007). Wal-Mart’s radio-tracked inventory hitsstatic. Wall Street J. February 15, B: 2. Available at http://online.wsj.com/article/SB117150681979009405.html (accessed dateNovember 16, 2012).

Meyer, H. 1990. Inventory accuracy–Is it worth it? A case study.Prod. Invent. Manag. J. 31(2): 15–17.

Millet, I. 1994. A novena to Saint Anthony, or how to find inven-tory by not looking. Interfaces 24(2): 69–75.

Morey, R. C. 1985. Estimating service level impacts from changesin cycle count, buffer stock, or corrective action. J. Oper.Manag. 5(4): 411–418.

Neely, P. 1987. Simple mathematics applied to inventory accu-racy. Prod. Invent. Manag. 28(3): 64–68.

Raman, A., N. DeHoratius, Z. Ton. 2001. Execution: The missinglink in retail operations. Calif. Manag. Rev. 43(3): 136–151.

Roth, A. V. 2007. Applications of empirical science in manufactur-ing and service operations. Manuf. Serv. Oper. Manag. 9(4):353–367.

Sahin, E., Y. Dallery. 2005. Improving the performance of inven-tory systems subject to errors by deploying the RFID technol-ogy. J. Decis. Syst. 14(4): 73–91.

Shadish, W. R., T. D. Cook, D. T. Campbell. 2002. Experimental andQuasi-Experimental Designs for Generalized Causal Inference.Houghton Mifflin, Boston, MA.

Singer, J. D., J. B. Willett. 2003. Applied Longitudinal Data Analysis:Modeling Change and Event Occurrence. Oxford UniversityPress, New York, NY.

Whitaker, J., S. Mithas, M. S. Krishnan. 2007. A field study ofRFID deployment and return expectations. Prod. Oper. Manag.16(5): 599–612.

Zipkin, P. 2006. The best things in life were free: On the technol-ogy of transactions. Manuf. Serv. Oper. Manag. 8(4): 321–329.

Hardgrave, Aloysius, and Goyal: RFID-Enabled Visibility and Retail Inventory Record Inaccuracy856 Production and Operations Management 22(4), pp. 843–856, © 2013 Production and Operations Management Society

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