Bill Hardgrave (presenter) John Aloysius Sandeep Goyal Information Systems Department University of Arkansas

 Will RFID technology improve inventory record accuracy? (Study 1)  Can RFID technology ameliorate the effects of known causal predictors of inventory record inaccuracy? (Study 1 / Study 2)  What are the characteristics of product categories for which RFID technology is effective in reducing inventory record inaccuracy? (Study 2)

 All products in air freshener category tagged at case level  Interrupted Time-series design  Data collection: 23 weeks  13 stores: 8 test stores, 5 control stores  Mixture of Supercenter and Neighborhood Markets  Daily physical counts  10 weeks to determine baseline  Same time, same path each day

Results of Linear Mixed Effects VariablesEffect (Intercept) 8.004*** Sales Volume-0.953** Variety Item cost 0.040* Dollar Sales PRE 0.138** TRANS 1.875*** POST *** Notes p<.05, **p<.01, ***p<.001 Velocity = Number of units sold per day; Item Cost Cost of an item in cents; Sales Volume = Item Cost X Velocity; Variety = Number of unique SKUS carried in a store; PRE: Periods numbered consecutively for 40 day window around the adjustment; POST: Periods numbered O to 20 days before the adjustment, numbered consecutively after; TRANS: Numbered 0 before the adjustment, numbered 1 after Notes p<.05, **p<.01, ***p<.001 Velocity = Number of units sold per day; Item Cost: Cost of an item in cents; Sales Volume = Item Cost X Velocity; Variety = Number of unique SKUS carried in a store; PRE: Periods numbered consecutively for 40 day window around the adjustment; POST: Periods numbered O to 20 days before the adjustment, numbered consecutively after; TRANS: Numbered 0 before the adjustment, numbered 1 after

VariablesFixed effects (Intercept) 5.654*** Sales volume 2.356*** Variety Item Cost Dollar sales Test-1.630** Period Notes: ***p<.001, **p<.01 Sales volume = Number of units sold per day; Item Cost = Cost of an item in cents; Dollar Sales = Item Cost X Velocity; Variety = Number of unique SKUs carried in a store; Test: Dummy variable coded I for test stores and 0 for control stores; Period: Day 1 starting when RFID auto-adjust was made available in test store.

PI accuracy improved 23% Results were essentially what we expected Insight from DeHoratius and Raman (2008) variables Raises the question: what about other categories?

Untreated Control Group design with pretest and post-test Matched Sample 62 stores: 31 test stores, 31 control stores Mixture of Supercenter and Neighborhood Markets Spread across the United States Looked at both understated PI and overstated PI Control stores: RFID-enabled, business as usual ▫ Test stores: business as usual, PLUS used RFID reads (from inbound door, sales floor door, box crusher) to determine count of items in backroom  Auto-PI: adjustment made by system  For example: if PI = 0, but RFID indicates case (=12) in backroom, then PI adjusted PI: Perpetual Inventory

Five general merchandise categories Floorcare e.g., Powerforce vacuum, tough stain pretreat, Woolite gallon Air freshener e.g., Glade plugin, Febreeze paradise, Glade oil Formula e.g., Pediasure chocolate, Nutripal vanilla Ready to assemble furniture e.g., computer cart, pedestal desk, executive chair Quick cleaners e.g., wood floor cleaner, Readymop, Swiffer floor sweeper PI: Perpetual Inventory

Data collection Two waves (Pre and Post implementation), two months apart Same time, same path each wave Stock physical counts conducted over 5 days in each wave by an independent company Dependent variable: PI Absolute = |PI – Actual| Looked at both understated and overstated PI Pre-implementationPost-implementation RFID Implementation 5 days2 Months5 days

Data collection (contd.): Measures Item cost Cost of the item to the retailer Sales volume Quantity of item sold for two month preceding measurement Dollar sales Dollar amount of items sold for two month preceding measurement Density Total number of units in a category divided by linear feet of shelf space for that category Variety Total number of unique SKUs in a category PI: Perpetual Inventory

PI~TREAT + COST + SALESVOL + DOLLARSA + DENSITY + CATVAR + TREAT_XXX *** p <.01, ** p <.05, * p <.10

PI = β 0 + β 1 *Treatment 1.*** p <.01, ** p <.05, * p <.10 2.Significance of difference assessed by interaction term of treatment (pre-post) and group (test-control)

*** p <.01, ** p <.05, * p <.10

▫ RFID technology with case-pack tagging demonstrated to improve inventory inaccuracy by 16% to 81% depending on category characteristics ▫ Evidence that RFID technology is effective in ameliorating the effects on inventory inaccuracy of item cost, sales volume, dollar sales, density, and variety PI: Perpetual Inventory

 RFID technology is more effective in reducing PI inaccuracy in product categories which have:  higher sales volume,  lower item cost,  higher dollar sales,  greater SKU variety,  greater inventory density

 What is the economic impact of improving inventory accuracy (with RFID)?  Imagine inventory accuracy with item-level tagging …

Bill Hardgrave John Aloysius Sandeep Goyal For copies of white papers, visit Keyword: RFID

 Perpetual inventory (PI) record inaccuracy affects forecasting, ordering, replenishment  PI is inaccurate on 65% of items (Raman et al. 2001)  At any given time the retailer in this study manages about $32 billion in inventory

 Firms are skeptical about implementing new technologies based on pure faith, but need value assessments, tests, or experiments (Dutta, Lee, and Whang 2007)  Such empirical-based research requires “a well-designed sample, with appropriate controls and rigorous statistical analysis”

RFID Technology Inventory Visibility Inventory Record Inaccuracy Costs/ Profitability Research Gap Delen et al. 2007

 There is little empirical research in the field that demonstrates and quantifies the ability of RFID technology to improve inventory inaccuracy  There is no empirical research that characterizes product categories for which RFID technology may be effective in reducing inventory record inaccuracy

 Inventory visibility  Retailer’s ability to determine the location of a unit of inventory at a given point in time by tracking movements in the supply chain  Inventory record inaccuracy  Absolute difference between physical inventory and the information system inventory at any given time (Fleisch and Tellkamp 2005)  RFID-enabled auto-adjustment  A system that leverages RFID technology to correct for the absolute difference between physical inventory and the inventory management system inventory at any given time

Mechanisms which result in record inaccuracy Results in overstated PI? Results in understated PI? Can case-level RFID reduce the error? Incorrect manual adjustment Yes Improper returnsYes No Mis-shipment from DC Yes Cashier errorYes No PI: Perpetual InventorySource: Delen et al. (2007)

 There is evidence that RFID technology improves inventory visibility  Researchers assume that improved inventory visibility will result in improved inventory record inaccuracy and consequently impact costs and profitability  The current research experimentally manipulates inventory visibility in field conditions (by means of an RFID enabled auto-adjustment system) in order to assess the effect on inventory record inaccuracy

 Looked at understated PI only  i.e., where PI < actual  Treatment:  Control stores: RFID-enabled, business as usual  Test stores: business as usual, PLUS used RFID reads (from inbound door, sales floor door, box crusher) to determine count of items in backroom ▪ Auto-PI: adjustment made by system ▪ For example: if PI = 0, but RFID indicates case (=12) in backroom, then PI adjusted – NO HUMAN INTERVENTION

Backroom Storage Sales Floor Door Readers Backroom Readers Box Crusher Reader Receiving Door Readers

 Two comparisons:  Discontinuous growth model (Pre-test/Post-test)  PI = b 0 + b 1 *PRE + b 2 *POST + b 3 *TRANS  Linear mixed effects model (Test/Control)  Random effect: Items grouped within stores  Statistical software: R  Hardware: Mainframe

VariableMeanStd. Dev Sales Volume Item Cost ** 3. Dollar Sales *** 0.125*** 4. Variety *** 0.146*** 0.160*** 5. Treatment **0.059*** 6. PI- Inaccuracy *** ***0.182***0.030 Notes: ***p<.001, **p<.01

 Comparisons:  Linear mixed effects model (Pre-test/Post-test)  Random effect: Items grouped within stores  Statistical software: R  Hardware: Mainframe

MeanStd. Dev PI_ABS Cost ** 3Category Variety ** -.198** 4Sales Volume ** -.032** -.037** 5Dollar Sales **.356** -.177**.648** 6Density **-.217**.263**.170** -.114**