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RFID-Enabled Visibility and Inventory Record Inaccuracy: Experiments in the Field

Bill Hardgrave (presenter) John Aloysius Sandeep Goyal Information Systems Department University of Arkansas. RFID-Enabled Visibility and Inventory Record Inaccuracy: Experiments in the Field . Research Questions. Will RFID technology improve inventory record accuracy ?

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RFID-Enabled Visibility and Inventory Record Inaccuracy: Experiments in the Field

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  1. Bill Hardgrave (presenter) John Aloysius Sandeep Goyal Information Systems Department University of Arkansas RFID-Enabled Visibility and Inventory Record Inaccuracy: Experiments in the Field

  2. Research Questions • Will RFID technology improve inventory record accuracy? • Can RFID technology ameliorate the effects of known causal predictors of inventory record inaccuracy? • What are the characteristics of product categories for which RFID technology is effective in reducing inventory record inaccuracy?

  3. Hypothesis 1 • RFID-enabled auto-adjustment will decrease inventory record inaccuracy over and above existing inventory management systems

  4. Study 1 • 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

  5. Study 1 Results: Linear Mixed Effects(Pre-test/post-test comparison for test stores) • From DeHoratius and Raman (2008) • Item level • Item cost • Sales volume • Dollar volume sales • Distribution structure (fixed) • Store level • SKU variety • Audit frequency (fixed) • Inventory density (fixed)

  6. Study 1 Results: Discontinuous growth model(Interrupted time series for test stores)

  7. Study 1 Results: Linear Mixed Model for Test versus Control stores

  8. Study 1: Discussion • PI accuracy improved 23% • Results were essentially what we expected • Insight from DeHoratius and Raman (2008) variables • Raises the question: what about other categories?

  9. Hypothesis 2 (Study 2) • RFID-enabled auto-adjustment will ameliorate the inventory record inaccuracy due to high sales volume, low item cost, high SKU variety, high dollar volume of sales, and inventory density • (across multiple categories) PI: Perpetual Inventory

  10. Study 2 • 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

  11. Study 2 (contd.) • 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

  12. Study 2 (contd.) • 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 RFID Implementation Pre-implementation Post-implementation 5 days 5 days 2 Months

  13. Study 2 (contd.) • 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

  14. Study 2 Results: Ameliorating effects of RFID (Pre-test/Post-test) PI~TREAT + COST + SALESVOL + DOLLARSA + DENSITY + CATVAR + TREAT_XXX *** p < .01, ** p < .05, * p < .10

  15. Study 2 Results:Effect size for Treatment, Linear Mixed Model PI = β0 + β1*Treatment *** p < .01, ** p < .05, * p < .10 Significance of difference assessed by interaction term of treatment (pre-post) and group (test-control)

  16. Study 2 Results:Characterization of Categories *** p < .01, ** p < .05, * p < .10

  17. Contributions • 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

  18. Contributions (contd.) • 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

  19. Future Research Directions • What is the economic impact of improving inventory accuracy (with RFID)? • Imagine inventory accuracy with item-level tagging …

  20. Bill Hardgrave bhardgrave@walton.uark.edu 479.575.6099 John Aloysius jaloysius@walton.uark.edu 479.575.3003 Sandeep Goyal sgoyal@walton.uark.edu 479.575.6961 For copies of white papers, visit http://itri.uark.edu/research Keyword: RFID

  21. Business Problem and Motivation • 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

  22. Scientific Motivation • 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”

  23. Research Model: Making the Business Case for RFID Technology Research Gap RFID Technology Inventory Visibility Inventory Record Inaccuracy Costs/ Profitability Delen et al. 2007

  24. Research Gap • 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

  25. Key Terms • 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

  26. How does inventory inaccuracy occur? PI: Perpetual Inventory Source: Delen et al. (2007)

  27. Research Gap • 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

  28. Study 1 (contd.) • 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

  29. Box Crusher Reader Receiving Door Readers Backroom Readers Backroom Storage Sales Floor Door Readers Sales Floor Read points - Generic Store

  30. Study 1: Statistical Analyses • Two comparisons: • Discontinuous growth model (Pre-test/Post-test) • PI = b0 + b1*PRE + b2*POST + b3*TRANS • Linear mixed effects model (Test/Control) • Random effect: Items grouped within stores • Statistical software: R • Hardware: Mainframe

  31. Study 1 Results: Descriptive statistics (all stores, pooled across pre-test/post-test periods)

  32. Study 1 Results: Post Hoc Analysis

  33. Study 2: Statistical Analyses • Comparisons: • Linear mixed effects model (Pre-test/Post-test) • Random effect: Items grouped within stores • Statistical software: R • Hardware: Mainframe

  34. Study 2 Results: Descriptive Statistics

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