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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 . Bill Hardgrave John Aloysius Sandeep Goyal (presenter) Information Systems Department

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

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

  4. Business Problem and Motivation • Perpetual inventory (PI) record inaccuracy affects forecasting, ordering, replenishment • PI is inaccurate on 65% of items (Raman et al. 2001) • Simulation shows that inventory visibility provided 40 to 70% reduction in inventory cost (Joshi 2000) • At any given time the retailer in this study manages about $32 billion in inventory

  5. 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”

  6. 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

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

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

  9. 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

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

  11. Hypothesis 1 • RFID-enabled auto-adjustment will decrease inventory record inaccuracy over and above existing inventory management systems (IMS) • IMS is the automated system that tracks the records of inventory on hand in the supply chain PI: Perpetual Inventory

  12. Factors Influencing PI Inaccuracy (DeHoratius and Raman 2008) • Item level • Item cost • Sales volume • Dollar volume sales • Distribution structure • Store level • SKU variety • Audit frequency • Inventory density PI: Perpetual Inventory

  13. Hypothesis 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 PI: Perpetual Inventory

  14. Study 1 • All products in air freshener category tagged at case level • 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

  15. 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

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

  17. 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

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

  19. Study 1 Results: Linear Mixed Effects(Pre-test/post-test comparison for test stores)

  20. Study 1 Results: Discontinuous growth model(Pre-test/post-test comparison for test stores)

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

  22. Study 2 • Matched Sample • 62 stores: 31 test stores, 31 control stores • Mixture of Supercenter and Neighborhood Markets • Spread across the United States • 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

  23. 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

  24. 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

  25. 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

  26. Study 2 (contd.) • Dependent variable: PI Absolute = |PI – Actual| • Looked at both understated PI and overstated PI • 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 PI: Perpetual Inventory

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

  28. Study 2 Results: Descriptive Statistics

  29. Study 2 Results: Ameliorating effects of RFID (Pre-test/Post-test) PI~PERIOD + COST + SALESVOL + DOLLARSA + DENSITY + CATVAR + PERIOD_XXX

  30. Study 2 Results:Effect size for Treatment, Linear Mixed Model PI = β0 + β1*Treatment

  31. Study 2 Results:Characterization of Categories *** < 0.01; ** < 0.05; * < 0.1 Sales Volume:Number of units sold per day Dollar Sales: Sales in dollars Inventory Density: ItemCost: Cost of an item in cents SKUVariety: Number of unique SKUs carried in a store

  32. Contributions • RFID technology with case-pack tagging demonstrated to improve inventory inaccuracy by 23% • Some 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

  33. Contributions (contd.) • RFID technology is more effective in reducing PI inaccuracy in product categories which have greater SKU variety, high sales volume, higher dollar sales, lower cost, and greater inventory density

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

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