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

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

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

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

  2. 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

  3. 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”

  4. 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

  5. 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?

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

  7. 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

  8. 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

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

  10. Hypothesis 1 (Studies 1 and 2) • 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

  11. 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

  12. 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 PI: Perpetual Inventory

  13. 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

  14. 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

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

  16. 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

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

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

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

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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

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

  27. Study 2 Results: Descriptive Statistics *** p < .01, ** p < .05, * p < .10

  28. 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

  29. 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)

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

  31. 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

  32. 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

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

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