Does RFID Improve Inventory Accuracy? Bill Hardgrave University of Arkansas RFID Research Center Note: this document is copyrighted ( 2008) and confidential; do not distribute or cite without explicit permission.

Who are we? Purpose: to investigate the business value and implications of RF technologies ~ 15 faculty and ~100 students Privately funded: more than 50 companies provide funding and equipment for the Center Founding member of GRFLA

RFID Lab 10,000 sq. ft. lab in Hanna’s Candle Co. 4,500 sq. ft. lab in Zero Mountain (cold storage facility) Replicates RFID in supply chain: dock doors, conveyor, impact doors, forklifts, pallet wrappers, item level, etc. Serves as research and teaching facility Provides services to the industry (tag type, tag placement, reader/antenna type) EPCglobal Accredited Performance Test Center

RFID Lab Lab simulates the complete retail supply chain from supplier’s shipping dock to store shelf

RFID Lab “The [University of Arkansas] RFID Lab is the most advanced of any owned by a university …” Mark Roberti, Editor of RFID Journal,

RFID Lab In the past year, more than 1100 people from 500+ companies visited the lab

RFID Lab

Premise Does RFID improve inventory accuracy? Huge problem –Forecasting, ordering, replenishment based on PI –PI is wrong 65% of the time –Estimated 10% reduction in profit due to inaccuracy What can be done? –Increase frequency (and accuracy) of physical counts –Identify and eliminate source of errors

Causes of Inventory Inaccuracy PI inaccuracy causes Results in overstated PI? Results in understated PI? Can case-level RFID reduce the error? Incorrect manual adjustment Yes TheftYesNoPartial Damaged/spoiledYesNoPartial Improper returnsYes No Mis-shipment from DC Yes Cashier errorYes No

Examples – Manual adjustment  PI = 12  Actual = 12  Casepack size = 12  Associate cannot locate case in backroom; resets inventory count to 0  PI = 0, Actual = 12 (PI < Actual)  Unnecessary case ordered

Examples – Theft  PI = 20  Actual = 20  7 stolen  PI = 20, Actual = 13 (PI > Actual)  Sell 13 over the next week:  PI = 7; Actual = 0  ROP = 6; thus, no order placed; no more sales

Examples – Cashier error Product AProduct B PI10 Actual10 Sell 3 of A and 3 of B, but Cashier scans as 6 of A PI = 4 Actual = 7 (PI < Actual) PI = 10 Actual = 7 (PI > Actual)

The Study All products in air freshener category tagged at case level 23 weeks 16 stores (8 test, 8 control) –Mixture of Supercenter and Neighborhood Markets PI accuracy determined each day: PI – actual 10 weeks to determine baseline Same time, same path each day

The Study Looked at understated PI only –i.e., where PI<actual Treatment: –Control stores: nothing; 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

Results

12% -1% 12% - (-1%) = 13%Numbers are for illustration only; not actual

Results

Implications What does it mean? –Inventory accuracy can be improved (with tagging at the case level) –Is RFID needed? Could do physical counts – but at what cost? –Improving understated means less inventory; less uncertainty Value to Wal-Mart and suppliers? In the millions! –When used to improve overstated PI: reduce out of stocks even further –Imaging inventory accuracy with item-level tagging …

Bill Hardgrave For copies of white papers, visit Keyword: RFID