RFID Enabled Business Processes and Retail Stockouts

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RFID Enabled Business Processes and Retail
Stockouts

• Matthew A. Waller
• Oren Harris Chair in Logistics

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Agenda

• Business processes utilizing RFID to reduce OOS
• Overview of the study of RFIDs impact on store
  level OOS
• Covariates
• Results
• Implications for theory building next steps

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How RFID Can Reduce OOS

• Promotional display execution
• 19 lift in sales for Gillette study
• Finding SKUs in the backroom
• Reverse picklist
• PI accuracy improvement

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Insights Promotions
If the product needed to be on the floor by
10/10/05 for the promotion, store 567 just missed
an important window of opportunity for
themselves and for the supplier
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OOS Study - Overview

• Scope
• 12 test stores / 12 control stores
• 6 supercenters, 3 division I, 3 neighborhood
  markets
• Control stores chosen to match test stores
• Stores in Texas and southern Oklahoma
• Scanning
• February 14 to September 12
• Scanned daily for 210 days (29 Wal-Mart weeks)
• Scanned most sections of the store (some
  exceptions)
• Started approximately same time each day and
  followed the same path

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Study Overview

• Ended up with 6 test stores
• 4,077 SKU-Locations in NMs (17)
• 5,925 SKU-Locations in WMs (25)
• 13,980 SKU-Locations in SCs (58)
• 30 of SKU-Locations were in test stores
• Mean c.v. about 1.9

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CAUSES OF SHELF STOCKOUTS Covariates and the
Fixed Factor
INVENTORY POLICY
STORE-LEVEL EXECUTION
Case Pack Capacity Vis-à-vis Shelf Capacity
Safety Stock
Frequency of Replenishment
Management Competency
SHELF STOCKOUTS
Shelf Holding Capacity
Business Processes
Demand Uncertainty
Level Of Demand
DEMAND CHARACTERISTICS
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OOS Reduction by Velocity

• For products selling X
• units per day RFID reduced OOS by
• lt .1 ---------------------- no improvement
• .1 - .2 ---------------------- 32
• .2 - .3 ---------------------- 32
• .3 - .5 ---------------------- 20
• .5 1 ---------------------- 36
• 1 3 ---------------------- 29
• 3 7 ---------------------- 32
• 7 15 ---------------------- 62
• gt 15 ---------------------- no improvement
• .1 15 30

More than 90 of the tagged items were within
this range
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Sample - Results
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OOS in perspective

• Likely to vary by department
• Likely to vary by sales velocity
• Likely to vary by .
• Bulk of items in study were in the lower velocity
  categories. What if more faster moving items had
  been tagged
• Non-tagged SKUs improved in test stores as well

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Consumer Reactions to OOS

• National and worldwide average out of stock (OOS)
  of approximately 8
• Roughly 25 of OOS attributed to store
  replenishment processes (in the store, not on the
  shelf)
• Consumer responses to OOS
• Do not purchase 9 (Retailer, Supplier)
• Substitute different brand 26 (Supplier)
• Substitute same brand 19
• Buy item at another store 31 (Retailer)
• Delay purchase 15
• Potential sales loss to retailers 3.2 (40x8)
• Potential sales loss for suppliers 2.8 (35x8)

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Summary

• 26 improvement in test stores Metro reported an
  11 in stock improvement for test sites
• 30 reduction in out of stocks for products
  selling gt .1 and lt 15 units per day
  (statistically significant)
• Translated into for suppliers? For retailers?

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Theory Building

• Execution should be more a part of theory
• Nomological validity of measures and alternative
  measures
• New approaches to managing inventory
• Complexity of models
• Changing settings based on observations