1. Product Data Quality – the Game
Changer to Success in Retailing
rm/ecrasia/061010
Presentation Summary
• The presentation covers findings from
1. The data crunch exercise done on data received from Retailers &
Suppliers in India
2. Industry perspective on their current challenges/pain points through
survey/interviews
• The data crunch exercise has uncovered the extent
of data discrepancy in India Retail & CPG Industries.
The numbers are worrying!
• The survey findings show that there is substantial
financial impact on their business due to poor data
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2. • Study conducted under National Retail Committee of CII
(Confederation of Indian Industry)
• CII is India’s key Industry body with direct membership exceeding
8100 companies and indirect membership of 90,000 companies
from 400 Trade/Industry associations.
• CII founded 115 years ago. Currently has 64 offices in India/
overseas and 223 counterpart organisations in 90 countries
• National Retail Committee membership includes key retailers
representing most of Indian organised retail besides other Retail
related organisations including GS1 India, IBM
• Study conceptualised by GS1 India under CII in collaboration with
IBM India who undertook the detailed study
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Definitions of Terms used
EAN – European article Number (Unique Item identification number represented in a barcode)
GTIN – Global Trade Item Identification Number (Unique Item identification number represented
in a barcode)
GDSN – Global Data Synchronization Network
FMCG – Fast Moving Consumer Goods
MRP – Maximum Retail Price (India government mandates that every product should
have a maximum price at which it can be sold specified on it)
Shelf Life – The life of a product from manufacture to expiry
Case Configuration/Eaches in a case – Number of units in a case/carton
Each – Refers one unit of a product
4way Match – A particular attribute is common across 4 different Retailers/Suppliers
SME – Subject Matter Expert
Fill Rate – % of order fulfilled (EG: if 100 units orders & only 80 delivered, fill rate will be 80%)
Deductions – Retailers deduct certain amount from the supplier’s bill due to
Returns/deviations from agreed terms of trade etc..
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3. Background
• Data Accuracy is worldwide a key driver to synchronizing data
between Retailers & their Suppliers
• A fact finding exercise is underway in India to evaluate extent
of Data Inaccuracy and its impact
• The exercise covered data being received from Retailers and
Suppliers for identified set of parameters
• The exercise is limited to FMCG assuming the suppliers are
organized and have evolved processes
• The exercise is divided in to two phases, Phase I concentrates
on data crunch and Phase II on building a business perspective
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Phase I
Data Crunch Exercise

4. Item master data requested for the exercise
30 generic parameters sought Sample below
Parameter Requested Parameter Description
Retailer item code Number/Code of the item maintained internally by the retailer
Item Long Description Description of the Item (up to 60 characters)
Item short Description Description of the Item (up to 40 characters)
MRP -
MRP Manufacturer's recommended retail price for the item. This field is stored in the primary currency.
Barcode/EAN 13 Fill in the EAN Code of the product (Fill in multiples if more than one).
Standard UOM Unit of measure in which stock of the item is tracked/ maintained
Conversion factor between an "Each/unit" and the standard uom of the product. (e.g. if standard_uom = case and 1
case = 10 eaches/units, this factor will be 10). This factor will be used to convert sales and stock data when an item
is retailed in eaches but does not have eaches as its
UOM Conversion Factor standard unit of measure (UOM).
Vendor/Supplier Code Supplier/vendor Number
Item shelf life Item shelf life in number of days
Eaches in a inner Enter the number of eaches / other UOM in Inner
Eaches in a case Enter the number of eaches / other UOM in Case
Each Length Enter length of item
Each Width Enter width of item
Each Height Enter height of item
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Phase I – Steps followed
Step 1 Obtain data files from retail partners Step 1
Review each file for completeness
Step 2 Step 2
Matching of consumer unit and traded unit data between
Step 3 Step 3
retailer files
Step 4 Request supplier data Step 4
Review supplier files for completeness
Step 5 Step 5
Matching of consumer unit and traded unit data
Step 6 Step 6
between suppliers and retailers
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5. Phase 1
1. Retailer data Analysis
Retailer Data Summary
Retailer 1 Retailer 2 Retailer 3 Retailer 4
Initial Observations
Available EAN/GTIN codes for Analysis 1014 3265 1735 1313
Under the same retailer item code there are many EAN codes
attached
One EAN number attached to multiple item codes
Missing/Incorrect EAN/GTIN Codes
MRP Missing
Vendor, Supplier product code missing
Shelf life blank or zero
Case configuration/ eaches in a case missing
Each L,W,H dimensions missing/incorrect
Each weight missing
case L,W,H dimensions missing
case Weight Missing
70% - 100% Occurrence 40% - 70% Occurrence Under 40% Occurrence
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6. Study Challenges Summarized
• The Retailer data received had multiple EAN/GTIN codes attached
with an item and retailers are maintaining data at the item code
level. This makes it difficult to compare data across retailers.
• Every item can have multiple MRP values hence its unlikely to have
one MRP available with every retailer. In the data received we find
different MRP values being associated with one EAN/GTIN code
which poses a challenge.
• The units of measure and conversion factors used pose a problem
to have exact comparison done to a precision level.
• There are many fields where the value is 1 which makes it difficult to
judge whether it’s a genuine value or a dummy value
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Retailer Consumer Unit GTIN Analysis
Retailer Unique GTINS
4 way = 224
224 GTINs were same across 4
A 1013 Retailer files
B 3265
3 way = 687
GTIN occurs once in 3 retailer files
C 1735 includes 4 way match results
D 1313
2 way = 1670
GTIN occurs once in 2 retailer files
Includes 3 and 4 way match results
Analysis from raw GTINs provided
to unique de-duplicated GTINS
Note: For subsequent Analysis (Ref slides 11 & 12)
1. We have considered the 224 GTINs which were common to all 4 Retailer files
2. Few parameters (like dimensions) were only available in two of the retailer files. Hence 651 GTIINs which were
common in these two files were used to compare the data.
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7. Consumer Unit Attributes Match
Based on 224 GTINS common to 4 Retailer files
Exact Match
Attributes Attributes Attributes
matched matched matched
across all 4 across any 3 retailers across any 2 retailers
retailers
Eaches Per Case
1% 22% 66%
Shelf Life
7% 29% 65%
MRP
42% 82% 91%
• The shelf life data is critical for ensuring product freshness, Discrepancy
Under 40% Match here can have financial impact as well safety concerns
• Case configuration data if incorrect can also result in financial impact
40% - 70% Match if used in calculating the units received/invoiced
70% - 100% Match • MRP is the only parameter which is at a reasonable level (Discrepancy
attributable to human error and all systems not updated)
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Consumer Unit Attributes Match based on
651 GTINs common to 2 Retailer files
Attribute Exact Match Tolerance 10% +/-
Attributes Attributes
matched matched
across any 2 retailers across any 2 retailers
Each Length
1% 23%
Each Width
2% 13%
Each Height
7% 49%
Each Dimension Sum
3% 61%
Each Volume
1% 22%
Each Net Weight
51% 55%
Note: The data for these parameters was available with only two retailers hence we could analyze data for common set of records between these two retailers
Under 40% Match • Besides net weight the dimension data is in red. The dimensions did not match
between Retailers
40% - 70% Match
•Even after applying a tolerance of +/- 10% none of the parameters were in
70% - 100% Match green
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8. Phase I
2. Retailer Vs Supplier data Analysis
Summary :Average matched attributes to all 4 Retailers
4 Way Match
Supplier 1 Supplier 2 Supplier 3 Supplier 4
Eaches Per Case 3% 3% 0% 0%
Shelf Life 0% 0% 8% 0%
MRP 42% 23% 62% 33%
• There is significant discrepancy between Retailer and Supplier data.
Under 40% Match
• Ideally there should not have been any discrepancy if the data from Suppliers
was used by Retailers without any manual intervention.
40% - 70% Match
• This clearly shows that Retailers are maintaining their own version of data which
70% - 100% Match is further impacted by manual errors
• 0% implies mismatch in shelf life maintained across Retailers
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9. Summary: Average matched attributes to 2 Retailers
Note: The data for these parameters was available with only two retailers hence we could analyze data for common set of records
between these two retailers
WITH 10% Tolerance
Supplier 1 Supplier 2 Supplier 3 Supplier 4
Each Net Weight 45% 70% 43% 19%
Each Length 29% 0% 22% 38%
Each Width 10% 0% 24% 42%
Each Height 48% 0% 18% 48%
Each Volume 12% 0% 8% 23%
• There is significant discrepancy almost 98-99% between Retailer and Supplier
Under 40% Match dimension data when we do an exact match.
40% - 70% Match • Even with +/- 10% tolerance the discrepancy does not seem to go down too much
70% - 100% Match • This clearly shows that Retailers are maintaining their own version of data which
is further impacted by manual errors
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High Level Observations
• 3 of the 4 Retailers had 28% to 53% of their item codes associated with two or
more GTIN codes. ( Having multiple EAN/GTIN codes attached to a single item
code while makes the effort for new item creation easy however it can create
inefficiency in operations like shelf management, promotion handling, Planogram
management)
• When comparing Supplier data with Retailer data the average match was less
than 50% across parameters barring MRP, with measurements such as dimensions
showing close to 0% match in certain cases .(There is a duplication of effort from
the retailers in capturing the logistical data as we see there is hardly any match
between the data from retailers and suppliers.)
• Supplier data was much more complete when compared with retailer data
• Only two retailers were maintaining item level dimension data of the four
• Retailers maintain the master data at item code level which is linked to multiple
EAN/GTIN codes and multiple MRP’s.
• Not every retailer seemed to maintain accurate and exact data about shelf life
and case configuration
• Getting all the data was a challenge. We got a feedback that data resided in
multiple systems and even to get the data per our requested format proved to be
not an easy task
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10. Phase II
Setting Business Context
Questionnaire Prepared for the exercise
Questionnaire for the Suppliers Microsoft Excel
Worksheet
Questionnaire for the Retailers Microsoft Excel
Worksheet
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11. Phase II – Steps followed
Step 1 Prepare the questionnaire with relevant Step 1
business related questions
Review the questionnaire with GS1 &
Step 2 Step 2
Industry SMEs
Send the questionnaire to industry
Step 3 Step 3
participants
Step 4 Discuss the questionnaire with them Step 4
Through face to face meetings/Telecons
Receive and consolidate Step 5
Step 5
The responses
Arrive at industry averages and derive
Step 6 Step 6
Inference from the responses obtained
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Summary findings from the Survey
• Retailers quote Average fill rate loss from Suppliers due to data
errors to be 10% to 15%
• Approx 30% to 40% of the PO’s received by suppliers contain
errors
• 20-50% of Finance and Merchandising team’s time spent
reconciling PO’s,Invoices, Payments
• Suppliers quote 5-10% deductions on invoice value by
retailers
• 20-40% of time spent by DC executives on reconciling PO’s,
receipts,managing returns etc..
• Industry loosing 15-20% space utilization gain by
missing/incorrect product dimensions
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12. Thank You
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