Product Data Quality – the Game
                         Changer to Success in Retailing




                             ...
• Study conducted under National Retail Committee of CII
  (Confederation of Indian Industry)
• CII is India’s key Industr...
Background


• Data Accuracy is worldwide a key driver to synchronizing data
  between Retailers & their Suppliers

• A fa...
Item master data requested for the exercise

30 generic parameters sought Sample below

Parameter Requested        Paramet...
Phase 1
                                               1. Retailer data Analysis




                           Retailer D...
Study Challenges Summarized


    • The Retailer data received had multiple EAN/GTIN codes attached
      with an item and...
Consumer Unit Attributes Match
                                      Based on 224 GTINS common to 4 Retailer files


     ...
Phase I
                               2. Retailer Vs Supplier data Analysis




Summary :Average matched attributes to al...
Summary: Average matched attributes to 2 Retailers
Note: The data for these parameters was available with only two retaile...
Phase II
           Setting Business Context




Questionnaire Prepared for the exercise




Questionnaire for the Supplie...
Phase II – Steps followed


  Step 1         Prepare the questionnaire with relevant   Step 1
                      busine...
Thank You




            23
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Ravi mathur product data quality

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Transcript of "Ravi mathur product data quality"

  1. 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 2
  2. 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 3 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.. 4
  3. 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 5 Phase I Data Crunch Exercise
  4. 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 7 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 8
  5. 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 10
  6. 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 11 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. 12
  7. 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) 13 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 14
  8. 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 16
  9. 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 17 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 18
  10. 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 20
  11. 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 21 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 22
  12. 12. Thank You 23

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