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DeepakGupta(MDMStrategist)
PIM CASE STUDY- RETAIL
-By Deepak Gupta
PIM Advisor and Strategist
 Problem with Product Data and Processes.
 Enterprise Product Data Sheet
 Immediate Benefits
 Overall ROI.
 Proposed: Long Term Business Strategy.
 Implementation Strategy.
 Resource and Skill.
DeepakGupta(MDMStrategist)
CONTENTS
PROBLEM WITH
EXISTING PRODUCT
DATA
WHY TO IMPLEMENT
PIM?
DeepakGupta(MDMStrategist)
DeepakGupta(MDMStrategist)
System 1
System 2
Team A
Team B
Custome
r file
Supplier
file
Partner
file
System 3
App 1
App 2
…
Vendor
File
...
...
Multiple Interfaces Multiple Interfaces
• Scattered Data, Multiple Interfaces
• Multiple Teams, Multiple Systems
DeepakGupta(MDMStrategist)
Internal&ExternalSourceSystems
Customers
Partners
Suppliers
Email Files
Legacy
Lead time high for New Item Induction
Phone Call
Validate,
Match and
Create Item
in required
System
System 1
System 2
Important Data exist in Local
Desktop/Laptop
App 1
App 2
App 3
App 4
No Automation- All Manual Process
( High Lead Time and Error Prone)
DeepakGupta(MDMStrategist)
Major Examples
• Duplicate/Triplicate Master Items.
• Invalid/Special Characters in Item Description and other
attributes.
• Mandatory Attributes like dimension , size , category missing for
significant number of items.
• No Standard item classification tax-anomy in place.
• Significant number of Orphan Items ( Item exist in Child Org
without master).
• GTIN Missing for Significant number of Items.
• No Standardized Unit of Measure of items defined.
Very Poor Data Quality
DeepakGupta(MDMStrategist)
Major Examples
• Mass update of attributes (dimensions, purchasing status, etc.)
require IT Support
• Mass Copy of Item Codes for New Warehouse Induction and
Movement require IT Support.
• Any New attribute addition require IT Support.
• New Customer/Supplier Onboarding require IT.
• Every Business process is hard coded example, approval
workflows, Business validation. Hence any change would require
IT support.
Too Much IT Dependency
PRODUCT DATA
SHEET
DeepakGupta(MDMStrategist)
DeepakGupta(MDMStrategist)
S.N
O
Entity Count
1 Number of Warehouses 100 +
2 Volume of Master Items 500 K +
3 Volume of Org Items 2 million +
4 Number of Attributes 200 +
5 Volume of Attribute Values 10 million +
6 Number of Users 300+
7 Number of Product Data Sources 20 +
8 Number of Transaction Systems 10 +
9 Number of Item Created/Week 5000 +
IMMEDIATE BENEFITS
DeepakGupta(MDMStrategist)
DeepakGupta(MDMStrategist)
Single
Blended
Record
Standardize/Consolidate/Match Review and Load
Standard
Data
Non-Standard
Data
Internal&ExternalSourceSystems
Customers
Partners
Suppliers
MDM
Team
Email Files Manual
(Review &
Workflow)
Automatic
(Rules-Based)
- or -
StagingArea
Legacy ETL
Spread-
sheet
upload
FTP Files
PIMBlend/Match
Engine
Standardization
Engine
Interfaces
• Reduced Manual Steps- Automate most of Processes for Item
maintenance.
• Implement Data Governance to improve data quality
• Lead time reduced for new item creation.
• Oracle PIM is highly scalable.
DeepakGupta(MDMStrategist)
Oracle
PIM
System A
System B
Custome
r file
Supplier
file
Partner
file
Vendor
File
...
I
N
B
O
U
N
D
I
N
T
E
R
F
A
C
E
One Item
O
U
T
B
O
U
N
D
I
N
T
E
F
A
C
E
Common Interface Common Interface
Item Team
Oracle
EBS
• Single Source of Data
• Common Interfaces
• Single Team
• Single System
STANDARD, CLEAN AND VALID DATA
DeepakGupta(MDMStrategist)
PIM
After : Standardized and Clean Data
Standardize item description
Extract and standardize attributes
Classify in Item Master Catalog
Apply industry and company standards
Validate values
Check for Completeness( Mandatory)
1
2
3
4
5
Description
Item Class Brand Speed
Catalog Category Revenue Code Commodity Code
1
2
3
2
4
Processor Intel Pentium 2.80 GHz
Manufactured Box Revenue 847170
Processor: Intel Pentium G840 (2.80GHz, 3 MB)
Procesador: Intel Pentium G840 (2.80GHz, 3 MB)
Description
Item Class Brand Speed
Catalog Category Revenue Code Commodity Code
AMD Athlon™ B 26
Manufactured Box Revenue 847171
Before : Non-Standard Product Data
PROCINTELPENTIUM2.8GHZ3MB
AMD ATHLONB26DUALCORE
Customer 1
Customer 2
Процессор : Intel Pentium G840 (2,80ГГц, 3Мбайт)
AMD Athlon(TM) II X************2 Dual-Core processor B26
AMD Athlon™ II X2 デュアルコア プロセッサー B26
AMD 애슬론™ II X2 듀얼 코어 프로세서 B26
6
5
6
DeepakGupta(MDMStrategist)
IMPROVED TAXONOMY AND CLASSIFICATION
OF PRODUCTS
• User-defined category hierarchy
• User-defined attributes
for each category
• Attribute inheritance
for easy maintenance
Category
Grocery
UPC# ____
Manufacturer ____
Buyer # ____
Attribute N ____
Category
Tobacco
UPC # ____
Manufacturer ____
Buyer ____
Attribute N ____
Smokeless Type ____
Category
Dry
UPC # ____
Manufacturer ___
Buyer # ____
Attribute N ____
Category
Attributes
Category
Inherited
Attributes
H
i
e
r
a
r
c
h
y
Unique
Attributes
CEN Master Catalog
Classify Products & Components in
Primary Item Master Catalog
DeepakGupta(MDMStrategist)
COMPLETE VIEW OF PRODUCT INFORMATION
GTIN Information
Classification
Physical Dimension
UPC & PLU
Warehouse
Specific
Attributes
Audit
History
Change
Requests/
Change Orders
REDUCTION IN LEAD TIME FOR NEW
WAREHOUSE INDUCTION
DeepakGupta(MDMStrategist)
Copy Items from one warehouse to another using Excel Sheet reduces lead
time for building new warehouse.
LONG TERM
STRATEGY
DeepakGupta(MDMStrategist)
DeepakGupta(MDMStrategist)
Govern
Customers
Suppliers
Partners
Mergers
Legacy
External Source
Consolidate
Cleanse
Parse Enrich
Match & De-Duplicate
Role
Based
Security
Admin
Events &
Policies
Change
Management
Use
Sales
Publish &
Subscribe
(SOA/ESB)
Finance
Finance Users
Data Ware House
Order Management
Procurement
Marketing
SCM Users
SNP Users
Purchasing
Ecommerce
Online
Sales
Support
Master
Data
Hub
Marketing
ItemPortal
RETURN OF
INVESTMENT (ROI)
DeepakGupta(MDMStrategist)
DeepakGupta(MDMStrategist)
• Product Data
Maintenance in 3
systems.
• Order Fails because of
Data Quality Issues.
• Manual Process of Item
Creation.
• Existing System are not
scalable
• Multiple Interfaces
running on old technology
and platform.
• Product Data
Maintenance in Single
System.
• Reduction in Data Quality
Issues.
• Automated Process of
Item Creation.
• PIM is scalable to hold 1
trillion items.
• Common Interface on
latest Service Oriented
Platform.
Before PIM After PIM
ROI- IN TERMS OF SIMPLICITY/MAINTENANCE
DeepakGupta(MDMStrategist)
• No Security and
Governance on Item Data.
• No mass update using
MS- Excel facility. IT is
involved for any mass
update.
• No Mass Copy Code
Process.
• Item creation process
require lot of manual
work.
• Manual work to support
customer item requests.
• Role Based Security and
Policy on Item Data.
• Mass update using MS-
Excel facility. No IT
involvement.
• MS- Excel Mass Copy Code
Process.
• Zero touch item creation
process using excel upload..
• Automatic processing of
customer item requests.
Before PIM After PIM
ROI- IN TERMS OF EASE OF PROCESS

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Pim retail industry case study

  • 1. DeepakGupta(MDMStrategist) PIM CASE STUDY- RETAIL -By Deepak Gupta PIM Advisor and Strategist
  • 2.  Problem with Product Data and Processes.  Enterprise Product Data Sheet  Immediate Benefits  Overall ROI.  Proposed: Long Term Business Strategy.  Implementation Strategy.  Resource and Skill. DeepakGupta(MDMStrategist) CONTENTS
  • 3. PROBLEM WITH EXISTING PRODUCT DATA WHY TO IMPLEMENT PIM? DeepakGupta(MDMStrategist)
  • 4. DeepakGupta(MDMStrategist) System 1 System 2 Team A Team B Custome r file Supplier file Partner file System 3 App 1 App 2 … Vendor File ... ... Multiple Interfaces Multiple Interfaces • Scattered Data, Multiple Interfaces • Multiple Teams, Multiple Systems
  • 5. DeepakGupta(MDMStrategist) Internal&ExternalSourceSystems Customers Partners Suppliers Email Files Legacy Lead time high for New Item Induction Phone Call Validate, Match and Create Item in required System System 1 System 2 Important Data exist in Local Desktop/Laptop App 1 App 2 App 3 App 4 No Automation- All Manual Process ( High Lead Time and Error Prone)
  • 6. DeepakGupta(MDMStrategist) Major Examples • Duplicate/Triplicate Master Items. • Invalid/Special Characters in Item Description and other attributes. • Mandatory Attributes like dimension , size , category missing for significant number of items. • No Standard item classification tax-anomy in place. • Significant number of Orphan Items ( Item exist in Child Org without master). • GTIN Missing for Significant number of Items. • No Standardized Unit of Measure of items defined. Very Poor Data Quality
  • 7. DeepakGupta(MDMStrategist) Major Examples • Mass update of attributes (dimensions, purchasing status, etc.) require IT Support • Mass Copy of Item Codes for New Warehouse Induction and Movement require IT Support. • Any New attribute addition require IT Support. • New Customer/Supplier Onboarding require IT. • Every Business process is hard coded example, approval workflows, Business validation. Hence any change would require IT support. Too Much IT Dependency
  • 9. DeepakGupta(MDMStrategist) S.N O Entity Count 1 Number of Warehouses 100 + 2 Volume of Master Items 500 K + 3 Volume of Org Items 2 million + 4 Number of Attributes 200 + 5 Volume of Attribute Values 10 million + 6 Number of Users 300+ 7 Number of Product Data Sources 20 + 8 Number of Transaction Systems 10 + 9 Number of Item Created/Week 5000 +
  • 11. DeepakGupta(MDMStrategist) Single Blended Record Standardize/Consolidate/Match Review and Load Standard Data Non-Standard Data Internal&ExternalSourceSystems Customers Partners Suppliers MDM Team Email Files Manual (Review & Workflow) Automatic (Rules-Based) - or - StagingArea Legacy ETL Spread- sheet upload FTP Files PIMBlend/Match Engine Standardization Engine Interfaces • Reduced Manual Steps- Automate most of Processes for Item maintenance. • Implement Data Governance to improve data quality • Lead time reduced for new item creation. • Oracle PIM is highly scalable.
  • 12. DeepakGupta(MDMStrategist) Oracle PIM System A System B Custome r file Supplier file Partner file Vendor File ... I N B O U N D I N T E R F A C E One Item O U T B O U N D I N T E F A C E Common Interface Common Interface Item Team Oracle EBS • Single Source of Data • Common Interfaces • Single Team • Single System
  • 13. STANDARD, CLEAN AND VALID DATA DeepakGupta(MDMStrategist) PIM After : Standardized and Clean Data Standardize item description Extract and standardize attributes Classify in Item Master Catalog Apply industry and company standards Validate values Check for Completeness( Mandatory) 1 2 3 4 5 Description Item Class Brand Speed Catalog Category Revenue Code Commodity Code 1 2 3 2 4 Processor Intel Pentium 2.80 GHz Manufactured Box Revenue 847170 Processor: Intel Pentium G840 (2.80GHz, 3 MB) Procesador: Intel Pentium G840 (2.80GHz, 3 MB) Description Item Class Brand Speed Catalog Category Revenue Code Commodity Code AMD Athlon™ B 26 Manufactured Box Revenue 847171 Before : Non-Standard Product Data PROCINTELPENTIUM2.8GHZ3MB AMD ATHLONB26DUALCORE Customer 1 Customer 2 Процессор : Intel Pentium G840 (2,80ГГц, 3Мбайт) AMD Athlon(TM) II X************2 Dual-Core processor B26 AMD Athlon™ II X2 デュアルコア プロセッサー B26 AMD 애슬론™ II X2 듀얼 코어 프로세서 B26 6 5 6
  • 14. DeepakGupta(MDMStrategist) IMPROVED TAXONOMY AND CLASSIFICATION OF PRODUCTS • User-defined category hierarchy • User-defined attributes for each category • Attribute inheritance for easy maintenance Category Grocery UPC# ____ Manufacturer ____ Buyer # ____ Attribute N ____ Category Tobacco UPC # ____ Manufacturer ____ Buyer ____ Attribute N ____ Smokeless Type ____ Category Dry UPC # ____ Manufacturer ___ Buyer # ____ Attribute N ____ Category Attributes Category Inherited Attributes H i e r a r c h y Unique Attributes CEN Master Catalog Classify Products & Components in Primary Item Master Catalog
  • 15. DeepakGupta(MDMStrategist) COMPLETE VIEW OF PRODUCT INFORMATION GTIN Information Classification Physical Dimension UPC & PLU Warehouse Specific Attributes Audit History Change Requests/ Change Orders
  • 16. REDUCTION IN LEAD TIME FOR NEW WAREHOUSE INDUCTION DeepakGupta(MDMStrategist) Copy Items from one warehouse to another using Excel Sheet reduces lead time for building new warehouse.
  • 18. DeepakGupta(MDMStrategist) Govern Customers Suppliers Partners Mergers Legacy External Source Consolidate Cleanse Parse Enrich Match & De-Duplicate Role Based Security Admin Events & Policies Change Management Use Sales Publish & Subscribe (SOA/ESB) Finance Finance Users Data Ware House Order Management Procurement Marketing SCM Users SNP Users Purchasing Ecommerce Online Sales Support Master Data Hub Marketing ItemPortal
  • 20. DeepakGupta(MDMStrategist) • Product Data Maintenance in 3 systems. • Order Fails because of Data Quality Issues. • Manual Process of Item Creation. • Existing System are not scalable • Multiple Interfaces running on old technology and platform. • Product Data Maintenance in Single System. • Reduction in Data Quality Issues. • Automated Process of Item Creation. • PIM is scalable to hold 1 trillion items. • Common Interface on latest Service Oriented Platform. Before PIM After PIM ROI- IN TERMS OF SIMPLICITY/MAINTENANCE
  • 21. DeepakGupta(MDMStrategist) • No Security and Governance on Item Data. • No mass update using MS- Excel facility. IT is involved for any mass update. • No Mass Copy Code Process. • Item creation process require lot of manual work. • Manual work to support customer item requests. • Role Based Security and Policy on Item Data. • Mass update using MS- Excel facility. No IT involvement. • MS- Excel Mass Copy Code Process. • Zero touch item creation process using excel upload.. • Automatic processing of customer item requests. Before PIM After PIM ROI- IN TERMS OF EASE OF PROCESS