CON9012 Effective Multichannel Commerce with Product Information Management

1. CON9012Effective Multichannel Commerce with Product Information Management BruckAssefa Senior Product Strategy Manager, Oracle DhimanBhattacharjee Principal Product Strategy Manager, Oracle ShaibalTalukder Director of IT, The Pampered Chef Presenting with

2. Safe Harbor The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

3. Program Agenda • Business Challenges & Opportunities • Product Data Challenges & Solution Overview • Customer Case Study: The Pampered Chef

4. Business Challenges & Opportunities

5. Multi-Channel Buying Experience is Complex Browse Print Catalog Print Catalog Visit Stores In-Store Pickup Up-Sell Warranty Store Complete Order Customer Service Call Center Download Coupon Browse Reviews Review Order History Web Create Cart Scan Coupon Mobile Compare Prices Write Review Compare Products Tablet Tweet Check With Friends Tell Friends Like Social

6. Multi-Channel Commerce Spans Multiple Processes • Master Data Management is a Foundation for Effective Multichannel Commerce • Cross-Channel Order Fulfillment • Invoicing • Shipment • Delivery Fulfillment Channels • Cross-Channel Commerce • Targeting & Segmentation • Pricing • Promotions Sales Channels • Cross-Channel Experience • Guided Navigation • Search • Personalization • Order Orchestration • Source Selection • Splitting • Monitor Updates • Cross-Channel Order Capture • Orders • Order Changes • Cancellations Warehouse Distributors Drop-Shippers 3rd Party Store • Merchandising • Planning • Procurement • Replenishment Web Call Center Kiosk Mobile Store Master Data Management Product Data Customer Data

7. A Typical eCommerce Website • Rich and Accurate Product Data is Critical for Sales and Customer Satisfaction Product Catalogs Fulfillment Options Images/Videos Marketing Descriptions Sales Bundles Cross-Sell/Up-Sell Products Warranties Product Features/Specifications

8. Product Data Challenges & Solution Overview

9. Product Data Challenges in Multi-Channel Commerce 1. Product information resides in multiple fragmented systems CRM PLM Retail ERP 2. Suppliers send large volumes of product data via different channels (Portal, GDSN, CNET) contributing to siloed on-boarding processes Enrichment and Governance Sharing Consolidation Data Quality 3. Suppliers send product data including images in their own native file formats (Excel, CSV, XML) 4. There is lack of streamlined collaboration with trading partners

10. Consolidate Product Data from Suppliers and Multiple Systems Enforce Data Quality and Enterprise Business Rules During Consolidation PLM CRM Consolidate Suppliers ERP Enterprise Data Quality for Product Fusion Product Hub • Consolidate Items, GTINs, Packs, Structures, Trade Partner Items, Cross References, Related Items, Category Assignments and more • Leverage standard based integration framework for real time and batch integration: • Enforce enterprise data quality standards and validation rules irrespective of source data • Review data quality results and remediate errors prior to import • Enforce workflow policies to review and approve new item introductions and product data changes • Specify options for manual or automatic processing of import data • Gain key insights and metrics on important batch import activities Cleanse Govern Share

11. On-Board Supplier Specific File Formats Accelerate Product Data Induction from Suppliers/Trading Partners • Load item data received in supplier specific file formats • Create a one-time mapping of CSV,XML and other commonfile formats • Use direct mappings or expressions to transform supplier fields to master fields • Associate Import Maps to one or more suppliers • Upload File from Different Sources • Create Mapping for Different File Types including CSV, XML • Preview Supplier Data Mapped to Product Hub Fields Prior to Import • Drag and Drop to Map Supplier Data to One or More Master Data Fields

12. Product Hub Portal* for Vendors/Partners Download Category Specific Templates Review Validation Errors Grouped by Error Types Self Service UI and Effective Collaboration 1 3 Upload Product Data and Digital Assets in Various File Formats Remediate Data Errors and Re-Upload Files 2 4 * Near Term Roadmap

13. Product Data Challenges in Multi-Channel Commerce 1. 2. Products are often misclassified or miscategorized Product data including descriptions are non-standard, misspelt and poorly formatted Enrichment and Governance Sharing Consolidation Data Quality 3. Duplicate items are difficult to identify and exist across the enterprise

14. Auto-Classify and Categorize Products • Embedded Enterprise Data Quality for Product Data Poor Quality Supplier Data – Non-standardized across items Example: Item: RT0003 - 16:9 Kodak Black digital photo frame 1024x768 10 IN 8 GB 400:1 EDQP • Semantic recognition to determine meaning and appropriate category • Auto-classification of products in appropriate product taxonomy • Auto-categorizations to multiple alternate catalogs (eCommerce, UNSPSC, Merchandising and others) • User control to confirm suggested product classifications and alternate categorizations • Enforce proper classification and alternate categorizations at all points of data entry Staging area to review consolidated data quality results prior to on-boarding data

15. Standardize and Auto-Populate Product Data • Embedded Enterprise Data Quality for Product Data Poor Quality Supplier Data – Non-standardized across items Example: Item: RT0003 - 16:9 Kodak Black digital photo frame 1024x768 10 IN 8 GB 400:1 EDQP • Consistent and standardized product descriptions • Auto-generation of standardized short, medium, and long descriptions of various character lengths • Extraction and auto-population of standardized attribute values • User control to confirm standardized descriptions and attribute values • Enforce data standardization at all points of data entry Staging area to review consolidated data quality results prior to on-boarding data

16. Identify Potential Duplicates and Similar Products • Embedded Enterprise Data Quality for Product Data Poor Quality Supplier Data – Non-standardized across items Example: Item: RT0003 - 16:9 Kodak Black digital photo frame 1024x768 10 IN 8 GB 400:1 EDQP • Identify duplicates as well as exact, close or functionally equivalent matches • Create and tune match rules by source system or product category • User control to confirm potential matches or duplicates • Prevent duplicate products at all points of data entry Staging area to review consolidated data quality results prior to on-boarding data

17. Product Data Challenges in Multi-Channel Commerce 2. Product data enrichment requires input from multiple lines of business which is difficult and time consuming to orchestrate 1. Product enrichment requires storing structured and unstructured data that are difficult to centrally manage Cross-sell/Up-Sell relationships Attributes Bundles Catalogs Images/Videos Supply Chain Marketing Sales Regulatory Enrichment and Governance Sharing Consolidation Data Quality 5. Change management is slow, ungoverned and performed manually across multiple systems 4. Product data enrichment is prone to errors and lacks required business validations 3. Product data and catalogs vary based on target markets, sales channels and other business needs Total Weight = 1 lbs Unit Weight = 2 lbs Trading partners Channels Different Geographies

18. Centralize Management of Enterprise Product Data • Extensible and Scalable Enterprise Product Data Model Categorizations in eCommerce, Print, UNSPC, Merchandising, Financial Reporting and other alternate hierarchies Associations to internal stores, suppliers, target markets and channels Sales bundles and configurations Unlimited number of product attributes Business user defined attributes to model marketing attributes, product features, design specifications, etc Cross-sell, up-sell, warranty, and other seeded and user defined product relationships Seeded attributes for the operational needs of the enterprise (Sales and Order Management, Planning, Inventory…) Foundation for Enterprise Product Record People who can view and edit product data Digital assets including images, videos, user manuals and other unstructured data

19. Orchestrate Product Definition Across the Enterprise Induct and Enrich Product Data via a Governed Workflow Sales Catalogs Supply Chain Attributes Supply Chain Attributes Digital Assets Sales Attributes Sales Catalogs Sales Attributes Regulatory Attributes Regulatory Attributes Regulatory Attributes Marketing Attributes Sales Attributes Sales Attributes Supply Chain Attributes Supply Chain Attributes Sales Catalogs Sales Catalogs Marketing Attributes Regulatory Attributes Digital Assets Digital Assets Marketing Attributes Digital Assets Regulatory Attributes Sales Attributes Supply Chain Attributes Sales Catalogs Marketing Attributes Digital Assets Marketing Attributes Step 5: Review & Approve Step 1: Sales Step 2: Marketing Step 3: Regulatory Step 4: Supply Chain Collaborative New Item Introduction and Enrichment Workflow • Orchestrate product definition across the enterprise via a flexible and collaborative workflow • Enforce required attributes at each stage of the workflow to prevent potential oversight and associated product delays • Induct only products that have been reviewed and approved to prevent redundant or unauthorized products • Monitor and improve definition and approval cycle times to achieve faster new item introduction and time to market

20. Manage Product Data for Various Business Needs • Localize and Maintain Context Specific Product and Catalog Data Target Market Specific Product Data Master Catalog Spain Spanish Description France French Description US English Description Channel Specific Catalogs Alternate Business Categorizations eCommerce Catalog Print Catalog UNSPSC Merchandise Hierarchy Financial Reporting

21. Enforce Data Integrity via Robust Business Rules Enrich Product Data While Consistently Applying Rules • Validate product data at any point during consolidation, enrichment or publication • Receive immediate remediation feedback • Assess impact of new or modified business rules prior to adoption Example Product Data Rules to Enforce Your Governance Policies • Product Attributes: Products cannot be on-boarded from Suppliers unless they have Item Description, Brand and MSRP information • Product Catalogs: Products that are sold on the web must be assigned to the ‘eCommerce Catalog’ and have a corresponding ‘UNSPSC’ and ‘Financial Reporting’ classification • Supplier Associations: Supplier A cannot be associated to Electronics products since it is not an approved supplier for electronics • Product Relationships: Products cannot have cross-sell or up-sell relationships with obsolete products or services • Product Lifecycle Phases: Products must have a ‘manufacturer’ attribute populated prior to being in a commercial phase • Internal Store Associations: Products that are drop shipped cannot be assigned to internal stores

22. Streamline Change Management for Business Efficiency Implement Changes via a Governed Review and Approval Process Change Once and Propagate to All Consuming Systems Sales Channels No Check ifApprovalNeeded Implement Change Product Data Change Route forApproval Yes Order Capture Systems Rules & processes defined by business users Order Fulfillment Systems Governed and Streamlined Change Management Process • Implement product data changes through a governed review and approval process • Define change management rules and processes for your unique business needs • Change once and propagate to all internal and external systems eliminating redundant and manual processes • Monitor and improve change management cycle times for greater business efficiency Reporting & Analytics

23. Product Data Challenges in Multi-Channel Commerce 1. Costly point-to-point integrations are maintained to share product data making it difficult to adopt new channels, systems and target markets 2. Inaccurate, incomplete and non-validated product data is shared with consuming systems Brand Name Legacy CRM Website Data warehouse ERP Enrichment and Governance Sharing Consolidation Data Quality 3. There is lack of visibility about what product data is shared with what consuming system Sales Channel ERP Product A

24. Order Fulfillment Publish Standardized and Accurate Product Master Data Store Suppliers Finance Logistics Warehouse Share Clean and Reliable Product Data Within and Beyond the Enterprise Commercial Catalogs (Web Catalogs, Print Catalogs,…) Order Capture Web Call Center Kiosk Mobile Store Product Specifications, Marketing Descriptions, Images,… Up-Sell/Cross-Sell Products Fusion Product Hub (Complete Enterprise Product Record) Operational Attributes (Order Management, Fulfillment …) Pack Configurations Substitute/ Equivalent Products Other ERP, Retail, Supply Chain, Regulatory, BI and Legacy Applications

25. Validate Product Data Prior to PublicationEnsure Complete and Accurate Product Data is Shared with Consuming Systems • Enforce business rules to ensure: • All required attributes are populated • Valid cross-sell/up-sell/warranty relationships are defined • Products are assigned to appropriate Web, Print, UNSPSC and other alternate catalogs • Publish only products conforming to established business rules

26. Empower Business Users to Drive Product Data PublicationControl Which Product Data Entities are Published to Which Consuming Systems Consuming System: Web Channel Business Entities to Publish: Products and Catalogs Product Data Entities to Publish: Attributes, Relationships & Digital Assets Product Selection Criteria: ONLY Server Products Catalog Publication Criteria: ONLY eCommerce Catalogs

27. Print Catalog Publication Tablet App Template PDF Output Print Template Adobe ® InDesign Server Fusion Product Hub Adobe Digital Publishing Suite Silicon Paginator* Tablet Output Published Print Catalog * 3rd Party Plug-in

28. Customer Case Study: The Pampered Chef

29. Effective Multichannel Commerce with Oracle PIM ShaibalTalukder – Director of IT EAA

30. The Pampered Chef,A Berkshire Hathaway Company 30 The Pampered Chef, Ltd. A global company that offers a line of kitchen tools, food products, and cookbooks aimed for preparing food in the home Headquartered in Addison, Illinois and operates in five countries: USA, United Kingdom, Germany, Canada and Mexico. Products: Professional-quality tools for cooking and entertaining. More than 300 items are carried at any one time, with 80-90 percent of those products exclusive to The Pampered Chef®. Test Kitchens: The Pampered Chef® Test Kitchens are committed to developing timesaving tools, tips and recipes that enhance mealtime and suit busy lifestyles. A staff of home economists, food scientists, chefs, dieticians and product developers edit, test and develop more than 400 recipes per year. The Pampered Chef® is continually expanding its recipe and product collection to align with top flavor, ingredient and lifestyle trends. Distribution: Products are sold primarily through in-home cooking demonstrations known as Cooking Shows (1 million are held in the United States each year). Structure: Direct Sales. There are tiered career levels progressing from Pampered Chef® Consultant to National Executive Director. Our corporate web site is: www.PamperedChef.com.

31. Business Challenges – Product Data The Pampered Chef Inability to deliver in the fast paced dynamic external environment, renders the enterprise unresponsive in a growing world of complexity and change. 31 • Cyclical Product Introduction in five different countries twice a year across 26+ applications spanning five different business areas . • Inconsistent Product data across enterprise systems serving different sales channels – multiple web platforms, mobile platforms, etc. causing – • Increased call volume in Customer Support Centre • Increased Customer Dissatisfaction • Lost Revenue because of differences in price and high rate of returns caused by data inconsistency • Inflexible system making operational changes time consuming, expensive and difficult resulting in lost business opportunity • Lack of automation and work flow for product life cycle management resulting in cumbersome operations across departments • Redundant and time consuming effort to make any product data change across enterprise

32. Our Vision for Product Management We were We are People Processes Tools Tools Broken Processes People To be efficient we wanted to rely more on solid Tools and Processes to operate our business. 32

33. Opportunities The Pampered Chef 33 Maintain product attributes such as dimensions, color codes, price, etc. uniform across enterprise applications and web applications supporting multiple sales channels. Reduce multiple points of entry to a single central point of entry and thus reduce data inconsistency. Automate the product life cycle approval process from concept to fruition to retirement Maintain consistent product and customer information across the enterprise to provide better service and options to our customers Streamline product management process and make the enterprise system flexible enough to address business requirement readily

34. Solution / Option The Pampered Chef 34 • Deploy Master Data Solution for Product Data Management across enterprise. • Deploy PIM solution to manage product introduction from concept to fruition. • Deploy Service Oriented Integration platform integrating centralized Master Data Management system with multiple enterprise systems including web, ERP, CRM, mobile apps, compensation, etc.

35. Why Oracle ? The Pampered Chef 35 • Automated workflow for approval • Maintain history from concept to retirement • Maintain consistent centralized repository of product data integrating disparate enterprise systems • Restricted role based access • Ease of use from the business perspective, utilizing template based deployment • Service Oriented Application • Flexibility to add business specific attributes • Extensive proof of concept with Oracle was embraced by TPC business users. • Oracle SOA fusion middleware was the choice of integration tool and thus deployment technicality was aligned with TPC in-house and implementation resources • Product Maturity and fitment from technical and business use perspective were the driving force to select Oracle MDM

36. Oracle Product Data Hub Implementation 36 Effectively captured, maintained, secured and governed 300+ product attributes, relationships, structures, categories, and documents utilized by several business groups such as Product Development, Quality Assurance, Sales and Marketing, Supply Chain, Procurement, Distribution Center and Finance at The Pampered Chef Synchronized product information, product relationships, product structures, and product categories to Web, Mobile Apps and Oracle EBS using real time SOA integration Enforced Role Based Access to enable the data governance and security of 300+ product attributes captured in PIM Enabled effective management of the life cycle of products from conception to retirement. Able to capture the history and generate reports around the product life cycle. Leveraged PIM workflow to simplify communication and co-ordination of various steps involved in Product Life Cycle among different business groups such as Product Development, Quality Assurance, Sales and Marketing, Supply Chain, Procurement, Distribution Center, Finance Established Single Source of truth for the product information at The Pampered Chef. Enforced business rule validations to prevent any data errors at the time of data entry itself. Facilitated the Data Governance and Reporting of product data at The Pampered Chef.

37. Product attributes, Categories, Relationships are published to Web(www.PamperedChef.com) and Mobile Apps Product Life Cycle Management from Concept to Retirement is done in Product Data Hub. Product Data Flow OracleR12 Web Product attributes, Relationships, Categories are published to transactional system - Oracle ERP Mobile 37

38. Project Status The Pampered Chef 38 Live with MDM and SOA for over one year User acceptance test going for R12

39. Results The Pampered Chef 39 • Unified PDM approach. Centralized Repository and subscription model. • Single point of entry eliminating multiple manual point of entries. • Spoke and hub subscription model utilizing Oracle’s SOA fusion middle ware and thus eliminating cumbersome point to point code based integration. • Item status, description and availability is real time across Website, smartphone or if requested over the phone with call center.

40. Before and After

41. Business Benefits Realized 41 Product launch is owned and managed by the business Over 1500 product launches completed successfully since go-live Number of calls to the Solution Center for product information mismatches are eliminated from over 2000 calls during the week of product launch in old system to null in the new system Product launch used to be an IT intensive event. Now it is driven by the Marketing and Sales department and is a non-IT event. Over 25K SOA calls for product changes occurs per day successfully during product launch time, without user intervention Streamlined business processes around master data lead to effective collaboration among different departments Repository available to store and maintain business area specific product data, which is managed by each area and readily accessible Role based restricted user access level for secured product data

42. Business benefits realized… Solution center savings –expenses to process additional calls from product related discrepancies – 0.5 M annually IT resources reduction – 1.5 Full time managers & 3.5 resources – 1 M annually Returns due to unsatisfied customers ~ $ 2 M annually Lost customers due to incorrect item information – intangible Better reporting on product data Reduced time to market

43. What are we working on now ? • Data Quality – OEDQ • Driving Data Governance towards maturity – • Defined Data Strategy • Data Life Cycle – Retention, Archival and Purging • Data Retrieval and Reporting • Continuous Improvement

44. MDM is our Source of Truth and SOA facilitates the same across Enterprise Applications Shaibal Talukder Director of IT

45. Key Product MDM Sessions MASTER DATA MANAGEMENT

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