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Master data management (mdm) & plm in context of enterprise product management


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The presentation discusses the classical features and advantages of Master Data Management (MDM) system along with appropriate situations to use it. How do companies apply MDM who design, manufacture and sell their products in several geographies facing challenges in making appropriate decisions on their investment in PLM & MDM space?
Another important aspect covers the comparison/relation between a MDM system (or Product Master System) and Enterprise PLM system. How can you maximize your ROI on both PLM and MDM investments? With examples from different industries the key takeaways include whether your organization requires an MDM solution or not.

Published in: Business, Technology

Master data management (mdm) & plm in context of enterprise product management

  1. 1. Master Data Management (MDM) & PLM – Enterprise Product Management Copyright © 2013 Tata Consultancy Services Limited 1
  2. 2. Table of Contents 1 Key trends in Product Development, reshaping product data 2 Current MDM practices, Need for product master data in MDM 3 Understanding Product Master Data - Ontology & Approach 4 PLM and MDM relationship 5 Customer Case Reference 6 Benefits & Takeaways 2
  3. 3. Globalization, Innovation, Cost competitiveness and Sustainability bringing increased focus on Product data Engineering Systems Globalization and localization of Products Part Engineer Sales & Marketing Systems “Product Data” mutates to meet local business and IT needs Component Supplier Designer Mergers, Acquisitions and Divestitures Engineering Data Manufacturing Systems Mfg Data Sales Data Multidisciplinary View of Sales Products Data Continuous and ongoing Mfg activity of Product Data Plant synchronization .Understanding of “What Product Data is” is Contract Mfg Mfg different across organizations Manufacturer Plant Data Engineering Data Constant increase in complexity of information needed to Mfg comprehensively understand product data. Engineering Sales Data Evolving Biz needs Mfg increasing the scope of Data product data elements Data Data Mfg Data Designer Continuously evolving business needs on “Product Data”. Engineering Data Acquisition Future proof IT at lower TCO Mfg Plant Mfg Data Mfg Plant Mfg Data Engineering Data Scalable robust IT infrastructure built on new gen IT strategies . Standardization and Harmonization of IT systems and services Part Engineer 3
  4. 4. Organizations are constantly thriving to address their challenges in managing and sharing Product Data Complex Product data, fragmented across the organization Looking beyond CAD & Documents and tackling the Complex Part/BoM /Change relationships Increased software intensity in products Expanding functional footprint (Simulation, Digital Mfg Data, Compliance and Quality Data) Product Data, today is federated and exists in disconnected multiple systems resulting in data ownership, governance and security issues Lack of Data / process synchronization leading to Usage of old and duplicate product data Long cycle times for data reconciliation Ever increasing mergers & acquisitions leading to growing complexity in product data diversity and relationships Available Product Data is not trustworthy for decision making “A need exists for a system to store released product information available as a System of Record, defined as Product Master Data which can be referred across the organization” Are Today’s MDM practices able to establish a product Master to address these challenges ?? 4
  5. 5. Current established practices of MDM should enrich the scope and boundaries of Product Data Today, Product Master data under MDM is catering mainly to the Sales and Marketing needs of the organization (Product Catalog, pricing, availability , etc..) Data Accuracy Data Reuse Data Quality Data Ownership Data Stewardship Semantic consistency Data Accountability Data Accountability Data Integrity Product Master data Enterprise Data Warehouse ETL Engine Supplier Master data Data Mart Data Analytics & BI Data Mart Data Mart EAI/P2P/SOA CRM CRM CRM ERP ERP ERP MES MES MRO PDM Legacy Legacy 5
  6. 6. Understanding Product Master Data in the Context of Master Data Management Multiple R&D Systems across BUs Enterprise Integration Layer EAI/P2P//SOA 3 2 Sales Sales Sales 4 Legacy Legacy Legacy (BU1) Product Catalog Sales Configurations Quotes & Orders Mat’l safety data sheets Mfg Product Master data PDM PLM PDM (BU1) Supplier Master data Derivable views for MTS, CTO, ETO etc Mfg Mfg Service Product Master Data 1 Plant BoMs Plant routings Work instructions Test plans and procedures Parts Data Design Data BOMs& Routings Docs/Specification Configuration Data Installed Base Spare Parts Catalog Service Bulletins Service Service Work Instructions ALMALM ALM (BU1) Supply Chain Supply Supply Chain Chain Material Sourcing Prod Quality Data ALM ALM Acquisitions Product Compliance Data Product Service Data 6
  7. 7. Ontology of Product Master Data Product Master Data Parts / Components Product Structure Product History / Change Mechanical BOM Electrical / Electronic Relationships Product Quality Data Rules Product Compliance Data Software Documents Visual Representation Product Planning Info Routings Process Instructions 2D 3D PDF / Word Product Service Data Product Technical Information Service Parts Attributes Product Re-use Info Classification Characteristics 7
  8. 8. A Closer look at the Data elements and interrelationshipsOntology of the Product Master Data (But not limited to..) Organization Business Unit Location Department Region Workflow Employee Workflow Task Mechanical Features Product Spare Part Master BoM Part Family Alternate Part Product Configuration Hardware Issue Supplier Software Eng. Change Cost Part Part Substitute Part BoMs Material Routings Substance Work Instructions Documents Tech. Manuals Specification Design Spec Tech. Spec Material Spec Test Spec Drawing PDF Model JT Images 8
  9. 9. PLM Systems will complement the creation of Product Master Data systems PLM PLM is the Source for Work in Progress (WIP) Product Data MDM MDM is the source for Released Product Data aggregated to a Product Master Authoring Space for Product Data Gate Keeper of Product Master Data for different consumers Focus on Multidisciplinary engineering functions (Hardware/ Focus on Product Master Data needed for enterprise level transactions Mechanics/Software) Process centric approach to creation of Product Data Structured feed of data elements which can constitute the Product Master data System of Record for establishing the single source of truth Leveraging the MDM infrastructure to feed different consumers like Sales, Supply Chain, Analytics etc PLM and Product Master data Systems are complementary and not redundant 9
  10. 10. Systemic View of the PLM MDM setup with other cross functional enterprise systems Data Migration Setup Sales Multiple Sources Extract Target Systems (Load) Staging (Cleanse/ transform) Configurator Pricing Warranty Orders MES MDM/PLM PDM/PLM Scheduling Testing / Quality Serialization As Built BOM Product Master PDM/PLM Authorin g Tools Product Data from Engineering/ R&D ALM Product Data needed by other functions (maintained in MDM) ERP Plant Specific Changes Planning Production Orders Localization Compliance Simulation Supplier Master Service Customer Master Integration Employee Master Data Load Service BOM Spares Maintenance Schedule 10
  11. 11. Enabling integration between the Engineering/ R&D systems, MDM and cross functional systems 1 PLM 3 2 MDM Request Maturity Lifecycle Engineering Business Processes Change Sales Mfg Response Product Master Changes Cross functional integration Service Supply Chain 11
  12. 12. Key Use Cases and solution scenarios Standard Based Meta Model Definition 1 • Meta model definition of the Product Master • Standards based metal model for structure, relationships and information exchange Data Ownership Identification & Transfer Mechanism • Identify ownership mechanism in various Engineering Systems • Ownership definition in Product Master system • Role & Access Control setup in Product Master System • Engineering attribute & Product Master attribute identification 4 Automated Continuous Data Migration Mechanism 2 • Automatic transfer situations from Engineering (R&D) systems to Product Master data system • Identify triggering mechanisms (synchronous / asynchronous) • Workflow modifications / status change triggers modification for data transfer Closed Loop Engineering Change Process 5 • Process definition for Engineering release to Product Master • Mechanism to inform Product Master stakeholders regarding the change • Change Number format harmonization in all Engineering Systems On Demand Trigger for data migration • Establishing the triggers from Engineering systems • 3 Identify additional attributes for transfer Integration between 6 engineering (R&D)systems and Product Data master system • Transform engineering system data to Product Master schema • Data validations before transfer • Development mechanism identification for source and destination systems • Queuing, Scheduling and Monitoring mechanism 12
  13. 13. Benefits for Globalization, Innovation, Cost competitiveness and Sustainability Data Governance, Risk & Compliance Process Simplification & Standardization Scalable and Simplified IT Landscape • • • • Standardized & uniform product data in Product Master system across the organization Single source for truth for Sales, Mfg, Supply chain & Service Ease in making Product Ownership changes Increased data quality for decision making • • • • Improved change management : Control on Global/Local changes Ease in engineering/mfg change analysis and implementation mproved business agility & speed to customer response Improved traceability of product data, helping in compliance & Issue identification • • Reduced number of interfaces (Low AMS costs) High Scalability due to standard practices and established models 13
  14. 14. 6 Key Learnings for Product Master Data fitment into Overall MDM Strategy 1 Business Unit / Product Division level variations in Product Data will lead to more time for interfaces harmonization 2 No standard data ownership identification mechanism. Organization specific rules need to be developed to classify ownership 3 Data conflicts identification & resolution is time consuming (especially for legacy data) 4 Historical data migration, could lead to development of complex migration mechanisms 5 Changes in upstream & downstream systems calls for lot of coordination among different organization functions 6 Standards driven Meta Model definition and data exchange like JT2Go, STEP, PLCS etc should be evaluated and established 14
  15. 15. Recommended reading  Open Methodology Solution on MDM ( ment_Solution_Offering).  Microsoft Definition on MDM (  David Butler. Master Data Management, an Oracle White Paper, 2011.  Dr Rob Bodington, Patrick Houbaux. What is PLCS – STEP AP239, 2011. 15
  16. 16. Thank You