DIGITAL BACKBONE A JOURNEY TO COMPANY WIDE PRODUCT DATA QUALITY MANAGEMENT

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2 DIGITAL BACKBONE A JOURNEY TO COMPANY WIDE PRODUCT DATA QUALITY MANAGEMENT Rolf Sint, IT Architect

3 Company Introduction

4 SWAROVSKI GROUP EUR 3.5 billion annual turnover generated by the Swarovski Group 32,000 employees distributed across 170 countries 3 companies Swaroski Crytsal Business, Swarovski Optik, Tyrolit Headquartered in Austria and Switzerland 4

5 SWAROVSKI CRYSTAL BUSINESS (SCB) Business Units & Brands CONSUMER GOODS BUSINESS SWAROVSKI PROFESSIONAL GEMSTONES BUSINESS ATELIER SWAROVSKI LIGHTING BUSINESS SWAROVSKI TOURISM SERVICES SWAREFLEX Executive Board Von links: Daniel Cohen, Nadja Swarovski, Mathias Margreiter, Markus Langes-Swarovski, Robert Buchbauer 5

6 FACTS SWAROVSKI CRYSTAL BUSINESS (SCB) EUR 2.7 billion turnover ,000 employees worldwide Stores worldwide 1,500 Swarovski Stores 1,500 Partner Stores Production locations in: Austria, India, Thailand, Vietnam, Serbia, USA 6

7 7 SCB LOCATIONS WORLDWIDE 3,000 Stores worldwide 7

8 Status quo and the product master data management challenge

9 CHALLENGE - PRODUCT DATA QUALITY AS A KEY COMPANY ASSET Increasing complexity of our product assortment Digital Initiatives (Online Shops, Mobile Apps, Online Plattformen, ) Increasing expectations to the Time to Market Data Protection Data Analytics State of the art IT landscape 9 DIGITAL BACKBONE

10 DIGITAL INITIATIVES (ONLINE SHOPS, MOBILE APPS, ONLINE PLATFORMS, ) IINCREASING NUMBER OF DIGITAL CHANNELS Through different channels (e.g. ecommerce) data (quality) becomes more visible to our customers Insufficient customer experience and lack of ability to show all available products or connect additional digital channels Quality issues and inefficient processes Inefficient processes, redundant data and inflexibility in IT systems are the reason for manual rework and a low degree of automation 10 DIGITAL BACKBONE

11 INCREASING COMPLEXITY OF OUR PRODUCT ASSORTMENT Current Assortment Swarovski has currently about materials (loose crystals) in our product assortment Each material describes a single crystal defined by key attributes like cut, color, size, effects and foiling. Each material is stored in SAP and has a unique material number. Future Assortment: (MTO) Our MTO assortment covers about. Cuts 533 Sizes 3 Average available sizes per cut Colors 70 Average available colors per cut Effects 55 Average available effects per cut Foiling 3 Average available The estimated maximum potential product variants cover about 18 Million variants of our crystals 11

12 THE MTO CHALLENGE We have to create and maintain feasibility information for crystals. 30% of these variants are technically not feasible, and another 30% are not feasible from a marketing point of view. So about 6 Million variants will comprise the final configurable assortment. The user should be able to start the configuration on any arbitrary criteria. E.g. size, color, etc. A lot of product configurators on the market only provide the linear configuration of products. E.g. Most car configurators. Complex logic within our product assortment: Some cuts are only feasible in specific sizes, some effects can only be applied on specific sizes and geometries. Some refinements can only be used on specific pretreatments. Indeed, various dependencies are possible and their detailed analysis and definition will be part of the project. Integration The rules will build the core of our future product assortment. So this information has to be integrated by several platforms, e.g. EShop, B2B Ecommerce, SAP, 12 PRESENTATION TITLE

13 BACK-END 3D USE CASES FRONT-END 3D DESIGNS ARE BECOMING ENTERPRISE ASSETS 3D DESIGN IS BECOMING AN ENTERPRISE ASSET Virtual try-ons Visual merchandising Online & retailer catalogues Communication Product costing Production automation Tooling & fixture production Demand planning How to guarantee the correctness of the data? What is the best way to distribute the data? 13

14 IT systems, data interaction and data objects

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16 PRODUCT DEVELOPMENT MAJOR SYSTEMS Siemens NX Siemens Teamcenter SAP ERP MDM Transactions Digital Backbone: Focus on MDM processes in Siemens NX, Siemens Teamcenter, SAP ERP and PCM Hybris PCM Hybris Commerce Mobile App HCP B2B Online Shops 16

17 STATUS QUO Highly customized IT application landscape No MDM system is currently in use Major systems für product master data are Siemens NX, Siemens Teamcenter and SAP ERP Siemens Teamcenter and SAP ERP are highly customized and have been developed over the last decades For a realignment and modernization of these systems the required processes as well as the future requirements have to be defined 17 PRESENTATION TITLE

18 In SCOPE: DEFINITION OF PRODUCT MASTER DATA MANAGEMENT OBJECTS IN PROJECT SCOPE Product development, data governance and data consumption Out of SCOPE: Pricing Stock Transactional data MES Sales and Demand Planning Data Launch planning CRM Product Master Data creation, maintenance and control Data Requirement: Customer requirement Product specifications Assortment data Launch information Siemens Teamcenter / Siemens NX # ID Descriptions Classification Geometric Data CAD Data Weight ERP # ID Descriptions, Classification Geometric Data, Weight Varian Configuration Plant specific data Sales specific data BOM Routing Product Documents PIM # ID Marketing Data Translations Media Assets Care instructions Data Consumption: Purchasing Production Logistic Sales / E- Commerce Analytics 18 DIGITAL BACKBONE

19 Project objectives of the digital backbone project

20 WHAT IS THE DIGITAL BACKBONE What is the digital Backbone about? The main objective of the digital backbone project is the identification of existing pain points in the product creation and maintenance process. the implementation of quick wins throughout the project as well as the development of an implementation roadmap to establish a state of the art IT architecture to support current and future business needs For this the following three dimensions have to be considered: Business Processes IT Systems and Tools Product Master Data Quality 20 DIGITAL BACKBONE

21 WHAT IS THE DIGITAL BACKBONE What is the result of this project? As a result of the digital backbone a future IT architecture in the areas of product master data will be established. This will form the digital backbone as a basis for the realization of future requirements in product management. 21 PRESENTATION TITLE

22 PRINCIPLES Product master data is maintained fully, correctly and on time Product master data is accessible be requesting persons and systems Individual responsibilities regarding data quality management processes are transparent and executed professional KPIs which indicate a high master data quality level are measured and checked regularly regarding completeness, consistency, correctness and actuality Causes of data quality issues are detected and fixed rapidly 22

23 THE 6 BUILDING BLOCKS FOR PRODUCT DEVELOPMENT IN A DIGITAL WORLD PROCESS PEOPLE TECHNOLOGY How does the process have to change so that we can significantly improve along the defined dimensions? ORGANIZATION Which skills and compentencies are required to effectively operate in such a product development environment? Which digital technologies can fuel the product development process? CAPABILITIES DATA 23

24 DIGITAL BACKBONE IS A MAJOR ENABLER OF SWAROVSKI S DIGITALIZATION AND FOCUSES ON EACH OF THESE BUILDING BLOCKS Digital Business Models Processes Organization System / Architecture Digital Customer SWAROVSKI s Digital Backbone Product master data Digital Enterprise Roles & Skills Capabilities Data 24 DIGITAL BACKBONE

25 Roadmap

26 DIGITAL BACKBONE PHASE 1 Timeline: 26 weeks Evaluation Phase Deliverables/Activities: Activity 0 Identify Stakeholder 1 Define business capabilities and requirements 2 Map future system architecture to capabilities 3 Asses status quo data objects and processes 4 Define current "data owner 5 Define future IT architecture 6 System application assessment (cleanup, adopt, replace) 7 Define implementation activities 26 DIGITAL BACKBONE

27 A JOURNEY TO COMPANY WIDE PRODUCT DATA QUALITY MANAGEMENT Identify relevant product masterdata objects Develop monitor data quality 6.Monitor 2.Define guidelines and data quality rules Digital Backbone Data quality and governance 5.Improve 3.Determine current data quality 4.Analyze reasons for quality issues and impact 27

28 IT OPPORTUNITIES & NEXT STEPS IT opportunities Establish a state of the art software architecture to support efficient business processes Introduce a MDM Tool to realize data governance ( including Workflows and Rule engines) (e.g. MDG / BRO / ) Introduce workflows to support processes Increase efficiency by increasing the level of Standardization Next Steps: High level documentation and status quo analysis of involved IT systems Get Business Vision, Business Objectives and Business Requirements Establish a target architecture together with business and based on business requirements Establish implementation roadmap 28

29 FUTURE IT ARCHITECTURE Establish a target architecture including. MDM SYSTEM SAP S/4 HANA Workflows Business Rules USER Interaction ACTIVE WORKSPACE Variant Configuration and CPQ 29

30 THANK YOU