Data Management, Information Quality. 3-5 November 2010

Transcription

1 Data Management, Information Quality & DW/BI Conferences Europe November 2010 Produced by:

2 Corporate Data Strategy Insights From Proven Approaches Using the Example of Nestlé Dr. Boris Otto, Karsten H. Muthreich Dr. Boris Otto, Karsten H. Muthreich London, November 5 th, 2010

3 Agenda 1 The strategic dimension of Corporate Data using the case of Nestlé 2 Nestle s experiences and future Plans 3 Insights and examples from the CC CDQ Community 3

4 Nestlé: Good Food, Good Life The world s leading nutrition, health and wellness company 4

5 Nestlé at a glance: Key figures CHF 108bn sales in 2009 EBIT CHF15.7bn Over 280, employees 449 factories Operations in 83 countries 5

6 Nestlé products and brands: instantly recognizable 10,000 different products Around 1 billion products sold every day A product for every moment of every day, from morning to night and from birth to old age 6

7 The GLOBE 1 program at Nestlé JULY 2000 The GLOBE Program is launched, under the sponsorship of Nestlé CEO, Peter Brabeck-Letmathe GLOBE will transform the highly successful federation of independent markets into one global Company that's my dream, that's what I hope to achieve, and we will achieve this through leveraging the size of the Group as a strength and not a liability (Peter Brabeck-Letmathe) 1) GLOBE: Global Business Excellence. 7

8 The original three GLOBE objectives 1 Implementation of harmonized Nestlé Business Excellence Best Practices 2 Implementation of Data Standards and Data Management: "Managing Data as a Corporate Asset Implementation of standardized information systems 3 and technology 8

9 Nestlé s s transormation From a Super tanker to an agile, change-ready fleet 9

10 Agenda 1 The strategic dimension of Corporate Data using the case of Nestlé 2 Nestle s experiences and future Plans 3 Insights and examples from the CC CDQ Community 10

11 GLOBE implementation status 89 Nestlé Markets/Businesses are operating with GLOBE processes, dataand systems Users 806 factories (378 Nestlé, 428 Co-Packers) Distribution Centers 591 Sales Offices March

12 From implementation to leverage Principles changing from: Implementation of harmonized Nestlé Business Excellence Best Practice To: Move from "Best in Nestlé" to "Best in Class" best practices confirmed with external benchmarking Implementation of Data Move from historical/explanatory Standards and Data Management reporting to forward looking/real "Managing Data as a Corporate time/predictive information with a focus Asset on customers and consumers Implementation of Standardized Information Systems and Technology Enable a fast, focused and flexible "front line" with a slim, cost efficient "back-line" 12

13 Data Management at Nestlé: Where are we today? GLOBE Business Framework GLOBE Applications: SAP the HEART APO HR DSP CRM FICO Composite Apps WM all other are represented by internal organs Data represents the blood connecting all organs/applications Our KPI Framework represents the nervous system Our infrastructure represents the skeleton 13

14 Data Management Our goal is to enable Nestlé to be the leading Nutrition Health and Wellness company through high quality data empowering our Consumers, Employees, Business Partners and Stakeholders with trusted information and supporting excellence in end to end process execution. Figures Master Data Repository 1 Terabyte Supplementary Data Repository 3 Terabyte Number of Material Records Number of Customer Records 2.7 Mio. Number of Vendor Record Number of Bank Records

15 Our Achievements From To «MY» Data «OUR»Data Before GLOBE: 500+ legacy Master Files held Vendor information = ~ 2 million records Before GLOBE: 500+ legacy Master Files held Finished Good (FG) information = ~ records With GLOBE we created ONE Vendor Master File with now ~ records = -65% With GLOBE we created ONE FG Master File with now ~ records = -85% 15

16 What s next Data Management Value Chain Evolution Insight Data Quality at Design Information Sharing Executed Globally Data Quality Framework Data Quality pollution prevention Implemented Globally Data Quality Framework Data Quality KPI Master Data Synchronization Defined Globally Standards Shared Master Data EDI processes with Customer Planned Projects: Data Management Simplification Address Validation, Duplication Check Renovation of Data Management KPI Material Discontinuation > 2001 Local or Regional Standards Shared Master Data 16

17 Our vision... Error-free process execution by 1 st time RIGHT Insight generation 17

18 Product coding practices before GLOBE UK KitKat Chunky Single Nordic KitKat Chunky NL KitKat Chunky Single (24x55gr) BE KitKat Chunky Single DE 3571 KitKat Chunky FR Kitkat 24x55g Chunky (47103) CH 5775 KitKat Chunky Single SP KitKat Chunky 24x55g GR KitKat Chunky Single PT KitKat Chunky 24x55g IT 2688 KitKat Chunky Single 18

19 Have we improved inter-market supply operations? Yes! KITKAT CHUNCKY 24x50g XL North + Iberia KITKAT CHUNCKY 24x50g XL Central + South Before GLOBE: 11 codes for 1 product With GLOBE: 2 codes for 2 products Now it is clear there are two wrappers with different language sets and Switzerland receives both! 19

20 Our current focus areas Corporate Data Ownership Best Practices for Sustain & Leverage Nestlé Best Practice Library Improved Data Quality Reporting Nestlé on the Move (Org Transformation) LEAN Data Mgmt Processes 20

21 Your One Stop Shop for Expertise! The Business Excellence Data Management Team delivers Data Standards Corporate Data Ownership External Standards Business Data Model Data Quality Data Organization 21

22 Agenda 1 The strategic dimension of Corporate Data using the case of Nestlé 2 Nestle s experiences and future Plans 3 Insights and examples from the CC CDQ Community 22

23 Strategic business requirements for Corporate Data 1 2 Risk Management Legal and regulatory requirements Contractual penalties, sales losses Reporting Single Point of Truth Standardization of reports and key performance indicators 3 4 Business Process Integration Leveraging synergies End-to-end - Processes Customer-based business models 360 -view on customers Expanding services business 5 6 Global spend Strategic Purchasing analyses Effective supplier development Complexity management IT und process consolidation Flexibility 23

24 The example of Syngenta In 2009, we established a new function, Syngenta Business Services, to integrate and standardize our transactional services across the organization. Together with developing new service models, investments in our IT infrastructure will [ ] provide integrated, standardized processes. Source: accessed on

25 The example of ZF Friedrichshafen Automotive supplier ZF Friedrichshafen AG restructures its aftersales and service organization. [ ] will be merged to the new business unit ZF Services. [ ] The merger of the previously separate business units establishes extensive growth and synergy potential for the ZF Group. [ ] more efficient processing of the existing customer portfolio. Source: zf.com, accessed on

26 Complexity drivers Size Revenue Nestlé 2009: 108 billion CHF Federal budget CH 2009: 59 billion CHF Data volumes RFID, customer loyalty programs etc. Hyper-connectivity New, external data sources, Data- Supply-chains etc. CDQ Global processes Multilingualism, Follow the sun - principle etc. Constant t Change Taylorism M&A, Divestments, Change Management Segregation of data creation and data use 26

27 Corporate Data Management is a cross-divisional effort Group Level Division A Division B Division C Business Units Business Units Business Units Business Processes Locations/Markets Departments Business Processes Locations/Markets Departments Business Processes Locations/Markets Departments Risk Management & Compliance Reporting Business Process Integration Customer-centric Business Models Strategic Purchasing Complexity Management 27

28 Typically, y, Corporate Data Quality evolves over time like this Corporate data quality Legend: CDQ Submarines (e.g. migrations, process errors, irregularities in management reporting). project 1 project 2 project 3 Time No risk management possible No chance to plan and to control budgets and resources No target values for corporate data quality No sustainability High recurring project costs (change requests, external consultants etc.) 28

29 Why a hundred percent data quality doesn t make sense Costs, C Total Costs of Data Quality (TCDQ) Costs of Data Quality Management t(dqm) C DQ Cost-optimal Data Quality Level Data Quality, DQ Costs resulting from defective data Legend: DQM - Data Quality Management. 29

30 Corporate Data Quality principles at an international retail group 30

31 Corporate directive at a leading automotive supplier Ensuring required framework within the organization: Scope: Corporate master data (cross-bu) Differentiation of master data classes Governance: Tasks, Overall responsibility Sustainable exercise Roles and authority Master data owner (central) Master data officer (central, local) Committees Methods, communication IT Procedure and collaboration model 31

32 Corporate directive at a leading automotive supplier (cont d) Executive Management corporate sector/ corporate department report overall responsibility for a master data class (specialist/organizational level) responsibility in relevant units (data maintenance/ application) Master data owner A Master data officer Master data Master data management owner X steering committee working group / Master data competence team officer Int erdisciplinary (M D Owner, IT,..) governance function concepts governance function concepts IT projects IT platforms, IT target systems master data class 1 master data class N e. g. Supplier master data Chart of accounts 32

33 Life-cycle cost of Corporate Data Costs Before Utilization Research & Development Labeling Master Data Management Costs During Utilization (of a Material Master Record) Documentation, 3,000 CHF p.a. Cataloguing + Procurement, Production, QM Sales Material Costs After Utilization Storage space in warehouses per m² Source: Lay, J.: Geld sparen mit Stammdatenmanagement. 20-Jahrfeier IWI-HSG, St. Gallen, 18 September

34 Consortium partners 1 in the Competence Center Corporate Data Quality Bayer CropScience AG Beiersdorf AG Daimler AG DB Netz AG Deutsche Telekom AG Hewlett-Packard GmbH E.ON AG ETA SA IBM Deutschland GmbH Migros Genossenschafts-Bund Nestlé SA Novartis Pharma AG Siemens Enterprise Communications GmbH & Co. KG Syngenta AG ZF Friedrichshafen AG 1) Overview comprises former and present partner companies. 34

35 Thank you for your attention! Dr. Boris Otto Karsten H. Muthreich University of St. Gallen Nestle S.A. Institute of Information Management BE Data Chair of Prof. Dr. Hubert Österle Phone: Phone: