Kontakt info. Elisabeth D. Tjørve, Matiq/Nortura. Geirr Winsnes, Itelligence

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2 Kontakt info Elisabeth D. Tjørve, Matiq/Nortura Geirr Winsnes, Itelligence

3 MDM Oversikt 3

4 Krav til masterdata oppdatering, Nortura Ønske om bruk av forretningsregler for å oppnå konsistente data Ved bruk av forretningsregler kan ikke den enkelte bruker endre feltverdier ukritisk, verdiene i feltene blir styrt av regler Ved bruk av regler kan vi utvide Materialene til Nortura og Norturas datterselskaper sine 67 fabrikker på en enkel måte Enkel måte å oppdatere data slik at korrekte verdier blir distribuert ut til alle fabrikker som skal benytte aktuelle materialer Ønske om forenkling og mer kontroll på dataene for Kunder, Leverandører og Materialer. 4

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6 Nortura masterdata objekter i it.mds Antall objekter i it.mds Material master: Kunde master: Leverandør master: Info post master: (Foreløpig) 6

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8 88 10/22/18 itelligence Master Data is the foundation

9 9 Consequences of poor master data quality. Inconsitencies in intercompany processes (missing / wrong dependend objects) Time to Market (uncontrolled, time-consuming process for creation and maintenance) Wrong stock (wrong planning and MRPdata on the material master) Typical problems due to bad master data maintenance and processes. Inconsistency and poor quality of master data leads to error-proned processes Unreliable Reports (Wrong data base due to wrong master data) Missing data for processes (missing tabs of the Material Master, MM-Inforecords,...) Wrong data for month closing (missing prices, MM- Inforecords, wrong valuation classes,...) 10/22/18 itelligence

10 10 Master Data Management - quality Rules Create, Change Organization Recurring data quality projects Constant data quality process Individual recurring data quality projects only "repair" the data stock for a short time -> Saw tooth effect Cleansing projects No long-term optimization of the master data Constant data quality process to manage company master data Provides a consistently high level of data quality 10/22/18 itelligence

11 New Master data governance structure in Nortura

12 Master data management in Nortura - Central governance & execution 12 PLM Master data Management Master data Production 12 S&OP Expedition Warehouse Input from departments: Data rules and dependencies itelligence Finance Sales Production 10/22/18 itelligence

13 STYRINGSMODELL FOR MASTERDATAFORVALTNING Governance-sponsor Harald Ullevoldsæter IT Governance Data Governance ledergruppe MD Pr.eier SAP Arkitektur Datavarehus Øystein Fuglestad Digitalisering Sigurd Myhre Roller og aut. Stig Henning Verpe It.mds forvaltning Elisabeth D. Tjørve Tore Ek Prosesseier Masterdata Kjell Sigmund Sandgrind MDM Arkitektur og governance NN Dataeier Dataeier Dataeier Dataeier Dataeiere Dataforvaltere Dataoperatører Dataeier Dataeier MD Ark Dataeier Dataeier Mål for Norturas styringsmodell for masterdataforvaltning: Muliggjøre standardisering på tvers av alle deler av Nortura med tydelig ansvar, samtidig som nødvendig fleksibilitet opprettholdes Tydeliggjøre ansvar for forvaltningstiltak og forvaltningsrelaterte aktiviteter, inkludert visjon og retning for spørsmål relatert til data Koordinere endringer i data- og informasjonsarkitekturen ved å sikre at prosesser, prosedyrer og standarder er på plass for å sikre at data forblir konsistent på tvers av organisasjonen Tydelighet i hvem som eier den globale datastrategien, og hvordan data skal integreres i hele bedriften Sikre at forretningens behov for data- og informasjonsbehandling blir håndtert på en disiplinert og konsistent måte Tydelig definere eierskap til data og informasjon Vise riktig beslutningsorgan for ulike typer saker relatert til data Definere oppsett og drift av governance-organisasjonen Fasilitere en kulturendring der riktig administrasjon av data ses på som et felles ansvar Datakonsumenter 13

14 DATA GOVERNANCE SAMHANDLINGSMODELL Dataoperatør forvalter data i henhold til retningslinjer og standarder utarbeidet av dataforvaltere, og godkjent av dataeier. Dataeier godkjenner og kommuniserer beslutning Dataforvalter analyserer data i produksjon, og ser at en strukturell endring i objektet vil medføre bedre datakvalitet og analysemuligheter, eller mulige prosessforbedringer for sitt område. Basert på dette utarbeides et endringsforslag sammen med fagleder for masterdata. Fagleder masterdata bestiller og følger opp eventuelle nødvendige endringer i løsninger/systemer (interne og eksterne), samt eventuelle masseoppdateringer Forslag til endring analyseres og konsekvens utredes av alle de ulike fagområdenes dataforvalterne for det aktuelle objektet Dataforvalter(e) oppdaterer rutiner og retningslinjer og sikrer at dataoperatører har nødvendig informasjon og opplæring Endringsforslag og konsekvensutredning behandles av dataeier, og eventuelt av data governance-ledergruppe dersom det er en særlig omfattende eller tverrfaglig endring Avdelingsleder masterdata sikrer at oppdaterte rutiner følges 14

15 it.mds in principle

16 16 it.mds (master data simplied) Leverage your existing business rules Define clear owners and responsibility to fields Central control Decentralized maintenance Automated masterdata management Significantly reduced time Quality improvement 10/22/18 itelligence

17 it.mds - principle and approach Sales SAP PLM Manuelt BADI BADI from Bods Rules profiles governance Production 17 22/10/2018 itelligence

18 it.mds - principle and approach LSMW (MDS) PLM Data Rules Profiles Dependencies Automatic OK ERP systems Data Data Maintenanc e it.mds Validation / completene ss check it.mds NO Master data SAP Dependent Locations & Dependent Objects Distribute Data External sources Data Department/ Responsibility Work Flow

19 19 It.mds definitions Object: Masterdata objects like Material, Customer, Vendor Subtype: Subtypes of an object. Example, split a material type into several subtypes Location Type: Type of location: Example: Production, Sales Location ID: Geographical location Location Profile: SAP Organizational unit Profile types: Divide profiles into different types like Definition, Source, Make, Plan Deliver,. Profiles: Combination of fields with a fixed value. Ref MRP profiles i ERP Rules: Stand alone business rules in which sets a values to a field for a specific part of the business. Ownership: Represent the ownership group in the business: Example: MDM, Finance, Procurement dep. 10/22/18 itelligence

20 Common issues which it.mds solves Lack of master data control Takes long time to create and change master data. Leads to delay in time to market. We would like to centrally control master data rules and principle, but people should still maintain the data them selves. Poor master data quality Inconsistency of Master Data across location. Incorrect master data Templates / Copy from material are generating too many errors. Due to lack of workflow, responsible. People are not doing what they should, when they should SAP Roll out to a new location 20 22/10/2018 itelligence

21 21 it.mds Profiles and rules All fields used by the company Basic Sales Purchase Planning Sort Warehouse fields into profile Finance types and profiles Profile types Profiles A A B B C C Plan Make External Subcontract. Internal Define profile content Source Factory X Factory Y External Deliver Standard 3rd party Direct Finance Std. cost Mov. averg. Split valuat. A B C Define dependent values MRP Type = PD Lot Size key = HB Consumption mode = 2 Forw. Cons. = 20 Backw. Cons. = 20 Etc. MRP Type = PD Lot Size key = FX Consumption mode = 2 Forw. Cons. = 30 Backw. Cons. = 30 Etc. From field 1 Procurement type= F Procurement type= F MRP Type = PD Lot Size key = EX Consumption mode = 1 Forw. Cons. = 0 Backw. Cons. = 30 Etc. From field 2 Mat. Group = 1000 Mat. Group = 2000 To field MRP controller = 100 MRP controller = /22/18 itelligence

22 22 it.mds - principle and approach Profile Profile Subtype Location Profile 22 Profile Manual entry Automated entry Profile 10/22/18 itelligence

23 23 Demo Demo in it.mds 10/22/18 itelligence

24 It.mds workflow

25 25 Process Execution: incomplete workflow Before a material is not considered complete we don t send it to sap No half finished materials can be used Based on Product and Lifecycle incompleteness can be defined Push approach Requester Production Planning Finance Approval Complete Data send to SAP SCM Total Leadtime Department / Responsibility Workflow 10/22/18 itelligence

26 26 Process Execution: Critical field workflow Purpose: To ensure that change of critical data are approved before transferred to SAP Up to 5 approval steps can be defined Can be dependent on Organizational level and unit Logical operators possible, e.g. Equal to, Greater than Change critical fields Approve data Update SAP 10/22/18 itelligence

27 Lessons learned

28 Hva er lærdommen Bruke mer tid på å analysere data - Dele inn i flere undertyper for en materialtype eller en kontogruppe - Bruke mer tid på å analysere feltene slik at man kan bygge profiler Gjennomføring av styringsmodellen - governance - Sikre eierskap i organisasjonen for den nye styringsmodellen Forretningens involvering - Involvering av nøkkelpersoner fra forretningen er et absolutt krav fra dag en til å lykkes. - Involvering av de ulike teamene i forretningsprosessene på et mye tidligere tidspunkt - Ikke jobbe i "silo", men snakke sammen - unngå ulike verdier i samme felt som benyttes av flere prosesser Analyze Validate Update business rules 28

29 Customer examples/success stories

30 30 it.mds in numbers In the market since Customers Typical customer 2007 Automation rate Total: : 21 Simplification Industry: High-tech, IMC, E&C, Process Industry, Wholesales/Trade, Pharma Target Group: 5+ plants International and regional based LOB: All working with master data maintenance (Material, PIR, Customer/Vendor, BOM, Routing, etc.) 2016 itelligence Governance >80% WF controlled processes Ownership on field level Simplified maintainence Reduced training Typical Consulting service man days (2017) 12 Countries Germany Denmark USA Norway Switzerland UK Spain France Netherlands Japan Poland Turkey 10/22/18 itelligence

31 31 Some of our it.mds customers 2015 itelligence 10/22/18 itelligence

32 32 Overview it.mds Data quality Rules are maintained in the system and are therefore mandatory when creating and changing master data. This ensures data quality. Automation / Time saving Processes and rules that fill fields are automatically executed when master data should be created and changed. Thereby manual entries are significantly reduced. Process control / responsibilities The responsible persons are assigned on the field level. They will be informed by workflow when they have to supplement manual data. Authorizations Detailed authorization is possible on the field and product group level. This results in much better control than in the SAP standard. User-friendly interface The frontend only displays the fields that are really relevant in the company and for which the user has the authorizations. 32 Dependent master data across locations Keeps master data in sync across locations Objects Master data maintenance is available for material, customer, vendor, and purchasing inforecord. BOM, routings master recipe, inspections etc. can leverage it.mds rules engine 10/22/18 itelligence