cdq good practice award 2019: submission data …...in europe customer proximity –global plants...

CDQ Good Practice Award 2019: Submission “Data Management in all Data Areas”Corporate Data Management

Cologne, December 4th, 2019

Three divisions – automotive OEM, Automotive Aftermarket and Industrial

Schaeffler at a glance

Automotive OEM | Systems Automotive Aftermarket | Segments Industrial | Sector Clusters

12/5/2019 CDQ Good Practice Award 2019: Data Management in all Data Areas

Chassis Systems

Engine Systems

Services

Heavy CommercialVehicles

Tractors & AgriculturalVehicles

Passenger Cars Light CommercialVehicles

Wind RawMaterials

Aerospace Railway

Offroad TwoWheelers

PowerTransmission

IndustrialAutomation

Hybrid and Electrical Drive Systems

Transmission Systems

2

In Europe

Customer proximity – global plants and R&D centers

Schaeffler at a glance


1 The regions represent the regional structure of the Schaeffler Group Number of plants in brackets * 2 plants Automotive, 1 plant Industrial

Germany

Bühl

Eltmann

Erlangen

Gunzenhausen

Herzogenaurach

Hirschaid

Höchstadt (2)

Homburg (3)*

Ingolstadt

Kaltennordheim

Lahr

Luckenwalde

Morbach

Nürnberg

Schweinfurt (2)

Steinhagen

Suhl

Unna

Wuppertal

Austria

Berndorf-St. Veit

France

Calais

Chevilly

Haguenau (2)

United Kingdom

Llanelli

Sheffield

Italy

Momo

Portugal

Caldas da Rainha

Spain

Elgoibar

Hungary

Debrecen

Szombathely

Romania

Braşov

Slovakia

KysuckéNové Mesto

Skalica

Czech Republic

Lanškroun

Svitavy

Regions1 Europe Americas Greater China Asia/Pacific

R&D centers 12 5 1 2

Plants 44 14 8 5

Automotive 32 10 6 4

Industrial 12 4 2 1

1

Stratford (2)

1Canada

USA

Cheraw (2)

Danbury

Fort Mill (2)

Joplin

Spartanburg

Troy

Wooster

Mexiko

Puebla

Irapuato

Ulyanovsk

Russia

Ansan

Changwon

Jeonju

South Korea

Yokohama

Japan

Anting

Nanjing

Suzhou

Taicang (4)

Yinchuan (2)

China

Brazil

Sorocaba (2)

South Africa

Port Elizabeth

India

Hosur

Pune

Vadodara

Savli

Biên Hòa City

Vietnam

Chonburi

Thailand

3

We view E-Mobility, Industry 4.0 and Digitalization as key opportunities for the future

Strategy “Mobility for tomorrow”


Digitalization

• Digitalization of internal processes and equipment to improve productivity

Digital Platform

Products & Services

Machines& Processes

Analyses& Simulation

User Experience &

Customer Value

Big Data Data Model

Advanced business models

Customer

Digital Agenda

E-Mobility

• Expertise in engine, transmission and chassis form the basis for ideal E-Mobility system solutions

Industry 4.0

• We combine existing technologies with new intelligent components – from sensor to cloud

4

Where data management is located at Schaeffler

Data management @ Schaeffler

12/5/2019 CDQ Good Practice Award 2019: Data Management in all Data Areas 5

Finance & IT

Executive Board Member Finance & IT

Corp. Information Technology (CIO)

Corporate Data Management

Data Management Methods

Material Master & Product Data

Business Partner

Identity & Access Management

Ro

llou

t D

M in

al

l are

as

Overview initiative

2019-12-04 CDQ Good Practice Award 2019: Data Management in all Data Areas 7

Project goal: Implement structured data management at the whole Schaeffler Group

Unstructured data management

• Many departments take care about data • Uncoordinated actions lead to double effort• Mainly SAP based – other systems

are often not taken into account

Structured data management

• Focused on master data management• Fully implemented data governance

according to CDQ reference model

Increase the share of structured data management to support Schaeffler strategy “Mobility for tomorrow”

2017

2018

Overview

Create domaindescriptionsfor the data domains

Our journey with data management in all Schaeffler data areas

CDQ Good Practice Award 2019: Data Management in all Data Areas

1

Definition ofdata domains andnomination of data domainmanagers

20182017

Training of the data domain managers

8

4

12/5/2019

Assessments and optimization of prioritized data domains

5

3

2

Developmentof themethodology forstructured datamanagement

6

Data modelcreation

2019

unstructureddata mgmt.

structureddata mgmt.

unstructureddata mgmt.

structureddata mgmt.

Overview


Results were achieved concerning data excellence, innovation and business value

3 training modules

developed

12 trainings conducted170+ participants trained

Collaboration with IT architecture,

BPM, InfoSec,digitalization office

Toolset for data architecture and data cataloging

Optimizations indata domains

Schaeffler Management Handbook: processes & procedures redesigned

Data Management Methods developed

Domains described

47 domains defined andnominated

Data modelling

Data culture: data essentials

Data Management

in all

Data Areas

Overview

Data quality KPIs developed, quality circles established

Data Excellence


Data excellence #1: Examples for data quality

Example 1: Data quality in data domain “Product & Materialaster Data”

• The DQ KPI “DQ Delivery Chain” helped to avoid issues within the Supply Chain

Example 2: Data quality in data domain “Product & Material Data”

• Structured data quality circles in 71 production plants

• 31 DQ indicators were implemented in the plant

Data quality KPI „DQ Delivery Chain“

Data excellence

Key success factors • Worldwide measuring and transparency • Utilization of internal / external audit• Audit force to take measures

Key success factors• Culture of open collaboration with regular meetings• Yearly plant workshops for quality analysis with

measurement discussion and training• Data Quality Awards


Data excellence #2: Examples for compliance & security

Information Security classification ofdata is drivenfrom two directions

Data model as global repository for thisinformation

Process managers& data managerscollaborate

Process 1 Process 2

Datadomain A

Data domain B

ProcessManager 1

Data DomainManager A

Data DomainManager B

ProcessManager 2

Data 2

From direction"process"

Fromdirection

"data"

1

2

Infor-mation

Data 3

Data 1

Data 2

Data excellence

ConfidentialityIntegrity ...


Data excellence #3: Examples for data culture

Data culture asimportant part ofdata strategy

Data essentials help 90.000 employees to handle datain the right way

Data excellence

2019-12-04 CDQ Good Practice Award 2019: Data Management in all Data Areas

Results summary: Data excellence

Data excellence

14

Innovation

16

Innovation #1: Integrated implementation approach of data management

CustomerEcosystem

IT Systems

Products & Services

People Processes EquipmentOrganization

Business Models

Strategy & Steering

Analytics

Data

Data Organization

Data MgmtProcesses &

Roles

Data People

Data MgmtSystems

Data Stores

Data Ecosystem

Customer Data

Data Model


Sensor Data, Machine D.

Data in / asProduct

Innovation


Innovation #2: Holistic data scope and nomination of 47 data domains in the Schaeffler organization

Innovation

Data Domains

care aboutall data

types

17

Data CatalogData Catalog


Innovation #3: Data Catalog and Data Models as Provider of Business Value and Speed

Data Models

Schaeffler Data Platform

Function, Division, Region

change, approve, inform

Innovation

…


Innovation #3: Data catalog and data models as provider of business value and speed

Let Pepper explain…

Innovation


Results summary: Innovation

Business value


Results: Overview of business value

Collaboration

Availability Process reliability

Processspeed

Customer satisfaction

New businessmodels

Processefficiency

Projectenablement

Business Value of Data Management in All Data Areas

Cost efficiency

Datasecurity

Transparency

Business value

Complaints

Business value #1: Cost efficiency by sharing data and through utilization in many domains

2312/5/2019

Data sharing of

business partners

Derived cost savings

Corporate data league

17

m

emb

ers

Business Value

CDQ Good Practice Award 2019: Data Management in all Data Areas

I use data in my processes or in

analytics!

Data User


Business value #3: Efficient collaboration thanks to an implemented data network

Data ContentResponsible

Data Maintainer

Corporate DataManagement

Data Domain Manager

Data Coordinator

I support the Data Domain manager

in his tasks!

I am responsiblefor the data

content!

Round Table

Business value

I control and monitor the data management

of my domain!

I maintain and edit data!

I am responsible for controlling and monitoring

the corporate-wide data management network!


Business value #4: Transparency and project enablement through well defined domains

Business value

Data Domain Data Domain ManagerMasterplanning Data Heike Pees-Huschka - SP/HZA-ILC

Description of the data domain, (Typical content and, if possible, data objects)

Sho

rt p

rofi

le

Delivery

Container

Charge

Warehousing / retrieval / rearrangement processes within the warehouse

Production order

Order type

Production order paper

KBK box accompanying card

Handling unit

Handling unit type

Internal purchase requisition

Internal order

Inventory data / inventory documents

Warehouse order

Warehouse structure

Warehouse number

Warehouse type

Warehouse location

Warehouse location type

Sensitive DataPersonal data: Personal data is included (e.g. feedback) , these are contact data (internal

communication data), which facilitate simplified processing conditions.

Particularly sensitive or critical data include: All personal and financial data is confidential. The same applies to data on

ordered, delivered and produced goods.

Monitoring / controlling data

Stocks

Error costs

Performance-KPIs

Planned order

Ressources (e.g. tugger train, timetables, stations)

Feedback data

Piece of rework, good rework, committee reworking,

Productive times / not-productive times (failure key)

Transportation order

Link of production order to drawing

BOM

Inspection plan

Work schedule

Incoming goods

The Data Domain is responsible for the data in the area of master planning in the plants and is responsible for the associated logistical data. This includes the data fromthe integrated planning, procurement and production of the products.

The following data objects are included, among others:

Find valid, detailed information on data?


Not long time ago, in a Company not far, far away.…

Find the responsible person? Identify the risks?

SOME MASTERDATA

OTHERDATA TYPES SOME MASTER

DATA

OTHERDATA TYPES SOME MASTER

DATA

OTHERDATA TYPES

R&D

Product structure is created & used by:

Requirements Mgmt.

Design & Development

Systems Engineering

Validation

…

Business value #5: Integrated product data model enables PEP projects and product quality

Business value

27

Production & SCM

Product structure is used by:

Industrial Engineering

Master Planning / Shop Floor

Purchasing

Quality

e.g. Hybrid and Electrical Drive Systems

Integrated product data model

CDQ Good Practice Award 2019: Data Management in all Data Areas12/5/2019


Summary: The project has fostered data excellence, business value and innovation

Data Management in all data areas

InnovationData

excellence

Businessvalue

Summary

cdq good practice award 2019: submission data …...in europe customer proximity –global plants...

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