turning industrial data into value

© Fraunhofer · Seite 1

Prof. Dr.-Ing. Boris Otto · Munich · February 7th, 2017

TURNING DATA INTO VALUELEVERAGING THE OPPORTUNITIES OF INDUSTRIAL DIGITIZATION


AGENDA

Digitization of the Industrial Enterprise

Smart Data Management

Leading Examples


Image sources: Audi (2016).Legend: AGV - Automated Guided Vehicle; VR – Virtual Reality.

Industrial digitization happens in all value creation processes – as the example of Audi shows

Autonomous AGVs for Modular Production Human Robot Collaboration Autonomous Tugger Trains

Drone Use in Assembly VR in Engineering Predictive Analytics in the Yard


These developments are a response to fundamental changes manufacturing companies need to cope with in terms of their production strategy

Production Volume per

variant

No. of Variants

1850

1913

19551980

2000

Ford Model T

VW Beetle

ProductionAudi Configurator

Mass

Production

Individualization

»Sharing Economy«

Complexity

Globalization

iPhone

3D Printed Car

Source: Koren (2010), in Bauernhansl (2014); image sources: Wikipedia (2015), Impulse (2015), Audi (2015), O2 (2015), computerbild (2015).


Image sources: ihs-gmbh.de (2016); silicon.de (2016).Legend: ERP – Enterprise Resource Planning; LAS – Logistics Assistance System; OEM – Original Equipment Manufacturer.

Data is the key resource in digital value creation networks


Required is a central information management instance – what Audi refers to as the »Tower«

Image sources: Audi (2016).


Image sources: Audi (2016).

The »Tower« is at the core of smart data management

Conceptual information model of the digital factory

Source of the »Digital Twins« and »Single Source of the Truth«

»Data Lake« functionality

Collection and analysis of manufacturing and supply chain

event data

Close to real-time process analysis

Backbone for data analytics and machine learning


AGENDA



Leading Examples


Legend: Information flow; Material flow.

Smart data management is the key capability of the digitized industrial enterprise

PublicData

Value Chain Data

Commercial

Services

Industrial

Services

Lot-Size 1

End-to-End Customer Process

Business Ecosystem

Hybrid Offerings

Smart DataManagement

Interoperability

Human-Machine-Collaboration

Autonomous Systems

Internet of Things

Customer

Production

Networks

Logistics

Networks

Digitized Value PropositionDataDigitized Value Creation


Industrial data has evolved into a strategic resource with an economic value

Time

Value Contribution

Data as process result

Data as process enabler

Data as product enabler

Data as a product


Source: Moody & Walsh (1999).

Despite its intangible nature, industrial data has a value which can be quantified

Number of users

Share of value

100% Data

Tangible Goods

Tangible Goods

ValueData

Usage Time

Potential value

Data

Data quality

Value

100%

Data

Integration

Value

Data

Volume

Value

Data


Source: Otto (2012); Otto (2015).

Many examples exist demonstrating the applicability of valuation procedures in the data domain

Company Industry Country Data domainValuationapproach

Value per record

Retail USCustomer data including shopping profile

Market value 1.6 EUR

Social Network US User data Market value 225 USD

Automation and drives

DE Master data on partsProduction costs

500 to 5.000 EUR

Agrochemical CH Material master data Use value 184 CHF


Source: Leveling et al. (2014).

Smart data management is aware of the heterogeneous nature of data

Peripheral data of greater fuzziness, volume, volatility, heterogeneity…

Peripheral data less controllable, critical, unambiguous…

Nucleus Data(Customer data, product data etc.)

Community Data(Spatial data, GTIN, addresses, ISO codes, EPCIS events etc.)

Open Big Data(Tweets, social media streams, sensor data etc.)

Megabytes

Gigabytes

Terabytes

Petabytes


Smart data management rests on a future-proof data service architecture

Industrial Data SourcesERP MES SCADA Installed Base etc.

Commercial Data SourcesCRM Loyalty Programs etc.

Social Data SourcesFacebook Twitter etc.

Cloud-based Data StorageData Source Connectors Data Space Infrastructure Shared Information Model

Industrial Data Service Architecture

Data Quality Assurance Mapping/Transformation Integration/Aggregation Data Provenance …

Data Analysis Data Mining Visualization Data Delivery …

Industrial Use-CasesPreventive Maintenance Digital Farming Supply Chain Visibility

Commercial Use-CasesSmart Home Mobility HealthCare

etc.

Internal Use-CasesData as a Process Enabler

Context-free UseData as a Product


Source: VDI (2015).

Smart data management enables digital twins of the real word

Reference Architecture Model Industry 4.0 Administrative Shell Concept

The Administrative Shell stores all data of a hardware or software component in production scenarios

It makes data and services related to that component available for Industry 4.0 scenarios in a standardized way


A set of design principles guides the transformation to smart data management

Design Perspective Design Principles Implementation Examples

Strategic principles Productizing of data Data products with clearly defined data elements or configuration, service levels …

Managing data as an asset Data valuation and pricing, data lifecycle management …

Data co-creating and sharing Collaboration in communities of interest and eco-systems

Organizational principles Governing data in participative ways Transparent responsibilities, digital sovereignty, data owners in control …

Managing data supply chains and life-cycles end-to-end Data acquisition, pre-processing, processing, distribution, use, retirement…

Recognizing data quality as probabilistic Dealing with fuzzy and volatile data with limited traceability

Systems and architecture principles

Deploying federated architectures Open platforms, linked data…

Decentralizing information security and data sovereignty Data tagging, blockchain technologies…

Sharing data processing resources Cloud platforms, intelligent devices, edge computing


AGENDA



Leading Examples


Source: Otto (2016).

The Industrial Data Space addresses the squaring of the datasovereignty circle

Interoperability

Data Exchange

»Sharing Economy«

Data-centric Services

Data Ownership

Data Privacy and Security

Data Value

Data sovereignty is the capability of a natural person or corporate entity for exclusive self-determination with regard to its economic data goods


Data flowMaterial flow

Legend: IDS – Industrial Data Space; LSP – Logistics Service Provider; IoT – Internet of Things.

The Industrial Data Space connects various digital platforms and the internet of things

Public context data

Weather

Factory/Warehouse

LSPElectronic Marketplace

Traffic

IoTCloud

IDS Broker

IDS

IDS

IDS

IDS

IDS

IDS

IDS

IDS

Supply chain planning data

Supply chain event data

Internal process data


Source: Cf. Kagermann (2015).

The Industrial Data Space defines the data architecture betweensmart services and the internet of things

Connected physical platforms Smart Products

Technical infrastructure Smart Spaces

Industrial Data Space

Service platforms Smart Services

Smart Data Services (Alerting, Monitoring, Data quality etc.)

Basic Data Services (Fusion, Mapping, Aggregation etc.)

Use restrictions attached to the data

Secure data supply chain

Data Fusion

Certified software endpoints

Multiple use scenarios

Federated governance models


NB: As per December 2016.

The initiative rests on solid and continuously growing industrycommitment organized in the Industrial Data Space Association

https://de.wikipedia.org/wiki/Datei:Logo_SICK_AG_2009.svg


Image source: Competence Center Corporate Data Quality (2016).

The »CDQ Framework« is a standard capability model for managing the data core


Source: CDQ AG; Corporate Data League (2016).

The Corporate Data League is a community approach for managing business partner data


Prof. Dr.-Ing. Boris Otto

Fraunhofer ISSTManaging Director

[email protected]

https://de.linkedin.com/pub/boris-otto/1/1b5/570

https://twitter.com/drborisotto

https://www.xing.com/profile/Boris_Otto

http://www.researchgate.net/profile/Boris_Otto

http://de.slideshare.net/borisotto

Your Contact Person!


Prof. Dr.-Ing. Boris Otto · Munich · February 7th, 2017

TURNING DATA INTO VALUELEVERAGING THE OPPORTUNITIES OF INDUSTRIAL DIGITIZATION