it’s all about data - from bits and bytes to business …...it’s all about data - from bits and...

It’s All About Data - From Bits and Bytes to Business Value@masscustom (Frank T. Piller)

Responsibilities

Head of RWTH Technology & Innovation Management Group and Professor of Management at TIME Research Area at RWTH Aachen University

Academic Director of RWTH Executive MBA

Co-Founder, MIT Smart Customization Group, Massachusetts Institute of Technology, Cambridge, MA

Research Interests and Expertise

Strategies for Customer-Centric Value Creation, like mass customization, innovation co-creation, additive manufacturing, managing the frontend of innovation

Open Innovation, i.e. technology transfer, R&D partnership models, crowdsourcing

Managing Disruptive Business Model Innovation and supporting organizational structures (facilitated by Industrie 4.0 and Digital Transformation)

Entrepreneurial Activities

Co-Founder, Investor, and/or Member of Board of Directors of several companies, includingThinkConsult (process management and concept testing), Competivation (innovation consultancy), Dialego AG (innovative online market research), Corpus-e AG (“best fit” solutions for eCommerce), Combeenation (product configurator as a service), and DOOB AG (3D printing and 3D modelling)

Real life achievements: Only German in “Top50 Profs on Twitter” list; Kloutscore >60; Google Scholar Citations ~10,050; H-index >44

Frank T. Piller

More info: frankpiller.comFollow me on Twitter: @masscustom

Digitalization mandates faster decision making

3

6Dexcom G4 CGM

7Dana M Lewis

#WeAreNotWaiting

„They told me as a child that there will

be a cure for diabetes, and I am still waiting.

But I will not wait for a better

Artificial Pancreas System (APS).“

The manufacturer Dexcom, Inc.: “Great ! We will bring this to market (open innovation rules). But this will take 5+ years for FDA

approval.”

Dana: „???? We are using this already !!!!“

User communities organized and got FDA approval in less than 5 months!

#WeAreNotWaiting

Why are user communities so much better organized than the world market leader

in a regulated industry?

12

It is all about data

Today, we create assets collecting this data purposefully:

Smart products & equipment

14

Sense

Act

Think Connect

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../../../AC_Projekte_Lst/ServiceFactory Adidas BMWI/Servicefactory_video_restricted_internal_use_only.mp4

How to create value for Adidas by data from smart products –

and what kind of challenges do you see?

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From smart shoes to smart manufacturing

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Integrate conventional silos of data into one manufacturing repository (digital

shadow), enabling data-driven operational excellence (OE and OEE)

Real-time simulation based on a “digital

shadow” and “digital twin” of plant,

equipment, and products

Source: Bechtold et al., 2015 / CapGemini

The “digital shadow / twin” of a product, component, or asset is the core

enabler of the Factory of the Future

A metaphor for the digital shadow

Google Map (simulation of reality in form of a map = Digital Twin 1.0)

Google Map with live traffic information, including reactions of drivers (life data = Digital Shadow)

Google Map with predictive traffic information

Google Map with dynamic suggestions for alternative routingsGoogle Map including alternative means of transportation (railway instead of car)

Google Map with suggestion where to build new highway to continuously avoid bottlenecks

Digital Twin 2.0

How can I produce product A44 in time when machine X24 is down?

The Engineering Valleyaround RWTH Aachen University

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The Internet of Production is our joint vision of the next level of Industrie 4.0 by a large consortium of research institutions at RWTH Aachen University – the German center of production technologies

Source: Brecher (2017), WZL RWTH Aachen / Fraunhofer IPT

Production is often characterized by a multi-lemma of objectives: Balancing conflicting industrial targets


Production is often characterized by a multi-lemma of objectives: Balancing conflicting industrial targets requires a deep understanding of the entire system


Existing approaches build on virtualization of products and machinery (simulation) and achieving process transparency by connecting assets and processes …


… allowing us to create a learning cycle based on causal relationships.


But today, this simulation-based cycle learning is limited by real life (stochastic) influences


New approach to better learning in a production system: A learning cycle based on causal relationships (simulation) + live machine data + learning models based on the digital shadow


The Infrastructure of the Internet of Production: the Smart Data layer connects existing data and application data on the shop floor with decision systems on the office floor

But there are still open questions

Source: Fraunhofer IPT, RWTH Aachen

Three core steps to digital production: Measure, Analyze, Optimize

cyber

physical

3

Make

measurable

& connect

Digitalization of

the production

Adaptive

optimization

Index GFG 250 II

VDF V 630

KH 35 CN Ravensburger

BöhringerVDF 250/315

Flachschleif-maschine

Index GFG 450 II

Index GFG 450 I

Index GFG 250 I

VDF V 630

VDF V 630

Kopfdreh-maschine

Reparatur

Ind

ex

GU

10

00

Traub

DigmaHSC 800

Mikron HSN 700

DigmaHSC 850

Huron MU4

Huron MU4

CNC Bettfräs-maschine UBF 2500/10 Kekelsen

SX B523 Huron

PFH 12-1400 Heller

CNC Bettfräs-maschine PFU 3 Heller

Ra-Bo-Ma

Kal.-Pr.

Optimization of

production based on

simulation and real

time data

3

Descri

pti

ve

3

Dia

gn

osti

c

3

Pre

dic

tive

3

Pre

scri

pti

ve

Analyze & Predict

Digital

image

Mirroring of

cyber- and physical world

And this was just the perspective of one firm /

plant. What about networks and platforms?

Development Cycle Production Cycle User Cycle

Smart Expert

Middleware+

Correlation

AnalysisCluster

Algorithms

Smart Data

Multi-Modal Information Access

Data IntegrationData

ModelData Storage

and Caching

Analytics

Management of data access to proprietary systems

Digital Shadow

Aggregation and Synchronization

Learning

AlgorithmsMeta

Heuristics

Context sensitive

processing

Application

Software

Raw Data Customer

Data

CRM

Machine

Data

MES

Product

Data

PLM

Test

Data

CAQ

Process

Data

ERP/

SCM

CAD

Data

CAD

Simulation

Data

FEM

Feedback

Data

BDE

Data Provision and Access

Decision Support

Agent Event-driven

Decisions A

Autonomous

Actions

Adaptive

Processes

From a business model perspective on manufacturing platforms (in terms of business ecosystems), this is just a “small” technical piece

44

Stake-

holder

Stake-

holderPlatform

Contri-

butorUser

Platform

Govern-

ance

Governance Tools

Platform Sponsor Platform Manager

Contri-

butorUser

Platform

Value

Capture

Value Appropriation

How to charge? Whom to charge?

Value Interaction

Value Proposition Value Consumption

Platform

Value

Creation

Contri-

butorUser

Infrastructure ComponentsPlatform

Infra-

structure

Contri-

butorUser

Core Peripheral

Platform-based

Business Models

are two- or multi-

sided markets

where a platform

operator

orchestrates

different user and

contributor groups,

who share

common

resources and

exchange data, but

also physical goods.

Development Cycle Production Cycle User Cycle

Smart Expert

Middleware+

Correlation

AnalysisCluster

Algorithms

Smart Data

Multi-Modal Information Access

Data IntegrationData

ModelData Storage

and Caching

Analytics

Management of data access to proprietary systems

Digital Shadow

Aggregation and Synchronization

Learning

AlgorithmsMeta

Heuristics

Context sensitive

processing

Application

Software

Raw Data Customer

Data

CRM

Machine

Data

MES

Product

Data

PLM

Test

Data

CAQ

Process

Data

ERP/

SCM

CAD

Data

CAD

Simulation

Data

FEM

Feedback

Data

BDE

Data Provision and Access

Decision Support

Agent Event-driven

Decisions A

Autonomous

Actions

Adaptive

Processes

From a business model perspective on manufacturing platforms (in terms of business ecosystems), this is just a “small” technical piece

Source: Based on: Porter et al: How Smart, Connected Products Are Transforming Competition, HBR 2014

The added value lies not so much in the smart product itself but in its connections

Potential Added Value | Added Management Complexity

THE crucial question:

App or Plattform?

THE STRATEGIC DECISION:

How to survive competition in a “platormed” industry: App or Platform?

For Platforms: How to manage (and build & nurture) the ecosystem?

For Apps: How to become a preferred partner in an ecosystem (in which one)?

How to set the right degree of openness?

48Ffwd: 60

365FarmNet as a platform for agriculture: Facilitated by Claas as an open platform for complementors, but also competitors!

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Designing digital business modelsaround smart data

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A five day executive program. More information: bmi.rwth-aachen.de

(1) Ecosystem Canvas & Platform Model- Design-thinking inspired approach to develop

business models alternatives by mapping all stakeholders of a business ecosystem

- Getting a holistic perspective of all partners and their contributions and expectations

- Canvas as a communication tool: Ability to map alternatives

- Can be complemented by the known Osterwalder canvas to go into more detail for one player

OUR APPROACH TO DESIGN DIGITAL, PLATFORM-BASED BUSINESS MODELS

* more information: bmi.rwth-aachen.de

(1) The well known Business Model Canvas ...

Canvas = template to build a BM prototype

… to analyze the status quo … to overcome barriers (confusion and

complexity) and to discover alternative business models, …

… to run “experiments” considering alternate combinations of the processes.

...is complemented by new canvas model to represent digital ecosystems

Canvas cards map Platform Business Model with its ecosystem in a holistic stakeholder landscape

A Canvas Card is used to describe a stakeholder and its requirements

Interaction cards illustrate the relationships and exchanges between stakeholders

Working procedure is equal to known BM innovation approach






(2) Platform design factors (PDF) representing patterns of digital BMs- Successful BM are based on recurring patterns- Derivation of specific digital BM patterns- Systematic problem solving based on TRIZ approach to

create new business models



(2) Platform Design Factors are patterns and proven elements of

digital platform-based business models

PDF Cards illustrate Platform Design Factors (recurring best practice configurations) and serve as a guideline to fill Canvas and/or Interaction Cards.

They are structured around an economic model of a business platform:

Platform

Governance

Platform

Infrastructure

• API

• Digital Supply Chain

• Filter

• Peer-to-Peer

• Shop-in-Shop

• Software

Development Kit

• Algorithmic Control Mechanism

• Content Monitoring• Decision Rights

Partitioning• Democracy• Formal Control• Gate Keeping• Informal Control• Insurance• Integrator• Market Control• Open Business Modell• Open Source• Orchestrator• Rating System• Risk Pooling• Social Contract• Social Sustainability

Seal

Platform

Value Capture

• (Digital) Lock-In

• Condition monitoring

• Connection as a

service

• Gamification

• Go Offline

• Guaranteed

Availability

• Layer Player

• Leverage customer

data

• Make more of it

• Push to Pull

• Recommender

Engine

• Sensor as a service

• Social curation

• (Digital) Add-on

• (Digital) Subscription

• Auction

• Crowdfunding

• Flat-Rate

• Freemium

• Long Tail

• Merchandising

• Pay per use

• Razor and (digital)

blade

Platform

Value Creation

(2) Platform Design Factors are patterns and proven elements of digital platform-based business models








(3) Rapid experimentation and validation in field- BMI means to develop alternatives – and to test

assumptions- Ability to generate quick and cheap experiments (e.g.,

5x5x5x5x5 logic by Schrage)- Experimentation template



(3) Rapid experimentation and validation of alternatives

BMI means to develop alternatives – and to test assumptions

This means to experiment and gather real validation data from real customers

Ability to generate quick and cheap experiments (e.g., 5x5x5x5x5 logic by Schrage, Pretendotypesby Google

Experimentation templates, Fast Business Case models








(3) Rapid experimentation and validation in field- BMI means to develop alternatives – and to test

assumptions- Ability to generate quick and cheap experiments (e.g.,

5x5x5x5x5 logic by Schrage)- Experimentation template



Conclusions

There are many ways to create value with smart data …

… but these ways to capture value from smart data and the digital shadow are

rather complex …

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Thankfully!

Otherwise is could not become a competitive advantage!

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Offers for further interaction

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Customization 4.0MCPC 2017: The 9th World Conference on

Mass Customization & PersonalizationHow smart products and digital manufacturing

enable a new level of customization & personalizationAachen, Germany, November 19-21, 2017

mcpc2017.com

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Become part of the RWTH Aachen Ecosystem by joining one of our consortia studies.

More information at www.kex-ag.com/en/our-products/consortium-studies/

https://www.kex-ag.com/en/our-products/consortium-studies/

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Dates and locations (BMW, Siemens, Festo …) for 2017:

theleadershipnetwork.com/courses/future-manufacturing

Exclusive Smart Factory Masterclasses & Factory Tours

RWTH Zertifikatskurs fürFührungskräfte:Business Model Innovation

5 Tage in zwei Teilen, plusunternehmensbezogenes Projekt

Dieser Kurs ist auch als individueller Inhouse-Kurs zu flexiblen Terminen möglich!

RWTH Zertifikatskurs „Business Modell Innovation“

Termine unter bmi.rwth-aachen.de

Lernen Sie die Methode für systematische Geschäftsmodell-Innovation

Berufsbegleitender EMBA Studiengang an der RWTH Aachen und Univ. St. Gallen mit Fokus auf Innovation, Technologie und Leadership

Start des 14. Durchgangs im Sept. 2017 | Studienleitung: Prof. Dr. Frank T. Pilleremba.rwth-aachen.de

The RWTH Executive MBA in Technology Management

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A unique blended-learning program in innovation for young professionals

www.academy.rwth-aachen.de/en/mme-time

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Our large platforms for cooperation on the RWTH campus

RWTH Aachen Invention Centerhttp://www.invention-center.de

RWTH Aachen Center Smart Services

http://center-smart-services.rwth-campus.com

Frank T. Piller, Prof. Dr.

RWTH Aachen University

TIME Research Area Technology, Innovation, Marketing & Entrepreneurship

Kackertstrasse 7 | 52072 Aachen | Germany

[email protected] @masscustom (Skype, Twitter, Facebook)time.rwth-aachen.de/tim | frankpiller.com

Expertly assisted by Monika Heer+49 (0)241 809-3577 | [email protected]

Contact industry alliances: Christian Gülpen+49 (0)241 809-6660 | [email protected]

Open for interaction

it’s all about data - from bits and bytes to business …...it’s all about data - from bits and...

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