WIVE workshop WIVE workshop Managing distributed innovation - - PowerPoint PPT Presentation

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WIVE workshop WIVE workshop Managing distributed innovation - - PowerPoint PPT Presentation

WIVE workshop WIVE workshop Managing distributed innovation processes in Virtual Organisations by applying the Virtual Organisations by applying the Collaborative Network Relationship Analysis Dr. rer. pol. Jens Eschenbcher Dipl. Inf. Heiko


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WIVE workshop WIVE workshop Managing distributed innovation processes in Virtual Organisations by applying the Virtual Organisations by applying the Collaborative Network Relationship Analysis

  • Dr. rer. pol. Jens Eschenbächer
  • Dipl. Inf. Heiko Duin
  • Prof. Dr.-Ing. Klaus-Dieter Thoben

PROVE 2009

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Content

  • Categories of innovation
  • Categories of innovation
  • Case study for distributed innovation in VO
  • Qualitative and quantitative methods to investigate

collaborative relationships in distributed innovation collaborative relationships in distributed innovation processes C ll b ti t k l ti hi l i

  • Collaborative network relationship analysis
  • Case study - revisited

y

  • Conclusions

WIVE workshop Jens Eschenbächer

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SLIDE 3

Categories of Innovationen

Innovation: Product innovation I-Phone, IPOD Product innovation Process innovation Global innovation processes (concept in US manufacturing in China test cases in Process innovation US, manufacturing in China, test cases in several countries, etc.) Service innovation Automatic update of Podcasts I-Tunes, platform for all types of new business ideas (talking books, radio stations, podcasts, …) Business model innovation technical Innovation, application innovation, empirical innovation, marketing innovation, structural Innovation Further models

WIVE workshop Jens Eschenbächer

(Reference: Granig 2007, S. 197, Geoffrey 2004, S. 62)

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SLIDE 4

Example innovation project: Intelligent front mirror

Market research Design Pre-development Product development production

x

Main innovation: Situation: Main innovation: Multi-touch governance of objects and information on an intelligent front mirror Situation:

  • 4 companies would like to collaborate (VO-
  • riented),
  • Sharing competencies is key,

g p y,

  • Technological and organisational

challenges and

  • Is there a market for intelligent front mirrors

( t ti t 1500 2500 E ) ? (cost estimate: 1500-2500 Euros) ? Objective:

  • Definition and analysis of needed

WIVE workshop Jens Eschenbächer

Definition and analysis of needed, collaborative network relationships

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SLIDE 5

Analysis of collaborative network relationships

Quantitative and qualitative methods

Qualitative-oriented methods – etimate Quantitative-oriented methods-measure

(Hollstein 2006, Eschenbaecher 2009, Jarimo And Korpiaho 2008, Wassermann and Fausst 2008):

  • Triangulation
  • Field research

(Ellmann 2007, Rank 2003, Wald 2003, Wührer 1995, Jansen 2006, Abreu and Camarinha-Matos 2008, S. S. Msanjila,

  • H. Afsarmanesh 2008, Wasserman/Faust 1994):
  • Interpretative procedures
  • Open interviews
  • Time series analysis

Relationship analysis (collaborative networks) )

  • Density of partner
  • Centrality
  • Inward-oriented relationships
  • Outward oriented relationships
  • Relationship analysis (collaborative networks)
  • Etc.
  • Outward oriented relationships
  • Closeness to other partner,
  • Benefit analysis
  • Value systems and trust management

WIVE workshop Jens Eschenbächer

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SLIDE 6

Quantitiative Network Analysis – various Indicators

Outward Orientation

(Rank 1998, Wald 2003,…)

Inward Orientation

(Renz 1998, Rank 2003, Wald 2003, …)

Network Density

(Ellmann 2008)

Network Centrality

(Ellmann 2008)

WIVE workshop Jens Eschenbächer

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Qualitative view: Collaborative relationships and intensities

A B C Identification of Collaborative Relationships Identification of Interaction Groups Collaboration Intensities Id tifi ti f I t ti I t ti h Identification of Collaborative Relationships Interactions are

  • f Different Category

Interactions have Intensities

WIVE workshop Jens Eschenbächer

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Collaborative network relationship analysis

Life-cycle

VO formation VO set-up VO dissolution nodes edges

Accurate

Collaborative network

Accurate understanding

Collaborative network relationship analysis Providing information on innovation processes in

time

Structural dimension p VO operation phase

WIVE workshop Jens Eschenbächer

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Collaborative network relationship analysis

  • 1. Determine competencies

and network structure Analysis and definition of competencies and network structures

  • 2. Present cooperation partners

Presentation of network by using a value-chain oriented analysis

  • f nodes

nning phase

p p 3.Define and measure cooperation intensities Deduction of interactions (cost estimates) and cooperation intensities between partners

Plan

cooperation intensities Configuration of Stage-Gate Model on Basis of necessary interactions and respective cooperation intensities

  • 4. Configure stage-Gate Model

tion phase

  • 5. Select and assign ICT-systems

Selection of appropriate tools and applications to support interactions within cooperative innovations process

Configurat

Accomplishment and evaluation of phases and gates

  • 6. Use and evaluate applications

sage/ Evalu- tion phase

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Us a

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Step 3: Method to define and measure collaboration intensities

Step 1 Identification of Interactions and their Categories Step 2 Definition of Variables for each Interaction Group Step 3 Investigation of the Collaboration Intensity Estimate about cooperation intensity by using a scoring system: Innovation-promoting interactions Tangible-means related interactions Legal interactions Definition of objectives ICT administration complexity by using a scoring system: 1 = difficult, 0.5 = medium,: 0 = simple Criteria Interaction Financial interactions Personal interactions ICT-related interactions interdisciplinary cooperation Conflict potential Adjustment necessity I1 I2 I3 I4 I5 Definition of

  • bjectives

0,5 0,5 1 ICT 0,5 1 1 0,5 Dependency of planning Information deformation management Complexity 0,5 0,5 1 1 0,5 0 – 2 points Step 4 Specification of the Collaboration Intensity by application of steps 1-3 Step5 Identification of the Collaborative Relationships Based on the Evaluated Interactions 2 – 4 points 4 – 5.5 points 5.5 – 7 points WIVE workshop Jens Eschenbächer 7 – 8 points

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SLIDE 11

Case revisited – main interactions

Automotive supplier‐ Soft‐ und Hardware

VE VT

I1 I5

PE PR RA

Innovation‐promoting interaction I1: Display technology I2: display contrast and brightness I5: Software concept i

VE

I1 I13

I7: First prototype I13: Feedback by living labs Tangibe means related interactions

VE PE PE

I2 I3 I4 I5 I11 I7

interactions I3: Derating I8: pre‐series models I10: technology test

PR PR EK

I6 I11 I10

Legal interactions I14: patent management Financial interactions

Automotive OEM Display producer

VT RA

I7 I8 I9 I12 I14

I4: House Engineer I6: sample management I11: project controlling Personal interactions

Implementation Engineering company Display producer Display technolgy

VE PE PR VT

Personal interactions I9: exchange of experts ICT related interactions I12: ICT infrastructure, Sharepoint Portal

WIVE workshop Jens Eschenbächer

g g p y Mechanic and construction

, p

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Collaboration Intensity – Example (1)

Example – Basic Assumptions

P j t ith 4 P t (N d )

P1 P2

– Project with 4 Partners (Nodes)

P4 P4 P3

Workplan

4 Tasks to be performed – 4 Tasks to be performed (effort in person-days, time in weeks)

P1 P2 P3 P4 Task 1 Task 2 Start Dur. 4 2 8 20.0 30 0 12.0 21 2 Totals 32.0 51 2 Task 2 Task 3 Task 4 2 8 6 6 8 6 30.0 14.0 21.2 10.0 18 0 30 0 51.2 24.0 48 0

WIVE workshop Jens Eschenbächer

Task 4 8 6 18.0 30.0 48.0

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Collaboration Intensity – Example (2)

Ass ming eq all distrib ted reso rces o er the Assuming equally distributed resources over the duration, the collaboration intensity can be calculated by dividing the totals by the duration of the task.

P1 P2 P3 P4 Task 1 Task 2 Start Dur. 4 2 8 20.0 30 0 12.0 21 2 Totals 32.0 51 2 Intens. 8.0 6 4 Task 2 Task 3 Task 4 2 8 6 6 8 6 30.0 14.0 21.2 10.0 18 0 30 0 51.2 24.0 48 0 6.4 4.0 8 0

WIVE workshop Jens Eschenbächer

Task 4 8 6 18.0 30.0 48.0 8.0

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Quantitiative Network Analysis – Common Indicators

(Wassermann und Fausst 1994, 2008, Knocke und Kulinski 2006)

,( )

i

N Out ijk

O z i j  

Outward Orientation i

index of actor i j index of actor j

1

i

j j

(Rank 1998, Wald 2003,…)

( )

N

O i j

j index of actor j k network k N total number of actors i t k k

Inward Orientation

(Renz 1998, Rank 2003, Wald 2003, …)

1

,( )

i

In ijk i

O z i j

 

in a network k

Network Density

(Ellmann 2008)

2 1 1

1 ,( )

N N k ijk i j

D z i j N N

 

   



( )

j

( )

N ijk ijk

z z 

Network Centrality

(Ellmann 2008)

1

,( )

ijk ijk j N N i ijk

C i j z

 

 

WIVE workshop Jens Eschenbächer

1 1 ijk i j  



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Additional Symbols to develop collaboration intensity indicator

Symbol Description

N N 1 1

,( )

N N ijk i j

z i j

 



Number of considered Interactions

ijk

z

interaction z (between the nodes i and j) of category k

ijk

interaction z (between the nodes i and j) of category k

ijk

z

x

Collaboration intensity of interaction

ijk

z

0zijk

t

Starting point (in time) for interaction

ijk

z

ijk

j

zijk

d

t

Continuity of interaction

ijk

z

ijk

z

g

Weighting factor of interactions

ijk

z

j ijk ijk

z z

x g

Product of collaboration intensity and weighting factor of interaction

ijk

z ( )

ijk

z

I t

Intensity of interaction

ijk

z

at point in time t

ijk

z

C

Cost of interaction

ijk

z

during the duration

zijk

d

t

  • f interaction

( )

ijk

z

c t

Cost of interaction

ijk

z

at point of time t

WIVE workshop Jens Eschenbächer

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Collaboration Intensity – Example (3)

Intensity

Then, the collaboration intensity can be shown over the time.

, ( ) ( ) , other cases

ijk ijk z z z ijk ijk ijk ijk

z z d z

x g t t t t I t         

Task 1 T k 2 Task 4

8

Task 2 T k 3 Task 3

4

Time

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2 4 6 8 10 12 14

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Collaboration Intensity – Example (4)

Accumulated Intensity

Adding the intensities results in the total collaboration intensity,

y

which is a step curve.

( ) ( )

ijk

N N z

GI t I t 

1 1

ijk

z i j  



12 8

Time

4

WIVE workshop Jens Eschenbächer

2 4 6 8 10 12 14

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Collaboration Intensity – Example (5)

Accumulated Intensity

Integrating the total intensities results in the accumulated

y

collaboration intensity.

8

t t N N

 

  

6

1 1

( ) ( )d ( ) d

ijk

z i j

AGI t GI t t I t t

 

       

  

4

Time

2

WIVE workshop Jens Eschenbächer

2 4 6 8 10 12 14

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Example: Intelligent front mirror - revisited

Market research Design Pre-development Product development Production

x

The project has been started without a collaborative network relationship analysis. Uncorrectly estimated work efforts in project management led immediatel to both cost increase and lead time dela s management led immediately to both cost increase and lead time delays. Due to economic crises innovation project is in hold position. The innovation project budget has been substantially underestimated.

WIVE workshop Jens Eschenbächer

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Conclusions and future outlook

Methods to support a more practical, qualitative analysis of collaborative network relationships are yet not available in y mature state. Current Reseach is focussing on the application of mathematical, quantitivative models These models are often very static and they quantitivative models. These models are often very static and they also imply a concrete understanding of real processes which probabaly remains difficult. The ideas of an indicator for analyzing collaborative relationships on information's delivered by managers can be seen as an attempt to combine analytical methods with qualitative information combine analytical methods with qualitative information. First steps in case studies show that the specification of collaboration intensities supports analytical thinking In a next step the authors will try to formalize the collaborative network relationship analysis by applying graph theory and media richness theory

WIVE workshop Jens Eschenbächer

media-richness theory.

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Thank you for your attention! Thank you for your attention!

This work has been partly funded by the European Commission through FP7 Project p g j EU-FP7-216256 COIN

WIVE workshop Jens Eschenbächer