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Enhancing the Prognostic Power of IT Balanced Scorecards with Bayesian Belief Networks. Stefan A. Blumenberg Daniel J. Hinz. Summary.

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Enhancing the prognostic power of it balanced scorecards with bayesian belief networks l.jpg

Enhancing the Prognostic Power of IT Balanced Scorecardswith Bayesian Belief Networks

Stefan A. Blumenberg

Daniel J. Hinz


Summary l.jpg
Summary

  • Balanced Scorecard (BSC) is a widely used performance measurement system. Causal relationships are an integral part, however it is neither thoroughly introduced in theory nor applied in practice

  • Bayesian Networks (BBNs) can be employed to improve Balanced Scorecard methodology

    • Balanced Scorecards and Bayesian Networks can be combined due to structural similarities

    • A combination of both approaches allows for an a priori validation of causalities with reduced effort in validity maintenance and better prediction of value chain figures

  • A sample IT scorecard shows the applicability of both methods

HICSS 39 12. März 2014


Balanced scorecard is widely adopted also in it management l.jpg
Balanced Scorecard is widely adopted, also in IT management

  • BSC is a widely adopted performance measurement method that provides „a fast but comprehensive views of all businesses“[Kaplan, R.S., Norton, D.P., 1992], that

    • Combines classical financial figures and „soft“ factors

    • Reduces information complexity

    • Consists of 4 perspectives in its generic design

  • BSC is also a common tool for IT management (e.g. IT governance or IT contribution to firm performance) [Graeser et al., 1998; Van der Zee, J.T.M., De Jong, B., 1999]

Financial

Perspective

Customers

Perspective

Internal

Business

Processes

Vision &

Strategy

Innovation &

Learning

Perspective

HICSS 39 12. März 2014


Causal relationships are the core and major strength of bsc l.jpg

IT support of

internal

communication

Customer

satisfaction

Net income

Causal relationships are the core and major strength of BSC

  • Thoroughly extracted cause-and-effect chains provide a good basis for a BSC to succeed [Kaplan, R.S., Norton, D.P., 1996] and allow

    • Prediction of value chain performance measures [Kaplan, R.S., Norton, D.P., 2001]

    • Communication and realization of the corporate strategy [Kaplan, R.S., Norton, D.P., 2001]

    • Incentives based actions [Malina, M.A., Selto, F.H., 2004]

  • 89% of surveyed companies agree to better understand the value of intangible assets with modeled cause-and-effect chains [Marr, B., 2004]

Cause-and-effect chain example

HICSS 39 12. März 2014


However most firms do not implement these powerful causal relationships l.jpg

23%

However, most firms do not implement these powerful causal relationships

Empirical findings

Causal relationships within BSC

Modelled

Not modelled

[Marr, B., 2004]

46%

[Ittner et al., 2003]

77%

Explanation for these findings

  • The theoretical description of causalities by Kaplan and Norton within BSCs is only vague [Norreklit, H., 2000]

  • From a practical point of view, extraction of corporate causalities and their reliability maintenance is complex and tedious [Malina, M.A., Selto, F.H., 2001; Grey, C., 2001]

HICSS 39 12. März 2014


Bayesian belief networks offer a promising method to substantiate bscs l.jpg

BBN Principles

  • A BBN models the cause-effect relationships of its nodes

  • Each node is a conditional probability distribution describing the effect of the parent nodes on itself Prob(node|parent nodes)

  • Network allows for simulation in both directions

    • Given a change in a parent node, how do the target figures change?

    • Given a desired target value, which values do the parent nodes have to have?

F

C

P

I

I

C

P

F

Bayesian Belief Networks offer a promising method to substantiate BSCs…

Balanced Scorecard

…substantiated by …

Bayesian Belief Network

HICSS 39 12. März 2014


Because of the structural similarities of both approaches l.jpg
… because of the structural similarities of both approaches

Balanced Scorecard (BSC)

  • Consists of entities (called figures), grouped within perspectives

  • Directed edges indicate causal relationships

  • Loops are allowed, but should be omitted to be compatible with BBNs

Bayesian Belief Networks (BBN)

  • Consists of entities (called nodes), may be grouped graphically

  • Directed edges describe causal relationships and are used to calculate conditional probabilities

  • Loops are not allowed (graphs has to be directed and acyclic)

HICSS 39 12. März 2014


Example a sample bsc from literature l.jpg

Financial approaches

Perspective

Customer

Perspective

Internal

Perspective

Learning and Growth

Perspective

Example: A sample BSC from literature…

Source: Journal of Management Information Systems,

Van der Zee, J.T.M.; De Jong, B., 1999)

Net income growth

  • BSC from Journal of Management Information Systems

  • Implemented by a European bank

  • Boxes contain partial strategies (not already chosen figures)

Standard sales growth

Operating costs down

Improve „low cost“ perception of

potential & current customers

Make products more

accessible through new and old

distribution channels

Start up call centers and

automated electronic distribution

channels for Internet

Reduce costs by designing

IT enabled processes

Increase employee productivity

through ubiquitous capabilities

Learn employees to use

productivity tools

Improve awareness about IT

opportunities in the context

of business activities

HICSS 39 12. März 2014


Can be transformed into a bbn l.jpg
… can be transformed into a BBN approaches

Transformation process

The simple steps…

  • Transform every figure of BSC into a BBN node

  • Transform every dependency arrow into a directed edge

  • Eliminate recursive loops (n/a in this example)

    … and the challenge

  • Discretize node values

  • Add probability tables for each node

  • Some dependencies are easy to determine (e.g. impact of sales growth on net income growth), others are not

  • Each assumption of the BSC has to be thoroughly revisited

HICSS 39 12. März 2014


More detail can be added by breaking down partial strategies l.jpg
More detail can be added by breaking down partial strategies approaches

Simulation 1

  • 20% of all employees have yearly business and IT trainings

  • 80% only every trhee years

  • Awareness increases by 0.9 points

    Simulation 2

  • 80% of all employees have yearly business and IT trainings

  • 20% only every trhee years

  • Awareness increases by 2.1 points

HICSS 39 12. März 2014


Limitations and further research l.jpg
Limitations and further research approaches

Limitations

  • BSC does allow loops, BBNs do not

  • Integration of time component is difficult in BSC as well as BBN

  • Empiricial validation can be done in two areas

    • Companies in the process of implementing a BSC:To demonstrate effectiveness in scorecard ramp-up

    • Companies with a working BSC:To test improvement capabilities of the proposed approach against traditional double loop learning

Further research

HICSS 39 12. März 2014


Contact l.jpg
Contact approaches

  • Stefan A. Blumenberg

    [email protected]

  • Daniel J. Hinz

    [email protected]

HICSS 39 12. März 2014


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