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Enhancing the Prognostic Power of IT Balanced Scorecards with Bayesian Belief Networks

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

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  1. Enhancing the Prognostic Power of IT Balanced Scorecardswith Bayesian Belief Networks Stefan A. Blumenberg Daniel J. Hinz

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. … 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

  8. Financial 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

  9. … can be transformed into a BBN 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

  10. More detail can be added by breaking down partial strategies 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

  11. Limitations and further research 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

  12. Contact • Stefan A. Blumenberg blumenberg@wiwi.uni-frankfurt.de • Daniel J. Hinz dhinz@wiwi.uni-frankfurt.de HICSS 39 12. März 2014

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