The week's pick
Reseach Article
Design of Prediction System for Key Performance Indicators in Balanced Scorecard
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 72 - Number 8 |
| Year of Publication: 2013 |
| Authors: Ahmed Mohamed Abd El-mongy, Alaa El-deen Hamouda, Nihal Nounou, Abdel-moneim A. Wahdan |
10.5120/12512-6016
|
Ahmed Mohamed Abd El-mongy, Alaa El-deen Hamouda, Nihal Nounou, Abdel-moneim A. Wahdan . Design of Prediction System for Key Performance Indicators in Balanced Scorecard. International Journal of Computer Applications. 72, 8 ( June 2013), 10-14. DOI=10.5120/12512-6016
Abstract
The balanced scorecard (BSC) is a performance management system that supplements traditional financial measures with the criteria that measure the performance from different perspectives. For strategists, they need to predict the KPIs future values to make good decisions during the design of BSC and the determination of the suitable target for each objective and KPI. From historical data, the dependency between KPIs can be discovered through developing traditional prediction model. Hence, the KPI future values can be predicted. However, such prediction does not consider the nature of KPIs in the BSC. The historical values of KPIs depend on the previously settled targets for objectives and KPIs. This raises the challenge of finding a solution to make more accurate prediction that considers the real values of KPIs beside the previous settled targets. For achieving that, we propose a solution that uses fuzzy logic to categorize the KPI values and then predict the future KPI values. Then, we develop a third predictor model as a data fusion module to predict the KPI values depending on both previous values and category predictors. We find that the prediction accuracy of our proposed solution significantly overcomes the normal values prediction of KPIs.
References
- Leen Yu M and et al. 2009. The e-balanced scorecard (e-BSC) for measuring academic staff performance excellence. Journal of Higher Education 2009; 57(6):813–828.
- Alan Meekings, Steve Briault and Andy Neely. 2011. How to avoid the problems of target-setting. Measuring Business Excellence, Vol. 15 Iss: 3, pp. 86 – 98.
- Gomes R. 2009 The Balanced Scorecard as a Performance Management Tool for Third Sector Organizations: the Case of
- the Arthur Bernardes Foundation, Brazil. Brazilian Administration Review, Curitiba; 6(4) , art. 5:354–366.
- Zhang T and Gao L. 2008. Study on the Application of Dynamic Balanced Scorecard in the Service Industry. IEEE
- International Conference on Intelligent Computation Technology and Automation; 1:1158-1162. (20).
- Amaratunga D and et al. 2002 Application of the balanced score-card to develop a conceptual framework to measure facilities management performance within NHS facilities. International Journal of Health Care Quality Assurance; 15(4):141-151.
- J. Shana and T. Venkatachalam. July 2011, Identifying Key Performance Indicators and Predicting the Result from Student Data, International Journal of Computer Applications (0975 – 8887) Volume 25– No. 9.
- Alaa Hamouda, "BSCBAS. 2012 A Balanced Scorecard Based Appraisal System for improving software organizations performance". Journal of Software Maintenance and Evolution: Research and Practice, Willy Publisher, 30 JUL 2012. DOI: 10. 1002/smr1566.
- Sanjeev Rao and Priyanka Gupta. JAN - March 2012. Implementing Improved Algorithm Over APRIORI Data Mining Association Rule Algorithm. INTERNATIONAL JOURNAL OF COMPUTER SCIENCE and TECHNOLOGY (IJCST), V 3.
- M. Hellmann. March 2001. Fuzzy Logic Introduction .
- YASHPAL SINGH and ALOK SINGH CHAUHAN. 2005 - 2009. Journal of Theoretical and Applied Information Technology.
- ]Gaurav L. Agrawal and Prof. Hitesh Gupta, Optimization of C4. 5 Decision Tree Algorithm for Data Mining Application; March 2013; International Journal of Emerging Technology and Advanced Engineering; Volume 3, Issue 3, March 2013.
Index Terms
Keywords