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Call for Paper - May 2015 Edition
IJCA solicits original research papers for the May 2015 Edition. Last date of manuscript submission is April 20, 2015. Read More

Design of Prediction System for Key Performance Indicators in Balanced Scorecard

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International Journal of Computer Applications
© 2013 by IJCA Journal
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 and Abdel-moneim A Wahdan. Article: Design of Prediction System for Key Performance Indicators in Balanced Scorecard. International Journal of Computer Applications 72(8):10-14, June 2013. Full text available. BibTeX

@article{key:article,
	author = {Ahmed Mohamed Abd El-mongy and Alaa El-deen Hamouda and Nihal Nounou and Abdel-moneim A. Wahdan},
	title = {Article: Design of Prediction System for Key Performance Indicators in Balanced Scorecard},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {72},
	number = {8},
	pages = {10-14},
	month = {June},
	note = {Full text available}
}

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.

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