EVA AND KEY PERFORMANCE INDICATORS: THE CASE OF AUTOMOTIVE SECTOR IN PRE-CRISIS, CRISIS AND POST-CRISIS PERIODS

The choice of a suitable measure for company's performance and identification of key performance indicators are among the most frequently discussed topics in the field of corporate management strategizing. This paper shows how the value-based measure represented by Economic Value Added (EVA) an...

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Bibliographic Details
Published in:Economics & sociology 2018-01, Vol.11 (3), p.78-95
Main Authors: Pavelková, Drahomíra, Homolka, Lubor, Knápková, Adriana, Kolman, Karel, Pham, Ha
Format: Article
Language:English
Subjects:
automotive industry
business cycle
Business Economy / Management
economic value added
Financial Markets
key performance indicators
sensitivity analysis
stochastic frontier analysis
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Summary:The choice of a suitable measure for company's performance and identification of key performance indicators are among the most frequently discussed topics in the field of corporate management strategizing. This paper shows how the value-based measure represented by Economic Value Added (EVA) and its pyramidal breakdown could act as facilitators in revealing value drivers. The univariate sensitivity analysis and the Stochastic Frontier Analysis are employed to identify the key performance indicators. The analysis is based on the samples of original equipment manufacturers and suppliers in Czech automotive sector. The automotive industry, in general, is sensitive to the business cycle. Therefore, KPIs of the multiple EVA/Sales distinguished for the samples in the Precrisis, Crisis and Post-crisis periods are identified. The detailed sensitivity analysis reveals several differences in these periods in both samples and across companies of different sizes. Some of the results are further confirmed by the Stochastic Frontier Analysis. Besides other indicators, value added is demonstrated as the key driver with the highest positive impact and personnel cost with the highest negative impact on EVA in all periods although the magnitude of these effects is changing. Analysis of the technical efficiency scores reveals that companies in the crisis periods are more similar to each other and are closer to the best-performing companies than in other periods.
ISSN:2071-789X
2306-3459
DOI:10.14254/2071-789X.2018/11-3/5