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Comparative Study of Earned Schedule Method With Earned Value Method


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1 NICMAR, Hyderabad, India
     

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ES is considered a breakthrough technique to integrated performance management and EVM theory and practice. Earned Schedule (ES) is a method of extracting schedule information from Earned Value Management (EVM) data. The method has been shown to provide reliable schedule indicators and predictors for both early and late finish projects. ES provides time based measure of schedule variance (SV(t)) and Schedule Performance Index SPI(t) metrics with behavior analogous to the EVM cost based counterparts. Traditional Earned Value Management (EVM) index-based methods for Cost Estimate at Completion (CEAC) of an ongoing project have been known for their limitations inherent with both the assumption that past EVM data is the best available information and early-stage unreliability. In an attempt to overcome such limitations, a new CEAC methodology is proposed based on a modified index-based formula predicting expected cost for the remaining work. The proposed model shows itself to be more accurate and precise in all early, middle, and late stage estimates than EVM.
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  • Comparative Study of Earned Schedule Method With Earned Value Method

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Authors

Abhineet Tripathi
NICMAR, Hyderabad, India
Darshan B. Patel
NICMAR, Hyderabad, India
Patel Rushil Ramanabhai
NICMAR, Hyderabad, India
Raja Sekhar Mamillapalli
NICMAR, Hyderabad, India

Abstract


ES is considered a breakthrough technique to integrated performance management and EVM theory and practice. Earned Schedule (ES) is a method of extracting schedule information from Earned Value Management (EVM) data. The method has been shown to provide reliable schedule indicators and predictors for both early and late finish projects. ES provides time based measure of schedule variance (SV(t)) and Schedule Performance Index SPI(t) metrics with behavior analogous to the EVM cost based counterparts. Traditional Earned Value Management (EVM) index-based methods for Cost Estimate at Completion (CEAC) of an ongoing project have been known for their limitations inherent with both the assumption that past EVM data is the best available information and early-stage unreliability. In an attempt to overcome such limitations, a new CEAC methodology is proposed based on a modified index-based formula predicting expected cost for the remaining work. The proposed model shows itself to be more accurate and precise in all early, middle, and late stage estimates than EVM.