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A better project performance prediction model using fuzzy time series and data envelopment analysis

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Journal of the Operational Research Society

Abstract

Earned value management (EVM) is a critical project management methodology that evaluates and predicts project performance from cost and schedule perspectives. The novel theoretical framework presented in this paper estimates future performance of a project based on the past performance data. The model benefits from a fuzzy time series forecasting model in the estimation process. Furthermore, fuzzy-based estimation is developed using linguistic terms to interpret different possible conditions of projects. Eventually, data envelopment analysis is applied to determine the superior model for forecasting of project performance. Multiple illustrative cases and simulated data have been used for comparative analysis and to illustrate the applicability of theoretical model to real situations. Contrary to EVM-based approach, which assumes the future performance is the same as the past, the proposed model can greatly assist project managers in more realistically assessing prospective performance of projects and thereby taking necessary and on-time appropriate actions.

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Notes

  1. For more detail information the reader can refer to Coelli et al (2005)

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Acknowledgements

The authors would like to thank anonymous reviewers for their constructive comments on the earlier draft of this manuscript. They also would like to sincerely thank Dr Morteza Bagherpour and Dr Mohammad Mahdi Asgari Dehabadi for their priceless support and assistance.

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Authors and Affiliations

  1. University of Calgary, Calgary, Canada

    Mostafa Salari

  2. George Washington University, Washington DC, USA

    Homayoun Khamooshi

Authors
  1. Mostafa Salari
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  2. Homayoun Khamooshi
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Correspondence to Mostafa Salari.

Appendix

Appendix

In Gangwar and Kumar (2012) studies, a variable named ρ is proposed to partition the time series data using the following expression:

Where E max and E min are maximum and minimum of time series data, respectively. Subsequent to determination of ρ, the fuzzy relations are defined for each partition. For instance, in case of CPI and SPI t data for project No. 1, ρ is calculated as below:

According to their model, the number of partition for CPI and SPI t will be 2 and 1, respectively. In the conclusion part of their studies, it is indicated that if ρ ⩾ N/2, (N = no. of time series data), then there will be not sufficient data in each partitions for prediction. Here, the project No.3 has met this condition:

The model rounded ρ value to the nearest upper integer value, so the value of ρ for both SPI t and CPI will be equal to 4. This causes the fact that the aforementioned condition where ρ ⩾ (N)/(2) happens:

Consequently, there is no sufficient data in each partition, and the model cannot be applied in the case of project No. 3.

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Salari, M., Khamooshi, H. A better project performance prediction model using fuzzy time series and data envelopment analysis. J Oper Res Soc 67, 1274–1287 (2016). https://doi.org/10.1057/jors.2016.20

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