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Bayraktar, M E, Hastak, M, Gokhale, S and Safi, B (2011) Decision Tool for Selecting the Optimal Techniques for Cost and Schedule Reduction in Capital Projects. Journal of Construction Engineering and Management, 137(09), 645–55.

Commuri, S, Mai, A T and Zaman, M (2011) Neural Network–Based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes. Journal of Construction Engineering and Management, 137(09), 634–44.

Dai, J and Goodrum, P M (2011) Differences in Perspectives regarding Labor Productivity between Spanish- and English-Speaking Craft Workers. Journal of Construction Engineering and Management, 137(09), 689–97.

González, V, Alarcón, L F, Maturana, S and Bustamante, J A (2011) Site Management of Work-in-Process Buffers to Enhance Project Performance Using the Reliable Commitment Model: Case Study. Journal of Construction Engineering and Management, 137(09), 707–15.

Goodrum, P M, Haas, C T, Caldas, C, Zhai, D, Yeiser, J and Homm, D (2011) Model to Predict the Impact of a Technology on Construction Productivity. Journal of Construction Engineering and Management, 137(09), 678–88.

Hwang, S (2011) Time Series Models for Forecasting Construction Costs Using Time Series Indexes. Journal of Construction Engineering and Management, 137(09), 656–62.

Lin, G, Shen, G Q, Sun, M and Kelly, J (2011) Identification of Key Performance Indicators for Measuring the Performance of Value Management Studies in Construction. Journal of Construction Engineering and Management, 137(09), 698–706.

  • Type: Journal Article
  • Keywords: Performance characteristics; Value engineering; Measurement; Questionnaires; Construction industry; Performance characteristics; Value engineering; Measurement; Questionnaires;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000348
  • Abstract:
    Value management (VM) is widely regarded as a useful tool for management to meet the challenges, such as limited resources and tight schedules arising in the construction industry. A rigorous measurement on the performance of VM studies is likely to improve the implementation of the VM methodology and enhance the confidence of clients about their investment in VM. The identification of key performance indicators (KPIs) is an essential first step in developing a proper performance measurement framework. This paper aims to identify the KPIs for measuring the performance of VM studies in construction. Delegates of international VM conferences hosted by SAVE International and Hong Kong Institute of Value Management during the period 2005 to 2007 were used as the target group for a questionnaire survey. The survey results identified 18 KPIs out of 47 potential performance indicators. They are divided into three groups: predicting indicators, process-related indicators, and outcome-related indicators, according to their characteristics. Three principal components were identified by using factor analysis of the KPIs, which reveals the interrelationship among the KPIs. Details on how to implement these KPIs, such as data providers, weightings, and scoring methods, are also presented.

Wambeke, B W, Hsiang, S M and Liu, M (2011) Causes of Variation in Construction Project Task Starting Times and Duration. Journal of Construction Engineering and Management, 137(09), 663–77.