Course of planned, actual and earned cost curves of diverse construction investments

References

• Abba W. 2000. How earned value got to primetime: a short look back and glance ahead. In Proceedings of the project management institute annual seminars & symposium; Project Management Institute (PMI), Houston, TX. Newtown Square.
   Google Scholar
• Al-Jibouri S. 2003. Monitoring systems and their effectiveness for project cost control in construction. Int J Proj Manag. 21(2):145–154.
   Google Scholar
• Anysz H, Zawistowski J. 2017. Cost-time control of the progress of construction works using the earned value method using MS Project (in Polish: Kontrola kosztowo-czasowa postępu robót budowlanych metodą wartości wypracowanej z wykorzystaniem programu MS Project). In Principles and standards of cost estimation of construction works for awarding entities, investors and contractors (in Polish: Zasady i standardy kosztorysowania robót budowlanych dla zamawiających, inwestorów i wykonawców); p. 71–88.
   Google Scholar
• Araszkiewicz K, Bochenek M. 2019. Control of construction projects using the Earned Value Method – case study. Open Eng. 9(1):186–195.
   Web of Science ®Google Scholar
• Ballesteros-Pérez P, Mohamed Elamrousy K. 2018. On the limitations of the Earned Value Management technique to anticipate project delays. Responsible Des. Deliv. Constr. Proj. ; p. 1–6.
   Google Scholar
• Ballesteros-Pérez P, Sanz-Ablanedo E, Mora-Melià D, González-Cruz M, Fuentes-Bargues J, Pellicer E. 2019. Earned Schedule min-max: two new EVM metrics for monitoring and controlling projects. Autom Constr. 103:279–290.
   Web of Science ®Google Scholar
• Ballesteros-Pérez P, Sanz-Ablanedo E, Soetanto R, González-Cruz M, Larsen G, Cerezo-Narváez A. 2020. Duration and cost variability of construction activities: an empirical study. J Constr Eng Manag. 146(1):04019093.
   Web of Science ®Google Scholar
• Barraza G, Back W, Mata F. 2004. Probabilistic forecasting of project performance using stochastic S curves. J Constr Eng Manag. 130(1):25–32.
   Web of Science ®Google Scholar
• Bauer-Celny A, Konior J, Szóstak M, n.d. Initial, Monthly, Close-out Reporting of Bank Investment Supervision. PM Group Polska Sp. z o.o. (2006–2015) and 3EPCM Sp. z o.o. (2016–2019).
   Google Scholar
• Bhargava A, Labi S, Chen S, Saeed T, Sinha K. 2017. Predicting cost escalation pathways and deviation severities of infrastructure projects using risk-based econometric models and Monte Carlo simulation. Comput Civ Infrastruct Eng. 32(8):620–640.
   Web of Science ®Google Scholar
• Chao L, Chen H. 2015. Predicting project progress via estimation of S-curve’s key geometric feature values. Autom Constr. 57:33–41.
   Web of Science ®Google Scholar
• Chao L, Chien C. 2009. Estimating project S-curves using polynomial function and neural networks. J Constr Eng Manag. 135(3):169–177.
   Web of Science ®Google Scholar
• Chao L, Chien C. 2010. A model for updating project s-curve by using neural networks and matching progress. Autom Constr. 19(1):84–91.
   Web of Science ®Google Scholar
• Chen H, Chen W, Lin Y. 2016. Earned value project management: improving the predictive power of planned value. Int J Proj Manag. 34(1):22–29.
   Web of Science ®Google Scholar
• Chen Z, Demeulemeester E, Bai S, Guo Y. 2020. A Bayesian approach to set the tolerance limits for a statistical project control method. Int J Prod Res. 58(10):3150–3163.
   Web of Science ®Google Scholar
• Cheng M, Roy A. 2011. Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines. Int J Proj Manag. 29(1):56–65.
   Web of Science ®Google Scholar
• Cheng M, Tsai H, Liu C. 2009. Artificial intelligence approaches to achieve strategic control over project cash flows. Autom Constr. 18(4):386–393.
   Web of Science ®Google Scholar
• Cheng Y, Yu C, Wang H. 2011. Short-interval dynamic forecasting for actual S-curve in the construction phase. J Constr Eng Manag. 137(11):933–941.
   Web of Science ®Google Scholar
• Chin-Keng T, Shahdan N. 2015. The application of Earned Value Management (EVM) in construction project management. J Technol Manag Bus. 2(2):1–11. Available online: https://www.semanticscholar.org/paper/The-Application-of-Earned-Value-Management-(EVM)-in-Keng-Shahdan/4a282f7dac42a66f4c551aba3648fdcd1d30d9a6. (accessed on 12 May 2020)
   Google Scholar
• Chou J. 2011. Cost simulation in an item-based project involving construction engineering and management. Int J Proj Manag. 29(6):706–717.
   Web of Science ®Google Scholar
• Cristóbal J. 2017. The S-curve envelope as a tool for monitoring and control of projects. Procedia Comput Sci. 121:756–761.
   Google Scholar
• Czarnigowska A. 2008. Earned value method as a tool for project control. Bud Archit. 3:15–32.
   Google Scholar
• Czarnigowska A. 2009. Monitoring of project progress using the earned value. Przegląd Bud (in Polish). 80:50–55.
   Google Scholar
• Czemplik A. 2014. Application of earned value method to progress control of construction projects. Procedia Eng. 91:424–428.
   Google Scholar
• De Marco A, Narbaev T. 2013. Earned value-based performance monitoring of facility construction projects. J Facilities Manag. 11(1):69–80.
   Google Scholar
• De Neufville R, Scholtes S, Wang T. 2006. Real options by spreadsheet: parking garage case example. J Infrastruct Syst. 12(2):107–111.
   Web of Science ®Google Scholar
• Dziadosz A, Kapliński O, Rejment M. 2014. Usefulness and fields of the application of the Earned Value Management in the implementation of construction projects. Bud Arch. 13(4):357–364.
   Google Scholar
• Gunduz M, Almuajebh M. 2020. Critical Success Factors for Sustainable Construction Project Management. Sustainability. 12(5):1990.
   Web of Science ®Google Scholar
• Howes R. 2000. Improving the performance of Earned Value Analysis as a construction project management tool. Eng Const Arch Man. 7(4):399–411.
   Google Scholar
• Hsieh T, Wang M, Chen C, Chen C, Yu S, Yang H, Chen T. 2006. A new viewpoint of s-curve regression model and its application to construction management. Int J Artif Intell Tools. 15(02):131–142.
   Google Scholar
• Hsieh T, Wang M, Chen C. 2004. A case study of s-curve regression method to project control of construction management via T-S fuzzy model. J Mar Sc Technol. 12:209–216.
   Google Scholar
• Kasprowicz T. 2017a. Quantitative assessment of construction risk. Arch Civ Eng. 63(2):55–66.
   Web of Science ®Google Scholar
• Kasprowicz T. 2017b. Quantitative identification of construction risk. Arch Civ Eng. 63(1):63–75.
   Web of Science ®Google Scholar
• Kerzner H. 2003. Project management: a systems approach to planning, scheduling, and controlling. New York (NY).: John Wiley & Sons, Inc
   Google Scholar
• Khamooshi H, Golafshani H. 2014. EDM: Earned Duration Management, a new approach to schedule performance management and measurement. Int J Proj Manag. 32(6):1019–1041.
   Web of Science ®Google Scholar
• Kim B, Reinschmidt K. 2007. An S-curve Bayesian model for forecasting probability distributions on project duration and cost at completion. CME 2007 Conference - Construction Management and Economics. p. 1449–1459.
   Google Scholar
• Klinowski M. 2014. Analysis of the current and forecast of the future realization of the project on the basis of earned value method. Res Pap Wrocław Univ Econ (in Polish). 335:105–115.
   Google Scholar
• Konior J, Szóstak M. 2020a. Methodology of planning the course of the cumulative cost curve in construction projects. Sustainability. 12(6):2347.
   Web of Science ®Google Scholar
• Konior J, Szóstak M. 2020b. The S-curve as a tool for planning and controlling of construction process—case study. Appl Sci. 10(6):2071.
   Google Scholar
• Konior J. 2018. Feasibility study of construction project in compliance with bank investment supervision requirements. J Civ Eng Archit. 12:831–883.
   Google Scholar
• Konior J. 2019. Monitoring of construction projects feasibility by bank investment supervision approach. cea. 7(1):31–35.
   Google Scholar
• Konior J. 2019b. Significance risks evaluation of commercial construction projects. Arch Civ Eng. 65(2):19–33.
   Web of Science ®Google Scholar
• Kumar V, Hanna A, Adams T. 2000. Assessment of working capital requirements by fuzzy set theory. Eng Const and Arch Man. 7(1):93–103.
   Google Scholar
• Lam K, So A, Hu T, Ng T, Yuen R, Lo S, Cheung S, Yang H. 2001. An integration of the fuzzy reasoning technique and the fuzzy optimization method in construction project management decision-making. Constr Manag Econ. 19(1):63–76.
   Google Scholar
• Leśniak A, Kubek D, Plebankiewicz E, Zima K, Belniak S. 2018. Fuzzy AHP application for supporting contractors’ bidding decision. Symmetry. 10(11):642.
   Google Scholar
• Lipke W, Zwikael O, Henderson K, Anbari F. 2009. Prediction of project outcome. The application of statistical methods to earned value management and earned schedule performance indexes. Int. J. Proj. Manag. 27(4):400–407.
   Web of Science ®Google Scholar
• Love P, Ahiaga-Dagbui D, Irani Z. 2016. Cost overruns in transportation infrastructure projects: sowing the seeds for a probabilistic theory of causation. Transp Res Part A Policy Pract. 92:184–194.
   Web of Science ®Google Scholar
• Love P, Wang X, Sing C, Tiong R. 2013. Determining the probability of project cost overruns. J Constr Eng Manag. 139(3):321–330.
   Web of Science ®Google Scholar
• Martens A, Vanhoucke M. 2020. Integrating corrective actions in project time forecasting using exponential smoothing. J Manag Eng. 36(5):1–34. 
   Web of Science ®Google Scholar
• Mohagheghi V, Meysam Mousavi S, Vahdani B. 2017. An assessment method for project cash flow under interval-valued fuzzy environment. J Optim Ind Eng. 22:79–80.
   Google Scholar
• Pakalapati K, Khalafalla M, Rueda-Benavides J. 2020. Using moving-window cross-validation algorithm to optimize bid-based cost estimating data usage. Int J Constr Manag. DOI: 10.1080/15623599.2020.1827694.
   Google Scholar
• Plebankiewicz E, Zima K, Wieczorek D. 2017. Quantification of the risk addition in life cycle cost of a building object. Tech Trans. 5:35–45.
   Google Scholar
• Połoński M, Komendarek P. 2011. Earned value method for operational cost control of civil structure. Quantit Meth Econ. 12:279–290.
   Google Scholar
• Połoński M. 2018. Management of construction investment process. Warszawa (Poland): Wydawnictwo SGGW. ISBN: 9788375830873
   Google Scholar
• Project Management Institute 2017. A guide to the project management body of knowledge (PMBOK Guide). 6th ed. Newtown Square (PA):Project Management Institute (PMI). ISBN: 9781935589679
   Google Scholar
• Przywara D, Rak A. 2017. The time-cost analysis of schedule monitoring using the earned value method. Tech Trans. 5:57–66.
   Google Scholar
• Saeed T. 2019. Road infrastructure readiness for autonomous vehicles [doctoral dissertation]. Purdue University Graduate School.
   Google Scholar
• Salari M, Khamooshi H. 2016. A better project performance prediction model using fuzzy time series and data envelopment analysis. J Oper Res Soc. 67(10):1274–1287.
   Web of Science ®Google Scholar
• Tepeli E, Taillandier F, Breysse D. 2019. Multidimensional modelling of complex and strategic construction projects for a more effective risk management. Int J Constr Manag. 1–22. DOI: 10.1080/15623599.2019.1606493.
   Google Scholar
• Tijanić K, Car-Pušić D. 2017. Application of S-curve in EVA method. In Proceedings of the 13th International Conference “Organization, Technology and Management in Construction; p. 103–115.
   Google Scholar
• Vandevoorde S, Vanhoucke M. 2006. A comparison of different project duration forecasting methods using earned value metrics. Int J Proj Manag. 24(4):289–302.
   Google Scholar
• Vanhoucke M, Coelho J, Batselier J. 2016. An overview of project data for integrated project management and control. J Mod Proj Manag. 3 (2):6–21.
   Google Scholar
• Vanhoucke M, Vandevoorde S. 2007. A simulation and evaluation of earned value metrics to forecast the project duration. J Oper Res Soc. 58(10):1361–1374.
   Web of Science ®Google Scholar
• Vanhoucke M. 2019. Tolerance limits for project control: an overview of different approaches. Comput Ind Eng. 127:467–479.
   Web of Science ®Google Scholar
• Wang H, Hu Y. 2011. Research on the system of the construction project life cycle cost risk management. AMM. 94-96:2329–2332.
   Google Scholar
• Waris M, Khamidi M, Idrus A. 2012. The cost monitoring of construction projects through earned value analysis. J Constr Eng Proj Manag. 2(4):42–45.
   Google Scholar
• Yao J, Chen M, Lu H. 2006. A fuzzy stochastic single-period model for cash management. Eur J Oper. Res. 170(1):72–90.
   Web of Science ®Google Scholar
• Yaseen Z, Ali Z, Salih S, Al-Ansari N. 2020. Prediction of risk delay in construction projects using a hybrid artificial intelligence model. Sustainability. 12(4):1514.
   Web of Science ®Google Scholar
• Yeung H, Ridwan T, Tariq S, Zayed T. 2020. BEAM Plus implementation in Hong Kong: assessment of challenges and policies. Int J Constr Manag. DOI: 10.1080/15623599.2020.1827692.
   Google Scholar
• Zavadskas E, Turskis Z, Tamošaitienė J. 2010. Risk assessment of construction projects. J Civ Eng Manag. 16(1):33–46.
   Web of Science ®Google Scholar
• Ziółkowska A, Połoński M. 2016. Application of the EVM method and its extensions in the implementation of construction objects. Eng Struct Technol. 7(4):189–196.
   Google Scholar