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Model for Quantifying the Various Levels of Construction Productivity

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Abstract

Increasing construction productivity appears to be a complicated process due to the interconnected nature of construction work and the absence of a standardised method for measuring different construction activities. In this study, grounded theory was adopted as the research methodology for the theoretical development of the productivity model. This paper suggests a theoretical productivity measurement model (PMM) for the construction industry to address the challenges associated with productivity assessment. The proposed model consists of three levels: operational efficiencies (OE), management/administration efficiencies (ME), and industry/sector efficiencies (IE). Each level represents a different aspect of productivity and contributes to the overall performance of the construction sector. The paper discusses the detailed elements, responsibilities, and efficiency measures that are associated with each productivity level and provides a comprehensive framework for productivity evaluation. The suggested PMM enables the collection of data about all the inputs provided and outputs produced, thereby, facilitating effective management and control of construction activities. By adopting this suggested standardized approach to productivity measurement, the construction industry can achieve long-term development and enhance its contribution to the economy.

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(Source: https://www.statista.com/statistics/1069818/india-gdp-contribution-by-construction/)

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(Source: Authors)

References

  1. J.K. Yates, Productivity improvement for construction and engineering. Am. Soc. Civil Eng. (2014). https://doi.org/10.1061/9780784413463

    Article  Google Scholar 

  2. M.E. Georgy, L.-M. Chang, L. Zhang, Prediction of engineering performance: a neurofuzzy approach. J. Constr. Eng. Manag. 131(5), 548–557 (2005). https://doi.org/10.1061/(asce)0733-9364(2005)131:5(548)

    Article  Google Scholar 

  3. P.M. Goodrum, D. Zhai, M.F. Yasin, Relationship between changes in material technology and construction productivity. J. Constr. Eng. Manag. 135(4), 278–287 (2009). https://doi.org/10.1061/(ASCE)0733-9364(2009)135:4(278)

    Article  Google Scholar 

  4. B. Andersen, J.A Langlo, Productivity and performance measurement in construction. In Conference: CIB World Building Congress (2016) (Issue June, pp. 23–44). https://doi.org/10.1201/b10260-3

  5. P.M. Goodrum, C.T. Haas, J.D. Borcherding, E. Allmon, Case studies of U.S. construction labor productivity trends, 1970–1998. Proc. Constr. Congr. VI: Build Together Better Tomorrow Increasingly Complex World 278, 808–817 (2000). https://doi.org/10.1061/40475(278)85

    Article  Google Scholar 

  6. K.M. El-Gohary, R.F. Aziz, Factors influencing construction labor productivity in Egypt. J. Manag. Eng. 30(1), 1–9 (2014). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000168

    Article  Google Scholar 

  7. W. Zhan, W. Pan, Formulating systemic construction productivity enhancement strategies. J. Constr. Eng. Manag. 146(8), 05020008 (2020). https://doi.org/10.1061/(asce)co.1943-7862.0001886

    Article  Google Scholar 

  8. J. Dai, P.M. Goodrum, W.F. Maloney, Construction craft workers’ perceptions of the factors affecting their productivity. J. Constr. Eng. Manag. 135(3), 217–226 (2009). https://doi.org/10.1061/(asce)0733-9364(2009)135:3(217)

    Article  Google Scholar 

  9. K.C. Iyer, K.N. Jha, Factors affecting cost performance: evidence from Indian construction projects. Int. J. Project Manage. 23(4), 283–295 (2005). https://doi.org/10.1016/j.ijproman.2004.10.003

    Article  Google Scholar 

  10. M.-J. Lee, A.S. Hanna, W.-Y. Loh, Decision tree approach to classify and quantify cumulative impact of change orders on productivity. J. Comput. Civ. Eng. 18(2), 132–144 (2004). https://doi.org/10.1061/(ASCE)0887-3801(2004)18:2(132)

    Article  Google Scholar 

  11. W. Yi, A.P.C. Chan, Critical review of labor productivity research in construction journals. J. Manag. Eng. 30(2), 214–225 (2014). https://doi.org/10.1061/(asce)me.1943-5479.0000194

    Article  Google Scholar 

  12. V. Bureš, A. Stropková, Labour productivity and possibilities of its extension by knowledge management aspects. Procedia. Soc. Behav. Sci. 109, 1088–1093 (2014). https://doi.org/10.1016/j.sbspro.2013.12.592

    Article  Google Scholar 

  13. A.A. Tsehayae, A.R. Fayek, Data-driven approaches to discovering knowledge gaps related to factors affecting construction labor productivity. Constr. Res. Congr. 2014(2008), 837–846 (2014). https://doi.org/10.1061/9780784413517.086

    Article  Google Scholar 

  14. H. Nasir, C.T. Haas, C.H. Caldas, P.M. Goodrum, An integrated productivity-practices implementation index for planning the execution of infrastructure projects. J. Infrastruct. Syst. 22(2), 04015022 (2016). https://doi.org/10.1061/(asce)is.1943-555x.0000275

    Article  Google Scholar 

  15. J.C. Kellogg, G.E. Howell, D.C. Taylor, Hierarchy model of construction productivity. J. Constr. Div. 107(1), 137–152 (1981). https://doi.org/10.1061/JCCEAZ.0000940

    Article  Google Scholar 

  16. K.M. El-Gohary, R.F. Aziz, H.A. Abdel-Khalek, Engineering approach using ANN to improve and predict construction labor productivity under different influences. J. Constr. Eng. Manag. 143(8), 1–10 (2017). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001340

    Article  Google Scholar 

  17. S. Ayele, A.R. Fayek, A framework for total productivity measurement of industrial construction projects. Can. J. Civ. Eng. 46(3), 195–206 (2019). https://doi.org/10.1139/cjce-2018-0020

    Article  Google Scholar 

  18. D. Karthik, C.B. Kameswara Rao, Identifying the significant factors affecting the masonry labour productivity in building construction projects in India. Int. J. Constr. Manag. (2019). https://doi.org/10.1080/15623599.2019.1631978

    Article  Google Scholar 

  19. K. Haugbølle, J.N. Larsen, J. Nielsen, Construction productivity revisited: towards measuring performance of construction output. Eng. Constr. Archit. Manag. 26(5), 794–813 (2019). https://doi.org/10.1108/ECAM-03-2018-0094

    Article  Google Scholar 

  20. E. Bamfo-Agyei, C. Aigbavboa, T.W. Didibhuku, Measuring labour productivity in labour intensive works on the road construction in Ghana. In Advances in Human Factors, Sustainable Urban Planning and Infrastructure (Vol. 600, pp. 515–523). Springer International Publishing. (2019) https://doi.org/10.1007/978-3-319-94199-8_50

  21. A. Kazaz, S. Ulubeyli, Drivers of productivity among construction workers: a study in a develo** country. Build. Environ. 42(5), 2132–2140 (2007). https://doi.org/10.1016/j.buildenv.2006.04.020

    Article  Google Scholar 

  22. S.B.S. Ahmad, M.U. Mazhar, A. Bruland, B.S. Andersen, J.A. Langlo, O. Torp, Labour productivity statistics: a reality check for the Norwegian construction industry. Int. J. Constr. Manag. 20(1), 39–52 (2020). https://doi.org/10.1080/15623599.2018.1462443

    Article  Google Scholar 

  23. M. Salehi, H. Shirouyehzad, R. Dabestani, Labour productivity measurement through classification and standardisation of products. Int. J. Prod. Qual. Manag. 11(1), 57 (2013). https://doi.org/10.1504/IJPQM.2013.050568

    Article  Google Scholar 

  24. H.-S. Park, S.R. Thomas, R.L. Tucker, Benchmarking of construction productivity. J. Constr. Eng. Manag. 131(7), 772–778 (2005). https://doi.org/10.1061/(asce)0733-9364(2005)131:7(772)

    Article  Google Scholar 

  25. S. Dixit, S.N. Mandal, J.V. Thanikal, K. Saurabh, Study of significant factors affecting construction productivity using relative importance index in Indian construction industry. E3S Web Conf. 140, 09010 (2019). https://doi.org/10.1051/e3sconf/201914009010

    Article  Google Scholar 

  26. V. Tran, J. Tookey, Labour productivity in the New Zealand construction industry: a thorough investigation. Australas. J. Constr. Econ. Build. 11(1), 41–60 (2011). https://doi.org/10.5130/ajceb.v11i1.1841

    Article  Google Scholar 

  27. R. Haloub, ZUD Babar (2020). Grounded theory in pharmacy practice research. In: ZUD Babar (eds) Pharmacy Practice Research Methods. Springer, Singapore. https://doi.org/10.1007/978-981-15-2993-1_8

  28. A.M. Jarkas, M. Radosavljevic, Motivational factors impacting the productivity of construction master craftsmen in Kuwait. J. Manag. Eng. 29(4), 446–454 (2013). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000160

    Article  Google Scholar 

  29. A.Y. Kazaz, S. Ulubeyli, A different approach to construction labour in Turkey: comparative productivity analysis. Build. Environ. 39(1), 93–100 (2004). https://doi.org/10.1016/j.buildenv.2003.08.004

    Article  Google Scholar 

  30. R.V.K. Vigneshwar, S. Shanmugapriya, Investigating the factors affecting construction site productivity—a case of India. Eng. Constr. Archit. Manag. 30(2), 963–985 (2023). https://doi.org/10.1108/ECAM-06-2022-0529

    Article  Google Scholar 

  31. R.V.K. Vigneshwar, S. Shanmugapriya, Potential management factors influencing construction projects. GRAĐEVINAR 74(12), 1037–1046 (2023). https://doi.org/10.14256/JCE.3563.2022

    Article  Google Scholar 

  32. R. Almamlook, M. Bzizi, M. Al-Kbisbeh, T. Ali, E. Almajiri, Factors affecting labor productivity in the construction industry. Am. J. Environ. Sci. Eng. 4(2), 24 (2020). https://doi.org/10.11648/j.ajese.20200402.13

    Article  Google Scholar 

  33. M.R. Abdul Kadir, W.P. Lee, M.S. Jaafar, S.M. Sapuan, A.A.A. Ali, Factors affecting construction labour productivity for Malaysian residential projects. Struct. Surv. 23(1), 42–54 (2005). https://doi.org/10.1108/02630800510586907

    Article  Google Scholar 

  34. A.M. Jarkas, M. Radosavljevic, L. Wuyi, Prominent demotivational factors influencing the productivity of construction project managers in Qatar. Int. J. Product. Perform. Manag. 63(8), 1070–1090 (2014). https://doi.org/10.1108/IJPPM-11-2013-0187

    Article  Google Scholar 

  35. A.T. Gurmu, A.A. Aibinu, Construction equipment management practices for improving labor productivity in multistory building construction projects. J. Constr. Eng. Manag. 143(10), 04017081 (2017). https://doi.org/10.1061/(asce)co.1943-7862.0001384

    Article  Google Scholar 

  36. G. Espinosa-Garza, I. Loera-Hernández, N. Antonyan, Increase of productivity through the study of work activities in the construction sector. Procedia Manuf. 13, 1003–1010 (2017). https://doi.org/10.1016/j.promfg.2017.09.100

    Article  Google Scholar 

  37. P. Drucker, Managing for the future. Routledge (1993). https://doi.org/10.4324/9780080938059

    Article  Google Scholar 

  38. J.H. Wong, A. Rashidi, M. Arashpour, Evaluating the impact of building information modeling on the labor productivity of construction projects in Malaysia. Buildings 10(4), 66 (2020). https://doi.org/10.3390/BUILDINGS10040066

    Article  Google Scholar 

  39. H.R. Thomas, W.F. Maloney, R.M.W. Horner, G.R. Smith, V.K. Handa, S.R. Sanders, Modeling construction labor productivity. J. Constr. Eng. Manag. 116(4), 705–726 (1990). https://doi.org/10.1061/(ASCE)0733-9364(1990)116:4(705)

    Article  Google Scholar 

  40. M. Dhal, Labor stand: face of precarious migrant construction workers in India. J. Constr. Eng. Manag. 146(6), 04020048 (2020). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001761

    Article  Google Scholar 

  41. Indian migrant workers (2022) https://en.wikipedia.org/wiki/Indian_migrant_workers_during_the_COVID-19_pandemic

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Authors and Affiliations

  1. Department of Civil Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamilnadu, India

    R. V. K. Vigneshwar

  2. Department of Civil Engineering, Coimbatore Institute of Technology, Coimbatore, Tamilnadu, India

    S. Shanmugapriya

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Contributions

Vigneshwar R V K (Author 1)—study conception and design, analysis and interpretation of results, draft manuscript preparation, review, and revision. Shanmugapriya S (Author 2)—study conception and design, draft manuscript preparation and review.

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Correspondence to R. V. K. Vigneshwar.

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Vigneshwar, R.V.K., Shanmugapriya, S. Model for Quantifying the Various Levels of Construction Productivity. J. Inst. Eng. India Ser. A (2024). https://doi.org/10.1007/s40030-024-00820-6

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