1. Introduction
The construction industry faced considerably large challenges because of COVID-19 with both direct and indirect adverse effects due to lockdowns. This study focuses on investigating cost management of construction, with specific reference China State Construction Engineering Corporation's approach to the various points of the building life cycle. The pandemic brought disruption to the supply chains in factories while the others were adhering to safety regulations and endeavoring to keep cost under control [1].
Such ancillary costs with greater delays lead to the searching and implementation of new financial measures. According to research, increased cost can arise from safety equipment [2] and works are disrupted due to the stress of schedules [3]. The fact that challenges can lead to more central cost and delay shipment platforms has been understood by studies carried out by Berger et al.[4] and by Ivanov [5].
This work compliments the grey correlation and value engineering models that help in cost management. Effort is on a tunneling of vital MBIs to vacant [6], and free of the deficit spending [7].A case of China Construction provides an example of a pro-active financial strategies by the company during crises.
Literature as a matter of fact brings a number of different perspectives on addressing construction costs in crises. Differentiated in use of technology, Getachew et al. [8] prove to be the obvious choice to fight against the pandemic. Whereas Wang et al. [9] and Zhao et al. [10] bring in relevance of electronic and remote means of control. As well as ensuring that the application of adaptive program management strategies, as finalizing timelines and allocating the resources, is also important [11].
The report concludes by the summarization of the findings so that a wide-ranging perspective on cost containment during crises may be established. The study therefore gives a number of hands-on recommendations for construction firms and serves as an example for other firms to grow their resilience [12].
2. The impact of COVID-19 on project costs for China construction companies
2.1. Changes in schedule of real estate projects and anti-epidemic projects
The study selects four representative projects, namely the Huoshenshan Hospital Project, the North Lantau Hospital Hong Kong Infection Control Centre Project, the Beijing Daji Dangerous Building Renovation Project, and the China Construction Jinxiu Capital Project.
Table 1: Project Duration Change Table (Source: China Construction Annual Reports)
Project | Start Date | Estimated Completion Date in 2018 Annual Report | Estimated Completion Date in 2020/2021 Annual Report |
Huoshenshan Hospital | January 2020 | - | February 2020 |
North Lantau Hospital Hong Kong Infection Control Center | October 2020 | - | January 2021 |
Beijing Daji Reconstruction Project | January 2003 | December 2021 | December 2025 |
Zhongjian Jinxiu Capital | August 2018 | December 2021 | December 2024 |
2.2. Some projects under construction are behind schedule
The following is the progress of important projects of China Construction:
Table 2: Progress of major construction projects in China in 2021 (Source: China Construction Annual Reports)
Project Progress | Interest Capitalization Accumulated Amount | This Year Interest Capitalization | Interest This Year Capitalization rate (%) | |
China Construction Third Engineering Bureau Beijing Headquarters Base Office Building Project | 100% | 209,501 | 22,084 | 5.78 |
China Construction Jinxiu Tiandi Construction Project | 100% | 135,000 | 19,730 | 4.50 |
Songjiang Yunzhu in Deep Blue Construction Project | 99% | 6,716 | 413 | 4.75 |
Others | Not Applicable | 99,234 | 39,851 | Not Applicable |
Table 3: Progress of Major Construction Projects in China in 2020 (Source: China Construction Annual Reports)
Project Progress | Interest Capitalization Accumulated Amount | This Year Interest Capitalization | Interest This Year Capitalization rate (%) | |
China Construction Third Engineering Bureau Beijing Headquarters Base Office Building Project | 89% | 187,417 | 63,556 | 5.78 |
China Construction Jinxiu Tiandi Construction Project | 69% | 115,270 | 43,455 | 4.50 |
Songjiang Yunzhu in Deep Blue Construction Project | 96% | 6,303 | 2,718 | 4.75 |
Others | Not Applicable | 59,383 | 29,282 | Not Applicable |
The Songjiang Yunzhu Deep Blue construction project originally planned to be completed in 2020 was 96% completed at the end of 2020, and 99% completed at the end of 2021. Not completed as planned. The following uses the earned value method to analyze its project progress.
2.3. Evaluating Construction Time and Cost Using the Earned Value Method
Table 4: Earned Value Analysis (Source: China Construction Annual Reports)
Item | Content |
Estimated Cost of Work to be Performed | The budgeted cost of work performance (BCWP) for the Songjiang Yunzhu Deep Blue construction project is 360,000 yuan for 69,289.28m², with 96% completed in 2020. BCWP = Completed work × Budgeted unit cost. Estimated value in 2020: 345,891,000 yuan. |
Budgeted Costs of Planned Projects | The budgeted cost of scheduled work (BCWS) is 360,000 yuan for the Songjiang Yunzhu Deep Blue project to be completed by September 2020. BCWS = Planned workload × Budgeted unit cost. Estimated cost: 360,000 thousand yuan. |
Actual Cost of Completed Work | The actual cost of work performed (ACWP) for the Songjiang Yunzhu Deep Blue project by December 2020 is 367,836.48 thousand yuan for 66,517.7m². ACWP = Completed work × Actual unit cost. Actual cost: 367,836.48 thousand yuan. |
Cost Variance | Cost Variance (CV) = BCWP - ACWP. For the China Construction Jinxiu Tiandi project: BCWP = 345,891,000 yuan, ACWP = 367,836.48 thousand yuan, CV = -21,945 thousand yuan (over budget). |
Schedule Deviation | Schedule deviation (PS) = BCWP - BCWS. For the China Construction Jinxiu Tiandi project: BCWP = 345,891 thousand yuan, BCWS = 360,000 thousand yuan, PS = -14,109 thousand yuan (behind schedule). |
The following is a summary table of earned value method indicator evaluation data:
Table 5: Earned Value Method Indicator Evaluation Data Table (Source: China Construction Annual Reports)
Index | Number |
BCWP | 345,891 |
BCWS | 360,000 |
ACWP | 367,836 |
CV | -21,945 |
PS | -14,169 |
3. Grey relational static evaluation model
3.1. Selection of evaluation indicators
Since the factors affecting cost are mainly construction period, construction organizational structure and unpredictable factors (including price and manpower) [13], the change amount of projects under construction, the cost of important raw materials, and employee wages and personnel expenses are used as evaluation indicators.
The following are the selected parent indicators and evaluation indicator values:
Table 6: Parent indicators and evaluation index values (Source: China Construction Annual Reports)
Year | Cost | Changes in construction in progress | Cost of important raw materials | Employee salaries and personnel expenses |
2019 | 1,262,226,200 | 5,408,977 | 365,217,797 | 64,378,787 |
2020 | 1,440,131,634 | 5,054,101 | 371,054,984 | 77,421,854 |
2021 | 1,677,136,509 | 6,675,256 | 408,088,395 | 85,716,394 |
3.2. Calculation of grey correlation coefficient
The following is the correlation coefficient result graph:
Table 7: Correlation Coefficient Result Graph
Changes in Construction in Progress | Important Raw Material Costs | Employee Compensation and Personnel Expenses | |
2019 | 0.8280111900471137 | 1 | 0.8520278099733023 |
2020 | 0.7629973148145222 | 0.9096426038363667 | 0.7881191975802553 |
2021 | 0.6913216440026381 | 0.8231984651845129 | 0.713839449524261 |
3.3. Calculation of grey relational value
The following is the correlation result graph:
Table 8: Correlation Result Graph
Evaluation Items | Correlation | Ranking |
Important Raw Material Costs | 0.911 | 1 |
Employee Compensation and Personnel Expenses | 0.785 | 2 |
Changes in Construction in Progress | 0.761 | 3 |
4. Conclusion and Discussion
The post-pandemic period has construction sector with new approaches to cost control related to the modified operation environment. Reducing construction periods of non-epidemic projects is an effective means to reduce costs for construction, especially in the real estate sector.
Project times in China's pursuit of real estate could often prolong to a time that is beyond the limit with acceptable scope. Tracking progress and re-scheduling, as part of 'plan-do-check-act', provide critical foundation to cost-effectiveness analysis. Program scheduling must be set up to prevent these problems from arising.
Being knowledgeable about the local downtimes as well as the production systems is one of the cost-efficient systems. Inaccuracies can be the reason for postponing projects and added expenditures for the contractor. Smart management emphasizes proactive change that match with the policy updates and performance results.
Smart management of resources, especially the workforce, optimization derives higher output and on-time completion. The effects of the pandemic have been to slow projects and to increase costs. Some workers deploying the efficiency of the workers while the subcontractor's reliability are other key factors for experience delays and costs.
Implementing mature technologies means the activities will be ensured a high-performance level during uncertainty. The correct resource management is the powerful indicator of the success. Energy management and specialized sourcing strategies are the keys to realizing the budget cutting effectiveness.
The pandemic has propelled the construction sector to address efficiency, cutting costs, and anticipation to develop the capacity in the post-pandemic world.
References
[1]. Niaz M, Nwagwu U. Managing Healthcare product demand effectively in the post-covid-19 environment: Navigating demand variability and forecasting complexities. Am J Econ Manag Bus. 2023;2(8):316–30. doi:10.58631/ajemb.v2i8.55.
[2]. Enshassi A, Mohamed S, Abushaban S. Factors affecting the performance of construction projects in the Gaza strip. J Civ Eng Manag. 2009;15(3):269–80. doi:10.3846/1392-3730.2009.15.269-280.
[3]. Osei-Kyei R, Chan APC. Review of studies on the Critical Success Factors for Public–Private Partnership (PPP) projects from 1990 to 2013. Int J Project Manag. 2015;33(6):1335–46. doi:10.1016/j.ijproman.2015.02.008.
[4]. Berger N, et al. Risk management of supply chain disruptions: An epidemic modeling approach. Eur J Oper Res. 2023;304(3):1036–51. doi:10.1016/j.ejor.2022.05.018.
[5]. Ivanov D. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp Res Part E Logist Transp Rev. 2020;136:101922. doi:10.1016/j.tre.2020.101922.
[6]. Azhar S, Carlton WA, Olsen D, Ahmad I. Building information modeling for sustainable design and LEED® rating analysis. Autom Constr. 2011;20(2):217–24. doi:10.1016/j.autcon.2010.09.019.
[7]. Sweis G, Sweis R, Abu Hammad A, Shboul A. Delays in construction projects: The case of Jordan. Int J Project Manag. 2008;26(6):665–74. doi:10.1016/j.ijproman.2007.09.009.
[8]. Getachew E, et al. Digital Health in the era of COVID-19: Reshaping the next generation of Healthcare. Front Public Health. 2023;11. doi:10.3389/fpubh.2023.942703.
[9]. Wang X, et al. The role of e-leadership in ICT Utilization: A Project Management Perspective. Inf Technol Manag. 2022;24(2):99–113. doi:10.1007/s10799-021-00354-4.
[10]. Zhao R, Chen Z, Xue F. A Blockchain 3.0 paradigm for Digital Twins in Construction Project Management. Autom Constr. 2023;145:104645. doi:10.1016/j.autcon.2022.104645.
[11]. Lishner I, Shtub A. Enhancing Strategic Planning of Projects: Selecting the Right Product Development Methodology. Information (Basel). 2023;14(12):632. doi:10.3390/info14120632.
[12]. Chan JF-W, Yuan S, Kok K-H, To KK-W, Chu H, Yang J, Xing F, Liu J, Yip CC-Y, Poon RW-S, Tsoi H-W, Lo SK-F, Chan K-H, Poon VK-M, Chan W-M, Ip JD, Cai J-P, Cheng VC-C, Chen H, Hui CK-M, Yuen K-Y. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet (Br Ed). 2020;395(10223):514–23. doi:10.1016/S0140-6736(20)30154-9.
[13]. Kaming PF, Olomolaiye PO, Holt GD, Harris FC. Factors influencing construction time and cost overruns on high-rise projects in Indonesia. Constr Manag Econ. 1997;15(1):83–94. doi:10.1080/014461997373132.
Cite this article
Guo,Y. (2024). Study on Cost Control of Construction Enterprises in the Context of the COVID-19 - A Case Study of China Construction. Advances in Economics, Management and Political Sciences,93,31-36.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Niaz M, Nwagwu U. Managing Healthcare product demand effectively in the post-covid-19 environment: Navigating demand variability and forecasting complexities. Am J Econ Manag Bus. 2023;2(8):316–30. doi:10.58631/ajemb.v2i8.55.
[2]. Enshassi A, Mohamed S, Abushaban S. Factors affecting the performance of construction projects in the Gaza strip. J Civ Eng Manag. 2009;15(3):269–80. doi:10.3846/1392-3730.2009.15.269-280.
[3]. Osei-Kyei R, Chan APC. Review of studies on the Critical Success Factors for Public–Private Partnership (PPP) projects from 1990 to 2013. Int J Project Manag. 2015;33(6):1335–46. doi:10.1016/j.ijproman.2015.02.008.
[4]. Berger N, et al. Risk management of supply chain disruptions: An epidemic modeling approach. Eur J Oper Res. 2023;304(3):1036–51. doi:10.1016/j.ejor.2022.05.018.
[5]. Ivanov D. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp Res Part E Logist Transp Rev. 2020;136:101922. doi:10.1016/j.tre.2020.101922.
[6]. Azhar S, Carlton WA, Olsen D, Ahmad I. Building information modeling for sustainable design and LEED® rating analysis. Autom Constr. 2011;20(2):217–24. doi:10.1016/j.autcon.2010.09.019.
[7]. Sweis G, Sweis R, Abu Hammad A, Shboul A. Delays in construction projects: The case of Jordan. Int J Project Manag. 2008;26(6):665–74. doi:10.1016/j.ijproman.2007.09.009.
[8]. Getachew E, et al. Digital Health in the era of COVID-19: Reshaping the next generation of Healthcare. Front Public Health. 2023;11. doi:10.3389/fpubh.2023.942703.
[9]. Wang X, et al. The role of e-leadership in ICT Utilization: A Project Management Perspective. Inf Technol Manag. 2022;24(2):99–113. doi:10.1007/s10799-021-00354-4.
[10]. Zhao R, Chen Z, Xue F. A Blockchain 3.0 paradigm for Digital Twins in Construction Project Management. Autom Constr. 2023;145:104645. doi:10.1016/j.autcon.2022.104645.
[11]. Lishner I, Shtub A. Enhancing Strategic Planning of Projects: Selecting the Right Product Development Methodology. Information (Basel). 2023;14(12):632. doi:10.3390/info14120632.
[12]. Chan JF-W, Yuan S, Kok K-H, To KK-W, Chu H, Yang J, Xing F, Liu J, Yip CC-Y, Poon RW-S, Tsoi H-W, Lo SK-F, Chan K-H, Poon VK-M, Chan W-M, Ip JD, Cai J-P, Cheng VC-C, Chen H, Hui CK-M, Yuen K-Y. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet (Br Ed). 2020;395(10223):514–23. doi:10.1016/S0140-6736(20)30154-9.
[13]. Kaming PF, Olomolaiye PO, Holt GD, Harris FC. Factors influencing construction time and cost overruns on high-rise projects in Indonesia. Constr Manag Econ. 1997;15(1):83–94. doi:10.1080/014461997373132.