MỤC LỤC
Nurul Afroze Zainal Abidin (2018) reviewed the causes leading to CSC disruption focusing on Malaysian Public Sector Construction Industry by analyze the emergent vulnerability and capability factors of the public sector supply chain in coping with supply chain disruptions, elaborate clearly on empirical aspect of CSC disruption in public sector’s project, so the study was not satisfied to whom does not involve in public projects. The objectives of the research outlined in this research are (1) to identify the causes affecting to CSC disruption, (2) to explore the underlying interrelationships among the constructs affecting to CSC disruption as well as how it will affect to the project performance, and (3) to propose a process for avoiding any possible CSC disruption.
However, despite the extensive literature on the topic, there is a lack of research that specifically focuses on the construction industry's supply chain disruptions with regard of interaction among constructs of causes leading to CSC disruption as well as the assessment of impacting to project performance. Therefore, this master thesis aims to contribute to the existing literature by providing a comprehensive understanding of the cause leading to supply chain disruptions on the construction industry and identifying the strategies that can be adopted to manage such disruptions.
Scope of research
From the investor's point of view, a competent and well-coordinated contractor is the most important factor leading to the possibility of winning the bid and the success of the project in the future, the factors of bid price are just one of them, some of the following criteria for evaluation (source Policy & Resources Committee, 2001). Considering that information, materials, services, and financial flows among project ‘s participants have a significant influence on the SCS system [4], and certain risks could be addressed by relevant stakeholders, it is crucial to incorporate the stakeholder perspective for proper supply chain risk management [24].
- The results point out significant weaknesses that must be addressed by (a) introducing appropriate capability initiatives to counter these SCV and develop value-enhanced resilient supply chains in IC and (b) developing an envisaged action framework for addressing the identified SCV in IC to serve as a launchpad for additional research and development. - This study helps project participants have an overview of the supply chain, identify risk factors that have an important influence on supply chain efficiency at factory projects, and then offer appropriate and strategic solutions for the company to contain the risks in the supply chain process.
4 Information’s exchange [2, 5], Expert interview Lack of capability as signed contract, late delivery 5 Inadequate change management [2, 5], Expert interview Lack of capability as signed contract, late delivery. 1 Item damaged during transportation [1, 2, 5] Take time for replacement, rework, late delivery 2 Weather problems (precipitation,. temperature) [3, 33] Delaying in transportation, during production.., late delivery time. 3 Transportation vehicle damage [1-3] Downtime, leading to late arrival, interruption of CSC. accidents) [1-4] Late arrival to the site caused by traffic or.
At the end of the pilot survey, experts revised two causes leading to the construction supply chain disruption, and decided amend to " Slowly in information’s exchange among stakeholders" and “Wrong in storing and keeping the materials, products” as showed in table 3.1, experts commented with the cause that “Transport restriction” needs to be explained more clearly, such as transporting large-sized components or heavy loads (super-long-range, super weight) to ensure the content is clear and understandable to the respondents. Additionally, SEM, also referred to as path analysis with latent variables, is currently a commonly utilized technique in the behavioral and social sciences for modelling dependence (and possibly "causal") interactions in multivariate data [38].
Data processing
Comment: The survey results show that there are 33/125 people working in the construction field for 10-15 years, accounting for a high rate of 26.4%; and there are 42/125 people working for 15-20 years, accounting for 33.6%, and there are 14/125 people working for more than 20 years, accounting for 11.2%, which means that most of the respondents have experience in the construction field. Comment: The survey results of working roles in projects with a construction contractor partnership show that there are 48/125 people working for the Construction contractor, accounting for 38.4%; there are 21/125 people working for the Supervision Consulting contractor, accounting for 16.8%; there are 14/125 people working for the Investor/PMU, accounting for 11.2%; 17/125 people work for the Suppliers, accounting for 13.6%; there are 16/125 people working for PMC, accounting for 12,8% and 7.2% working for Design contractor.
There are 3 causes having highest means value of above 4.0, pertaining to lacking of labor, late payment to subcontractors/suppliers and finance problems, it illustrates the real situation of Vietnam construction industry which are still high labor intensive due to lag behind of technology and almost projects are facing with financial problem, these issues often appear in the developing countries. There are 8 causes with their means from 3.90 to 3.99, they are popular in the Vietnam construction projects such as lack of materials, equipment, lack of trust among parties, using competitive bidding, especially the respondents have got the experience of Covid -19 pandemic, and have high level of agreement of construction supply chain disruption post pandemic, and till now we are still facing with difficulties of CSC disruption.
There are 9 causes having lowest mean value (range from 3.63 to 3.68), which would bring about supply chain disruption in large scale of projects/ or offshore project such as oil and gas projects…so that have less respondents agreed on these causes. SX1 - Speed of manufacturing (specially in producing interior products). Comment : The Cronbach's Alpha coefficient for the group of manufacturing- related causes is 0.888, meaning the scale has good reliability [35]. The correlation coefficients of the total variables of the causes are all greater than 0.3 and the Cronbach's Alpha coefficient if the variable is excluded are all less than 0.888. So this scale is qualified, no observed variables are removed. Group of causes related to Logistic:. 9: Cronbach's Alpha coefficient for the group of 6 causes related to logistic. Cronbach's Alpha Number of variables. size and heavy weight).
This factor explains 6.068 of the total variances in the data, the fourth ranked among factors and This factor encompasses various causes such as make-to-order production, speed of manufacturing (especially in producing interior products), long lead time during manufacturing, item damage during manufacturing, inadequate change management, and insufficient resources (components or raw materials). Factors such as mitigating transportation problems and accidents, addressing transportation restrictions for oversized or heavy items, implementing measures to prevent transportation vehicle damage, safeguarding items from damage during transportation, and considering weather conditions and their impact on transportation logistics are crucial for minimizing disruptions in the construction supply chain related to transportation.
This factor explains 3.127 of the total variances in the data, the eighth ranked among factors and This factor encompasses various causes such as the existence of hazardous substances, issues related to scraping and disposal, governmental regulations regarding environmental concerns, and challenges associated with the immobility and large size of materials. It is established when the AVE estimate of a construct is greater than the highest squared correlation with other constructs [39] As presented in Table 4.24, each AVE estimate along the diagonal of the interconstruct correlation matrix is greater than the squared correlations among the constructs in the corresponding column or row, indicating that the measurement model satisfies discriminant validity.
Four commonly used indices were based on in this study to assess the overall fit of the model: the ratio of X2 to the degree of freedom (df), the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA). The acceptance levels of these model fit indices were based on previous research recommendations [43, 44]. The specific details of the fit indices for the measurement model of construction supply chain disruption are presented in Table 4.24. These fit indices are as follows:. These results provide evidence that the measurement model of construction supply chain disruption demonstrate a strong fit with the collected data. Relationship Estimate S.E. Relationship Estimate S.E. Based on the table of regression coefficient results of the SEM model, reject 3 hypotheses with P-value > 0.05, corresponding to hypotheses H3, H7 and H12. Looking at the structure model more closely, we see that their relationships are found positive influence but they are weak with standardized coefficients value of 0.06, 0.23 and 0.19, respectively. Therefore, these three relationhips are believed that not true or at least not consistent with this collected data. So we reject these three hypotheses and re-run the structure model, the result of the final structure model showing in Figure 4.10. 26: Regression coefficient of the final structure model of interaction among constructs. Relationship Estimate S.E. Relationship Estimate S.E. Discussions and implications:. This implies that stakeholders should control and relief VDC in order to relief impacting to other factor as well as to reduce the impact to CSC disruption because of all those factors directly impact to influence of CSC disruption. 0.62), demonstrated that all these causes have strong influence on the VDC. Overall, VDC factor has significant impacts on HD, SX, VC, GN, KB, CT, CLR, it implies that during handling these factors with their risks, stakeholders should also monitor and control simultaneously the risks of VDC (External influences and Regulatory Environment) factor, which interactions among factors have confirmed and shown in the structure model as figure 4.10.
Overall, this research identifies the causes leading to construction supply chain disruption and provides valuable insights into the interrelationships among these causes and their impacts on project performance. It is hoped that the findings of this thesis will contribute to the development of effective strategies and practices for managing construction supply chains, ultimately leading to improved project outcomes and success in the construction industry.
Proposal a process to coordinate
Purpose
Objective
Procurement organization
After material approval, Project Procurement Manager of his representative select two (2) or three (3) vendors from approved vendor list or vendor approval as explained sub paragraph 4.1.3 and request selected vendor to submit their quotation providing specification requried. For Type – A material, Purchase Department request Project Procurement Manager to prepare a technical bid evaluation(TBE) report in accordance with the Preparation Procedure for Technical Bid Evaluation for approval by Client.
Procurement status reporting
Sholeh, "Risk study on supply chain management in construction (Case study: Building projects in Indonesia)," in IOP Conference Series: Materials Science and Engineering, 2019, vol. Hayat, "Effects of risk attitude and controllability assumption on risk ratings: Observational study on international construction project risk assessment," Journal of management in engineering, vol.
Figure 2. 1: Features of a project
Figure 4. 1: Chart of working time in construction field
Figure 5. 1: Purchasing procedures
Table 3. 1: The causes leading to construction SC disruption during the construction stage
Table 4. 1: Working time in construction
16: Percentage of explanations for variables and total variance extracted