Simulation Analysis of Supply Chain Resilience of Prefabricated Building Projects Based on System Dynamics
Abstract
:1. Introduction
2. Literature Review
2.1. Prefabricated Building
2.2. Supply Chain Resilience
2.3. Supply Chain Resilience of Prefabricated Building Project
3. Materials and Methods
3.1. Supply Chain Resilience Evaluation System for Prefabricated Building Project
3.2. Analytic Hierarchy Process
- (1)
- Judgment matrix construction and weight value calculation
- (2)
- Weight vector calculation
- (3)
- Consistency test
3.3. System Dynamics Model
- (1)
- Causal loop diagram
- (2)
- System stock-flow diagram
- (1)
- State variables
- (2)
- Rate variable
- (3)
- Auxiliary variables and constants
4. Case Analysis and Results
4.1. Project Overview
4.2. Data Processing
- (1)
- Determination of evaluation index weight
- (2)
- Constructing a cause and effect diagram
- (3)
- Constructing stock flow diagrams
- (4)
- Determination of simulation model formula
4.3. Model Imitation and Analysis
- (1)
- Supply chain resilience prediction analysis of YWC project
- (2)
- Comparative analysis of subsystem change schemes
- (3)
- Comparative analysis of secondary sub-factor change schemes
5. Discussions and Suggestions
5.1. Discussions
5.2. Suggestions
- (1)
- Augment risk prevention and management awareness.
- (2)
- Cultivate internal and external collaborative mechanisms.
- (3)
- Enhance logistics competencies.
6. Conclusions
- (1)
- The supply chain within prefabricated building projects exudes complexity, entailing a myriad of participants and a tightly-knit connectivity amongst them. Consequently, the entirety of the system can be accurately characterized as an integrated, nonlinear, multi-feedback dynamic system. The deployment of System Dynamics (SD) for crafting a system dynamics model affords not only a more precise reflection of the intricate causal interrelations among factors but also a quantitative depiction of the system’s lateral evolution under the influence of assorted variables.
- (2)
- Insights derived from simulation forecasting illuminate a notable fragility in the overall risk resilience, principally attributed to an experiential deficit in refining technology, management, and emergency response within the supply chain system of prefabricated building projects during initial phases. However, a subsequent elevation is observable in the level of assembly technology, capital allocation among chain participants, risk awareness of node enterprises, and collective risk mitigation capacities, propelling the overarching anti-risk caliber of the prefabricated assembly construction project supply chain along an exponential growth trajectory.
- (3)
- Perturbations in disparate subsystems give rise to divergent risk-resilience levels within the prefabricated assembly construction project supply chain. Notably, the absorptive capacity subsystem emerges as a pivotal entity, exerting a prominently amplifying effect on the risk-resilience caliber of the prefabricated building engineering supply chain, thereby situating itself as a linchpin within the entire risk-resilience framework of the prefabricated building engineering supply chain.
- (4)
- An examination into the modulation of single-factor variables reveals that the most sensitive secondary sub-factors within each subsystem encompass risk awareness, logistics support level, collaboration degree, supply chain reconfiguration aptitude, and management strategy decision-making capability. These elements, therefore, crystallize as paramount factors in enhancing the resilience level of the supply chain within prefabricated building projects.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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First Grade Indexes | Second Index | Reference |
---|---|---|
Forecast capacity | Supply chain structure (FC1) | [3,33,34] |
Supply chain complexity (FC2) | [35,36] | |
Risk awareness (FC3) | [3] | |
Uptake capacity | Prefabricated construction level (UC1) | [3,33] |
Logistics support level (UC2) | [3,33,37] | |
Supplier management (UC3) | [34,36] | |
Components production flexibility (UC4) | [34,36,37] | |
Adapt capacity | Information management capability (AC1) | [11,37,38] |
The degree of collaboration (AC2) | [11,26] | |
Inventory redundancy (AC3) | [35,36,38] | |
Risk management level (AC4) | [38,39,40] | |
Recovery capacity | Funds scheduling capacity (RC1) | [20,37] |
Emergency response capability (RC2) | [20,38] | |
Resource reengineering capability (RC3) | [37,38] | |
Supply chain reconfiguration capability (RC4) | [37,39] | |
Growth capacity | Organizational learning ability (GC1) | [36,38] |
Assembly technology innovation investment (GC2) | [3,37,39] | |
Management strategic decision-making ability (GC3) | [16,38,40] |
Scale | Explanation |
---|---|
1 | The two factors are equally important when compared |
3 | Comparing the two factors, the former is slightly more important than the latter |
5 | Comparing the two factors, the former is significantly more important than the latter |
7 | Comparing the two factors, the former is very important than the latter |
9 | Comparing the two factors, the former is extremely more important than the latter |
2, 4, 6, 8 | The median value of the above neighboring judgments |
Order | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|
RI | 0 | 0 | 0.58 | 0.9 | 1.12 | 1.24 | 1.38 | 1.41 | 1.46 |
First Grade Indexes | Weight | Second Index | Weight |
---|---|---|---|
Forecast capacity | 0.1207 | Supply chain structure (FC1) | 0.1834 |
Supply chain complexity (FC2) | 0.1652 | ||
Risk awareness (FC3) | 0.6514 | ||
Uptake capacity | 0.3525 | Assembly construction level (UC1) | 0.1365 |
Logistics support level (UC2) | 0.3378 | ||
Supplier management (UC3) | 0.2994 | ||
Components production flexibility (UC4) | 0.2263 | ||
Adapt capacity | 0.2153 | Information management capability (AC1) | 0.1911 |
The degree of collaboration (AC2) | 0.2671 | ||
Inventory redundancy (AC3) | 0.1072 | ||
Risk management level (AC4) | 0.4346 | ||
Recovery capacity | 0.0968 | Funds scheduling capacity (RC1) | 0.1073 |
Emergency response capacity (RC2) | 0.3412 | ||
Resource reengineering capacity (RC3) | 0.1791 | ||
Supply chain reconfiguration capacity (RC4) | 0.3724 | ||
Growth capacity | 0.2148 | Organizational learning capacity (GC1) | 0.2535 |
Assembly technology innovation investment (GC2) | 0.2193 | ||
Management strategic decision-making capacity (GC3) | 0.5272 |
SD Equation | |
---|---|
The influence level of forecast ability | INTEG (forecast capacity change, 0) |
Forecast capacity variation RFC | 0.1834 × FC1 + 0.1652 × FC2 + 0.6514 × FC3 |
The influence level of uptake ability | INTEG (uptake capacity change,0) |
Uptake capacity variation RUC | 1.0875 × forecast capacity influence level + 0.1365 × UC1 + 0.3378 × UC2 + 0.2994 × UC3 + 0.2263 × 1.0375 × UC4 |
The influence level of adapt capacity | INTEG (adapt capacity change, 0) |
Adapt capacity variation RAC | 1.1875 × forecast capacity influence level + 1.475 × uptake capacity influence level + 0.1911 × AC1 + 0.2671 × 1.4375 × AC2 + 0.1072 × AC3 + 0.4346 × 1.4063 × AC4 |
The influence level of recovery capacity | INTEG (recovery capacity change, 0) |
Recovery capacity variation RRC | 1.3688 × adaptive capacity influence level + 0.1073 × RC1 + 0.3412 × RC2 + 0.1791 × RC3 + 0.3724 × 1.2813 × RC4 |
The influence level of growth capacity | INTEG (growth capacity change, 0) |
Growth capacity variation RGC | 1.2 × adaptability influence level + 1.1963 × recovery capacity influence level + 0.2535 × GC1 + 0.2193 × 1.4313 × GC2 + 0.5272 × 1.0125 × GC3 |
Supply chain resilience of prefabricated building project | 0.1207 × forecast capacity influence level + 0.3525 × uptake capacity influence level + 0.2153 × adaptability capacity influence level + 0.0968 × recovery capacity influence level + 0.2148 × growth capacity influence |
Time (Month) | Original State | FC Increased by 30% | UC Increased by 30% | AC Increased by 30% | RC Increased by 30% | GC Increased by 30% |
---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 |
10 | 68.04 | 71.86 | 100.2 | 112.57 | 74.23 | 97.84 |
20 | 314.00 | 330.49 | 397.69 | 406.26 | 334.08 | 373.61 |
30 | 834.45 | 877.27 | 990.39 | 977.63 | 876.14 | 923.86 |
40 | 1727.52 | 1815.26 | 1977.8 | 1924.82 | 1798.53 | 1846.74 |
50 | 3092.93 | 3249.13 | 3460.98 | 3347.53 | 3200.96 | 3241.95 |
60 | 5031.96 | 5285.25 | 5542.59 | 5347.06 | 5184.73 | 5210.79 |
Subsystem | Factor Changes | Final Toughness Level | Subsystem | Factor Changes | Final Toughness Level |
---|---|---|---|---|---|
Forecast capacity | FC1 + 0.5 | 5481.32 | Growth capacity | GC1 + 0.5 | 5693.52 |
FC2 + 0.5 | 5305.6 | GC2 + 0.5 | 5624.23 | ||
FC3 + 0.5 | 5915.82 | GC3 + 0.5 | 5956.82 | ||
Uptake capacity | UC1 + 0.5 | 5292.24 | GC4 + 0.5 | 5297.48 | |
UC2 + 0.5 | 6153.88 | Recovery capacity | RC1 + 0.5 | 5742.69 | |
UC3 + 0.5 | 5760.83 | RC2 + 0.5 | 5551.14 | ||
UC4 + 0.5 | 5546.93 | RC3 + 0.5 | 5824.37 | ||
Adapt capacity | AC1 + 0.5 | 5550.12 | |||
AC2 + 0.5 | 6169.53 | ||||
AC3 + 0.5 | 5279.71 | ||||
AC4 + 0.5 | 6547.91 |
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Liu, W.; Liu, Z. Simulation Analysis of Supply Chain Resilience of Prefabricated Building Projects Based on System Dynamics. Buildings 2023, 13, 2629. https://doi.org/10.3390/buildings13102629
Liu W, Liu Z. Simulation Analysis of Supply Chain Resilience of Prefabricated Building Projects Based on System Dynamics. Buildings. 2023; 13(10):2629. https://doi.org/10.3390/buildings13102629
Chicago/Turabian StyleLiu, Wei, and Zixuan Liu. 2023. "Simulation Analysis of Supply Chain Resilience of Prefabricated Building Projects Based on System Dynamics" Buildings 13, no. 10: 2629. https://doi.org/10.3390/buildings13102629