Mitigating Making-Do Practices Using the Last Planner System and BIM: A System Dynamic Analysis
Abstract
:1. Introduction
2. Literature Review
2.1. Theoretical Understanding of Making-Do Waste
2.2. Technical Understanding of Making-Do Waste
2.3. LPS-BIM Mitigation Strategies for MD Practices
2.4. System Dynamics Modeling
2.5. System Dynamics Applications in Lean Construction Research
3. Materials and Methods
3.1. Data Collection for LPS-BIM (Questionnaire Survey)
3.2. Structural Equation Modeling
3.3. System Dynamic Modeling (SDM)
4. Results
4.1. Data Analysis
4.1.1. Descriptive Analysis for Questionnaire Data
4.1.2. Exploratory Factor Analysis (EFA)
4.1.3. Confirmatory Factor Analysis (CFA)
4.1.4. Structural Equation Model Assessment
4.2. Causal Loop Diagram (CLD)
4.3. Stock and Flow Diagrams
4.3.1. Work Progress
4.3.2. Productivity
* impactOfWorkSpaceLimitation[SUBSTAGES] * impactOfBIMonProductivity * impactofLPSonProductivity
4.3.3. Resources
4.3.4. Cost and Location Subsystems
4.3.5. The Dynamic Interaction with LPS Functions and BIM Functionalities
4.4. Simulations
4.4.1. Validation Projects
4.4.2. Model Assumptions
4.4.3. Testing for Model Unit Consistency
4.4.4. Model Stability Testing
4.4.5. Parameter Variation Testing
5. Discussion
6. Conclusions
- Social-technical factors directly influence MD in construction management systems. MD is a form of improvisation that masquerades in the short run as innovation, which reduces delivery time and related costs, but in the long run, several wastes could emerge and even snowball across the project delivery time; more than 80% of MDs are NVAs or a source of NVAs. This percentage can be prevented when proper production planning and control are employed, such as the LPS.
- This study investigates the impact of the integrated form of the LPS and BIM on Making-Do mitigation, using the system dynamics modeling method to strategically assist project stakeholders in assessing lean–BIM policy in tackling this waste and its impacts.
- The study found that MD is not widely known among professionals, and even some lean practitioners have not heard about it; similarly, construction management research has shown little interest in investigating MD, except for a few attempts from academics working in LC research.
- This research presents a novel MD model based on system thinking theory, which simulates the feedback mechanisms in construction management and measures the accumulation levels of construction constraints, Making-Do incidents, and emerging wastes.
- The accuracy of the simulation results of variables (MD, constraints, waste, cost, and completion rate) for the baseline scenario is considered acceptable compared to data collected from Projects A, B, and C. The average percentage of collected data divided by estimated data is MD 98.24%, constraints 99.52%, waste 98.80%, completion rate 95.99%, and additional costs 97.34%.
- Four scenarios have been applied: Scenario I with LPS technical factors, Scenario II with the application of LPS technical factors in addition to collaboration (COO) factors, Scenario III with the application of LPS socio-technical parameters, and Scenario IV with full LPS and BIM parameters.
- After a series of dynamic simulations for each scenario and compared to the baseline simulation. The dynamic simulation results show that after applying LPS-BIM, construction projects can reduce the number of unresolved constraints, MD decisions, and waste generated by MD, such as material waste, quality deviation, defects, and reworks.
- Schedule pressure impacts the level of pushing work without proper screening for constraints, which may lead to mishandling uncertainty. However, cost overruns and failure to meet pressures are not considered in the scope of this paper, which is planned for future research.
- BIM functionalities have a high impact on collaboration but a minimal impact on MDK, while MDK has the maximum value once LPS functions are implemented in integration with BIM.
- Practical implications include enhancing overall planning reliability, coordination, and control and avoiding wasting resources and time. BIM improves stakeholder communication, while SDM facilitates decision-makers and analyzes multiple outcomes. Thus, further research with interventions to offer construction professionals adequate training to increase their awareness of MD and encourage preventive management measures is needed.
- This study relies only on SDM, which entails analyzing the system at the strategic level with high levels of aggregation. Such a limitation may hinder a compelling discussion on the entire LPS hierarchy at the operation level. Further research is recommended to utilize SDM in coordination with ABM to incorporate advanced social interaction. Furthermore, there is an exception regarding validating the current SDM because it was validated using only residential projects. It is suggested that the findings be validated with other types of construction (e.g., industrial, healthcare, and transportation projects) to increase the study’s external validity.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Responses | Percentage |
---|---|
Total questionnaires sent out | 336 |
Total submitted responses | 118 (35.12%) |
Discarded responses | 2 |
Total usable responses | 116 (34.52%) |
Years of experience in the construction industry | |
0–5 years | 23.61% |
6–10 years | 22.22% |
11–15 years | 19.44% |
16–20 years | 8.33% |
Above 20 years | 12.50% |
No | Variable | Mean | Cronbach’s Alpha | Rank |
---|---|---|---|---|
VA24 | Identify and resolve time and space clashes using BIM Clash Detection tools. | 3.736 | 0.945 | 1 |
VA23 | Report task information in alignment with product specifications to ensure accuracy. | 3.722 | 0.945 | 2 |
VA25 | Facilitate the exchange and communication of Making-Do practices through online BIM models. | 3.722 | 0.944 | 3 |
VA22 | Utilize 4D planning to visualize constraints and their impact on project timelines. | 3.681 | 0.945 | 4 |
VA5 | Provide coaching, training, and seminars for superintendents and forepersons. | 3.653 | 0.945 | 5 |
VA11 | Ensure the availability of BIM models, design drawings, and site layout plans for reference during the Last Planner System implementation. | 3.611 | 0.944 | 6 |
VA21 | Facilitate daily discussions between trades to address constraints and coordinate activities. | 3.583 | 0.945 | 7 |
VA2 | Ensure high-level coordination among project stakeholders. | 3.542 | 0.946 | 8 |
VA20 | Collaboratively design operations using BIM for digital prototyping. | 3.486 | 0.945 | 9 |
VA12 | Maintain transparency by keeping all plans publicly accessible. | 3.472 | 0.945 | 10 |
VA14 | Apply constraint analysis proactively to identify and address potential issues as a team. | 3.472 | 0.945 | 11 |
VA9 | Facilitate knowledge exchange and the sharing of experiences among different companies. | 3.458 | 0.945 | 12 |
VA3 | Facilitate discussions to address concerns and foster consensus. | 3.444 | 0.945 | 13 |
VA7 | Establish a data bank to clarify misconceptions regarding lean construction, Making-Do, and Last Planner System principles. | 3.444 | 0.946 | 14 |
VA1 | Handle disagreements and interests effectively to foster collaboration. | 3.431 | 0.947 | 15 |
VA6 | Process and translate knowledge from experiential learning into actionable insights. | 3.403 | 0.946 | 16 |
VA13 | Utilize guiding information across digital and physical environments to enhance understanding. | 3.403 | 0.946 | 17 |
VA17 | Involve stakeholders in constraint management processes to enhance collaboration in mitigating MD. | 3.347 | 0.944 | 18 |
VA8 | Learn from past incidents of Making-Do. | 3.306 | 0.946 | 19 |
VA16 | Encourage stakeholders to communicate and share any constraints that may impede progress. | 3.278 | 0.945 | 20 |
VA10 | Compare and analyze multiple cases to understand how Making-Do is managed. | 3.264 | 0.946 | 21 |
VA4 | Adapt local adjustments to align with organizational requirements. | 3.181 | 0.947 | 22 |
VA18 | Maintain a workable backlog of tasks to prioritize and manage the workload effectively. | 3.153 | 0.944 | 23 |
VA15 | Delay tasks with uncertain constraints to avoid potential disruptions. | 2.931 | 0.947 | 24 |
VA19 | Break down tasks from processes to operations and further to individual tasks for clarity of management and control. | 2.889 | 0.947 | 25 |
Constraints | MD Categories | MD Impacts | |||
---|---|---|---|---|---|
P1 | External Conditions | CAT1 | Access and Movement | I1 | Decreased Productivity |
P2 | Information | CAT2 | Component Adjustment | I2 | Material Waste |
P3 | Interdependent Tasks | CAT3 | Equipment/Tools | I3 | Quality Deviation |
P4 | Labor | CAT4 | Sequencing | I4 | Rework |
P5 | Materials and Components | CAT5 | Workspace | I5 | Unfinished Works |
P6 | Space |
Project A | Project B | Project C | |
---|---|---|---|
Enterprise Code | M | N | K |
Country | Brazil | Brazil | France |
Start and finish dates | March 2016–March 2021 | March 2020–September 2023 | February 2019–March 2021 |
Project type | Construction | Construction | Rehabilitation |
Building type | Multi-storey condominium | Multi-storey condominium | Multi-storey building |
Description | Three towers | Two towers | One tower |
Floors/tower | 20 | 15 | 7 |
No. of units | 480 | 45 | 140 |
Land use (m2) | 9445 | 2860 | 1223 |
Category | Cost Increase ($) | Actual Completion Rate (%) | Total MD (Tasks) | Total Constraints (Tasks) | Total Waste (Tasks) |
---|---|---|---|---|---|
Baseline A | 76,849.337 | 82.540 | 209.126 | 1956.066 | 3600.587 |
Project A data | 75,950.000 | 80.570 | 205 | 1951 | 3590 |
Baseline B | 29,094.560 | 87.996 | 182.637 | 973.859 | 2427.597 |
Project B data | 27,200.000 | 82.010 | 180 | 968 | 2350 |
Baseline C | 11,134.500 | 85.652 | 180.345 | 865.970 | 1700.781 |
Project C data | 11,100.000 | 83.213 | 177 | 861 | 1699 |
Tested Variable | Involved Parameters | Values | |
---|---|---|---|
Scenario I | LPS technical factors enabled | VA10, VA11, VA12, VA13, VA14, VA15, VA16, VA17, VA18, VA19, VA20, VA21 | All values set to five |
Scenario II | LPS technical factors enabled, associated with collaboration factors | VA6, VA7, VA8, VA9, VA10, VA11, VA12, VA13, VA14, VA15, VA16, VA17, VA18, VA19, VA20, VA21 | All values set to five |
Scenario III | LPS socio-technical factors enabled with the association of Making-Do knowledge factors | VA1, VA2, VA3, VA5, VA6, VA7, VA8, VA9, VA10, VA11, VA12, VA13, VA14, VA15, VA16, VA17, VA18, VA19, VA20, VA21 | All values set to five |
Scenario IV | LPS socio-technical factors + BIM enabled | VA1, VA2, VA3, VA5, VA6, VA7, VA8, VA9, VA10, VA11, VA12, VA13, VA14, VA15, VA16, VA17, VA18, VA19, VA20, VA21, VA22, VA23, VA24, VA25 | All values set to five |
Variable | Baseline | Scenario I | Scenario II | Scenario III | Scenario IV | |
---|---|---|---|---|---|---|
MD Categories | CAT1 | 23.754 | 15.572 | 15.572 | 11.795 | 11.284 |
CAT2 | 99.032 | 74.655 | 74.093 | 66.208 | 66.455 | |
CAT3 | 11.760 | 7.351 | 7.194 | 5.159 | 4.629 | |
CAT4 | 53.450 | 43.508 | 43.315 | 38.878 | 39.017 | |
CAT5 | 21.130 | 15.760 | 15.366 | 12.315 | 11.587 | |
Constraints | P1 | 49.814 | 39.381 | 38.622 | 33.528 | 30.993 |
P2 | 158.584 | 142.457 | 141.579 | 135.694 | 125.442 | |
P3 | 221.538 | 163.799 | 157.679 | 122.372 | 109.533 | |
P4 | 572.133 | 457.476 | 446.363 | 381.997 | 344.119 | |
P5 | 766.068 | 559.156 | 544.587 | 430.870 | 382.813 | |
P6 | 187.928 | 124.644 | 121.702 | 85.208 | 75.007 | |
MD Impacts | I1 | 292.140 | 199.919 | 194.741 | 97.086 | 92.304 |
I2 | 322.522 | 213.900 | 209.476 | 129.549 | 124.458 | |
I3 | 354.241 | 235.027 | 229.363 | 112.556 | 109.578 | |
I4 | 1247.717 | 766.449 | 747.751 | 417.300 | 404.727 | |
I5 | 1383.967 | 941.289 | 941.289 | 507.161 | 498.680 | |
Completion Rate (%) | 82.540 | 94.845 | 94.794 | 95.351 | 98.962 | |
Cost | $ | 76,849.337 | 73,566.85 | 69,040.08 | 61,036.00 | 60,893.31 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Karaz, M.; Teixeira, J.M.C.; Amaral, T.G.d. Mitigating Making-Do Practices Using the Last Planner System and BIM: A System Dynamic Analysis. Buildings 2024, 14, 2314. https://doi.org/10.3390/buildings14082314
Karaz M, Teixeira JMC, Amaral TGd. Mitigating Making-Do Practices Using the Last Planner System and BIM: A System Dynamic Analysis. Buildings. 2024; 14(8):2314. https://doi.org/10.3390/buildings14082314
Chicago/Turabian StyleKaraz, Mahmoud, José Manuel Cardoso Teixeira, and Tatiana Gondim do Amaral. 2024. "Mitigating Making-Do Practices Using the Last Planner System and BIM: A System Dynamic Analysis" Buildings 14, no. 8: 2314. https://doi.org/10.3390/buildings14082314