1. Introduction

2. Literature Review

3. Materials and Methods

4. Results

4.1. Data Analysis

4.1.1. Descriptive Analysis for Questionnaire Data

Figure 4a illustrates the distribution of educational qualifications among survey respondents. Most respondents, 59.72%, hold master’s degrees, followed by 25.00% with bachelor’s degrees, and those with doctoral degrees make up 8.33%, and the remaining 6.94% have a high school diploma or equivalent, some college but no degree, and an associate degree. Figure 4b depicts the distribution of job titles among the respondents. Field engineers and Project managers represent the most prominent groups at 15.28% and 13.89%, respectively. Similarly, designers and BIM specialists represent a significant presence, constituting 11.11% of the respondents. Superintendents comprise 6.9%. They were followed by Researchers, Schedulers, Quality Control Managers, Safety Coordinators, and others, respectively (as shown in Figure 4b).

The data in Figure 5 reflects the respondents’ background in LC, the LPS, and BIM by asking if they ever attended courses, training, workshops, or reading. Most respondents lack formal education in LC, with 54.17% indicating no such education; the case is not the same with BIM education, which forms 65.25% of the respondents who have received some formal education or training. Figure 5a assesses the extent of knowledge regarding MD waste among the respondents. Despite the term’s inception in 2004, 67.9% of respondents demonstrate a lack of familiarity or utilization of this terminology or analogous terms in their professional capacities. Nevertheless, 14.3% of respondents indicate some level of awareness, while an additional 17.8% incorporate related concepts such as task requirements, delivery checklists, lists of work security, quality checklists, and constraints checklists into their understanding.

As shown in Figure 5b, the application of LC and the LPS varies, with a significant percentage indicating no use, 63.89% and 70.83%, respectively. Meanwhile, 18.06% of the participants experienced the LC philosophy in their workflows for 1-5 years, making up 15.28% of the participants who used the LPS for production planning and control in their enterprises for the same period. However, BIM education and application exhibit higher involvement, with 41.67% having BIM education and 26.39% applying BIM for 1-5 years.

Figure 6a presents an estimation that respondents were asked to fill out, which reflects the percentage of MD expected in the construction workflows: 18.06% of the respondents realize that? that their workflows are free of MD practices, 23.61% estimated that MD constitutes 25% of their production, 34.72% projected MD practices infect half of their workflows and 22.22% confirmed that MD is presented in more than 75% of their production. This estimation is a rough quantification of MD and might lack clarity or formal measurement, but it reflects that once the MD concept was introduced to the participants, they perceived that MD waste is an integral part of their decisions across the construction lifecycle. Figure 6b reveals the respondents’ perspectives regarding the primary stakeholders involved in MD within their workflows. The findings indicate that specialty trades and project managers are considered the most significant contributors to MD decisions, accounting for 27% and 17%, respectively. Designers follow closely with 66.2%, clients at 63.5%, project managers at 62.2%, regulators at 48.6%, and consultants at 44.6%.

4.1.2. Exploratory Factor Analysis (EFA)

Reliability analysis assessed the internal consistency of the variables related to using BIM and LPS to mitigate MD practices in construction projects. A total of 25 variables were tested for their importance in MD mitigation according to the participant’s perspective, and the Likert scale consistently reflects the construct of the study set out to measure. Accordingly, Cronbach’s alpha coefficient of reliability (α) was calculated for the variables using Equation (1).

$$\alpha =\frac{N}{N-1}\left(1-\frac{\sum _{i=1}^n{\sigma }^2}{{\sigma }_T^2}\right)$$

(1)

In this context, N represents the total number of questions. Each question has a score variance denoted by σ where i ranges from 1 to n. The overall test score’s total variance, not in percentage form, is represented by the σ_T. Cronbach’s alpha α has a value from 0 to 1, and the higher the value of (α), the greater the internal consistency of data (Field, 2005). It is generally believed that a value of α = 0.7 is acceptable, and α > 0.8 depicts good internal consistency. The calculated α for this study is 0.9475, demonstrating an excellent internal consistency. The 25 variables were then ranked using the descriptive statical mean as the ratio of importance. The results of the reliability analysis and ranking of the variables are shown in Table 2.

The exploratory factor analysis (EFA) method aims to discover “underlying” structures associated with the variables revealed in the literature. Its goal is to determine the set of dimensions forming variables as the basis of their structure, using the reductionist method to substitute them with fewer uncorrelated principal components. The resulting procedures have the added advantage of deleting redundant (highly correlated) variables while at the same time preserving the integrity of the original data. In this research, factor analysis was done by IBM SPSS 27 employing Principal Component Analysis (PCA) with oblique rotation (varimax) of 25 variables. PCA was employed for factor extraction, and varimax rotation was used as a rotation procedure. The Kaiser-Meyer-Olkin (KMO) measure for the sampling adequacy got a value of 0.873, which is higher than the recommended threshold of 0.5, while Bartlett’s Test of Sphericity resulted in a p-value of 2.45 × 10⁻¹⁰⁴ (less than 0. 5) suggesting substantial evidence against the null hypothesis of an identity matrix.

The demonstration previously mentioned confirms that this data set is suitable for factor analysis. The PCA results reorganize the list of variables into four factors, which account for the total variance of 57.876%, as shown in Supplementary Table S1. Reliability mainly refers to how consistent the measurements used within a study are relative to the construct. A construct is reliable when Cronbach’s alpha coefficient is greater than the threshold of 0.70 [51], enabling confidence in the correctness of the measures or items given a reliable instrument. The study is illustrated by the results of the construct reliability assessment, which was based on Cronbach’s Alpha. The results show that collaboration, having four items, makes its reliability index (0.815), and the making-do knowledge scale, subjected to five items (α = 0.811), yielded satisfactory levels of reliability. Also, the LPS Functions scale, made of twelve items (α = 0.873), and the BIM functionalities scale, four items (α = 0.843), proved a reliable inter-consistency value.

Accordingly, the groups were deduced and categorized based on the assigned variables. For further information, please refer to Supplementary Table S2, which provides detailed component labels and their corresponding criteria from the exploratory factor analysis. The groups include Group A (VA1 to VA4), denoted by the COO, which describes Collaborative Commitment during Planning and Control towards MD Mitigation and Adaptation Towards LPS and MD-free culture. Group B (VA5 to VA9) is denoted by MDK, by Active Learning of People Inside Organization for MD Incident Resolution. Group C (VA10 to VA21), denoted by LPS, describes the last planner system functions within short-, medium-, and long-term planning. Group D (VA22 to VA25), denoted by BIM, describes Integrated Production and Product Information parameters using BIM functionalities.

4.1.3. Confirmatory Factor Analysis (CFA)

Confirmatory Factor Analysis (CFA) implemented in AMOS software served to verify the validity of the measurement model. This factor analysis investigation involved looking at factor loadings for every item. It was found that three items, i.e., VA4, VA10, VAR21, had low factor loadings (VA4 = 0.46, VA10 = 0.45, VA21 = 0.48) which are all less than the accepted threshold of 0.5. Therefore, they were taken out of the study. As a composite of CMIN/df, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Residual (RMSEA), and Standardized Root Mean Square Residual (SRMR), these model-fit indices were used for the overall evaluation of the model. Significantly, the means of all calculated statistics were within the established standard values, as defined in previous research [67,68,69,70]. The four-factor model, as visualized in Figure 7, comprising COO, MDK, LPS, and BIM, demonstrated a satisfactory fit to the data, as indicated by the following fit indices: CMIN/df = 1.490, CFI = 0.932, TLI = 0.911, SRMR = 0.08, and RMSEA = 0.065 (Table 3 includes model-fit indices).

Construct Reliability was evaluated using Cronbach’s Alpha and Composite Reliability (CR). The Cronbach’s Alpha coefficients for each construct in the study exceeded the recommended threshold of 0.70 [71]. CR values also ranged from 0.813 to 0.839, surpassing the 0.70 benchmark [60]. Therefore, CR was established for each construct in the study, as documented in Table 4. The convergent validity of the scale items was assessed using Average Variance Extracted (AVE) [60]. The AVE values for BIM functionalities and LPS technical measures exceeded the threshold value 0.5 [60]. However, Collaboration, MD knowledge, LPS Functions, and BIM Functionalities exhibited AVE scores below 0.5. Nonetheless, given that the CR values exceeded the required threshold, it can be inferred that these constructs maintain adequate convergent validity for the present study, as summarized in Table 4.

The discriminant validity of the research was assessed by utilizing both the Fornell and Larcker Criterion approach and the Heterotrait-Monotrait (HTMT) ratio. The Fornell Larcker Criterion allows discriminant validity when the square root of a construct’s Average Variance Extracted (AVE) surpasses the correlation with other study constructs [72]. Nevertheless, the Fornell and Larcker Criterion has been the source of recent criticisms, and scholars have accepted new methods like the HTMT Ratio as alternative techniques. Although the Fornell and Larcker Criteria did not provide evidence of discriminant validity in this study, all HTMT ratios were below the 0.85 threshold value, as Henseler et al. (2015) recommended. Therefore, the Heterotrait Monotrait (HTMT) ratio was used to confirm the discriminant validity. The presented content of Supplementary Table S3 is the results of a closely carried out discriminant validity analysis, where the coefficients and significance values of discrimination are highlighted.

4.1.4. Structural Equation Model Assessment

A structural equation model generated through AMOS was used to test the relationships. The structural model’s fitness indices are as follows: CMIN/df = 1.418, TLI=0.932, CFI= 0.946, SRMR= 0.0597, and RMSEA =0.060 achieve an excellent fitting model. The squared multiple correlation was 0.62 for MDK; this shows that LPS Functions, COO, and BIM Functionalities account for 62% of the variance in MDK. The square multiple correlation for collaboration was 0.44; this shows that LPS Functions and BIM Functionalities account for 44% variance in collaboration. The study assessed the impact of Collaboration, LPS Functions, and BIM Functionalities on MDK. The effect of LPS on MDK was positive and significant (b= 0.698, t= 3.159, p<0.001); hence H2 was supported. The impact of BIM on MDK was negative and insignificant (b= -0.013, t=-0.085, p = 0.932). Therefore, H1 was not supported. The effect of COO on MDK was positive and insignificant (b=0.118, t= 1.030, and p = 0.303); hence, H1 was not supported. The impact of LPS on COO was positive and significant (b =0.803, t = 4.309, and p < 0.001); hence, H2 was supported. However, the impact of BIM on COO was negative and insignificant (b= -0.257, t= -1.415, and p = 0.157). Thus, H1 was not supported. Model fit indices and Hypothesis results are presented in Table S4.

Figure 8. Mediation Analysis for LPS, BIM, COO, and MDK.

Figure 8. Mediation Analysis for LPS, BIM, COO, and MDK.

A mediation analysis was carried out to investigate the mediation effect of LPS and BIM on the association between COO and MDK. There was insignificant mediation between the Making-Do Knowledge and Collaboration through BIM (b = 0.033, p = 0.314), rejecting Hypothesis 1. On the contrary, the mediation effect was significant for LPS Functions (b = 0.268, p = 0.02); evidence favoring Hypothesis 2 was established. Moreover, the direct impact of the COO on MDK with the mediators was insignificant (b = 0.262, p = 0.028). So, LPS can partially be seen as a channel between Collaboration and MDK. The result of the mediation analysis is shown in Table 5.

4.3. Stock and Flow Diagrams

The CLD was translated into SFD in Anylogic to test and simulate the system; in other words, the development of SFD quantifies and operationalizes the CLD, which requires two steps [65]. The first step is to set boundary conditions and provide the model assumptions to stabilize the system behavior to prevent unpredicted responses and force the system to behave in a way that is like reality. Secondly, the dynamic model can be dismantled into subsystems that shape the overall behavior. The model comprises six subsystems which include exogenous (external) and endogenous (internal) factors: (1) Work progress, (2) productivity factors, (3) resources, (4) making-do, and (5) MD impacts, (6) LPS social and technical functions, and BIM functionalities as shown in the generic Figure 10. These subsystems are described thoroughly in the following subsections. Accordingly, the internal factors are formed by parameters like initial values, including project definition, planned duration, and allocated resources. For the second group, the actual completion time of processes and the total project duration were considered. Thirdly, the productivity factors include the number of resources used in each task, the number of functions being processed or waiting to be processed, the project cost, the number of MDs for each category, and the related constraints and waste.

The formulation of parameters that form socio-technical LPS and BIM functionalities were added to the model to reflect the 5-point Likert scale ratings and formulated using SEM. For instance, the change in the LPS technical factors was calculated according to the rating for parameters (VA10 to VA20). The technical aspects of the LPS refer to functions that provide the production schedule according to the LPS hierarchy of planning and scheduling, which includes the master schedule, phase schedule, lookahead schedule, and short-term schedule [37]. Note that the equations used in the model are in the Additional materials in this article (Table S7 – Dynamic equations), and table functions or lookup tables are elaborated in (Table S8-Table Functions).

4.3.1. Work Progress

A customary initiation point for a planning system involves defining project goals, typically input by the user as a constant to provide an initial estimate for the quantities or number of tasks allocated to each stage. This input serves as the baseline value for the stages allotted throughout the project phases, while milestones dictate the progression from one stage to another and often serve as benchmarks for gauging the project’s strategic-level performance. The primary determinant of change within the “stages” stock is the rate at which tasks are transitioned by the planning team from master planning to the “ToBeProcessed” state for execution or advancement in planning. This rate of change is conceptualized as a flow entity denoted as “BackLogRate,” measured in tasks per month.

The magnitude of the “BackLogRate” is contingent upon the task counts within both the “stages” and “WIP” stocks. Specifically, when the number of tasks in “ToBeProcessed” equals or surpasses the number of functions in the “stages” stock, the BackLogRate diminishes to zero. Conversely, if the task count in “WIP” falls below half of the tasks in the “stages” stock, the BackLogRate escalates to its maximum level, determined by user-defined constants.

Figure 11. Work Progress Subsystem.

Figure 11. Work Progress Subsystem.

Acknowledging that the “WIP” stock facilitates arrays that categorize the five phases from the “stages” stock into 11 work packages is imperative, and the arrays used in this research are explained in Table S9. This functionality aligns with the principles of the last planner system, advocating for the breakdown of projects into manageable, measurable, and controllable work segments. However, it is noteworthy that the SDM method does not accommodate the granular breakdown of work into operations and tasks due to the inherent abstraction level in SDM.

The efficacy of constraints analysis is paramount in mitigating the adverse effects of MD practices. Its effectiveness is contingent upon two primary factors: the proportion of scheduled tasks within each phase, which influences the timeframe for identifying constraints, and the chosen production policy, whether it adheres to a pull or push strategy. Notably, when a push strategy is employed, there tends to be a higher incidence of encountered constraints later in the project, whereas the opposite is observed with a pull strategy. The functionality of constraints analysis directly impacts MD incidents and indirectly influences waste generation, as elucidated in Section (Quantifying MD Practices). The accumulation of unresolved constraints necessitates reevaluating and adjusting affected tasks to facilitate problem-solving discussions with the pertinent teams. Consequently, in this framework, the quantity of each constraint is integrated into each stage’s stock, necessitating additional time for the re-planning processes in stages where constraints are identified but not resolved.

The classifications are adopted for task prerequisites, MD categories, and their impacts on the literature [21,25,43], as shown in Table 6; this paper discusses and evaluates the relationship between these variables.

4.3.2. Productivity

Finished Works Stock is one of the objective metrics for evaluating the change rate based on the number of MDs, which is calculated based on the resource productivity rate in processing tasks from WIP stock. The rate influences how individuals complete tasks, which depends on resource allocation by the resources subsystem and productivity ratio [74]. Productivity is the ratio of total output to the sum of inputs, including items, for example, labor, material, equipment, energy, and capital, as in Equation (1). This Equation sums the productivity levels in each workflow to determine the total project productivity; such definition should also govern the impact of space, as indicated in LBMS, which considers that operations should be seen as the Movement of labor and equipment across locations [79,80]. This research ignored the output value of equipment and energy outputs due to insufficient data collected; however, their contribution to the total production was considered and subtracted.

Prod[SUBSTAGES] = effFatigueProductivity[SUBSTAGES] * overtime[SUBSTAGES] * normalProductivity
* impactOfWorkSpaceLimitation[SUBSTAGES] * impactOfBIMonProductivity* impactofLPSonProductivity

(1)

Figure 12. The dynamic variables affecting the construction productivity.

Figure 12. The dynamic variables affecting the construction productivity.

4.3.3. Resources

A trade analysis approach is employed, leveraging observed resources from case studies to estimate the number of tradespeople and engineers required to complete construction tasks. Subsequently, the current cost per labor hour can be calculated, providing insight into the workforce’s productivity, expressed as the hours required for monthly output. The workflow needed in Equation (2) is contingent upon conditional logic (IF and THEN), which diagnoses whether the project is completed and if any Work-in-Progress (WIP) remains. In such cases, the maxflow is assigned the value of the required flow, with the maximum workflow “maxWorkflow” values set from SS1 to SS11 denominated in the “Tasks” unit. If the diagnosed condition is not met, the requiredWorkflow takes the maximum value between ToBePrecessed divided by remainingTime. The xidz function is employed to avoid division by zero, returning the maxWorkflow value if division by zero occurs to prevent not a number (NaN) value.

changeInResources = newWorkForce [RESOURCES]/resourceAdjustementTime

(2)

projectIsDone[SUBSTAGES] != 0 && WIP[SUBSTAGES] < 0 ? 0: max(xidz(WIP[SUBSTAGES]), remainingTime[SUBSTAGES], maxWorkflow[SUBSTAGES] ), maxWorkflow[SUBSTAGES])

(3)

Figure 13. The Dynamic Subsystem for Resources.

Figure 13. The Dynamic Subsystem for Resources.

The dynamic variable resourceGap regulates the required resources over time if the condition is satisfied. Additionally, this dynamic function determines the number of trades to be dismissed after passing “half month,” as assumed in the model. The dynamic variable Tot_Resources aggregates the values between the new workforce hired and the resources working on assigned tasks in the project. The change resources divide the value of newWorkForce by the user-input value resourceAdjustmentTime, which is assumed to be “2 months”.

4.3.4. Cost and Location Subsystems

The additional cost associated with MDs and their impacts is taken into account in the project cost subsystem as indicated in Figure 14a; this system assumes that additional costs are caused by delays to rectify production problems, wastes that emerged across the system, and overtime due to schedule pressure. The location subsystem in Figure 14b adopts the principles of Location-based-Management to calculate the number of resources moving across the locations; it is assumed that the location change (locationUtlizationRate) is determined by multiplying available locations by the division of the division of the number of remaining tasks and resources. The workspace available is an essential variable for productivity variable calculation.

4.3.5. The Dynamic Interaction with LPS Functions and BIM Functionalities

This subsystem represents the LPS, COO, and MDK levels. The change in these variables across time is determined according to the structural equation modeling. Figure 15 displays how the different parameters interact in the dynamic model. Note that the relationship from the COO affects LPS_S by CCO_F, as shown in Figure 15a, and the impact of BIM is determined by the BIMF_F, as shown in Figure 15b.

5. Discussion

6. Conclusions

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