Critical factors for successful implementation of just-in-time concept in modular integrated construction: A systematic review and meta-analysis (2024)

Table1

Disagreement between previous studies on the importance level of some CFs to apply JIT.

2.1. Just-in-time (JIT) in modular integrated construction (MiC)

JIT is a lean construction approach, which aims to deliver required materials on-time, at the right location, and with the required quality and quantity (Ballard and Howell, 2010). JIT is built on six fundamental principles. They are pull system, waste elimination, smooth workflow, total quality management, supplier relations, and top management commitment (Pheng and Chuan, 2001). The pull system requires that materials are sent only after receiving authorized orders from the demand side, which eliminates the need for high inventory levels. The waste elimination principle identifies and eliminates waste activities that do not add value to the final product. Hence the waste elimination principle emphasizes the pull system principle by considering the inventory as a waste activity. Since JIT targets zero inventory, the principle of smooth workflow tries to maintain an uninterrupted workflow through the different activities along the supply chain. The principle of smooth workflow cannot be implemented without ensuring good quality of the received materials through the principle of total quality management. To receive the required materials on-time, with the required quality and quantity, mutually beneficial and long-term customer-supplier relationships are of utmost importance. Finally, the benefits of JIT cannot be achieved without the commitment of top management and involvement of employees in the process. These benefits are inventory reduction, reduction of procurement costs, improvement of market competitiveness, building long-term supplier relationships, and enhancement of forecasting (Akintoye, 1995).

Due to the advantages of JIT, qualitative and quantitative studies have discussed the implementation of JIT in the MiC supply chain. As for quantitative models, Kong etal. (2017) have proposed an approach that optimizes the production sequence of prefabricated components for JIT delivery to construction sites. Similarly, Kim etal. (2020) have demonstrated an algorithm that considers multiple uncertainties to solve the sequencing problem of prefabricated components. Recently, Hammad etal. (2020) have developed a mixed-integer non-linear programming model that solves the job shop scheduling problem in MiC factories. Regarding logistics in MiC, Kong etal. (2018) have developed a mathematical model that optimizes the number of delivery batches of prefabricated components, the number of these components in each batch, and the delivery time of each batch. Hsu etal. (2018) have also developed a two-stage stochastic programming model that determines the optimal dispatch time, ordering quantities and transportation time of MiC modules. Likewise, Yusuf etal. (2019a) have utilized a stochastic simulation-based optimization model to find the near-optimum sequence of precast components, while Yusuf etal. (2019b) have considered supply chain risks in the developed simulation model. Recently, Li etal. (2020) have proposed an improved artificial bee colony algorithm, which finds the near-optimum route planning of prefabricated components within time windows.

As for qualitative studies, many researchers have discussed the CFs required for the successful implementation of different lean approaches, including JIT. For instance, Nasrollahzadeh etal. (2016) have used the fuzzy analytic hierarchy process and Delphi techniques to evaluate CFs required for implementing lean techniques in MiC. Asri etal. (2016b) have summarized these CFs by conducting a literature review. Also, Yunus etal. (2017) have identified CFs of lean thinking in MiC and evaluated them on a Likert scale. Besides identification and assessment of the importance of CFs, Rahimah Mohd Noor etal. (2018) have adopted the interpretive structural modeling and Matrice d’impacts croi-sés multiplication appliquée an classment (MICMAC) to identify the relationships between the CFs. Other studies identify and evaluate the CFs of JIT and lean techniques in the construction industry of different countries. These include South Africa (Aigbavboa etal., 2016), Ireland (Spillane and Oyedele, 2017), Nigeria (Amade etal., 2019), and USA (Demirkesen and Bayhan, 2019).

Overall, the above-mentioned studies show that academic attention towards studying CFs of JIT is increasing. Also, as MiC continues to draw more attention, many review studies have been published, as mentioned in section 1. However, a systematic review of CFs required for the successful implementation of JIT in MiC is missing. Moreover, the previous studies on CFs for implementing JIT have quantified the importance of each CF based on the opinions of its respondents. Therefore, a synthesis of the results presented in these studies is required to provide a more reliable evaluation of CFs. Consequently, meta-analysis is selected to achieve this objective in a transparent and replicable manner.

2.2. Meta-analysis

Meta-analysis provides a statistical synthesis of results collected from different studies to give a more reliable indication of the population mean than that provided by individual studies (Horman and Kenley, 2005). Meta-analysis is usually conducted with a systematic review to combine consistent results (Borenstein etal., 2009). The systematic review is required to find relevant studies. In meta-analysis, each individual study is assigned a weight based on the number of respondents (sample size) in it by using a standard statistical approach (Borenstein etal., 2009). In a narrative review, the researcher gives credence to each included study, and hence his/her conclusions are subjective. Accordingly, the obtained conclusions cannot be used without caution. Therefore, meta-analysis is deemed to be more reliable and transparent than a narrative review. However, the adoption of meta-analysis is not free of some challenges (Glass etal., 1981). Inconsistent studies that have used different measuring techniques or addressed different topics may be erroneously combined with each other. The risk of publication bias is another issue, where peer-review journals tend to publish only significant results. However, these issues can be tackled by a proper design of the systematic review and meta-analysis.

Some researchers have meta-analytically synthesized some previous studies on lean-related topics in the business management research field. For instance, Abreu-Ledón etal. (2018) have provided a meta-analysis study on the relationship between the application of lean production and the performance of firms. Recently, Liu etal. (2018) have meta-analysed the relationship between lean practices and enterprise performance. This relationship has been analysed by using the Bayesian network based on the meta-analysis results. In the construction management research field, few researchers have exploited the full-benefits of meta-analysis. Horman and Kenley (2005) have used meta-analysis to quantify the percentage of wasted time in construction activities from the different percentages reported in relevant studies. Sancini etal. (2012) have conducted meta-analysis to evaluate the trend of fatal and nonfatal injury in the construction industry. In the same manner, Umer etal. (2018) have educed the prevalence of different musculoskeletal symptoms among construction workers. Regarding using meta-analysis to evaluate the importance of CFs, Alruqi and Hallowell (2019) have assessed the correlation values of multiple safety leading indicators from results reported in previous studies. Despite that meta-analysis has been used frequently in the medicine, social, and business research fields (Hunter and Schmidt, 2004), its adoption in the construction management research field is still limited.

To the best of the authors’ knowledge, this study is one of the first that conducts a systematic review of CFs for JIT implementation. Also, it performs a meta-analysis of results from multiple quantitative studies. Hence, it provides a more reliable assessment of the importance of the CFs required for implementing JIT in MiC.

3.1. Specification of research question and objectives

Identification of a clear and feasible research question is the first step in any meta-analysis study (Tawfik etal., 2019). In this study, the main research question is as follows: what is the prioritization of CFs to implement JIT in MiC? Answering this question requires firstly identification of CFs to apply JIT in MiC by conducting a systematic literature review methodology. Secondly, the importance of each identified CF needs to be quantified by using meta-analysis. Besides, secondary research questions are defined as follows: Are there any factors that might affect the importance level of these CFs? If yes, what are these factors? And how can they affect the evaluation of CFs?

3.2. Preliminary research and validation of the research idea

Conducting preliminary research is paramount to 1) ensure the novelty of the research idea; 2) assure that there are enough studies to perform systematic review and meta-analysis; 3) gain a deep understanding of the field of study; 4) collect a wide variety of keywords related to the study topic to help in retrieving relevant studies; and 5) help in defining the inclusion and exclusion criteria (Tawfik etal., 2019). The authors have conducted a preliminary search on Google Scholar and Scopus by using the following keywords: ‘critical factors’ AND ‘just in time’ AND ‘modular construction’ OR ‘modular integrated construction’. This search has resulted in finding only two studies by Asri etal. (2016a) and Asri etal. (2016b). Through a literature review, the former has identified key success factors to integrate JIT in Malaysian MiC projects, while the latter has identified CFs for the successful application JIT to overcome transportation and delivery challenges of MiC. These results are justifiable due to the relatively new advent of MiC to the construction industry and the lack of awareness of JIT, as a lean tool, in the construction field (Innella etal., 2019). Also, this preliminary search indicates that the statistical meta-analysis approach has not been used before to prioritize CFs to apply JIT in either MiC or the traditional construction.

3.3. Definition of inclusion and exclusion criteria

Specification of inclusion and exclusion criteria is a critical step in the meta-analysis study. This step aims to filter the retrieved studies and keep only the studies that answer the main research question (Wohlin, 2014). In this study, the inclusion criteria would include: 1) studies which contain critical success factors of JIT in the construction industry; 2) studies which include factors that affect on-time delivery of construction materials; 3) no restrictions regarding countries, publication year or publication type to avoid the risk of publication bias (Higgins and Green, 2011); and 4) quantitative studies which include sufficient statistical data to use it for conducting meta-analysis. The exclusion criteria are: 1) studies published in languages other than the English language; 2) studies without available full text; and 3) duplicated studies or overlapping data.

3.4. Determination of search databases and search strategy

At least two search databases are required to achieve accurate retrieval of relevant studies (Shea etal., 2017). The more the number of databases, the more comprehensive results are obtained. In this study, the authors have used Scopus, Web of Science, and Google Scholar to retrieve relevant studies. After the specification of search databases, the authors have adopted two search strategies to search for studies. The first strategy is the keywords-based search method. In this method, configurations of relevant keywords constituted by deploying Boolean operators such as (AND & OR) are used. With a full understanding of the critical role of keywords, representative keywords and their similar nomenclatures of “critical factors”, “just in time” and “modular construction” have been used to find relevant studies. Examples of alternative keywords of “modular construction” include “prefabricated/precast”, “industrialized construction”, “Prefabricated Prefinished Volumetric Construction” and “off-site construction”. These keywords have been used to search for relevant papers in multiple review papers on MiC such as critical risk factors (Wuni etal., 2019); critical success factors (Wuni and Shen, 2019); lean techniques (Innella etal., 2019); supply chain vulnerabilities (Ekanayake etal., 2020); and adoption barriers (Wuni and Shen, 2020a). The second strategy is a manual search method called snowballing search strategy (Wohlin, 2014). The first step of this strategy is to identify a start set of papers. These papers are identified after filtering the studies obtained from the keywords-based search method. Then, each study in the start set will be used to conduct backward and forward snowballing. In the backward snowballing, the researcher searches for relevant studies in the reference list of each study in the start set. In the forward snowballing, the researcher uses the list of papers that cited the article under investigation to find new articles. The newfound articles constitute a new start set which undergoes another cycle of backward and forward snowballing. This cyclic process is iterated until no new papers are found. As shown in Fig.2, the adopted search strategies follow the steps of Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA), which is a widely accepted protocol adopted in systematic reviews and meta-analysis studies (Moher etal., 2009).

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Fig.2

See Also

Kanban ZoneJust-in-Time (JIT): Definition, Example, and Pros & ConsAdvantages & Disadvantages of Just-in-Time InventoryJust in Time (JIT) Method

PRISMA flow diagram for evaluating and selecting relevant studies.

3.5. Evaluation of retrieved studies

Screening the retrieved studies undergo three consecutive processes (Moher etal., 2009). Firstly, the title and abstract of each obtained study are evaluated according to the predefined eligibility criteria. The eligibility criteria are inclusive rather than exclusive because sometimes the title and abstract provide insufficient information. At this stage, the authors have included any study which addresses logistics issues in the construction field. Secondly, duplications are removed manually. In this step, duplications emerge only from using multiple databases. Then, eligible articles are downloaded for full-text evaluation. Thirdly, the evaluation of the full text is implemented according to the inclusion and exclusion criteria discussed in section 3.3. Then, the included studies are classified into qualitative and quantitative studies. The difference between them is that the former only discuss CFs to apply JIT without providing any statistical data to evaluate their importance. Forty-six out of 150 studies report statistical data to assess the CFs, as shown in Fig.2. Only quantitative studies are applicable to meta-analysis.

3.6. Data extraction and cleaning

In this stage, the authors have collected data from the full-text articles in a structured Excel sheet. In this study, the extracted information is as follows: authors name, publication type (journal, conference, report, thesis, etc.), publication year, country of the conducted case study, type of project under study (buildings, infrastructure or MiC), CFs to apply JIT, the number of respondents (sample size), research method and the reported statistical data (mean and standard deviation (SD)). Only these data will be used to conduct meta-analysis.

Before the commencement of meta-analysis procedures, an examination of the extracted statistical data is essential to determine the dominant measuring technique among the retrieved studies. Determination of this dominant measuring technique allows us to include only studies that deploy this measuring technique. This step is necessary to prevent one of the limitations of meta-analysis, where a researcher mistakenly combines studies that use different measuring techniques or address different topics. In the meta-analysis jargon, this limitation is called mixing “apples and oranges” (Borenstein etal., 2009).

It is found that all of the 46 studies have used the Likert scale to evaluate the CFs except three studies. Hence, these three studies have been excluded. Then, the authors find that the remaining 43 studies have used different Likert scales. However, the five-point Likert scale has been adopted by 30 out of the 43 studies. In the five-point Likert scale, the scale is ranged from 1 to 5 where “1” indicates the least important and “5” represents the most important. It is worth mentioning that it is possible to convert the results obtained by other Likert scales into the five-point Likert scale (IBM Support, 2018). This conversion is possible only in the case that raw data are available and not only the statistical results such as the mean and SD. This case is applicable only for one study, which is the study by Kawish (2017), who has used the seven-point Likert scale. Regarding the statistical measures used in these studies, the authors find that 19 out of the 30 studies have used the mean and SD to prioritize the CFs, while only six out of the 30 studies have deployed other measures such as correlation coefficient and Cronbach’s alpha. Hence, these six studies have been removed. The remaining five studies of the 30 studies report only the mean, which is not enough to perform meta-analysis, and thus these five studies have been excluded. Finally, 20 studies have been identified to undergo meta-analysis procedures. However, after rechecking the statistical results of the 20 studies, it is found that two of them have precisely the same results, which is not possible. After close examination, it is found that one of them is a journal paper, and the other is a conference paper, but both of them belong to the same researchers and the same case study. Therefore, the authors decide to exclude the conference paper. In summary, we have 19 studies applicable to perform meta-analysis. These studies will be mentioned in section 4.1.

Before starting meta-analysis, cleaning of the data extracted from the 19 studies is vital to enter these data to the meta-analysis software effortlessly (Tawfik etal., 2019). The data cleaning can be done by organizing an excel sheet for each factor containing the information mentioned above in a structured way.

3.7. Procedures of meta-analysis

In this study, standard meta-analysis procedures are followed. These procedures are adapted from studies by Borenstein etal. (2009) and Higgins and Green (2011). These studies are deemed among the most widely accepted references to perform meta-analysis. Again, the objective of doing meta-analysis in this study is the prioritization of CFs to apply JIT in MiC. This objective is accomplished by the calculation of an aggregated mean and SD for each factor. Consequently, the meta-analysis is applied to each of the identified CFs. Each factor has to be reported in at least two studies to perform meta-analysis. The following sections explain how the aggregated mean and SD are calculated.

4.1. Data extracted from the included studies

Table5 shows the 19 studies which meet the inclusion criteria to conduct the meta-analysis. Also, Table5 lists the information required to implement sub-group analysis such as publication type, publication year, country, and project type. Moreover, the table shows that the included studies have evaluated the importance of CFs by using questionnaires, interviews and case studies. As shown in Table5, the publication year of the included studies ranges between 2009 and 2019. Therefore, the authors divided the included studies equally into two groups to conduct the sub-group analysis based on the publication year. The first group contains studies published from 2009 to 2015, while the second group includes studies published from 2016 to 2019.

Table6 summarizes 42 CFs to implement JIT identified from the included studies, concomitant with the definition and the frequency of occurrence of these CFs in the included studies. The seven most frequently reported CFs in the previous studies are CF27 “Knowledge and awareness of JIT” (17 times), CF1 “Top management commitment” (15 times), CF15 “Quality assurance and quality control (QA/QC)” (15 times), CF22 “Culture and human attitude” (15 times), CF34 “Collaboration among supply chain members” (14 times), CF35 “Communication and information sharing” (13 times) and CF28 “Training” (12 times). In addition, the 42 CFs are categorized into nine categories, which are managerial, technical, cultural and human, educational and knowledge, financial, communication-related, skills and expertise related, governmental, and logistics factors.

4.2. Statistical results of meta-analysis

Table7 and Fig.3summarize the results of the meta-analysis for each CF. Meta-analysis has been performed by using Review Manager (RevMan) 5.3, which is a standard meta-analysis software (RevMan Web, 2020). The second column of Table7 shows the results of heterogeneity tests before and after conducting the sensitivity analysis. The third column shows whether there is a statistically significant difference between sub-groups in each of the four criteria mentioned in section 3.7.3. Fig.3 shows the weighted mean (summary effect) and the weighted SD for each CF in an error bar.

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Fig.3

Weighted mean±weighted SD of each critical factor.

The heterogeneity results are represented by three values, namely, χ2, P-value and I2. Heterogeneity is detected in case of high values of χ2 and P-value < 0.05 based on a 95% level of confidence and quantified by the value of I2. The heterogeneity results indicate widely dispersed data represented by high values of I2 (I2 > 75%) in the majority of CFs even after conducting the sensitivity analysis. However, the sensitivity analysis is applicable only when we find a justification for removing studies whose results vary substantially from the other studies. A justification here means a feature of a study, based on publication year, publication type, country’s economy, or project type, which distinguishes it from the other studies. For instance, we have removed studies by Abdullah etal. (2009) and Yunus etal. (2017) in the sensitivity analysis of CF2 because the first one is the oldest study among the studies reported CF2, while the second one is unique by its project type which is MiC. The potential reasons behind these high levels of heterogeneity and how such heterogeneity has less impact on the findings of this study are discussed in section 5.

The results of the sub-group analysis for each CF are summarized in the third column of Table7. Again, the sub-group analysis is applicable only if each group has at least two studies. For the publication type criterion, the results indicate that there is no statistically significant difference between the results published in journal papers and the results published elsewhere in all CFs except CF35 “Communication and information sharing”. The results related to studies published in journals indicate that the summary effect (the importance level) of CF35 is 3.27 on the five-point Likert scale, while the studies published in other types of publications show that its summary effect is 4.05. However, these results of CF35 are characterized by high heterogeneity values. Similarly, there is no statistically significant difference between the results published in relatively old studies (between 2009 and 2015) and that published in recent studies (between 2016 and 2019) for all CFs.The sub-group analysis based on the project type is applicable only for 21 out of the 42 CFs. This means that the majority of studies focused mainly on building projects and ignored other project types. Interestingly, the sub-group analysis reveals that the importance levels of eight CFs vary significantly depending on the project type. These eight CFs are CF1 “Top management commitment”, CF3 “Extensive planning and scheduling”, CF15 “QA/QC”, CF16 “Agreed implementation methodology to implement JIT”, CF25 “Employee morale and motivation”, CF27 “Knowledge and awareness of JIT”, CF32 “Contract and incentives” and CF39 “Supportive governmental regulations”. For the country’s economy criterion, the sub-group analysis indicates that whether having the country an advanced or stable economy (within the G20) does not affect significantly the importance of the majority of CFs except three CFs. These CFs are CF6 “Decentralization of decision making”, CF29 “Education and research” and CF39 “Supportive governmental regulations”.

Finally, Fig.3 shows the weighted mean and the weighted SD for each CF after conducting the sensitivity analysis. Apparently, all CFs have mean values higher than “3” on the five-point Likert scale. As a result, all of the 42 CFs deserve to be considered for successful JIT implementation. However, only seven CFs have weighted means higher than “4” which indicates that these factors have significant importance. This threshold has been adopted in previous studies by Lam etal. (2007) and Chan etal. (2004). These seven CFs are CF21 “Production planning”, CF42 “Route planning”, CF14 “Willingness to invest in JIT practices”, CF16 “Agreed implementation methodology to implement JIT”, CF40 “Political and economic stability”, CF7 “Market strategy to adopt JIT” and CF27 “Knowledge and awareness of JIT”.

4.3. Analysis of studies on MiC

This section summarizes the CFs and their statistical results reported in studies on MiC. Only three out of the 19 included studies focused on MiC projects. Table8summarizes the CFs mentioned in these studies and the weighted mean and SD of each CF. As shown in Table8, 30 out of the 42 CFs are mentioned in the three studies. As for the CFs mentioned in only one study, we have just put the mean and SD reported in the study. On the contrary, the weighted mean and weighted SD are calculated for CFs mentioned in multiple studies. All the 30 CFs have mean values higher than “4”, as shown in Table8. Hence these CFs are significant for JIT implementation in MiC, except six CFs. These CFs are CF5 “Integrating risk management and supply chain management”, CF18 “Site layout planning”, CF20 “Standardization of activities”, CF34 “Collaboration among supply chain members”, CF35 “Communication and information sharing” and CF41 “Inventory management”.

The high heterogeneity is observed despite the small number of studies and the fact that all the three studies were conducted in Malaysia. Interestingly, it is observed that the study by Jaafar and Mahamad (2012) is repeated in the vast majority of CFs characterized by heterogeneity. Also, this study is not found in any CF characterized by hom*ogeneity. This observation indicates that the results reported in the study by Jaafar and Mahamad (2012) are heterogeneous from the results reported in the studies by Yunus etal. (2017) and Rahimah Mohd Noor etal. (2018). To validate this finding, removing the results of Jaafar and Mahamad (2012) from CF15 reduces the heterogeneity, from I2=87% to I2=69%.

4.4. Prioritization of critical factors to implement JIT in MiC

In the previous section, the meta-analysis results indicate that seven CFs have mean values higher than “4” on the Likert scale and the other CFs have mean values between “3” and “4”. Based on these results, it would be valuable for stakeholders of MiC projects to provide them with a prioritization of the most important factors to allocate their limited resources to these factors optimally. Prioritization of the CFs by using only their mean values, as mentioned in section 4.2, might be flawed because the variability of these values is ignored. Therefore, the authors adopted the signal to noise ratio (SNR) to prioritize the CFs. The SNR is frequently used within the context of the design of experiments to find the best parameter setting. The SNR is simply the reciprocal of the coefficient of variation, as shown in Eq. (8) (Holmes and Mergen, 1995; George and Kibria, 2012).

Where SNR=the signal to noise ratio; x¯ and s=the sample mean and sample standard deviation, respectively.

Table9 shows the ranking of CFs with considering all project types (global rank) and MiC only (MiC rank). The global rank is based on SNR values calculated from the values of the weighted mean and SD of each CF mentioned in section 4.2. On the other hand, the MiC rank depends on SNR values estimated from weighted means and SDs mentioned in section 4.3. The higher the SNR value, the more important the factor is on the Likert scale with considering variability. Consequently, Table9 prioritizes the 42 CFs. Hence, the most seven critical factors with considering all project types are CF14 “Willingness to invest in JIT practices”, CF11 “Resources management”, CF10 “Well-established long-term supplier-client relationships”, CF26 “Trust among supply chain members”, CF13 “Adopt new management concepts”, CF12 “Procurement management” and CF21 “Production planning”. Fig.4shows the forest plots of these seven CFs. In each forest plot, the size of the solid squares reflects the weight given to the respective study (Wi∗), while the diamond represents the summary effect (WM). The location of the diamond signifies whether the CF is significant (i.e., it’s mean>4 on the five-point Likert scale) to the successful implementation of JIT. However, the diamond width reflects the precision of the summary effect. Just for visualization purposes in RevMan 5.3, we have entered the means reported in each study subtracted by four, which is the adopted threshold, to judge the significance of CFs.

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Fig.4

Forest plots of the most influential CFs based on SNR values.

Despite that Table9 provides a prioritization of the 42 CFs, it does not show whether there is a significant difference between the importance of these CFs. Also, if there are some CFs that are more important than the others significantly, it would be valuable to identify these factors. Therefore, a one-way analysis of variance (ANOVA)-F test has been performed to test if there is a significant difference between the importance of the 42 CFs. If there is a significant difference, then Tukey’s method is used to identify which CFs are more important than the others. Tukey’s method is a multiple-comparison method in ANOVA.

The first step to implement ANOVA is to collect the mean values of each CF from the studies which addressed this factor. Then, these data have been identified to Minitab®18 statistical software to conduct the one-way ANOVA and the Tukey’s method. Fig.5shows the results of the ANOVA-F test and Tukey’s method. Before the discussion of the ANOVA results, it is essential to check the conditions required for a valid ANOVA F-test. Fig.5(a) shows that the normality assumption is verified by observing that the central points in the normal probability plot of residuals stick to the line (Montgomery, 2012). Also, Fig.5(b) shows that there is no distinct pattern like an outward-opening funnel in the plot of residuals versus fitted values. Hence, the population variances do not differ significantly. Based on Fig.5(a) and (b), the conditions to apply ANOVA are satisfied. As shown in Fig.5(c), the P-value is less than 0.05, highlighting that there is a significant difference between the importance of the 42 CFs. However, the multiple pairwise comparisons test conducted by using Tukey’s method suggests that there is no significant difference between the importance of the 42 CFs. This contradiction between the results of ANOVA and Tukey’s method might be attributable to the high number of pairwise comparisons between the 42 CFs (Lee and Lee, 2018). Besides, some CFs have small sample sizes (a small number of studies).

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Fig.5

Results of Tukey’s method and checking of the model adequacy.

Table10

Ranking of CFs based on no. of frequencies, mean and SNR values.

Table11

Values of weighted means of the eight CFs whose importance differs significantly between the different project types.

5.1. Theoretical implications

This study provides a generalized assessment of the importance level of CFs required for successfully implementing JIT in MiC. Relying solely on the frequency of occurrence in previous studies to rank CFs may lead to flawed assessment (Wuni and Shen, 2019; Ekanayake etal., 2020). Therefore, using meta-analysis contributes to the theory development of JIT implementation by providing a more reliable assessment of these CFs that when thoroughly considered result in the successful application of JIT and, hence reaping its benefits. Moreover, sub-group analysis identifies the impact of some predictive criteria or moderators on the assessment of CFs.

In this study, the impact of four criteria is studied. These criteria are time, publication type, project type and country’s economy. However, through extending the examination of literature, other criteria should be studied to further enrich the knowledge and help in the theory development of JIT in the construction industry. For instance, the sensitivity of the supply chain to disruptions and disturbances may affect the successful implementation of JIT in MiC (Wuni and Shen, 2020b). Also, order behaviour of the client is another potential factor that is likely to change how JIT is implemented in MiC (Mostafa and Chileshe, 2018). More importantly, understanding how JIT practices can enhance the performance of the MiC supply chain is critical to the theory development of JIT in MiC.

MacKelprang and Nair (2010) have meta-analytically studied the correlation between different JIT practices (e.g. schedule adherence, Kanban, pull system and preventive maintenance) and some performance measures, such as quality, cost, inventory levels and cycle time in different industries. However, as MiC is a cleaner construction alternative, assessment of the impact of JIT practices on different environmental metrics, such as embodied energy, global warming potential and acidification potential is imperative (Švajlenka etal., 2018). To complete the third triangular point of sustainability, Švajlenka and Kozlovská (2020) have included some social criteria to evaluate the efficiency of the off-site construction from the perspective of end-users. Using such sustainability criteria to evaluate the performance measures of JIT implementation calls for innovative methods for data collection and analysis. Hammad etal. (2019) have proposed a BIM-based approach to help in quantifying the sustainability impact of MiC through a life-cycle assessment. Others suggest using data analytics for monitoring sustainability (Eugene etal., 2017). The above-mentioned criteria and their relationships with JIT practices have not been addressed yet.

Through a systematic review of previous studies, this meta-analysis research uncovers multiple gaps in the current theoretical knowledge of JIT application in MiC. This study could serve as a stepping stone for future studies to complete the theory development of lean implementation in MiC.

5.2. Managerial implications

Given the challenges of MiC logistics and the potential of JIT to address them, the managerial implications of this study are invaluable. The lack of manuals to guide practitioners to implement lean techniques, including JIT, is one of the main barriers to adopt lean techniques in the construction industry (Demirkesen and Bayhan, 2019). Therefore, this study provides two important deliverables to convert JIT from mere theory to practice. Firstly, Table6 provides checklists of CFs that when thoroughly considered, will aid in the successful application of JIT. Secondly, Table9 provides a meta-analytical prioritization of these CFs. This ranking helps MiC stakeholders to optimize resource allocation strategies for a better implementation of JIT. Through a sub-group analysis, this study shows that the importance of these CFs is changing with different countries and project types. Moreover, the high heterogeneity between the included studies highlights that other factors might have an impact on the assessment of CFs. Therefore, stakeholders implementing JIT should note that ignoring the project characteristics (e.g. type, duration, budget, etc.) and the environment in which JIT is implemented may not end with obtaining the desired outcomes.

5.3. Implications for lean construction

Despite that this study examines CFs required for implementing JIT, many of these CFs are also associated with lean construction. JIT is a fundamental part of lean, and the concepts and techniques developed under lean are similar to that of JIT (Schonberger, 2007). Therefore, this study provides valuable theoretical and managerial implications for lean construction. In particular, this study shows that the importance of some CFs is sensitive to the type of project and the territory it is implemented. There is also the possibility that these CFs are also sensitive to additional criteria or moderators. Therefore, it would be valuable that researchers examine the sensitivity of CFs of other lean construction techniques (e.g. last planner system, six sigma, 5S, visual stream mapping, etc.) to potential moderators. Also, the study findings imply that lean construction practitioners should be aware that the importance of CFs to apply lean construction is dynamic with the project’s characteristics and environment.

5.4. Impact of JIT concept on the sustainability of MiC

The successful implementation of JIT is directly and positively correlated with satisfying the triple bottom line of sustainability that concentrates on economic, environmental and social concerns (Green etal., 2019; Solaimani and Sedighi, 2020). From an economic perspective, the application of JIT can contribute to cost reductions and profit increase (Dehdasht etal., 2020). For instance, considering both CF42 “route planning” and CF21 “production planning” lead to the on-time delivery of MiC modules to the construction site (Kong etal., 2018). This would reduce the financial penalties associated with the early and late delivery of MiC modules and hence avoid project’s cost overrun. Besides, JIT aims at reducing buffer inventory at the different MiC supply chain stages by considering CF41 “inventory management”. Storage of such heavy and bulky materials requires preparation of sheltered, secured and vast storage areas on the construction site to protect them from bad weather and vandalism. For congested construction sites, storage at consolidation centres is the solution (Magill etal., 2020). Both of these storage options imply a cost increase without adding value. Also, through CF11 “resources management”, JIT tries to maximize the resources utilization, which results in the reduction of the project’s direct costs (Kong etal., 2018). Moreover, JIT aims at delivering of MiC modules to the construction site in the required quality without any dimensional or geometric deficiencies through CF15 “QA/QC”. This would avoid additional costs associated with repairing of defective modules on-site or returning them to the factory (Enshassi etal., 2019). Other factors such as CF28 “training”, CF20 “standardization of activities” and CF25 “employee morale and motivation” also contribute to defect and cost reduction (Kumar etal., 2020). Last but not least, through CF7 “market strategy to adopt JIT”, successful JIT application could provide the organization with a sustainable competitive advantage and improve its market image which in turn increase its market share (Green etal., 2019). On the other hand, the successful implementation of JIT requires initial costs to consider factors such as CF28 “training”, CF31 “availability of resources”, CF33 “incentives and reward systems”, CF37 “existence of certified and qualified lean personnel” and CF38 “experienced and skilled workers”.

JIT implementation contributes significantly to environmental sustainability (Klassen, 2000). JIT aims at reducing inventory levels by maintaining more frequent deliveries (Min and Sui Pheng, 2007). Consequently, some researchers indicated that JIT might have negative impacts on environmental sustainability due to the inefficient utilization of the truck capacity and the increase in carbon emissions (Longoni and Cagliano, 2015). Conversely, in MiC, the truck can only transport one module at a time. Therefore, such environmental concerns of applying JIT are not applicable to MiC. However, Kong etal. (2018) have demonstrated that transportation of such heavy materials during peak hours increases the emissions. Through CF42 “route planning”, JIT can maintain environmental sustainability by delivering MiC modules during off-peak hours or by using uncrowded routes. Besides, carbon emissions and energy consumption associated with the double handling of MiC modules could be decreased by considering CF41 “inventory management” (Lu and Yuan, 2013). Also, CF41 “inventory management” and CF15 “QA/QC” ensure the reduction of construction wastes (Dehdasht etal., 2020). CF11 “resources management” contributes to the reduction of environmental pollution by maximizing resources utilization (Rothenberg etal., 2001). Finally, factors such as CF8 “human resources management”, CF28 “training”, CF34 “collaboration among supply chain members”, CF4 “stakeholder management” and CF22 “culture and human attitude” are necessary to engage respective stakeholders of MiC in the adoption of eco-friendly practices and techniques (Martínez-Jurado and Moyano-Fuentes, 2014).

The successful application of lean techniques, including JIT, contributes to the social sustainability of the built environment (Mellado and Lou, 2020). For instance, factors such as CF18 “site layout planning” and CF20 “standardization of activities” help in providing a safe working environment (Sanad etal., 2008). Also, consideration of CF42 “route planning” would reduce the social impacts (e.g., emissions and traffic accidents) associated with the transportation of such bulky materials in urban areas (Kong etal., 2018). Besides, reduction of inventory levels (through CF41 “inventory management”), especially for constriction sites in dense urban areas, contributes to the social welfare of their neighbours by reducing social impacts such as the noise and traffic congestion. CF35 “communication and information sharing” would help in avoiding disputes and claims among stakeholders and mitigating corruption risks (Faisal, 2010). In addition, paying attention to factors such as CF22 “culture and human attitude”, CF24 “knowledge sharing”, CF25 “employee morale and motivation”, CF26 “trust among supply chain members”, CF28 “training”, CF8 “human resources management”, CF6 “decentralization of decision making”, CF10 “well-established long-term supplier-client relationships” and CF4 “stakeholder management” would increase the engagement, motivation, communication, teamwork among respective stakeholders which in turn lead to a socially sustainable working environment (Martínez-Jurado and Moyano-Fuentes, 2014; Baliga etal., 2019). On the other hand, some researchers claimed that the successful application of lean techniques would add more workloads over employees (Hines etal., 2004). Such social concerns are applicable to factors such as CF3 “extensive planning and scheduling”, CF13 “adopt new management concepts”, CF20 “standardization of activities” and CF27 “knowledge and awareness of JIT”. However, considering factors such as CF1 “top management commitment”, CF6 “decentralization of decision making” and CF25 “employee morale and motivation” help in reducing the work stress (Conti etal., 2006).

Based on the previous discussion, considering CFs for successful application of JIT would contribute more to the economic, environmental and social sustainability of MiC. Fig.8shows a schematic system dynamics model to summarize how CFs of JIT contribute to the three sustainability aspects. This model can be extended in the future to estimate how each CF contributes to MiC sustainability and understand their dynamic behaviours over a time period.

Open in a separate window

Fig.8

A schematic system dynamics model of JIT CFs for a more sustainable MiC.

5.5. Study limitations

Despite the obvious importance of this study, some limitations do exist. Firstly, combining and assessing CFs from several international studies overlook the sensitivity of these CFs to project characteristics such as the project’s type, budget, territory, etc. However, these sensitivities may be overlooked for theoretical development or situations where bespoke research is not feasible or economical, but considering project characteristics is imperative in real-life projects (Wuni and Shen, 2019). The high heterogeneity that exists in this study is another limitation. Similarly, other meta-analysis studies in the construction management research field have reported high heterogeneity, such as studies by Horman and Kenley (2005) and Alruqi and Hallowell (2019). The high heterogeneity reflects the uncertainty embedded in the construction domain. Finally, although the number of MiC studies is limited in comparison with the number of studies on other project types, the results indicate a consistency between these studies in the evaluation of all CFs except for seven CFs which are more important in MiC projects. Therefore, including studies on other project types besides MiC has increased the reliability of the evaluation of the majority of CFs. However, the limited number of MiC studies shows the need for more empirical studies on CFs to apply lean techniques in MiC. Hence, it is worthy to replicate meta-analysis with more studies in the future.

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