Abstract
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2021, VOL. 20, NO. 1, 78–87 https://doi.org/10.1080/13467581.2020.1800471 CONSTRUCTION MANAGEMENT a b c Hyeon-Seung Kim , Sung-Keun Kim and Leen-Seok Kang a b BIM Development Team, Seoyeong Engineering Co. Ltd., Seoul, Korea; Department of Civil Engineering, Seoul National University of Science & Technology, Seoul, Korea; Department of Civil Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Korea ABSTRACT ARTICLE HISTORY Received 18 January 2019 Currently, various guidelines regarding building information modelling (BIM) technology policy Accepted 16 July 2020 are being developed in different countries. However, for many companies, the cost- effectiveness of BIM investment remains unclear. Some studies suggest a return on investment KEYWORDS (ROI) as the result of cost-effective analysis calculations, which can be obtained by the BIM; BIM performance introduction of BIM. However, a lack of research has led to inconsistent metrics being applied assessment; K-means to the calculation of BIM-ROI for various types of projects. The purpose of this study is to clustering; ROI develop a system to evaluate the performance of BIM using a K-means clustering algorithm and ROI analysis to reflect the cost-effectiveness of BIM investment. The proposed system also includes methods for determining best-case projects with high similarities from existing case projects and benchmarking their evaluation know-how, and its usability was verified through experienced BIM users. 1. Introduction ROI on the value of BIM introduction and benchmark- ing their assessment capabilities. Recently, many governments have proposed policies For many construction companies, including com- for the development of building information model- panies without BIM experience, this system will be ling (BIM) delivery guidelines or the mandatory intro- useful for planning further improvement and deter- duction of BIM. Nevertheless, McGraw Hill Construction mining whether to introduce BIM by considering spe- (2014) indicated that, owing to the BIM performance cific projects and company environments. To validate assessment system, the corporate sector is far from the reliability and accuracy of the proposed system, obtaining a clear expectation value and objective a practical survey was conducted by BIM experts. opportunity cost, which complicates decision-making regarding the introduction and operation of BIM for many construction companies. 2. Research background and contribution Traditional BIM performance assessment tools are 2.1. Previous research for BIM performance focused on qualitative aspects rather than the financial assessment assessment aspects. These tools can measure how successfully a BIM project is progressing or the ability In terms of research into BIM capability assessments, of a company to successfully complete a BIM project Lee and Yu. (2013) proposed the relationships among (Won and Lee 2016). Although qualitative BIM perfor- different metrics related to BIM acceptance by utilizing mance assessment tools are effective for determining the technology acceptance model (TAM). Kim and Park BIM capabilities and for establishing an operational (2012) suggested necessary process improvements strategy, whereas the cost-effectiveness of BIM invest- and a change of organization for construction projects ment (i.e., the financial benefit expected from BIM introducing BIM. They compared BIM promotion poli- introduction) is often difficult to determine. cies between the USA and the UK as references for the To resolve this problem, this study suggests a BIM domestic introduction and promotion policy of BIM. performance assessment system that enables both the Succar (2009) and Succar and Kassem (2015) derived assessment of the current BIM execution ability and assessment areas for technology, process, and institu- prediction of the cost-effectiveness before and after tional aspects of the BIM capability assessment, and implementation of BIM projects through classified BIM capability into three phases: object- a comparison with other projects. The method is based modelling, model-based collaboration, and net- based on a K-means clustering algorithm and ROI ana- work-based integration. Becerik-Gerber and Rice lysis. This includes finding the most similar case project (2010) analysed the substantial benefits and character- among many case projects, predicting the expected istics of BIM from the perspective of cost-effectiveness, CONTACT Leen-Seok Kang lskang@gnu.ac.kr Gyeongsang National University, Jinju 52828, Korea © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 79 proving that a greater BIM performance experience development environment; i.e., nationality, organiza- leads to greater profitability and cost-cutting effects tion, and entity (Hoffer 2014; Poirier, Staub-French, and due to the reduced duration. Chien, Wu, and Huang Forgues 2015; Neelamkavil and Ahamed 2012). For this (2014) analysed the risk factors required to consider reason, the ROI analysis results of BIM projects have a swift counterstrategy for various risk elements and a large range of 229–39,900% (Azhar 2011). This is increase the possibility of BIM project success, then because the metrics utilized for ROI analysis are not suggested 13 risk factors including aspects of technol- quite consistent. Most are developed through surveys ogy, management, human resources, finance, and or expert interviews or result from the application of ROI legal procedure. Bryde, Broquetas, and Volm (2013) metrics utilized in the field of information technology. derived standard metrics for determining the success Therefore, it is necessary to develop specific metrics for of a BIM-based project, reporting both positive and BIM. As various BIM functions are utilized across the negative effects that will follow the introduction of entire life cycle of a project, the revenue structure can BIM. Lee et al. (2013) proposed an ROI predictive ana- differ depending on the phase of application. Therefore, lysis calculation formula that reflects controllable and tangible effects from the BIM functions, such as clash uncontrollable factors when evaluating the value of detection and reduction of the construction period, and BIM introduction regarding a change of plan. the intangible effects, such as image improvement of Regarding the cost-effectiveness analysis of BIM, the corporation, should be converted into financial gain. Barlish and Sullivan (2012) defined the return metrics as change orders, requests for information (RFIs), and 3. Development of ROI measure items for BIM schedules, and the investment metrics as design costs performance assessment and contractor costs, while suggesting a calculation method for each index. Suermann (2009) developed 3.1. Total cost of ownership for BIM-ROI six performance indicators, including quality control Recently, due to the lower cost of BIM-related hard- (rework reduction), schedule conformance (delay ware and the shift away from permanent licensing to reduction), total cost (cost reduction), unit (square paying for software annually, operating costs are feet)/man-hour, cost/unit (square feet), and safety higher than the initial acquisition costs. For this reason, (delayed man-hour reduction), and calculated the the valuation factors for BIM investment cost using cost and effect of BIM. Love et al. (2014) suggested Total Cost of Ownership (TCO), which takes into a framework for the investment value evaluation of account the operating costs of hardware and software, BIM comprising four essential questions that reveal are selected in this study. how a property owner can earn business values from In this study, the metrics for BIM investment are investment in BIM through benefit realization manage- largely classified into direct and indirect costs. Direct ment (BRM). Sen (2012) derived the factors of BIM and costs comprise outsourcing, development of applica- virtual design and construction (VDC) that influence tions and the database, and construction of BIM func- the ROI through interviews with experts, case studies, tions, as well as personnel expenses related to the and research results, and suggested a finance model introduction of BIM. In particular, the introduction of for ROI calculation. Giel and Issa (2011) conducted BIM functions includes modelling, calculation of supply a comparative analysis between an actual project and costs, interference review, and simulation. This case where BIM was applied and a project case where allows us to calculate the cost of BIM introduction, BIM was not applied. The BIM construction cost was which is predominantly equipped with a specific func- assumed to be 0.5% of the total cost, and the effect of tionality. Indirect costs are classified into one-time BIM considered the cost of duration reduction per day indirect costs and durable indirect costs. One-time and interest rates for the duration reduction. indirect costs include the costs for purchasing HW/ Reizgevičius et al. (2018) presented the methods and SW and other equipment, alterations to the office factors of ROI evaluation for small design companies environment due to BIM operation, and modification and performed a comparative analysis of Autodesk’s costs, as well as the costs for education and purchasing ROI assessment methods. assets necessary for introducing the BIM system. The durable indirect costs include the costs for upgrading 2.2. Contribution comparing with previous HW/SW and other equipment for the efficient opera- research tion of BIM, updating applications, and continual development of BIM applications. Traditional research on BIM performance assessment defines the performance capability required for an orga- nization or an individual, then develops a maturity 3.2. Tangible/intangible effects of BIM-ROI model and metrics for evaluating said organization’s or individual’ capability. However, the metrics sug- In traditional IT-ROI analyses, the benefit of the IT gested by traditional research differ with the project is calculated as a cost through the analysis of 80 H.-S. KIM ET AL. tangible and intangible effects (Carratta, Dadayan, and As shown in Figure 1, this study uses the K-means Ferro 2006). The tangible effect can be converted to algorithm in the process of searching for the similar financial profits and is diversified into the improve- project group. ment of labor productivity, improvement of process A K-means algorithm is the most widely used one in productivity, and so forth. The intangible effect is cluster analysis and is known to present quick and a potentially beneficial nonfinancial profit that can be stable results. Because the K-means algorithm is effec - diversified into superior brand value and intellectual tive in identifying the characteristics of different types property, for example. Based on these diversifications, of assessment items, it can be used for identifying the we developed metrics for the tangible and intangible characteristics of the type of data for this study. effects of BIM investment. The tangible effects of BIM The algorithm is processed by inputting project infor- include a reduction of duration, a request for informa- mation, setting metrics and weighted values, calculating tion (RFI) and process modification, and reduction of similarities, searching for similar projects, and extracting the construction costs. The intangible effect enables similar projects. The K-means clustering algorithm is used calculation of the strategic superiority of a company to classify the case projects into multiple clusters. The and intellectual property with regard to the employees type of data used for these clusters is then determined by and includes the improvement of customer preference the similarity ratio between the case project and target by the introduction of BIM, expansion of the BIM mar- project. The similarity ratio defined in this research is the ket, increased employee productivity, etc. ratio of similar parts derived through a comparison In general, the benefits of these tangible and intan- between case project and target project characteristics. gible effects are difficult to objectively calculate. The Therefore, in this study, relevant articles were analysed costs estimated by construction managers with a high when suggesting metrics in order to enable comparison understanding of BIM projects are used in this study. of project characteristics. When the clustering analysis is conducted using the similarity ratio, the case projects are provided in categories. Among these, the cluster with the 4. K-means clustering algorithm-based model highest similarity ratio was determined to derive the for searching similar project project with the highest similar ratio, termed the best project, which was utilized for benchmarking. Through 4.1. K-means clustering algorithm-based process various integrated analysis processes, different types of BIM has a wide range of applications from design to information were provided for the target project to refer maintenance phases. Therefore, efficient implementa- to, including the metrics, measured values, costs, and tion of BIM requires knowledge of appropriate pro- usage frequency. cesses for the requirements of the user, the purpose of BIM introduction, its scope of application, the orga- 4.2. Metric development for searching similar nization in charge of its implementation, and the types projects of software employed. Considering this, a reasonable evaluation method should be applied in accordance The metrics derived in this study will evaluate the simi- with various types of BIM implementation at the BIM larity of BIM environments between the case project performance management level. Using the searching and the target project. The metrics can contain informa- process shown in Figure 1, evaluation methods and tion on projects and organizations including the scale of metrics for conducting ROI in various BIM project envir- the project, composition of the organization, and onments are analysed, and the most similar projects retained technologies. This information is included in are extracted to act as a benchmark. the BIM guidelines and BIM implementation plan (e.g. Figure 1. Process for searching of similar projects using the K-means clustering algorithm. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 81 Table 1. Metrics for analysing the BIM environment. can be applied differently according to the country Metrics Description and region. Second, items such as the purpose of Project information Project name, project type, project cost, BIM, functions requiring BIM and their effects, improve- country, region, etc. ment of productivity, and the risk reduction are input Goal of BIM Improvement of productivity, schedule introduction reduction, cost reduction, etc. for the purpose of BIM introduction. Third, in the BIM BIM tool Revit architecture, Bentley architecture, tool, technologies retained by the company and their ArchiCAD, Navisworks, Vico control, etc. BIM application Design phase-conceptual, design phase-detail, utilization capabilities are evaluated through the BIM phase construction phase, etc. S/W and other equipment information such as Performance Organization, technology, management Autodesk, Navisworks, and Bentley Architecture. capability maturity Fourth, in the BIM application phase, a BIM information exchange system and BIM process specifically modi- BIM Project Execution Planning Guide V2.1). During fied for detailed tasks were reflected by designating construction of the BIM environment, a phase is the specific step of a task or specific task requiring BIM required in which the project’s information and purpose application. Finally, the performance capability utilizes for introducing BIM are analysed to clarify the user’s the BIM maturity model to evaluate the intellectual primary goal. These preconditions are determined by capability of the staff or the technological level of the the performance capability and technologies owned by organization in order to perform BIM. the target company and determine whether the com- pany is indeed capable of introducing BIM. Through these results, deficiencies related to conducting BIM 4.3. Data organization for K-means are finalized and complemented to establish an efficient algorithm-based clustering analysis BIM implementation plan. As shown in the top panel of Figure 2, the information Table 1 shows five types of metrics derived from the input to the metrics exhibits various forms shown by analysis of the reference and BIM implementation letters and numbers. plans (AEC 2012; CIC Research Group 2011; Building This data must be standardized into numerical data and Construction Authority 2012; Autodesk 2014). The for application to the K-means clustering algorithm. The metrics are classified into project information, goal of method for digitizing the input values employs the BIM introduction, BIM tool, BIM application phase, and frequency number or ratios occurring in similar projects performance capability. These were determined after compared with the target project. However, when using a discussion with working-level staff in order to effi - values calculated using identical frequency numbers or ciently comprehend the performance capability of the ratios without considering the characteristics of each target company and the user requirements. item, the correlation between the metrics cannot be First, basic project information is input to the pro- reflected. For example, the types of software input to ject name, including the type of project such as bridge, the BIM tool cannot be compared; i.e., the effect of the road, or dam, and total cost of the project. Notably, the software on BIM performance is difficult to compre- location of the project can be specified by the level of hend. To solve this problem, the data for each metric country and region (Kang 2012) and the ROI metrics Figure 2. Calculation process of similarity ratio. 82 H.-S. KIM ET AL. are expressed in a unified unit, which is defined by the C ¼ jZmaturity Zmaturity j i i main similarity to which the target project is compared. First, among the six metrics, four variables used for Matusr : similarity of performance competency for the clustering analysis are selected (type of project, organization; Xmaturity ; Ymaturity ; Zmaturity i i i cost of project, purpose of BIM introduction, and per- : organization capability; technical capability formance capability maturity) and their weighted and management capability for case project values are input. Then, similarity ratios are calculated iXmaturity ; Ymaturity ; Zmaturity main main main for each variable. The calculation formula for the simi- : organization capability; technical capability larity ratio between the case project and target project is categorized into three types. and management capability for target project Csr ¼ Cost � Cost � 100%; Cost Cost < 0 Third, Formula 4 is used to evaluate the similarity i main i main i rate between the case project and target project Csr ¼ Cost � Cost � 100%; Cost Cost > 0 i i main main i regarding the performance capability of the organiza- Csr : cost similarity; Cost : cost of case project i i i tion, which is categorized into organizational capabil- Cost : cost of target project main ity, technical capability, and management capability. (1) These are derived from the maturity model developed in the previous research. The similarity ratio is input The project cost information is utilized to calculate the from 0 to 5 points according to each capability, based similarity of project scale (Formula 1). The cost of the on the difference between the case project and the project is expected to exhibit greater differences target project. Finally, the similarity ratio between each between the case and target projects than the other metric is then calculated in the range of 0 to 100 and variables. However, due to the fact that the difference can be integrated, along with the application of would be shown in the type of interval-based variable, a weighted value (determined in accordance with the for example, the similarity in this factor is simply calcu- importance of weights), depending on its degree of lated using the ratio of costs between both projects. importance. The integrated data are then applied to This problem can be partially resolved by conducting the K-means clustering algorithm. a relative correction in the weighted-value calculation phase. Facsr ¼ ComItem � TotItem � 100% i i i 5. Development and application of BIM Facsr : Item similarity i performance assessment system ComItem : number of items commonly included in 5.1. System process according to assessment (2) the case and target project timing TotItem : total number of items in the case and In this section, a BIM performance assessment system target project; Exceptforduplicateitems was developed based on previously suggested methods. Second, to measure the similarity ratio of the variable Application of the BIM performance assessment can that represents various items, such as the purpose of differ according to the purpose and timing of the BIM introduction, Formula 2 is used, which reflects assessment; it is classified into a first phase (i.e. validity detailed items. For variables that are input with of BIM introduction) and a second phase (i.e. effective - detailed items, specific software may be used, as for ness analysis of BIM implementation), as shown in the metric of the BIM tool. This means that the input Figure 3. items are correlated with each other, which is useful The first-phase assessment is conducted to evaluate when searching for an item that is highly correlated the expected value that can be obtained by the intro- through two coincidental items. Therefore, this study duction and investment of BIM, prior to conducting uses the support calculation formula (Formula 3) to a project. This can help support reasonable decision- calculate the similarity of the items. making among stockholders who decide the introduc- Number of transactions that tion and investment of BIM. This phase is utilized when include both items A and B no BIM-related experience exists and when relevant SupportðA ) BÞ ¼ (3) Total transactions information is lacking. By inputting only minimal infor- mation, the validity of the introduction and investment Matusr ¼ ½15 ðA þ B þ CÞ�=15� 100% (4) i i i i of BIM can be approximately determined. The second phase assessment is conducted to eval- A ¼ jXmaturity Xmaturity j; i i main uate whether BIM management is properly promoted during the operation of BIM. Continuous BIM perfor- B ¼ jYmaturity Ymaturity j; i i main mance assessment is conducted during BIM operation JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 83 Figure 3. Assessment process by BIM operation phase. to monitor the achievement of the planned BIM objec- study, at least a certain level of data is required. But, tive. As such, the requirements of the site can be BIM-related information in the case projects alone determined and relevant feedback can be provided, makes it difficult to obtain the appropriate data for thereby gradually establishing a system for effective the evaluation items. For this reason, hypothetical BIM performance management. data were generated and used for the case study by constructing information from the case projects to suit the evaluation items through consultation with 5.2. First-phase case study experts. First, the characteristic information of the BIM The first-phase assessment aims to derive similar case Introduction environment is input to reflect the envir- projects and secure approximate BIM performance onmental conditions (Figure 4). The project character- information. Therefore, this case was analysed to deter- istic information is applied to the calculation of the mine the expected construction costs, tangible effects, similarity ratio used as basic data for clustering the intangible effects, etc., for a company wishing to intro- similar projects. As the clustering analysis differs with duce BIM. For the case study of the developed system, the similarity ratio, care should be taken to best reflect the previously collected data of case projects are orga- the characteristics of the target project. nized into a data set that is appropriately categorized There are six metrics in the calculation of similarity into the evaluation items of BIM-ROI and BIM imple- ratios, which allow the user to select data types. mentation environment. To perform a reliable case Figure 4. Input data and similarity rate analysis. 84 H.-S. KIM ET AL. Therefore, the metrics that will be used for the analysis similar projects, can be determined using the graph of the BIM introduction environment can be selected shown in the bottom panel of Figure 5. Types of and require a process for assigning weighted values to metrics are then considered for introducing and con- each metric. As shown in Figure 4, the metrics for structing the BIM, and the types of metrics that gen- project characteristics are selected as “BIM tools” and erate investment and revenue in the similar projects “BIM goal,” and the similarity ratio calculation is con- can be determined. ducted by inputting the weighted values, resulting in In this case study, the individual BEP (the best break- a similarity ratio for “BIM tools” and “BIM goal.” even point of 0.9 years) and integrated BEP (average Through this, the case project that is most similar to break-even point of 1.32 years) for 18 similar projects these two metrics can be confirmed, while excluding are shown in Table 2. In addition, the average cost and projects with low similarity ratios from the cluster. metric frequency (maximum 18, minimum 1) of the After inputting the number of cluster k and conducting Total Cost of Ownership (TCO) were identified. For the clustering analysis, the result of the K-means clus- the tangible and intangible effects of BIM, the profit tering analysis can be confirmed, as shown in the costs and metric frequency (maximum 18, minimum 2) upper right corner of Figure 5. This information is were identified then used to obtain the characteristics of the cluster Considering that this result is calculated by only and distribution of individual projects. The results inputting minimum information regarding “BIM tools” prove that the best of the three clusters is cluster No. and “BIM goal,” it is relatively specific. This information 2. This is because the clustering analysis was con- is therefore considered sufficient for nonprofessional ducted on the basis of the similarity ratio; therefore, corporations with no BIM experience to decide the cluster that is farthest from the origin is considered whether to introduce BIM. the best. However, as this cluster has a higher average distance within the cluster and it includes the farthest 5.3. Second phase case study case, this anomaly should be calibrated and reconsidered. This case study is performed to consistently evaluate When the similar projects are derived through the the effect of BIM during BIM operation in a large-scale cluster analysis, approximate information, including project with multiple on-site construction zones, the major metrics and costs of each item related to where the BIM is analysed and integrated for each Figure 5. Results of the K-means clustering and ROI analysis. Table 2. Results of the first-phase case study. BEP Avg. 1.32 (max. 0.9) Investment cost ($) Avg. 10,619 ROI 145% (3 years) Major Investment cost Sub-constrictors cost (18 times), metrics HW cost, SW cost, etc. Tangible and Planned duration vs. actual duration (three times), number of design intangible changes [unit], improvement of labor productivity, etc. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 85 Figure 6. Analysis of ROI and integration of common metrics. construction zone. The basic information of the project usage frequency, as shown in Table 3. The decision on to be created is input in a module screen (Figure 6) and whether to use these as common metrics for each site can be utilized as version management information for was made through consultation with practitioners. further consistent BIM performance management. Through these efficient discussions, the planned con- After inputting the basic information, the metrics of struction duration and actual duration, a number of TCO and BIM benefits for BIM performance assessment reviews requested, and a number of rework occurrences are selected. The metrics can be selected through dis- were obtained as common metrics. These metrics were cussions among internal staff or manually by creating delivered to all sites of the project so that BIM perfor- new metrics. Moreover, as mentioned in the first-phase mance could be consistently monitored using identical analysis, the similar projects derived from the K-means metrics at each site. Likewise, the metrics with high clustering analysis can be utilized to select the metrics. usage frequency can be consistently reviewed and Then, the values of each metric are input, and ROI designated as common. Repeating this will eventually analysis is performed. The measured values of ROI constitute an appropriate package for the project. comprise quantity and unit price. To ensure the cred- ibility of these data, criteria are required for the calcu- 6. Reliability and accuracy of the developed lation. After inputting the measured values, the return system flow, NPV, ROI, BEP, etc., are analysed to review the validity of investment. To analyse the reliability and accuracy of the BIM per- Table 3 shows that the break-even point for this formance assessment system proposed in this study, case study is 1.59 years, which means that the cumu- 30 practitioners (average experience of 11 years, more lative revenues are higher after approximately 1 year than two previous experiences with BIM projects and and 7 months and the cost-effectiveness is highest at ROI) were surveyed. A total of 11 questions were asked 2400% in Sector No. 2. about system reliability, system convenience, and Figure 6 (lower panel) shows how a general manager overall accuracy (Table 2), and a frequency and relia- can aggregate the results of each site to improve BIM bility analyses were conducted using SPSS 21. Each performance management across the entire project. item employed the Likert 5-point scale, where 5 point The data of each site (November 2014–January 2015) represents “high agreement” and 1 point represents were collected and analysed to derive metrics with high “poor agreement”. As a result of analysing the reliabil- ity by questionnaire, Cronbach’s alpha was found to be more reliable than 0.860 (Table 4). Table 3. Results of the second-phase case study. The system reliability, convenience, and overall BEP Avg. 1.59 (max. 0.5, Sector No. 2) accuracy were 3.97, 3.92, and 3.95, respectively. In Investment cost ($) 30,619 particular, the value of the metrics and the objectivity ROI 350% (Max. 2400%, Sector No. 2) Frequently Investment User education cost and re-education of assessment timing were the highest at 4.0 in the used cost cost, system reliability. In terms of system convenience satis- metrics HW cost, SW cost, etc. faction, the assessment procedure satisfaction was the Tangible and Planned duration vs. actual duration, intangible number of reviews for opinions [unit], highest at 3.93. For the overall accuracy, benchmarking number of reworks [unit] etc. information was the highest at 4.00 or higher. These 86 H.-S. KIM ET AL. Table 4. Reliability and accuracy results. Cronbach’s Type Description α System Reliability of BIM-ROI analysis procedure and analysis timing (first and second phase), reliability of BIM-ROI metrics 0.912 reliability System Satisfaction of BIM-ROI analysis procedure and BIM-ROI metrics, etc. 0.860 convenience Overall Overall BIM-ROI accuracy 0.886 accuracy (vs. existing ROI) results indicate that the proposed methodology and implementation environment and ROI evaluation system are highly useful as a benchmarking tool for items were required, but, in reality, it was difficult to BIM performance and investment analysis. collect such case data. This is because the BIM-ROI information provided by most existing case projects is different and inconsistent. In the future research, it is 7. Conclusion necessary to obtain enough case data based on the evaluation items presented in this study and system- In this study, an assessment system for BIM perfor- atize evaluation items and measurement methods for mance was suggested to predict and evaluate the cost- the types of BIM projects. And, it is also necessary to effectiveness of the introduction and operation of BIM. suggest key evaluation items and to objectify their First, to suggest an ROI analysis model for BIM, weights through the analysis of the association among a traditional IT-ROI technique was utilized to develop the evaluation items. a BIM-ROI evaluation model, employing metrics sug- gested in traditional BIM performance analysis and in projects or research related to BIM-ROI analysis. 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Journal
Journal of Asian Architecture and Building Engineering
– Taylor & Francis
Published: Jan 2, 2021
Keywords: BIM; BIM performance assessment; K-means clustering; ROI
