Bridging the gap: a framework for monetizing BIM by integrating industry insights for informed decision-making

Abstract

Purpose

This study addresses the critical imperative of quantifying building information modeling (bimalliance) benefits by augmenting existing methodologies, with a focus on monetization. Engaging industry practitioners, the research develops a comprehensive framework through an exhaustive literature review and a survey in the Swedish construction industry, incorporating insights from 128 respondents.

Design/methodology/approach

The framework, validated by industry experts, systematically assesses tangible BIM benefits against associated costs. It introduces a novel method in construction, addressing the lack of a unified approach. The resulting framework facilitates nuanced feasibility determinations by systematically evaluating BIM benefits against costs.

Findings

Despite its acknowledged limitations, the framework effectively captures a comprehensive range of costs and benefits, providing a more accurate and detailed estimation of BIM’s impact on project outcomes.

Practical implications

With practical implications, the framework enhances BIM understanding and application, contributing to effective project management throughout the construction supply chain lifecycle. Moreover, it aims to improve efficacy within the architecture, engineering, construction and operations industry.

Originality/value

The study empowers organizations and decision-makers with a bespoke tool for evaluating BIM feasibility, contributing to decision-making through a clarified numerical representation.

Citation

Gharaibeh, L., Lantz, B. and Eriksson, K.M. (2024), "Bridging the gap: a framework for monetizing BIM by integrating industry insights for informed decision-making", Smart and Sustainable Built Environment, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SASBE-07-2024-0259

Introduction

Assessing the financial performance of Building Information Modeling (BIM) stands out as a pivotal undertaking, particularly at the project’s finalization (Apeesada et al., 2021). Given that the adoption of BIM necessitates a substantial investment, decision-makers are tasked with validating whether the monetary returns derived from its application surpass the associated costs (Jasiński, 2020). Presently, the economic evaluation of BIM is underway within various large corporations and by industry analysts, each employing their unique methodologies (PWC, 2018). However, a notable debate persists regarding the feasibility of BIM (Munir et al., 2019). This ongoing discourse underscores the complexity of gauging BIM’s economic value and emphasizes the need for a comprehensive understanding of its monetary implications, providing decision-makers with the necessary insights to ascertain the technology’s viability in relation to financial outcomes (Hosseini et al., 2018).

Nonetheless, the accurate translation of BIM effects into numerical values encounters several limitations (McGraw-Hill, 2012; PWC, 2018). The measurement of BIM effects relies on a multitude of assumptions when compared to scenarios where BIM is not implemented (Barlish and Sullivan, 2012). Consequently, numerous prior studies have assigned numerical values to BIM benefits in their respective case projects; however, these studies often fall short in providing intricate details regarding the specific assumptions and calculation processes employed (McGraw-Hill, 2012; PWC, 2018). The resultant wide range of BIM benefits values reported in these cases raises concerns about the reliability of BIM applications and fosters a negative perception of their effectiveness (Lee and Lee, 2020). This discrepancy emphasizes the need for a more transparent and standardized approach to evaluating and reporting the quantitative impact of BIM to enhance the credibility and understanding of its financial implications (Gharaibeh et al., 2024).

A comprehensive understanding of the business benefits associated with BIM is instrumental in aiding organizations to make informed decisions regarding the adoption of BIM technology and its effective implementation (Apeesada et al., 2022). This understanding empowers organizations to assess the potential Return on Investment (ROI) and weigh the benefits against the costs by delineating specific advantages that BIM can offer, including cost savings, enhanced productivity, and error reduction (Abdelbary et al., 2020; Chou and Pei-Yu, 2017). Moreover, a nuanced understanding of BIM’s business benefits facilitates the formulation of a well-defined implementation strategy, allowing organizations to maximize the utility of BIM (Guoqian et al., 2021). Importantly, better BIM implementation not only enhances current practices but also opens avenues for more advanced applications, including robotics, artificial intelligence (AI), and digital twin technologies, propelling organizations toward cutting-edge advancements in the construction industry (Aktürk and Irlayıcı Çakmak, 2024; Heng and Zhe, 2022; Stas and Abrishami, 2024; Walid et al., 2023).

This study addresses the need to quantify BIM benefits, acknowledging the lack of established methodologies. Building on previous efforts, it aims to create a comprehensive framework by engaging industry practitioners. The goal is to empower organizations and decision-makers with a tool for evaluating BIM feasibility in their specific contexts. The research involves an extensive literature review to identify relevant metrics, followed by a survey to validate and enhance these metrics based on industry insights. The subsequent proposal of quantification methods and equations will undergo validation by construction industry experts, leading to the development of a robust BIM benefits quantification framework.

Research methods and techniques

This study aims to establish a systematic methodology for quantifying the benefits of BIM in construction projects, addressing gaps in prior research. The research will follow a sequential approach, starting with a comprehensive literature review. A survey within the Swedish construction industry will validate gathered insights. The culmination will be the formulation of metrics and calculations within a comprehensive framework, evaluating tangible BIM benefits while considering associated costs. The framework’s validity will be determined through assessment by a panel of industry experts, ensuring the sufficiency of the formulated calculations and providing a basis for feasibility determinations. The research methodology is illustrated in Figure 1.

Phase 1: attributes identification

The initial phase of the methodology aimed to identify quantifiable indicators representing tangible benefits of BIM. These indicators, linked to measurable outcomes and assigned specific values, were derived from a literature review, including industry reports and case studies. This review served to compile identified attributes and associated quantified values. The literature also addressed costs linked to BIM investment. To establish precise values, a questionnaire was developed with each attribute corresponding to a distinct question, presented as an ordinal Likert scale. Industry practitioners validated metric values based on their experiences and project data.

The literature review included relevant publications from the past two decades, focusing on diverse assessment methodologies. Limiting the publication date to the past two decades ensured the inclusion of contemporary economic methodologies. Only English-language journal articles were considered, and Endnote software facilitated article refinement and duplicate removal. A content analysis using NVivo software identified research patterns, highlighted gaps, and suggested future directions. The final literature review comprised 75 research articles, with a standardized data extraction form capturing quantitative and qualitative details such as title, year, source, method, attribute, project phase, and reported results type.

Phase 2: questionnaire survey

The survey served the dual purpose of validating the values extracted from prior research and addressing the lack of studies specific to the Swedish construction industry. This alignment ensured that international figures and values were adapted to suit the unique characteristics of the Swedish construction market. Considering the diverse and expansive nature of the Swedish construction sector, the questionnaire survey method was chosen as the most effective approach for gathering insights from a wide range of industry practitioners. This approach aligns closely with the research objectives by enabling a comprehensive and in-depth exploration of professionals’ perspectives on the percentages and values associated with BIM benefits in the construction field.

A detailed questionnaire was carefully developed and refined using a professional survey design platform, employing a web-based format for efficient distribution and response collection. The online survey method was essential in enhancing the precision and reliability of the responses. This was achieved by using mandatory questions to ensure a response for each question, thereby minimizing the occurrence of incomplete or missing data. The questionnaire included sections to gather demographic information from the sample and to assess perceived benefits using a set of 12 predefined items sourced from previous literature. For each item in the survey, participants were provided with a range of ordinal values and were asked to select the interval that most accurately reflected the impact of each benefit on their project’s cost, based on their project-specific data. Furthermore, the collected data were systematically analyzed to understand the trends and variations in the perceived benefits of BIM across different segments of the industry.

Sampling

The survey aimed to encompass diverse stakeholders in the Swedish construction industry, including clients, consultants, designers, manufacturers, suppliers, contractors, facility managers, and building operators. Utilizing purposive and convenience sampling, the survey was electronically distributed through a web-based platform. Initial recipients, totaling 204, were chosen based on predefined criteria, emphasizing individuals with BIM experience in the Swedish construction industry. Encouragement was provided for recipients to extend the survey within their networks. Additionally, a digital survey link was publicly shared through professional networks associated with the Swedish construction industry. As a result, we received a total of 128 completed surveys after conducting an initial quality check to ensure the responses were both complete and accurate. Given the diverse distribution methods employed, calculating an exact response rate posed challenges; however, it is noteworthy that response rates ranging from 10% to 12% are not uncommon in research related to construction management (Bing et al., 2005).

Phase 3: establishing quantification methodology

Each quantifiable benefit required a distinct approach to convert its tangible impact into costs, necessitating an analysis of each attribute to determine the most suitable quantification method. The equations for each attribute encompassed numerous variables, some of which were specific to each case and needed to be filled based on project data, while variables related to the value of the BIM benefit were assumed based on the metrics gathered from the survey, which confirmed previous research findings.

Once the appropriate equation for each attribute was determined, all the inputs were integrated into the comprehensive framework, essentially aggregating all benefits, and subtracting costs, while also considering project-specific factors.

Phase 4: validating the framework

Following the development of the framework for quantifying BIM benefits, it was essential to validate the established equations and techniques utilized for each attribute. The framework was subjected to validation by presenting it to a panel of industry practitioners. For each attribute, a discussion was conducted, and feedback and considerations raised by the panel were carefully incorporated into the final version of the framework.

Literature review

The primary focus of this literature review was to compile and critically evaluate the quantified benefits of BIM from previous studies. Several methods have been proposed for assessing the economic impact and investment value of BIM. One widely suggested method is the Return on Investment (ROI) approach (Apeesada et al., 2022; Lee and Lee, 2020). This methodology quantifies the profit, gains, and losses resulting from an investment in BIM, represented as a percentage of the invested amount and adjusted for contributions and withdrawals (Sompolgrunk et al., 2023). While ROI provides a straightforward and easy-to-understand metric, its application in BIM assessment is often limited by the difficulty in capturing all relevant financial flows and accurately attributing them to BIM adoption. As such, several studies argue that ROI might oversimplify the complexities associated with BIM, and therefore, caution should be exercised when relying solely on this metric for decision-making (Ardani et al., 2022; Qian, 2012).

Another commonly used approach is the Cost-Benefit Analysis (CBA), which evaluates the economic feasibility of adopting BIM technology in the construction industry (Weisheng et al., 2014). The use of CBA in the context of BIM allows for a more comprehensive assessment by comparing the costs—such as hardware and software acquisition, employee training, and workflow modifications—with the potential benefits, including enhanced collaboration, reduced rework, and improved project outcomes (Chahrour et al., 2020). However, the accuracy of CBA is contingent upon the ability to quantify both tangible and intangible benefits and costs. Studies by Barlish and Sullivan, (2012) and Zakeri et al. (2023) have highlighted the methodological challenges of including intangible benefits, such as improved collaboration and decision-making, which are often subjective and harder to measure. Moreover, the effectiveness of CBA is limited by the variability of cost and benefit data across different contexts and project types, which hinders the generalization of results.

Although both ROI and CBA provide valuable insights into the economic viability of BIM, they are not without limitations. A key critique emerging from recent studies is that these methods often lack standardized procedures, leading to inconsistencies in their application and results (Barlish and Sullivan, 2012). Additionally, many studies offer incomplete descriptions of their methodologies, making it challenging to assess their accuracy and reliability (Ardani et al., 2022). Consequently, while ROI and CBA remain prevalent, there is a need for more comprehensive, context-sensitive, and standardized methodologies that can better accommodate the diverse range of benefits and challenges associated with BIM adoption.

To attain a comprehensive understanding of prior research endeavors, the literature review categorized the selected sources based on various dimensions, including their relation to the project lifecycle and the impact of BIM on different stakeholders. Some studies encompassed the entire project lifecycle (Walasek and Barszcz, 2017) while others specifically analyzed distinct phases, such as examining BIM benefits for consultants (Chahrour et al., 2020), facility managers (Weiwei et al., 2018), asset owner (Munir et al., 2019) and manufacturers (He et al., 2021).

The literature review identified a notable research trend, with a significant focus on quantifying specific BIM benefits, often associated with distinct functions or project phases. Predominantly, research concentrated on the design stage, exploring the impacts of design optimization, error reduction and clash detection (Abdelbary et al., 2020; Chou and Pei-Yu, 2017; Porwal et al., 2020), and stakeholder coordination (Xian-yong et al., 2019). Approximately 24% of the articles emphasized the integration of design and construction, delving into topics like quantifying change order costs (Myungdo and Ung-Kyun, 2020; Poirier et al., 2015) and assessing how BIM enhances opportunities for prefabrication and offsite construction (Barkokébas et al., 2021; Poirier et al., 2015). Post-construction effects of BIM were examined in 12% of the analyzed articles, evaluating the investment value for operators and facility managers (Mohammed et al., 2022; Wang et al., 2013). Additionally, a substantial number of studies addressed sustainability and optimized energy consumption during building operations. Table 1 compiles quantified attributes extracted from the literature review, categorized based on the measured benefits.

Classification of benefits

Previous research endeavors focused on quantifying BIM benefits, revealing diverse advantages throughout a project’s lifecycle. To streamline calculations, a dual classification system was adopted in this study. The initial categorization considered both the beneficiary stakeholder and the project phase. This aimed to efficiently allocate quantified benefits and clarify the impact on specific stakeholders. Subsequently, a detailed classification based on the nature of the impact was implemented. For instance, time-saving benefits in the design phase were grouped as “efficiency savings.” This classification enhanced the framework’s user-friendliness by reducing user input. Figure 2 illustrates benefit grouping across project phases and identifies key stakeholders experiencing these benefits.

Likelihood

Prior studies have highlighted the connection between the degree of development and the realized benefits of implementing BIM (PWC, 2018; UnitedBIM, 2022). While BIM experts and researchers aim for full BIM implementation, some argue that complete adoption is not necessary to reap anticipated benefits, and even partial utilization can significantly impact project performance (Dainty et al., 2017). Nevertheless, evidence suggests that the higher the adoption level and sophistication of the BIM model, the greater the applications and, consequently, the perceived benefits (Eadie et al., 2013). In line with this notion, this research introduces the term “likelihood” to express the probability of a given case experiencing the quantified benefit. This likelihood is determined by linking the level of BIM implementation in the company or project with the potential to realize the intended effect.

Benefits quantification methodology

The formulation of equations aimed to quantify benefits as cost savings, leveraging established mathematical relationships among key project success factors like cost, time, and quality, validated in prior research. Productivity improvements, for example, reduce time and resource needs, leading to cost savings across direct and indirect expenditures. Similarly, time-related benefits yield cost savings across various factors. Each benefit calculation underwent thorough individual examination. Attributes identified required two types of variables: metrics converting assumed BIM benefits into cost values (validated through research and questionnaires), and case-specific variables (project-specific figures). Table 2 details attributes and metrics for each calculation.

Formulation of equations

An equation was developed for each attribute to capture the monetary value associated with the presumed benefit. These equations were constructed based on prior research and established quantification methods commonly used in the construction industry Table 3. Lists the developed equations and the explanation of the variables for each attribute.

Assumed BIM investment associated costs (I)

The costs associated with BIM investment and implementation are systematically quantified and integrated into the overall BIM benefits assessment methodology. These costs are generally consistent across various BIM environments and include expenses such as establishing a common data environment (CDE), BIM management costs, expenses related to BIM training, Employer Information Requirement, Organization Information Requirements (OIRs), the cost of updating facilities management systems as required by the procuring authority, and the expenses incurred in maintaining the BIM model.

Verification of assumed metrics

The formulated equations for each item incorporate two types of variables crucial for quantifying the impact of BIM. The first type involves case-related variables, specific to the project or company, tailoring results to the context under study. The second type includes metrics from prior research and industry case analyses, where BIM benefits were quantified to a certain confidence level. These variables were collected, evaluated, and validated through the questionnaire survey in this research. Table 4 displays the extracted metrics from the survey data analysis. Participants selected the most accurate estimate of the BIM effect based on their experiences and projects, ensuring alignment with previous research for accurate estimations. The answer with the higher frequency was considered for each item.

Likelihood assumption

As discussed previously in various research literature, the perception of BIM effects is closely linked to the level of implementation. Therefore, it is crucial to calibrate the equations in alignment with the level of BIM implementation in the case under study. To achieve this, an initial qualitative assessment of the company is conducted to estimate its level of BIM utilization within its processes. The assessment results position the company on a scale of likelihood to perceive benefits, signifying that a higher level of advancement in BIM corresponds to a greater likelihood of reaping benefits.

In simple mathematical terms, the overall equation for the assessment methodology, incorporating all costs and benefits and calibrated using the qualitative assessment, can be expressed as follows:

(14)Implementation of BIM benefits total={(∑i=0nD+∑i=0nC+∑i=0nO)×l}−∑i=0nI

Where l is the likelihood coefficient, which is expressed as a percentage determined by the user based on the qualitative assessment for each case, D, C, and O values are as per Table 3. And represents the summation of benefits in the design, construction and operations, while I is the summation of investment costs also as per Table 3.

Validation of framework

The quantification methodology framework and the established equations underwent a continuous validation process with industry practitioners to ensure their adequacy for measuring the targeted impact and practicality in real-world scenarios.

Expert Selection and Participation: Experts were selected based on their extensive experience in the construction industry and familiarity with digital tools like BIM. Three company representatives participated in an initial workshop where a thorough review of the framework and equations was conducted. The framework was subsequently tested with two additional company representatives.

Validation Criteria: The validation focused on three key criteria: (1) the adequacy of the equations for capturing the intended impact, (2) the clarity of the methodology, and (3) the feasibility for users to obtain the necessary values for each variable.

Validation Process and Results: During the workshop, the experts provided valuable feedback on these aspects, and adjustments were made accordingly. In the testing phase, the representatives confirmed that the framework was clear and that they were able to obtain the required information to use it effectively. A final revision of the equations was conducted, incorporating the comments from both the workshop and testing phases to enhance clarity and applicability. Figure 3 presents the quantification assessment framework.

Discussion

In its final form, the framework is designed to conduct a cost-versus-benefits analysis of BIM implementation and generate a comprehensive conclusion regarding the feasibility of BIM for a specific establishment or project. The feasibility concept is rooted in the belief that the investment cost of BIM may not be fully offset by the benefits in certain cases. Therefore, the framework aims to provide an exploratory data sheet that distributes the costs and benefits throughout the project’s supply chain.

It is crucial for industry practitioners and decision-makers to recognize that the benefits of BIM are not all immediate. BIM can be considered a long-term investment, with some benefits, such as energy efficiency and facilitating building operations, being realized over an extended period. While certain benefits, like error reduction and efficiency improvements, can be immediate, decision-makers may exhibit hesitancy to invest in BIM due to a focus on short-term gains. This reluctance is influenced by the nature of the construction project supply chain, which typically involves multiple partners with varying perspectives on innovation and business development.

In parallel to the distribution of benefits, the costs associated with BIM investment are also spread across the project lifecycle, with a tendency for a larger portion of costs to be concentrated in the initial stages of the project. Consequently, the decision to implement BIM should ideally be made by clients and consultants during the early stages of the construction project. This early engagement allows subsequent stakeholders to participate in model development and the broader BIM environment.

The framework introduced in this study fills a significant research gap by providing a unified approach to quantify BIM benefits. A thorough literature review exposes the absence of consensus on a standardized methodology for accurately assessing the genuine investment value of BIM. Further scrutiny of previous research underscores a limited understanding of BIM’s economic impact, with general assertions prevailing in academic and industry sources. Existing methodologies may not be structured to aid industrial organizations in evaluating the perceived value of BIM aligned with their specific operational contexts. The literature review reveals diverse methodologies often concentrating on specific attributes, cases, or projects, emphasizing the necessity for a comprehensive cost-benefit model capable of measuring BIM’s return on investment throughout the entire construction supply chain lifecycle.

The framework exhibits several limitations, including the challenge of incorporating intangible benefits such as adaptability to changes, knowledge transfer, and regulatory compliance. Despite the difficulty in quantifying these advantages, their impact is discernible within organizations. Concurrently, the framework fails to address intangible costs, particularly considering their substantial variation across projects and dependence on organizational capabilities. For instance, the cost of developing a BIM model to advanced levels may be more significant for certain companies than others. Nonetheless, the utilization of BIM not only enhances project efficiency but also confers a competitive advantage to construction industry organizations through the stimulation of innovation, cost reduction, and the overall enhancement of project quality and sustainability.

Notwithstanding its inherent limitations, the framework is perceived to comprehensively encompass the costs and benefits pertinent to the specific case under consideration, offering a precise estimation of the overall expected effects of implementing Building Information Modeling (bimalliance). This analytical approach promises decision-makers a clearer numerical depiction, facilitating a more informed decision-making process. The resulting data sheet, derived from this quantification methodology, will position the examined case within a BIM feasibility context. Moreover, it will analyze the anticipated distribution of costs and benefits across the project lifecycle, thereby assisting companies in anticipating and strategically planning for the timing and impact of these effects.

Conclusion

This research delves into the complexities of Building Information Modeling (BIM), acknowledging its inherent limitations while highlighting its substantial potential through the introduction of a novel framework for quantifying the financial impact of BIM at the project level. Despite the challenges in quantifying intangible elements, the proposed framework provides a practical analytical tool that enables decision-makers to develop a detailed, numerical understanding of the anticipated effects of BIM implementation in specific contexts. The resulting data sheet, a concrete output of this methodology, serves as a strategic asset for organizations, helping them assess their position along a BIM feasibility spectrum. Moreover, the framework supports the analysis of projected costs and benefits throughout the project lifecycle, offering insights that empower stakeholders—including project managers, owners, and construction teams—to strategically plan for the timing and magnitude of BIM’s impact.

As BIM continues to reshape the construction industry, this study makes a significant contribution to both practical and theoretical domains. For practitioners and project stakeholders, it provides a systematic approach to assessing the feasibility and financial implications of BIM adoption, thereby supporting informed decision-making in real-world scenarios. For academics, it enriches the body of knowledge with a robust framework for BIM quantification, bridging the gap between theory and application in the construction sector.

To maximize the practicality and applicability of this research, it is crucial to rigorously test the proposed framework through multiple case studies across diverse project types and organizational settings. These case studies will not only validate the framework but also serve as a foundation for refining the model iteratively, based on empirical evidence and evolving industry practices. Empirical testing is essential to uncover any potential gaps that may only emerge in real-world contexts, allowing for targeted improvements to the framework.

Furthermore, results from these case studies will generate invaluable data, providing insights into the current industry landscape regarding the application and value accrued from BIM. These insights will help identify emerging trends that shape future BIM strategies, thereby informing both of industry practices and academic discourse. Expanding the scope of testing to include different geographical and regulatory environments will further enhance the framework’s robustness and adaptability, contributing to the ongoing advancement of methodologies and practices in BIM implementation and management. Ultimately, this research aims to benefit both project stakeholders and the broader field of knowledge, fostering a more informed, strategic approach to the adoption and management of BIM technologies.

Figures

Figure 1

Research methodology

Figure 2

Clustering of BIM benefits

Figure 3

BIM benefits quantification framework

Table 1

Extracted quantified attributed from the literature review

	Measured attribute reported values
ROI	Giel and Issa (2013) ROI of BIM varied greatly from 16 to 1,654%, Conde et al. (2020) ROI of 34.5%, Kim et al. (2020) ROI of 145% & 350%, Lee et al. (2012) A BIM ROI of 22–97% was derived by converting 709 design errors detected by BIM into rework cost savings, Lee and Lee (2020) The integrated BIM ROI to consider the overall effect of applying BIM was about 476.72%, McGraw-Hill (2012) 62% of the targeted sample reported a positive ROI, Stowe et al. (2015) ROI of 1.8%–10.5%, Ham et al. (2018) ROI of 94.41%, Won and Lee (2016) BIM ROI of 27%–400%
Productivity	Abdelbary et al. (2020) 50% reduction in labor works, Conde et al. (2020) productivity improvement exceeding 27%, Poirier et al. (2015) increase in productivity ranging from 75% to 240%, Qian (2012) Productivity Loss for Company ∼2 Months Downtime, Rafael Sacks and Ghang Lee, 2005 2.3% improved work productivity, Reizgevičius et al. (2018) productivity gain after staff training 31%, Sacks and Barak (2008) productivity gain for drawing production of 21%–61%, Succar et al. (2012) productivity gains of 15% and 41%
Prefabrication opportunities	Banihashemi et al. (2018) 50% increased possibilities for prefabrication, Khanzode et al. (2008) 100% pre-fabrication for the plumbing contractor, Kuprenas and Mock (2009) shop fabrication of $25,000, McGraw-Hill (2012) BIM increased prefabrication by 22%
Change orders and design errors	Abdelbary et al. (2020) BIM resulting in rework cost reduction of 49%, A total saving of 10%, generated by BIM clash detection and 32% reduction of change orders, Barlish and Sullivan, (2012) 42% reduction in change orders, Giel and Issa (2013) change orders was reduced by 40, 48, and 37%, Ham et al. (2018) rework due to design errors 5–20% in the total contract amount reduced by 47%, Honnappa and Padala (2022) 8.16% cost saving due to less changes, Lee et al. (2018) BIM impact on preventing rework $314,000, Lopez and Love (2012) design errors were revealed to be 6.85 and 7.36% of contract value, Love et al. (2013) 10% saving in contract value through clash detection. 40% elimination of unbudgeted change
RFI’s	Abdelbary et al. (2020) approximate reduction of 90% of RFI’s, Barlish and Sullivan, (2012) 30% in RFI’s, Conde et al. (2020) reduced by 25%, Giel and Issa (2013) RFI’s was reduced by 34%, 68%, 43%
Schedule	Abdelbary et al. (2020) schedule reduction of 57, Barlish and Sullivan, (2012) 67% less delays, Honnappa and Padala (2022) 11.52% time saving, Khanzode et al. (2008) 6 months’ savings on the schedule, Kuprenas and Mock (2009) savings of time value of $10,000, Love et al. (2013) 7% reduction in schedule, Paneru et al. (2023) reduce the time to complete a project by 7%, PWC (2018) Time savings in design 6.3% and 36%, Time savings in build and commission 15.3%, Time savings in handover (12.5%), Sacks and Barak (2008) An overall reduction of between 15% and 41% of the hours required for a project
Environmental, sustainability, energy performance and waste management	Banihashemi et al. (2018) reduction of waste by 2%, Ferreira et al. (2023) 2–5% savings in energy consumption, Hasanain and Nawari (2022) design optimization 20%–60% less water consumption, Hussain et al. (2023) Carbon emissions are reduced by 32.94%, 14.92%, 28.40%, and 6.52% during the production, construction, operation, and demolition stages, Kamel and Kazemian (2023) 26% lower energy use, Motalebi et al. (2022) 24%–58.2% reduction in energy consumption, Tu et al. (2023) construction waste source reduction of 67%, 48%, and 4.6%, Won et al. (2016) BIM-based design validation prevented 4.3–15.2% of waste on sites
Facility management and operations	Love et al. (2013) cost of not using BIM is $680,000 over an asset’s operating life, PWC (2018) Cost savings in asset maintenance (60.7%), Tsantili et al. (2023) reducing yearly energy usage by 43.75%
Project outcomes	Abdelbary et al. (2020) A total saving of 10% ($10 million), generated by BIM, Barlish and Sullivan, (2012) 5% savings n contractors’ costs, Conde et al. (2020) 20% reduction in costs per project, Kim et al. (2017) BIM has contributed to identifying and/or resolving issues whose contractual values are as much as 15.92% of the total direct cost of the project, Kuprenas and Mock (2009) clash detection savings of $25,000, Love et al. (2013) 80% reduction in the time taken to generate a cost estimate with cost estimation accuracy within 3%, Paneru et al. (2023) decrease the time needed to generate a cost estimate by up to 80%, PWC (2018) 3.0% savings in total Cost savings in clash detection (1.8%), Wong et al. (2018) cost of drafting reduced by 80%–84% using BIM.
Investment cost	Barlish and Sullivan, (2012) design costs: 31% increase, 29% increase in 3D background model creator costs: 34% increase, Qian (2012) Investments for BIM Costs (per staff) of ∼S$18,000 to S$30,000, Reizgevičius et al. (2018) Expected productivity loss after starting to use BIM software 34%

Source(s): Authors’ own work

Table 2

List of attributes and metrics for calculations

Attribute	Assumed BIM impact	Variables needed to convert impact to cost (case related)	Required metric to calculate direct impact of BIM
D1	Assumed efficiency saving to internal management costs	Inhouse cost per month Design and construction duration	efficiency savings %
C1	This benefit will reduce associated contractor’s prelim costs	construction value prelims%	Time saving as % of overall project
C2	Assumed reduction of the programme and leading to reduced inflation costs	Project duration in years, Construction value, Annual inflation	efficiency savings %
C3	Assumed efficiency saving to contractors pricing levels	Tender value, user defined % reduction of tender prices	efficiency savings %
C4	Assumed efficiency saving to construction risk provisions	construction value used defined construction risk	efficiency savings %
C5	Assumed reduction in client held risk	construction value user defined client risk reduction	efficiency savings %
C6	Assumed efficiency saving to cost of BWIC	construction value M&E % of the total works, BWIC %	saving to cost of BWIC
C7	Assumed efficiency saving to management of changes during construction	Cost of handling one change	Reduction of changes %
O1	Assumed efficiency saving to transfer data at completion	Hourly rate Hours spent	efficiency savings %
O2	Assumed efficiency saving per annum of enhanced data management	time saving per request hr. Cost rate/hr for labor Nr of requests	efficiency savings %
O3	Assumed efficiency saving per annum to energy costs	Area of building (m2), energy use/m2, user % saving, rate of kWh operational period (yrs)	efficiency savings %
O4	Saving time and resources for each maintenance event during the operational stage	time saved per event (hr) cost rate/hr for labor number of maintenance events (Bosch-Sijtsema et al.)	efficiency savings %
O5	Savings due to combined maintenance tasks	LCC rate/m2, Gross Internal Area user% saving, operational period	efficiency savings %

Source(s): Authors’ own work

Table 3

Equations and variables for attributes

D1	Assumed efficiency saving to internal management costs (D1)
	The implementation of BIM aims to improve project efficiency, minimize delays, and decrease resource requirements for public bodies through enhanced cost predictability and stakeholder engagement via 3D modeling and visualization, resulting in time savings for the internal management team
	D1=Inhouse cost×Efficiency savings×Design and construction duration (1)
	In the equation, “in-house costs” represent the monthly cost of managing the design phase, “design and construction durations” denote the project’s duration in months, both of which are case-specific variables. The “efficiency assumption” is a percentage of the reduction in necessary management efforts resulting from BIM implementation, and this variable is defined based on prior research and validated through the survey
C1	This benefit will reduce associated contractor’s prelim costs (C1)
	BIM has the potential to yield time and cost savings in project scheduling and duration. These savings are achieved by enhancing the project schedule, increasing pre-fabrication and design for manufacturing and assembly, optimizing construction equipment, improving subcontractor briefings, and mitigating design risks. These benefits also lead to a reduction in the contractor’s preliminary costs
	C1=Construction value×prelims %×Reduction % (2)
	In the equation, “construction value” represents the total tender price upon which the contractor bases the preliminary costs, and “reduction percentage” indicates the assumed reduction in preliminary costs resulting from BIM implementation
C2	Assumed reduction of the programme and leading to reduced inflation costs (C2)
	BIM can improve project efficiency by streamlining design and construction processes, resulting in expedited project approvals, improved cost predictability, and reduced risk of delays and cost overruns, ultimately reducing overall project costs. This benefit is grounded in the assumption that BIM reduces project duration, thereby avoiding additional inflationary costs
	C2=Project durationx construction value×Annual inflation %×Reduction % (3)
	In the equation, the “reduction percentage” is like the variable described in (C1), and “project duration” is expressed in years
C3	Assumed efficiency saving to contractors pricing levels (C3)
	A model-based approach to procurement enables digital quantity take-offs and a comprehensive comprehension of project scope and risks, resulting in greater confidence in commercial costs, design, and scope, ultimately leading to more competitive tender prices
	C3=Reduction in tender prices assumed %×construction cost (4)
	The reduction in tender prices due to BIM implementation is a variable that has been established through previous research and confirmed through the survey
C4	Assumed efficiency saving to construction risk provisions (C4)
	BIM model will reduce un-coordinated design issues through the collaborative model. This will reduce the required contract risk sum during the construction phase
	C4=Reduction in contract risk assumed %×construction cost (5)
	The reduction in contract risk due to BIM implementation is a variable that has been established through previous research and confirmed through the survey
C5	Assumed reduction in client held risk (C5)
	Enhanced stakeholder engagement, improved design coordination, and increased cost predictability result in fewer project changes, thus reducing the client’s contingency allowance for risk during project development
	C5=Reduction in client risk assumed %×construction cost (6)
	The reduction in client risk due to BIM implementation is a variable that has been established through previous research and confirmed through the survey
C6	Assumed efficiency saving to cost of BWIC (C6)
	A fully coordinated BIM model provides cost predictability for Builders Work in Connection (BWIC) requirements through detailed modeling and a fully defined BWIC schedule. The impact of BIM results in reduced BWIC provisions, through decreased provisional sums or offsite manufacturing and assembly
	C6=assumed saving to BWIC %×% of M&E works×BWIC%×construction value (7)
	In the equation, “M&E works” represents the total electrical and mechanical components of the construction, “BWIC %” is the percentage of builders work in connection, and the “assumed saving %” is a variable established through prior research and verified through the survey
C7	Assumed efficiency saving to management of changes during construction (C7)
	Implementing a model-based procurement approach allows for more precise digital quantity take-offs and a thorough understanding of project scope and risks, leading to improved cost predictability, and resulting in more competitive tender prices
	C7=cost of mamaging one change×reduction in change events %×Nr. changes (8)
	In the equation, the “number of changes in the project” and the “cost of managing each change” are case-specific variables provided by the user, while the “percentage of reduction in changes” is a variable established through previous research and confirmed through the survey
O1	Assumed efficiency saving to transfer data at completion (O1)
	Developing a Project Information Model (PIM) that seamlessly integrates with the Asset Information Model (AIM) and can be aligned with operational facilities management systems lowers the cost of transferring data upon project completion
	O1=Time in Hrs.×hourly rate (9)
	Where the time is the number of hours required by the existing resources to transfer data
O2	Assumed efficiency saving per annum of enhanced data management (O2)
	Through a comprehensive asset information model, information can be readily sourced, accessed, and shared, eliminating the need to create new data for each maintenance event. BIM usage is assumed to generate annual savings in data management costs over the operational period of the facility, which can be calculated as follows
	O2=time saving per request×hourly rate×Nr. of requests×operational period. (10)
	In the equation, “time saving” is expressed in hours, the “operational period” is in years, and the “number of requests” is calculated within a one-year timeframe
O3	Assumed efficiency saving per annum to energy costs (O3)
	BIM will enable improved modeling of the energy performance of the proposed solution and the ability to test materials and construction techniques more effectively, resulting in enhanced energy efficiency. Additionally, when offsite manufacturing is employed, it leads to improved air tightness of the building, subsequently reducing energy costs during the operational stage
	O3=efficincy enhancement %×Area×energy use×rate of KWh x operational period. (11)
	In the equation, the variables for “energy use” and the “area of the building” are specific to the case, while the “efficiency enhancement percentage” is a variable established through prior research and validated through the survey
O4	Assumed saving time and resources for each maintenance event during the operational stage (O4)
	During the operational stage of the facility, when a fault is logged on the facility management desk, product parts, health and safety information, access details, and work methodology can all be automatically generated from the asset information system. Resulting in reduced resource costs for each maintenance event
	O4=Time saved per event×cost rate of labor×Nr. of maintenance events×efficincy % (12)
	The variables in the equation are all case related, in exception to the assumed savings in efficiency of handling maintenance due to BIM, which is a variable established through previous research and confirmed through the survey
O5	Assumed savings due to combined maintenance tasks (O5)
	By utilizing an asset information model, the opportunity to take a strategic view of medium-term maintenance activities across a portfolio of projects/assets is created. The structured asset data across this project portfolio allows for the bundling of procurement works for goods and services, resulting in improved value for money
	O5=LCC rate×gross internal area×o perational period×efficincy % (13)
	In the equation, “LCC” represents the Life Cycle Costs achieved through proactive and strategic procurement of lifecycle works, and this variable is specific to the case. The “assumed savings in efficiency of handling maintenance due to BIM” is a variable established through prior research and validated through the survey

Source(s): Authors’ own work

Table 4

BIM benefits variables as extracted from the survey analysis

BIM related item	Estimation of impact
BIM can reduce the project duration during the design by	21–40%
BIM can enhance the efficiency of the design by	41–60%
BIM can reduce tender (Contract/BOQ) prices by	6–10%
BIM can reduce changes during the construction by	21–40%
BIM can reduce the project delivery duration during the construction by	1–20%
BIM can increase safety on site during the construction by	1–20%
BIM can reduce requests for information on site during the construction by	21–30%
BIM can reduce Builders Work in Connection (BWIC) costs by	1–20%
BIM can increase possibilities for prefabrication by	>50%
BIM can facilitate the creation of As-built models by	1–30%
BIM can reduce energy consumption for new projects during operation by	8–10%
BIM can facilitate maintenance works by	21–40%

Source(s): Authors’ own work