2.1. Performance Analysis and Assessment of BIM-Based Construction Projects
Previous studies have presented a variety of methods for the performance measurement of BIM. First, the tools for macro-level measurement of the maturity of BIM from organizational and project perspectives are as follows: bimSCORE [
9], BIM Proficiency Matrix [
10], BIM Interactive Capability Maturity Model (I-CMM) [
11], BIM Maturity Measure (BIMmm) [
12,
13], BIM QuickScan [
14], BIm3 [
15], the macro-BIM adoption assessment model [
16], and a success level assessment model for BIM projects [
17]. Since these methodologies analyze the performance of BIM projects from a macroscopic perspective, there is a limit to analyzing the microscopic performance according to the input of BIM staff.
Second, studies that evaluate the positive impact of BIM adoption to construction projects through case studies are as follows. Both the analysis of return on investment (ROI) of BIM and the avoidance of economic losses due to design errors through BIM-based design validation were investigated [
6,
18]. Ham et al., (2018) classified the design errors identified through BIM-based design validation of high-rise construction projects into simple design errors, rework design errors (design errors likely to cause demolition and rework), and delay design errors (design errors likely to cause construction delay). Accordingly, the cost of loss due to design quality was quantified from the perspective of the contractor [
18]. Lee et al., (2012) used BIM-based design validation for ROI analysis based on the rework costs that may be incurred [
6]. Kim et al., (2017) quantified the value of BIM contribution to resolving issues that occurred in the construction phase [
19]. In addition, Bryde et al., (2013) performed a qualitative analysis of various projects through the generation of success criteria and the use of context analysis for the project management body of knowledge [
20]. In addition, some studies have performed qualitative and quantitative analysis on the ROI of the construction phase through various cases [
21]. In summary, the existing methods of BIM performance assessment are applied to the macroscopic dimension of BIM implementation, which leads to the limitation that the assessment may depend on the subjective judgment of individual assessors.
Therefore, there is a need for an analysis method that allows the measurement of the performance of BIM-based construction support to further improve performances from macroscopic perspectives. To this end, this study aims to analyze the characteristics of the situations wherein the BIM staff receive and respond to RFIs from BIM users who aim for active utilization of BIM for the project in the construction phase.
2.2. Properties of Request for Information in BIM-Based Construction Support
BIM-based design validation, a methodology for reviewing design errors in the pre-construction phase, aims to minimize the impact on the construction phase by resolving the issue of the consistency of the BIM model and the quality of the drawings before fully embarking on the actual construction process [
6,
18]. Further, as previous studies reported that design errors can cause a significant loss in the construction phase, in order to prevent such loss, design validation is performed at the end of the working drawing phase or during a short period before the construction begins after entering the construction phase [
6,
18].
Collaboration on an ideal BIM-based construction project can be supported by modern BIM tools. For example, Solibri can maximize collaboration and solve problems by verifying BIM models or integrating models from various fields to assure quality [
22]. Recently, tools such as BIMcollab and Revizto have been developed. These are tools that allow experts in various fields to collaborate, even in non-face-to-face situations, which supports live coordination beyond collaboration [
23,
24]. These tools not only verify the quality of the model, but also provide functions such as issue management and information exchange. If this BIM collaboration platform is used, various users can utilize the necessary information, as shown in
Figure 1. Even if the BIM collaboration platform is introduced in the field, a BIM staff capable of operating this tool well is essential for many users to obtain the necessary information from BIM. If the BIM collaboration platform is not applied to the field, the BIM staff must perform the server role for collaboration between various users.
In general, BIM-based design validation is performed with the input of a large BIM staff in a short period of time. In these cases, the focus is on resolving design errors through quality control of the drawings, and the types of design errors are classified into illogical design, discrepancies between drawings, and missing items [
6]. In addition, because the BIM staff put into the project in support of the construction phase provide services during the construction period, there is the problem of a small number of staff members having to respond to BIM RFIs from many project participants. As discussed above, the BIM staffs’ roles in responding to the BIM RFI requested by various users is very important, regardless of whether the BIM collaboration platform is used or not.
Ham et al. (2020) assumed the situation of waiting for the service described above, developed a queuing model and analyzed the performance of the system [
8]. To analyze the adequacy of the BIM staffing level, the satisfaction with the service of project participants, who are customers waiting for a response to the BIM RFI they have raised, was considered. If BIM-based design validation has not been performed properly, the focus of BIM-based construction support must be on the design review and checking. Conversely, for the sites where BIM-based design validation has been properly performed, the validated BIM data can be utilized for decision making. Project participants who seek to utilize BIM actively request a wide variety of information types and formats for the purposes of decision making and as data for reporting in their work. Therefore, to respond to such information requests from project participants, BIM staff must have relevant competencies above a certain level, and a strategy for efficient responses to a wide range of requirements is needed. In this dimension, we aim to assess the performance of BIM staff through analysis methods that make it possible to consider various types of RFI from project participants, determine the number of BIM staff members in the project and their competencies, and analyze how much the performance can be improved through the application of a priority policy based on the purposes of RFIs.
2.3. Queuing Model
In the construction sector, various methodologies have been developed for the modeling of repeatedly performed work processes and the measurement of productivity. In particular, Halpin [
25] developed the cyclic operation network (CYCLONE) to describe real job site processes. This is a management tool that enables productivity measurement and describes logical relationships between resources, operation time, and tasks in operations based on deterministic or probabilistic approaches [
26]. Process modeling techniques and simulation systems based on CYCLONE include the resource queuing network simulation system (RESQUE) [
27], construction object-oriented process simulation system (COOPS) [
28], and state- and resource-based simulation of construction processes (Stroboscope) [
29]. These methodologies are used to predict productivity through simulation, coordinate input resources through sensitivity analysis, and derive the optimal combination of resources. These methodologies can contribute to process optimization, but they have a limitation in that micro-level analysis for the quantitative effects of the allocation of input resources is not possible.
The measurement of work productivity from a microscopic point of view allows analysis on the effect of a given input (e.g., BIM staff) on the productivity of other inputs (e.g., project participants) [
30]. Therefore, to perform micro-level analysis of the effect of the BIM staff on project participants, the interactions between these two stakeholders should be considered [
31]. Work-flow management (WfM), which has been utilized since the early 1990s, facilitates improvements in the performance of business processes related to lead time, waiting time, service time, and resource utilization [
32]. There are many different techniques for analyzing a WfM system [
33,
34,
35,
36]; among these methods, a queueing model that can generate numerical results is generally used [
37,
38]. Queuing theory describes a queuing system that can be observed in the real world [
39]. Customers arrive at the queuing system individually and randomly to receive a service. If a customer cannot receive the service immediately upon arrival, the customer waits in the queue. Usually, one or more servers provide services. Each customer leaves the queuing system after receiving a service individually from a server [
38].
Figure 2 expresses the waiting situation between the BIM staff supporting BIM collaboration and project participants who want to obtain RFI from BIM as a queue system that considers customers and servers.
Many of these queuing systems are characterized by time-dependent changes in parameters [
40]. These time-dependent changes in parameters may have a substantial impact on the performance of queuing systems and should be considered in the design and control of these systems. Researchers from previous studies have applied the queuing model to various industries and sectors. The queuing model is applied to a wide range of decision making in sectors such as health care [
41], emergency services [
42,
43], repair facilities [
44], air traffic [
45,
46], transportation planning [
47], and the management of container terminals [
48]. However, in the construction sector, research with a queuing model is in the early stage of application for purposes such as the allocation of trucks for concrete work [
49], performance measurement of BIM staff in their responses to BIM RFIs, and optimal BIM staffing [
8,
50].
In other fields of research, active studies have been performed to achieve improvement in the system performance through application of a priority policy for customers arriving at the queuing system. In the healthcare field, the waiting time of outpatients was minimized by coordinating their reservation time [
51]. In the research of de Souza et al., (2015) for provision of patient care according to their medical seriousness, users with multiple priority classes were integrated into a system queue, and performance was evaluated for mean transfer time and mean waiting time for patients of different classes [
52]. In the software field, the fault detection process and fault correction process of a system were improved by utilizing a priority queuing model [
53]. In addition, Kim et al., (2013) analyzed the queue network of a call center and determined the priority of high-value customers to minimize damage to customer service [
54]. Nan et al., (2014) compared the priority-service and multi-service scenarios to solve the resource optimization problem for a multimedia cloud, and through the comparison, derived the minimum response time for cloud resources and the minimum resource cost [
55]. As can be observed from these prior studies in other research fields, it is possible to achieve improvement in the performance of systems where queuing occurs through changes in the policy of responding to customers’ requests at the microscopic level. However, there are few research methodologies and empirical cases in the construction industry.