Human Resource Planning for Building Construction Processes Through the Integration of BIM and Line of Balance

Abstract

Construction projects depend heavily on manual labor; however, workforce productivity is frequently constrained by poor planning and communication. This paper proposes a methodological framework that combines Building Information Modeling (BIM) with the Lines of Balance (LOB) technique to estimate, allocate, and visually coordinate crews in repetitive building work. Using a Design Science Research approach, the study draws on a systematic review of 29 eligible studies that identified 23 processes for human resource planning and allocation. These processes are structured into five planning categories: scope and duration, structuring and quantification, resource estimation and allocation, schedule baseline, and cost baseline. BIM support is operationalized through seven high-utility BIM applications identified by expert assessment (RUI > 0.75), including phase planning, scheduling, site utilization planning, and cost estimation. The framework connects model-based quantity takeoff and productivity assumptions with LOB-based sequencing and crew assignment. This integration enables early detection of spatiotemporal overlaps and workload imbalances through consistent BIM–LOB visualization. The method was implemented and calibrated in two residential case studies (one covering 295 m² over 3 months and the other 3660 m² over 22 months), resulting in workforce plans comprising 10 workers across five crews and 72 workers across nine crews. An evaluation involving 31 professionals indicates a high perceived utility, particularly in reducing errors in quantity and productivity estimation (RUI = 0.90) and crew quantification (RUI = 0.88).

1. Introduction

The construction sector is a cornerstone of the global economy, contributing approximately 13% of the world’s Gross Domestic Product (GDP) [1]. Despite its economic relevance and central role in shaping the built environment, construction continues to exhibit a persistent productivity gap relative to other sectors [2]. While industries such as agriculture achieved a compound annual growth rate of 4.5% over the period reported, construction recorded only 0.1% between 1947 and 2010 [3]. In Colombia, where the empirical cases analyzed in this study are located, official productivity accounts show that the construction sector recorded a 4.24% decrease in value added in 2023, while the total economy grew by 0.60%. In the same year, the contribution of total factor productivity to construction value-added growth was −4.89 percentage points, compared with −1.71 percentage points for the economy as a whole [4]. This weak performance is consistent with earlier evidence on the Colombian construction sector, where efficiency levels across construction-related subsectors ranged between 54% and 78%, and only one subsector showed cumulative productivity growth of 0.1% during the period analyzed [5]. More recent national accounts also indicate that, although construction value added recovered by 1.87% in 2024, labor services contributed −1.39 percentage points to its growth, mainly due to the negative contribution of hours worked [6]. These Colombia-specific statistics reinforce that the productivity challenge is not only an international or cross-sectoral concern, but also a sector-specific issue within the same country context. Closing this gap is therefore not only an economic imperative, but also a practical necessity for improving delivery reliability, strengthening competitiveness, and expanding employment opportunities. In this context, human resource planning emerges as a decisive lever, as deficiencies in workforce estimation, allocation, and coordination can undermine organizational performance and compromise project objectives [4,5]. Strategic workforce planning from early project stages is therefore fundamental for improving productivity outcomes in construction [7]. In this context, human resource management emerges as a decisive lever, as deficiencies in planning and administration can undermine organizational performance and compromise project objectives [8,9]. Strategic workforce planning from early project stages is therefore fundamental for improving productivity outcomes in construction.

Evidence suggests that effective human resource management can mitigate a wide range of project risks and, in some cases, may outperform other managerial strategies in its ability to stabilize project performance [9,10]. However, conventional scheduling approaches often fall short of supporting workforce planning needs in construction, particularly regarding process visualization and the efficient communication of information required for decision-making [11]. These limitations are consequential because staffing decisions influence critical project performance dimensions such as scope, cost, schedule, and quality, and their effectiveness depends on coordinated planning and control [12,13]. The scale of this challenge is reinforced by the sector’s labor intensity, with construction accounting for around 8% of global employment [14]. As a result, improving the reliability of human resource planning has direct implications for project outcomes and for broader socio-economic performance.

The human component is especially influential in building projects, where labor not only drives production but also conditions the performance of materials, equipment, and other resources required for execution [15,16]. Construction remains a comparatively low-technology sector with substantial reliance on manual labor [9], which increases sensitivity to quality management and coordination effectiveness. Consequently, human resource professionals and project teams must adopt strategies that maximize the use of human talent and reduce inefficiencies that propagate into performance losses [9]. From a project management perspective, effective planning and allocation require assigning the right personnel at the right time and in the right work fronts, whereas inadequate workforce management can degrade quality and disrupt execution flow [17,18]. This becomes even more critical given that labor constitutes a fundamental element in building construction processes [19,20].

Deficient workforce planning in construction is rarely caused by a single isolated problem; rather, it results from the combined effect of several technical, managerial, and informational factors. The most recurrent factors include incomplete definition of project scope and work packages, unreliable quantity takeoff, insufficiently validated productivity rates, inadequate estimation of labor demand, weak formulation of work crews, poor assignment of crews to activities, unclear predecessor–successor relationships, limited consideration of work-front availability, and fragmented communication among design, planning, and site teams [2,21,22,23,24]. These factors are particularly critical in building projects because labor demand varies according to construction sequence, spatial constraints, crew continuity, and the interaction between trades operating in shared work areas. In addition, changes in site conditions, late design adjustments, and limited visualization of space–time conflicts can generate workforce imbalances, idle time, over-allocation, and interruptions in production flow [21]. Therefore, workforce planning deficiencies should be understood not only as errors in estimating the number of workers required, but also as failures in connecting scope definition, quantities, productivity assumptions, sequencing logic, and crew deployment within a coherent planning system. This complexity supports the need for innovative strategies and technologies that enhance decision-making in building projects, particularly during early planning phases when leverage to prevent downstream deviations is highest [8]. Among the most promising approaches, Building Information Modeling (BIM) and Lean Construction have been increasingly recognized as enabling technologies and methodologies that strengthen planning, coordination, and resource management [17,25]. BIM models, for instance, can support the evaluation of alternative resource-planning scenarios while reducing the risks associated with trial-and-error decision-making in real projects [26]. Lean Construction, in turn, fosters interdisciplinary and collaborative practices that support decision-making and earlier detection of errors in resource allocation and scheduling [23,27].

When workforce planning is deficient, productivity declines, and project performance deteriorates [12]. The project manager’s role becomes pivotal because they must coordinate the construction process, develop and maintain a strategic workforce plan, adjust crews to evolving constraints, and ensure efficient resource deployment, a task that is intrinsically complex due to changing site conditions and interdependencies among activities [8,28]. This complexity supports the need for innovative strategies and technologies that enhance decision-making in building projects, particularly during early planning phases when leverage to prevent downstream deviations is highest [8]. Among the most promising approaches, Building Information Modeling (BIM) and Lean Construction have been increasingly recognized as enabling technologies and methodologies that strengthen planning, coordination, and resource management [17,25]. BIM models, for instance, can support the evaluation of alternative resource-planning scenarios while reducing the risks associated with trial-and-error decision-making in real projects [26]. Lean Construction, in turn, fosters interdisciplinary and collaborative practices that support decision-making and earlier detection of errors in resource allocation and scheduling [23,27].

Lean integration has been associated with improvements in overall management and constructability [29,30] by pursuing production systems that accelerate delivery, reduce waste, and strengthen planning, control, and resource management practices [31,32]. Within this methodological family, the LOB technique is particularly relevant for coordinating repetitive work and visualizing production flow through location-based representations of activities. By making crew continuity and workflow conflicts more explicit, LOB can support more coordinated workforce planning and reduce uncertainty in allocation decisions [33,34,35]. Nevertheless, while Lean-based tools can strengthen human resource planning, their impact can be amplified when integrated with digital information environments that enhance the accuracy and traceability of planning inputs.

BIM has demonstrated potential to enhance project management by supporting workforce planning across the project lifecycle, with positive implications for productivity and quality control in building construction [36,37,38,39]. BIM-enabled simulation of construction alternatives allows planners to anticipate project behavior over time and evaluate workforce strategies proactively by adjusting project characteristics and constraints [40]. Moreover, BIM implementation can reduce errors during planning and design while lowering the manual effort associated with information handling and coordination [41]. Despite these advances, deficiencies in workforce planning persist, indicating a continued need to integrate technologies and methodologies that improve the consistency between quantities, productivity assumptions, sequencing decisions, and staffing profiles.

Although the combined adoption of Lean principles and BIM has improved project management practices [42,43], an important knowledge gap remains regarding the integration of BIM with the Line of Balance (LOB) technique for workforce planning and allocation. Existing studies have advanced BIM-based quantity takeoff, 4D/5D simulation, resource estimation, and Lean-based production planning; however, limited methodological guidance is available on how to convert BIM-derived quantities and productivity assumptions into location-based crew allocation decisions that can be verified through LOB logic. This gap is particularly relevant in residential building projects, where repetitive work, crew continuity, work-front availability, and spatial–temporal coordination directly affect the reliability of workforce planning. The scientific novelty of this study lies in the development of a process-based BIM–LOB framework that explicitly connects model-based quantity information, productivity assumptions, crew sizing, crew assignment, and location-based sequencing within a single planning workflow. Unlike approaches that use BIM mainly for visualization or LOB mainly for schedule representation, the proposed framework integrates both methods as a traceable decision-support structure for workforce planning and allocation. The contribution is articulated in four research tasks: (1) to synthesize and structure the processes required for workforce planning and allocation in building construction projects; (2) to identify and prioritize BIM uses that support workforce-related planning decisions; (3) to develop BIM–LOB workflows that transform quantities, productivity rates, and crew configurations into location-based planning outputs; and (4) to implement, calibrate, and evaluate the framework in residential case studies using both expert judgment and controlled planning-stage performance indicators. Accordingly, this study contributes a methodological artifact that strengthens early-stage workforce planning by providing a structured pathway from process definition and BIM-based information management to LOB-supported crew allocation and planning verification.

2. Literature Background

2.1. Human Resource Management in Construction Projects

In conventional project management approaches, human resource management is a core determinant of construction project performance because labor remains the primary driver of production and a major component of direct cost. The PMBOK Guide, Sixth Edition, positions resource management as a structured set of processes that span planning, estimating, acquiring, developing, managing, and controlling resources to meet project objectives [44]. In this study, the “traditional methodology” is understood as the management of projects through standardized and widely adopted practices for planning, execution, monitoring, and closing, as formalized in the PMBOK framework [44]. Within that logic, planning human resource management is not limited to assigning personnel; it involves defining roles and responsibilities, required competencies, communication interfaces, and control mechanisms that ensure crews can deliver the planned production rates within the constraints of location, safety, and work packaging. Context conditions, site logistics, communication strategies, change management, skills development, and staff motivation further shape the ability to sustain productivity while respecting time and cost baselines [45]. Accordingly, this paper focuses on the planning dimension of human resource management, with emphasis on the accurate estimation, allocation, and operational coordination of labor resources. In this study, PMBOK was not used as a statistical source of evidence, but as a normative and organizational reference for structuring the logic of traditional project management processes. Its role was to provide a standardized vocabulary and a process-oriented backbone for resource planning, particularly in relation to defining how resources are planned, estimated, organized, and linked to schedule and cost baselines. However, PMBOK does not prescribe, at the level of operational detail required in this study, how construction crews should be formulated, allocated across work fronts, linked to model-based quantities, or verified through location-based production flows. For this reason, the systematic literature review was used to identify the construction-specific actions that operationalize human resource planning in building projects. Accordingly, the process structure proposed in this paper should be understood as an integration between PMBOK-based project management logic and process-level evidence observed in construction-planning literature.

Human resource management planning in construction requires translating the scope and work structure into labor demand profiles that are feasible in time and space. This translation typically depends on two inputs that are repeatedly cited as sources of error in practice: reliable quantity takeoff at the appropriate level of detail and defensible productivity assumptions for each trade and work package. It also requires explicit documentation of responsibilities and coordination rules across disciplines to reduce fragmentation and prevent misalignment between sequencing decisions and crew availability. Prior studies provide evidence of a linkage between structured human resource planning and project outcomes. Muntu et al. [12] highlight the influence of the project manager and the allocation and coordination of the project team on overall performance, while Maloney [46] argues that strengthened human resource management practices can improve efficiency, particularly in pre-construction and construction phases. These perspectives emphasize that workforce planning is a managerial system that connects organizational strategy with production control at the project level, rather than an isolated administrative function. From a planning perspective, these deficiencies can be grouped into four interrelated domains. The first domain concerns information reliability, including incomplete scope definition, inconsistent work breakdown structures, inaccurate quantity takeoff, and weak documentation of activity attributes. The second domain concerns production assumptions, particularly the use of productivity rates that are not sufficiently linked to project conditions, crew composition, learning effects, or spatial constraints. The third domain concerns sequencing and location-based coordination, including unclear precedence relationships, limited recognition of work-front availability, and insufficient analysis of interference among crews. The fourth domain concerns managerial coordination, including fragmented communication between planning and field teams, late incorporation of design or site changes, and limited mechanisms for verifying whether crew assignments remain feasible over time. These domains explain why workforce planning in construction requires more than estimating labor quantities; it requires a structured integration of scope, quantities, production rates, sequencing logic, and spatial coordination [21]. This rationale is directly aligned with the BIM–LOB framework proposed in this study, which seeks to improve the traceability of planning inputs and the visual verification of crew allocation across repetitive building work.

The relevance of robust workforce planning becomes more acute in an industry where productivity growth has lagged behind other sectors and where limited automation constrains planning accuracy and responsiveness [3]. Improving productivity through human resource management requires clarity in responsibility assignment, organizational stability, and effective communication between human resource functions and site production teams [47]. In parallel, the literature reports advanced decision-support models aimed at strengthening workforce-related decisions, including personnel selection and allocation mechanisms that improve the match between capabilities and project needs [48]. Process-oriented strategies have also been associated with performance gains by reducing rework and improving the reliability of resource allocation [49]. Despite these advances, an important gap persists in operationalizing digital construction technologies within human resource planning workflows. In particular, the limited integration of BIM-based quantity information with location-based planning methods restricts the ability to generate workforce plans that are simultaneously data-driven, visually verifiable, and aligned with the flow of repetitive work. Addressing this gap is essential for a framework that combines BIM and LOB to strengthen crew estimation and allocation, improve sequencing coherence, and reduce planning-driven deviations in schedule and cost performance.

2.2. BIM in Human Resource Management in Construction

BIM has progressively shifted from a design-support technology to an information management methodology that enables more reliable planning decisions throughout the project lifecycle [21]. Its central value in human resource management stems from its capacity to generate structured, traceable data from three-dimensional digital models, thereby improving the accuracy of quantity takeoff, constructability analysis, and the estimation of time and costs for construction work packages. By strengthening the link between scope definition and measurable production inputs, BIM provides a more defensible basis for translating quantities into labor demand through productivity rates, which supports earlier and more consistent decisions on crew sizing, trade sequencing, and staffing profiles across the schedule horizon [50]. Therefore, BIM contributes to human resource planning by reducing reliance on manual approximations and by enabling model-informed scenario analysis that improves the timeliness and robustness of workforce-related decisions. BIM also enhances coordination by facilitating a shared understanding of the planned work, thereby supporting cross-disciplinary communication and reducing misalignment between planned production and available personnel [51].

The integration of time and cost information into BIM environments further expands its relevance for workforce planning. Four-dimensional BIM supports the visualization of construction sequences and spatial constraints, allowing planners to anticipate overlapping work fronts, access limitations, and workspace conflicts that often drive unplanned crew interference. Five-dimensional BIM complements this capability by associating work packages with cost information, which includes labor-related components and enables a more explicit connection between staffing decisions and budget performance. In this context, Bohórquez et al. [52] propose a methodology based on 5D BIM simulation for human resource planning, reporting benefits that include improved crew allocation, more reliable estimation of productivity and activity duration, better coordination of material supply, and enhanced identification of risks that may compromise execution. These contributions reinforce the idea that BIM can support workforce planning when used as a platform to integrate quantities, production assumptions, and schedule logic within a single coherent planning environment.

The literature also discusses more advanced BIM “dimensions” that are frequently presented as extensions, with potential implications for project performance, including workforce planning; however, terminology beyond 5D remains heterogeneous and context dependent. While 4D and 5D are consistently defined and directly support human resource planning by linking work quantities with sequence and cost information, higher dimensions are described with varying emphases, which complicates their operational use for estimating, allocating, and controlling labor. For example, Piaseckienė [53] relates 9D BIM to Lean-oriented objectives such as waste reduction, process improvement, and operational transparency, which are conceptually aligned with stabilizing production flow and reducing variability in crew utilization. Other sources portray 9D as an enabler of resource optimization in industrialized settings [54]. Kulkarni et al. [55] associate 9D with the use of laser scanning to generate high-fidelity digital representations of the as-built environment, a capability that can support workforce control indirectly by improving progress verification and reducing rework driven by inaccurate field information. Effective adoption of BIM for workforce planning also depends on institutional readiness and on the definition of roles and competencies that enable consistent implementation. Adekunle and Aigbavboa [56] analyze employer preferences and labor market signals related to BIM, indicating strong demand for professionals with BIM skills and highlighting persistent ambiguity in role definitions and recruitment structures. This evidence is relevant to human resource management because the capability to leverage BIM for planning and control requires more than software access; it requires organizations to establish clear responsibilities, workflows, and competency pathways that connect digital modeling practices with production planning tasks. When these elements are weak, BIM may remain confined to documentation and coordination rather than supporting systematic workforce decisions.

Empirical evidence further suggests that BIM adoption can improve productivity through automation and enhanced coordination, particularly when it reduces rework and strengthens information flow among stakeholders. Mesároš et al. [57] report that BIM implementation can increase management productivity by supporting process automation and improving coordination, which indirectly benefits workforce performance. Despite notable progress, the full potential of BIM in human resource management remains underexploited, particularly in the operational allocation and balancing of crews across repetitive locations and work fronts. BIM can improve the quality of the inputs that underpin staffing decisions, yet it often provides limited support for explicitly representing labor flow and crew continuity over space and time. This gap motivates approaches that integrate BIM with location-based planning methods such as LOB, in which visual programming can translate BIM-derived quantities and production rates into crew-synchronized schedules, strengthening allocation decisions and improving the reliability of human resource plans in building construction processes.

2.3. LOB in Human Resource Management in Construction

LOB is a location-based scheduling technique particularly suited to projects characterized by repetitive units or continuous production along a spatial dimension. By representing time against location or work units, LOB makes the relationship between production rates, crew continuity, and workspace availability explicit. This representation is directly relevant to human resource management because it supports workforce planning as a flow problem rather than as a set of isolated activities. In practical terms, LOB enables planners to define and verify feasible crew paths across units, detect crew interference and idle time, and evaluate whether a given staffing strategy can sustain the required production rates without disrupting workflow continuity. These features make LOB a natural tool for translating labor demand into coherent allocation decisions, especially when the objective is to maintain stable crews and avoid productivity losses associated with frequent start–stop cycles.

Prior research has shown that LOB supports more transparent schedule evaluation and improved resource synchronization in repetitive construction. Hegazy [58] and Arditi and Albulak [59] report that combining LOB with CPM strengthens schedule logic while preserving the benefits of location-based visualization, improving the ability to coordinate resources, reduce discontinuities, and communicate large volumes of schedule information. Arditi et al. [60] further emphasize that dedicated LOB-oriented software can better leverage learning effects and reduce idle time, which are central concerns in workforce planning because learning and continuity are closely linked to crew productivity. At the same time, the effectiveness of LOB depends on iterative refinement as production assumptions evolve and variability emerges during execution, which reinforces the need to treat workforce plans as living baselines rather than static outputs.

Methodological extensions in the LOB literature also provide mechanisms that strengthen labor-related decision-making. Ungureanu et al. [61] propose global indicators to evaluate continuity, synchronization, and uniformity, providing a structured way to assess whether the planned crew flows are consistent without relying solely on visual inspection. Arditi et al. [62] incorporate learning rates through fuzzy set theory to adjust activity durations, which has direct implications for labor demand profiles and the timing of crew mobilization. Tokdemir et al. [63] integrate LOB with Monte Carlo simulation to assess delay risk, enabling managers to anticipate disruptions and define contingency strategies that often translate into temporary staffing adjustments, buffer policies, or resequencing of work fronts.

Beyond evaluation, LOB has been used to formalize workforce allocation and optimization problems. Su and Lucko [45] highlight LOB’s capacity to coordinate repetitive work by assigning crews across units in a manner that preserves continuity, while Zou et al. [64] address deadline compliance by allowing multiple crews to operate in parallel and by using mixed integer linear programming to improve allocation decisions under time and cost constraints. Resource leveling and utilization optimization have also been investigated through genetic algorithms and linear project models, showing that LOB-based approaches can reduce fluctuations in workforce demand, mitigate downtime, and sustain productivity by smoothing crew deployment over time [65]. Literature positions LOB as a workforce-centric scheduling method that makes crew continuity, productivity assumptions, and location constraints observable and testable. This role becomes more impactful when LOB is coupled with reliable quantity information and consistent work packaging, which motivates its integration with BIM to enhance the accuracy and feasibility of human resource plans in building construction processes.

2.4. Methodological Frameworks in Human Resource Management in Construction

Methodological frameworks are important for structuring human resource management in construction because workforce decisions are inherently cross-cutting, spanning scope definition, work packaging, sequencing, production control, and performance monitoring. Unlike isolated techniques, a framework formalizes how inputs, roles, decision rules, and control cycles interact, enabling repeatable planning processes and improving the traceability of staffing decisions across project phases. This is particularly relevant in construction, where labor demand is dynamic, strongly constrained by location and sequencing, and frequently affected by uncertainty in quantities, productivity assumptions, and site conditions. In this context, frameworks contribute by defining how projects translate scope into labor requirements, how crews are allocated across work fronts, and how corrective actions are triggered when deviations threaten schedule and cost objectives.

Prior research has proposed frameworks that incorporate digital technologies to strengthen workforce planning and control, although the focus and degree of automation vary considerably. For example, Bohórquez et al. [52] propose a 5D BIM-based framework that leverages simulation to support crew allocation and improve the estimation of productivity and activity durations. This type of contribution illustrates the potential of model-based information environments to improve the reliability and timeliness of workforce decisions by connecting quantities, sequencing, and cost implications within a single planning representation. More broadly, the literature suggests that technological integration can enhance human resource management by reducing manual data handling, increasing inter-disciplinary coordination, and improving the transparency of planning assumptions. Nevertheless, despite the growing emphasis on digital transformation, there remains limited consolidation of methodological frameworks that simultaneously address human resource planning and allocation while operationalizing automation through BIM and production-oriented methods commonly associated with Lean Construction.

To situate this study’s contribution,

Table 1. Studies addressing human resource planning in construction projects.

Reference	Country	Project Type	Project Stages	HR Type	Type of Contribution				Topic					HR Focus
					Case study	Literature Review	Methodological Framework	Impact Analysis	Recruitment and Selection of HR	HR Forecasting	HR Allocation and Optimization	Management Practices	Productivity in Construction	Focus on HR Planning (Yes/No)	Focus on HR Allocation (Yes/No)	HR Allocation Tools (Yes/No)
Bohórquez et al. [52]	Colombia	Building Construction	Pre-construction and Construction	Unskilled	√		√			√	√	√		Yes	Yes	Yes
Nguyen and Macchion [66]	Vietnam	Sustainable Building Construction	Pre-construction and Construction	Skilled and unskilled		√	√	√				√		Yes	Yes	No
Assaad et al. [67]	Australia and the United States	Prefabricated Construction	Design, assembly, and construction	Skilled and unskilled		√		√	√					Yes	Yes	No
Zhao et al. [68]	China	Various	Lifecycle	Unskilled		√				√		√		Yes	No	No
Aldahash and Alshamrani [69]	Saudi Arabia	Building Construction	Construction	Unskilled		√		√	√				√	Yes	No	No
Nguyen et al. [70]	Vietnam	Construction	Construction	Skilled	√		√					√		No	No	No
Gurmu [71]	Australia	Building Construction	Construction	Unskilled		√						√	√	Yes	No	No
Khodeir and Nabawy [72]	Egypt	Building Construction	Design and Construction	Skilled and unskilled		√	√		√			√		Yes	No	No
Tamosaitiene et al. [73]	Iran	Various	Post-construction	Unskilled	√		√					√		No	No	No
Dhabuwala [74]	India	Various	Pre-construction	Skilled and unskilled	√	√			√			√		Yes	No	No
Laksmana and Wijayaningtyas [75]	Indonesia	Building Maintenance	Postconstruction	Skilled and unskilled		√	√					√		No	No	No
Gurmu and Ongkowijoyo [47]	Australia	Building Construction	Lifecycle	Unskilled		√	√					√	√	Yes	No	No
Pinna et al. [76]	Italy	Building Construction	Construction	Unskilled		√	√					√		Yes	No	No
Mo et al. [77]	United States and China	Building Maintenance	Post-construction	Skilled and unskilled		√	√			√	√	√		Yes	Yes	No
Fini et al. [78]	Australia	Construction	Construction	Unskilled	√	√	√		√	√	√	√	√	Yes	Yes	Yes
Ghodrati and Wilkinson [79]	New Zealand	Construction	Construction	Unclassified		√		√				√	√	Yes	No	No
Pathasarathy et al. [80]	India	Building Construction	Construction	Unskilled		√								Yes	No	No
Gerek et al. [81]	Turkey	Construction	Construction	Unskilled		√							√	Yes	No	No
Terouhid and Ries [82]	United States	Various	Construction and Post-construction	Skilled and unskilled		√			√			√		Yes	No	No
Gurmu and Aibinu [83]	Australia	Construction	Construction	Skilled and unskilled		√						√	√	No	No	No
Terouhid and Ries [84]	United States	Post-Construction	Post-construction	Skilled and unskilled		√		√				√	√	No	No	No
Leite et al. [85]	Brazil	Construction	Construction	Skilled and unskilled	√	√	√		√		√	√		Yes	Yes	No
Clarke and Herrmann [86]	England, Scotland, Denmark, and Germany	Construction	Construction	Skilled and unskilled		√		√					√	No	No	No
Biga et al. [87]	Portugal	Post-Construction	Post-construction	Skilled and unskilled	√							√	√	Yes	No	No
Sacks et al. [88]	Israel	Construction	Construction	Skilled and unskilled	√					√		√		Yes	No	No
Brandenburg et al. [89]	United States	Construction	Construction	Skilled and unskilled			√	√				√	√	Yes	No	No
Thomas et al. [90]	United States	Bridge Construction	Construction	Skilled and unskilled	√	√		√				√	√	No	No	No
Lim and Alum [91]	Singapore	Construction	Construction	Skilled and unskilled				√					√	No	No	No
Srikanth et al. [92]	India	Construction	Construction	Skilled and unskilled		√	√	√					√	Yes	No	No
This study	Colombia	Building Construction	Pre-construction	Unskilled	√	√	√			√	√			Yes	Yes	Yes

synthesizes and classifies the most relevant literature identified through a systematic review, applying explicit inclusion and exclusion criteria. The selected publications are organized into four categories that reflect the dominant research designs: case studies, literature reviews, methodological frameworks, and impact analyses. In parallel, they are mapped to five topical areas that capture the principal human resource management perspectives in construction: recruitment and selection, forecasting, allocation and improvement, management practices, and productivity. Each study is further assessed against three workforce-oriented questions that align with the objective of this paper: whether it addresses human resource planning, whether it addresses human resource allocation, and whether it incorporates tools that support allocation decisions. This classification indicates that, while several studies contribute important insights or isolated methods, fewer provide an end-to-end framework that links estimation, allocation, and schedule feasibility through an integrated and operational workflow. Addressing this limitation, the present research proposes a methodological framework that enhances human resource planning and allocation by combining traditional project management practices with BIM-enabled information extraction and Line of Balance-based visual programming. The artifact was implemented, validated, and calibrated across two residential projects, providing empirical evidence of its applicability to repetitive building construction processes.

3. Research Method

Effective human resource planning in construction projects is a critical factor in ensuring project success, particularly in meeting deadlines, controlling costs, and meeting quality standards. In this study, the PMBOK Guide, Sixth Edition [44], was adopted as a reference framework, which divides resource management into key phases: (1) Resource Management Planning, (2) Activity Resource Estimation, (3) Resource Acquisition, (4) Team Development, (5) Team Management, and (6) Resource Control. This work focused on the first two phases, which form the foundation for accurate human resource planning in construction projects. The primary objective was to develop an artifact that facilitates the planning and estimation of required human resources from the early stages of these projects, integrating innovative methodologies such as Lean Construction, LOB, BIM methodology, and traditional processes. This artifact was created using the Design Science Research (DSR) methodology, which combines theoretical foundations with the practical realities of the problem, offering a valuable and effective solution.

Methodologically, DSR focuses on solving real-world problems by creating artifacts that provide both practical and theoretical value. As defined by Peffers et al. [93], the DSR process comprises five key stages: (1) problem identification, (2) definition of solution objectives, (3) design and development of the artifact, (4) evaluation and validation of the artifact, and (5) presentation of results. In this study, applying this approach to creating a framework for human resource planning and allocation, five phases were proposed for developing the method: (1) identifying the necessary processes for human resource planning, (2) designing BIM-LOB workflows, (3) developing the framework for human resource planning by integrating BIM, LOB, and general process map workflows, (4) validating and calibrating the human resource planning framework, and (5) feedback on the framework (see Figure 1). Detailed descriptions of these phases are provided in the following sections.

3.1. Identification of Processes for Human Resource Planning

This study employed a systematic literature review to identify and formalize processes related to human resource planning and allocation in building construction projects. The review was designed to ensure transparency, reproducibility, and analytical rigor, as the resulting corpus formed the empirical basis for defining the process structure later incorporated into the proposed BIM–LOB framework [94]. Rather than treating the literature search as a broad thematic exploration, the review was structured as an evidence synthesis process to capture studies that explicitly addressed workforce planning logic, allocation decisions, or process-oriented approaches relevant to building construction. The systematic review employed in this study was based on the four fundamental principles outlined by Briner and Denyer [95]: (1) the application of a systematic method to address a research question, (2) the use of a defined method for the study, (3) the replicability and repeatability of the process, and (4) the presentation of a summary and synthesis of the evidence. Guided by these principles, the literature review was structured into five stages: (1) formulation of the research question, (2) search for relevant studies, (3) selection of documents, (4) analysis of the evidence, and (5) synthesis of results, ensuring a thorough and comprehensive examination of the existing literature on human resource planning in construction projects.

The search process was carried out in Scopus, chosen for its coverage of peer-reviewed literature in construction management, project management, and engineering-related research. The complete Boolean search strings used in each database are reported in

Table 2. Keywords and Boolean operators used to locate relevant studies.

Keywords	B.O. *	Keywords	B.O. *	Keywords
Human	“AND”	Resource	“AND”
“OR”		“OR”
Labor		Management		Building
“OR”		“OR”		“OR”
HR		Planning		Housing
“OR”		“OR”		“OR”
Staff		Assignment		Residential
“OR”		“OR”
Workers		Allocation

* B.O.: Boolean operators used to establish logical relationships and search or filtering criteria within the analysis presented in the table.

, replacing the previous keyword-only presentation to enhance reproducibility. The search was limited to the period (1990–2025), with the following filters applied: English, articles only, and Engineering, Computer Science, Project Management, and Environmental Sciences. After retrieval, all records were exported to Mendeley Cite 2025 (v1.69.1), where duplicate detection was performed using DOI matching, title comparison, and manual verification of bibliographic metadata. This step resulted in a refined dataset for screening, preventing the same contribution from being evaluated more than once under different indexing forms. The screening process was conducted in two stages. First, titles and abstracts were reviewed to exclude clearly irrelevant records. Second, potentially eligible studies were assessed through full-text reading. This two-stage review was conducted by 5 researchers, with disagreements resolved through consensus discussions. This approach enhanced traceability and minimized the risk of selection bias during the eligibility assessment.

The inclusion criteria were defined to ensure direct relevance to the research objective: (1) the study had to explicitly address human resources within construction-project environments, excluding works that treated human resources only from a general organizational or administrative perspective without project-planning relevance; (2) it had to report processes, procedures, methods, or structured practices related to human resource planning or allocation in building construction, excluding documents that discussed productivity, labor, or staffing without process-level evidence relevant to planning or allocation, as well as studies focused on sectors outside construction or on non-building project domains; and (3) the publication had to provide accessible full text and sufficient methodological detail for evidence extraction, excluding duplicate records, editorials, notes, and other sources not suitable for systematic synthesis. After applying these criteria, the screening process reduced the initial pool of 1617 records to 29 studies for detailed analysis. To improve reporting transparency, the document selection process is now illustrated through a PRISMA-style flow diagram that shows the number of exclusions at each stage along with the reasons, providing a clearer and more auditable overview of the review pathway than the previous version of Figure 2.

To strengthen the reliability of the extracted evidence, the full-text studies that passed eligibility screening underwent a quality appraisal process before the synthesis phase. The appraisal was based on 3 evaluative criteria: clarity of the study’s objective, methodological soundness, explicit connection to building construction, specificity of human resource planning or allocation approach, and the extractability of process information relevant to the framework. Each study was scored using a Likert scale, e.g., 1–5 scale, and only those exceeding the predefined quality threshold were included for final evidence extraction. This step was included because thematic relevance alone was insufficient to determine the weight of each study during synthesis. By applying this additional filter, the review prioritized studies with stronger methodological consistency and more explicit procedural content, thereby enhancing the reliability of the 23 processes ultimately identified. The final sample was then analyzed through structured evidence extraction focused on planning stages, resource estimation logic, allocation rules, sequencing mechanisms, and supporting tools. This analytical approach facilitated the synthesis of a process-oriented corpus that directly informed the methodological artifact proposed in this paper and increased the internal validity of the conclusions derived from the review.

After the final sample of 29 studies was defined, the derivation of the 23 processes followed a structured coding and synthesis protocol. A standardized extraction matrix was first created to record, for each study, the project context, project stage, human-resource focus, planning or allocation procedures described, supporting tools, and explicit process-level actions. The coding unit was any sentence, table, figure, workflow, or methodological description that referred to a discrete planning or allocation action, such as defining work packages, estimating quantities, identifying resource requirements, assigning crews, verifying activity durations, or consolidating workforce demand by activity. Descriptive open codes were initially assigned using the terminology found in the source studies. These open codes were then normalized into action-oriented process labels using a verb-object structure, for example “define work packages,” “quantify work volumes,” “formulate work crews,” or “assign work crews to each activity.”

The coding procedure was conducted in three stages. In the first stage, two researchers independently reviewed the 29 full-text studies and coded all process-level evidence related to workforce planning and allocation. In the second stage, the preliminary codes were compared to identify exact matches, synonymous expressions, overlapping procedures, and inconsistent interpretations. Disagreements were discussed in consensus meetings with the participation of the research team. When disagreement persisted, the decision rule was to retain the code only if it met three conditions: it represented an explicit planning or allocation action, it could be linked to at least one documentary source, and it contributed to the construction of a coherent workflow for estimating, assigning, or verifying human resources. In the third stage, the consolidated codes were grouped into broader categories by comparing their function within the planning sequence and their relationship with the PMBOK Sixth Edition resource-management processes. PMBOK was used as a reference structure to organize and standardize the process logic, whereas the literature review provided the empirical evidence for process identification and contextual adaptation to construction projects. Inter-rater reliability was considered through qualitative agreement rather than through a statistical coefficient. This decision was adopted because the objective of the coding process was not to estimate the frequency of themes, but to synthesize process-level actions from heterogeneous studies into a reproducible methodological framework. Reliability was therefore addressed by independent double coding, comparison of coding outputs, explicit discussion of disagreements, consensus-based reconciliation, and preservation of an audit trail.

The relationship between PMBOK and the processes extracted from the literature was clarified through a source-classification procedure. PMBOK was used to define the general management logic, including the need to plan resources, estimate activity resources, structure work through the WBS, link resources to activity durations, and connect planning decisions to schedule and budget baselines. In contrast, the reviewed literature provided construction-specific evidence on how these generic management principles are translated into operational planning actions, such as defining construction phases, identifying work packages, quantifying work volumes, establishing productivity assumptions, formulating work crews, assigning crews to activities, and verifying crew continuity through location-based representations. Therefore, the final process set was not derived exclusively from PMBOK or exclusively from the literature; rather, it was produced through a qualitative synthesis that distinguished between PMBOK-inherited logic, literature-observed practices, and hybrid processes supported by both sources.

Since the purpose of this phase was to develop a methodological framework rather than to test statistical hypotheses, no inferential statistical analysis was conducted. The synthesis followed a qualitative evidence-integration approach, consistent with the Design Science Research orientation of the study. The descriptive percentages reported in the Section 4 were used only to characterize the thematic emphasis of the reviewed corpus and were not interpreted as statistical evidence of prevalence or causal association. The reconciliation between PMBOK and the literature followed four decision rules. First, when a process was explicitly defined in PMBOK and also appeared in construction-planning studies, the PMBOK terminology was retained, but its operational meaning was adapted to the construction context. Second, when the literature described a construction-specific action not explicitly detailed in PMBOK, the action was retained if it was necessary for estimating, assigning, or verifying human resources. Third, when PMBOK and the literature used different terms for similar planning actions, the final label was standardized using an action-oriented verb-object structure. Fourth, when a generic PMBOK process was not supported by process-level evidence in the reviewed studies or exceeded the planning scope of this research, it was not retained as a standalone process. These rules allowed the framework to preserve consistency with PMBOK while incorporating the operational specificity required for workforce planning and allocation in building construction projects.

3.2. Designing BIM–LOB Workflows

The integration of Building Information Modeling (BIM) into human resource planning for building construction projects required the development of a structured methodological framework grounded in clearly defined project lifecycle phases and supported by internationally recognized guidelines, including the BIM Project Execution Planning Guide, V.3.0, by Penn State [96] and the Lean Deployment Planning Guide [97]. This framework was designed as an operational architecture to align strategic objectives, information flows, and workforce allocation processes within a collaborative digital environment. Its development was organized into four interrelated stages. First, project-specific objectives and corresponding BIM uses were identified to ensure that each digital application addressed explicit human resource planning requirements. Second, the BIM execution process was defined through detailed workflows that established responsibilities, task sequencing, and mechanisms for interdisciplinary coordination. Third, the necessary information exchanges were defined to ensure consistency in personnel allocation decisions. Finally, a support infrastructure integrating the LOB methodology was configured to synchronize production scheduling with workforce distribution. This integration strengthened operational traceability, enhanced alignment between planning and execution, and consolidated a methodological framework that articulates digital modeling processes with systematic resource control in construction projects.

The initial design of the BIM-based methodological framework began with the rigorous identification of project objectives and specific BIM uses, following the structure proposed by Messner et al. [96]. This stage involved an analytical evaluation of BIM applications with the greatest potential to enhance the efficiency of human resource planning and allocation in building construction projects. To support this selection process, the Relative Utility Index (RUI) was adopted as a quantitative mechanism to assess the perceived relevance of different BIM uses within the study context. The analysis considered 25 uses identified in the Pennsylvania BIM Execution Planning Guide [96], widely recognized as a methodological reference for BIM implementation in construction. A structured data collection instrument was administered to project management experts with experience in BIM methodologies and workforce planning, using a five-point Likert scale to evaluate the utility of each identified use. The collected data were processed to calculate the RUI, thereby establishing a prioritization hierarchy based on expert perception. The selection criterion focused on the fourth quartile of the distribution, retaining BIM uses exceeding the 75% relevance threshold. The RUI was computed according to the formulation presented in Equation (1), providing a statistically grounded basis for integrating the most impactful BIM applications into the proposed methodological workflow.

$$RUI={\displaystyle \frac{{\displaystyle {\sum }_{i=1}^N}{U}_i}{N\times U_{máx}}}$$

(1)

U_i represents the utility value assigned to each BIM use by respondent i, N is the total number of respondents (N = 20), and U_máx is the maximum possible utility value (U_máx = 5).

Table 3. Profile of the surveyed experts.

ID	Profession (Degree)	Role	Years of Experience
E1	Civil Engineering, Ph.D.	Academic/Researcher	>10 years
E2	Civil Engineering, Ph.D.	Academic/Researcher	>5 years
E3	Civil Engineering, Ph.D.	Academic/Researcher	>15 years
E4	Architecture, M.Sc.	Builder	>15 years
E5	Civil Engineering, Spec.	Designer	>3 years
E6	Civil Engineering	Academic/Researcher	>3 years
E7	Architecture	Consultant/Supervisor	>5 years
E8	Civil Engineering	Consultant/Supervisor	>3 years
E9	Civil Engineering	Academic/Researcher	>3 years
E10	Civil Engineering	Academic/Researcher	>3 years
E11	Civil Engineering, M.Sc.	Builder	>5 years
E12	Civil Engineering, Spec.	Consultant/Supervisor	>10 years
E13	Civil Engineering	Builder	>5 years
E14	Civil Engineering	Academic/Researcher	>3 years
E15	Civil Engineering	Builder	>3 years
E16	Civil Engineering	Builder	>3 years
E17	Civil Engineering, Spec.	Academic/Researcher	>5 years
E18	Civil Engineering	Academic/Researcher	>3 years
E19	Civil Engineering	Academic/Researcher	>3 years
E20	Civil Engineering, Spec.	Academic/Researcher	>3 years

outlines the professional profiles of the experts who participated in the evaluation process and were selected based on predefined qualification criteria. These criteria required a minimum of three years of experience in building construction, BIM implementation, and workforce planning, thereby ensuring technical consistency and domain expertise across the collected responses. The RUI formulation was adapted from methodological developments previously reported in the specialized literature [98,99,100,101], thereby enabling the development of a quantitative procedure aligned with established academic standards. This methodological adaptation facilitated the estimation of the perceived utility of each BIM use for project management and strategic workforce allocation.

The expert panel used to prioritize BIM uses was configured through purposive sampling, consistent with the exploratory nature of this stage. The objective was not to obtain a statistically representative sample of the construction sector, but to collect technically informed judgments from professionals with sufficient knowledge of BIM implementation, project planning, workforce estimation, and building construction processes. Potential participants were identified through academic and professional networks related to construction management, BIM-based project delivery, design coordination, supervision, and residential building execution. Each expert was invited individually and completed the assessment independently, without group discussion or access to other participants’ responses. This procedure was adopted to reduce response dependence and minimize potential influence among evaluators. None of the experts was part of the author team, and the responses were processed anonymously and with equal weighting.

The inclusion criteria for the 20 experts were defined before administering the instrument. Participants were required to have at least 3 years of professional or academic experience in building construction, BIM implementation, project planning, workforce planning, design coordination, supervision, or construction execution. The panel intentionally included academics/researchers, builders, designers, consultants, and supervisors to reduce the risk of a single-role interpretation of BIM utility. Although purposive expert sampling may introduce selection bias, this risk was addressed by defining explicit qualification criteria, including participants from different professional roles, applying the same instrument to all experts, anonymizing responses, and avoiding group-based consensus during rating. This strategy was considered appropriate because the purpose of the RUI analysis was to identify BIM uses with high perceived relevance for workforce planning and allocation, not to estimate population parameters.

To strengthen construct validity, the instrument was based on the 25 BIM uses defined in the BIM Project Execution Planning Guide [96], which provided a recognized and standardized taxonomy for BIM implementation. Each expert evaluated the utility of each BIM use specifically in relation to workforce planning and allocation in building projects, using a five-point Likert scale. The construct assessed was therefore not general BIM usefulness, but perceived utility for supporting human-resource planning decisions, including quantity extraction, phase planning, scheduling, site utilization, cost estimation, construction-system definition, and visual control. The RUI was used to normalize the ordinal ratings and rank the BIM uses according to perceived utility, following prior applications of this index in construction-management research [98,99,100,101].

The RUI > 0.75 cut-off was adopted for two complementary reasons. First, from a measurement perspective, a value above 0.75 indicates that the average rating is located in the upper range of the five-point scale, corresponding to a mean score above 3.75 out of 5 and therefore reflecting high perceived utility. Second, from an empirical ranking perspective, the results showed a clear separation between the seventh-ranked BIM use, 3D Control and Planning (RUI = 0.77), and the eighth-ranked BIM use, Maintenance Scheduling (RUI = 0.67). This 0.10-point gap indicates a natural break in the ranking and supports the selection of the first seven BIM uses as the high-utility group. A sensitivity check was also performed. Reducing the threshold to RUI > 0.70 did not change the selected set of BIM uses, because no additional BIM use was located between 0.70 and 0.75. In contrast, increasing the threshold to RUI ≥ 0.80 would retain only Phase Planning, Programming, and Site Utilization Planning, excluding Cost Estimation, Site Analysis, Construction System Design, and 3D Control and Planning, which are operationally necessary for linking quantities, resources, sequencing, and visual verification within the BIM–LOB workflow. Therefore, RUI > 0.75 was retained as a balanced threshold that is selective enough to identify highly valued BIM uses while preserving the functional completeness required by the proposed framework.

To ensure a rigorous evaluation of BIM uses within the project, five strategic objectives were defined to guide the analytical framework: (1) promote collaborative workflows, (2) incorporate visual tools to support decision-making, (3) strengthen communication and coordination across multidisciplinary teams, (4) improve accuracy in resource estimation, and (5) increase productivity in residential building construction. These objectives were systematically compared with the BIM uses identified through the expert-based instrument, confirming alignment with the project’s operational goals. Based on this alignment, specific objectives were derived from the selected BIM uses, and corresponding applications were formally assigned to each objective. Subsequently, the design processes required to implement BIM across project activities were defined. Process maps were developed to represent the logical sequence of activities and their interdependencies, following the methodology proposed by Messner et al. [96]. These maps incorporated parameters that enabled the identification of inputs, outputs, and information requirements at each stage. This structured representation facilitated a detailed understanding of dependencies and critical interactions, integrating traditional practices derived from the systematic review and the PMBOK Sixth Edition [44] with prioritized BIM uses, thereby consolidating a coherent and operational methodological framework.

The structured process map integrates conventional planning procedures with the BIM uses identified as most relevant to human resource planning. This integration established an operational framework that links established management practices with digital tools to enhance decision-making reliability. To accurately assign LOB, the Lean Deployment Planning Guide proposed by Messner et al. [97] was adopted, presenting a structured implementation pathway through four interrelated stages: initiate, select, plan, and integrate. During the initiation stage, Project Conditions of Satisfaction (P-COS) were defined to establish measurable performance targets. In the selection phase, Lean methods were evaluated for their contribution to these conditions. The planning stage defined responsibilities, implementation timelines, and application mechanisms. Finally, the integration stage embedded the selected methods into the overall project structure, ensuring consistency between strategic planning and operational execution. This systematic approach strengthened the articulation between Lean principles and BIM-enabled processes within a unified methodological environment.

Within the specific context of this study, the scope was defined to evaluate the satisfaction conditions delivered by the Lean tool, including the visual identification of inconsistencies in precedence relationships, the detection of excessive activity durations, and the anticipation of tasks through space–time analysis derived from LOB. Based on this evaluation, LOB was selected as the tool that best fulfilled the established objectives. During the planning stage, key implementation questions were addressed by selecting scheduling software, designing graphical line diagrams, establishing activity durations, and assigning responsibilities for each task. The procedure was subsequently incorporated into the process map, clarifying how LOB supports both traditional planning processes and BIM-enabled workflows. Validation was conducted by applying the methodological framework to two case studies, thereby calibrating the framework and verifying operational consistency. This structured procedure enhanced workforce planning by integrating Lean principles with digital modeling, providing analytical traceability and methodological coherence in strategic human resource allocation.

3.3. Developing the Framework for Human Resources

The methodological framework was developed by integrating the processes outlined in the PMBOK Sixth Edition [44] with findings from a systematic review focused on human resource planning in building construction. This integration provided a solid conceptual foundation for the proposed artifact, aligning internationally recognized management standards with specialized empirical evidence. Based on this foundation, a methodological artifact was developed, incorporating a general process map structured under the conceptual hierarchy of “Level 1: General BIM Map” and “Level 2: Detailed BIM Use Process Map” proposed by Messner et al. [96]. This hierarchical configuration enabled differentiation between the workflow’s strategic overview and the operational breakdown of specific BIM uses across project phases. In parallel, the LOB tool was incorporated as an analytical mechanism to detect sequencing inconsistencies and scheduling deviations. The integration of traditional project management methodology, BIM-based modeling, and LOB analysis produced a comprehensive process map detailing the activities required to optimize workforce planning. The framework was developed within an iterative logic aligned with Design Science Research principles, promoting continuous refinement of the artifact through empirical validation rather than theoretical abstraction alone.

The validation and calibration phase was conducted by implementing the framework in real case studies, enabling assessment of its operational consistency and integration within residential construction contexts. This empirical application allowed adjustments to activity sequencing, refinement of critical interactions, and strengthening of traceability between strategic planning and on-site execution. The process map was subsequently updated based on observed outcomes, ensuring the artifact evolved in response to empirical evidence. As a result, the consolidated framework integrates three complementary dimensions: the formal structure of traditional process-based management, the visualization and modeling capabilities inherent to BIM, and the space–time analytical perspective provided by LOB. This methodological convergence establishes a more robust planning environment in which workforce allocation is grounded in structured, coherent, and verifiable information. From a systemic standpoint, the artifact not only digitizes existing procedures but also reconfigures planning logic through the explicit articulation among processes, information flows, and temporal control mechanisms. Consequently, the study contributes a reproducible methodological approach to enhancing efficiency and reliability in workforce allocation for residential building projects.

3.4. Validating and Calibrating the Human Resource Planning Framework

The developed methodological framework was rigorously validated and calibrated by implementing it in two real-world case studies to assess its operational consistency and integration capacity within residential construction environments. In both applications, the BIM methodology was deployed in coordination with specialized tools for generating LOB diagrams and construction scheduling software, enabling alignment between theoretical planning and temporal execution. To enhance interoperability, an external plug-in was incorporated to generate LOB charts in .csv format partially, facilitating structured data processing within integrated digital environments. In parallel, quantity extraction was automated in Dynamo (v3.2.1.5366) by developing a custom script to improve efficiency and precision in retrieving model-based metrics. An additional script was created to streamline data import for activity performance and explicitly link it to the Work Breakdown Structure (WBS). This technical configuration established direct traceability between modeling, planning, and temporal control processes. The framework implementation followed an iterative logic, enabling progressive refinements based on empirical observations, thereby strengthening the coherence of the integrated system.

The empirical validation phase was structured as a cyclical process to identify opportunities for improvement in the articulation among traditional management processes, BIM-based modeling, and space–time analysis using LOB. Each case study was developed with a high level of geometric and informational detail, ensuring precise representation of activities, productivity rates, and construction sequences (see

Table 4. Characteristics of the case studies.

Characteristics	Case #1: Single-Family Housing	Case #2: Vival del Carvajal
Status	Completed	Completed
Location	Cajicá, Colombia	Bogotá, Colombia
Built area	295 m2	3660 m2
Stages with BIM integration	Planning	Planning
Stages with LOB integration	Planning	Planning
Duration	3 months	22 months
Maximum workers	16 simultaneous workers	108 simultaneous workers
Cost ($)	COP 97,087,426 (USD 27,267)	COP 7,381,873,823 (USD 2,073,199)
Project scope	Single-family housing used for desktop testing consists of 2 floors and a reinforced concrete framed structure.	Mid-rise residential building with eight floors, one basement, 21 apartments, reinforced concrete framed structural system, and architectural components.
BIM Model
Application mode	Retrospective planning-stage implementation using completed-project documentation and BIM-based planning data.
Validation scope	Verification of workflow feasibility, model-based quantity extraction, productivity assignment, crew sizing, and LOB-based sequencing.
Field decision influence	The framework did not modify field execution decisions because the project had already been completed; it was used to reconstruct and test planning decisions.
Productivity-rate source	Project planning records, unit-price analyses, and expert review of production assumptions
Benchmarking approach	Sensitivity analysis of productivity assumptions and comparison with a traditional planning baseline

Note: Project values are reported in Colombian pesos (COP) and US dollars (USD) to facilitate interpretation for both local and international readers. USD equivalents were calculated using the official Colombian Representative Market Exchange Rate (TRM) certified for 24 April 2026: 1 USD = COP 3560.62. The conversion is provided for reporting purposes and does not include inflation adjustment or purchasing-power-parity correction.

). This modeling depth enabled examination of the consistency between initial resource estimates and outcomes from temporal simulation, facilitating calibration of workforce allocation parameters. The comparative analysis of the two cases provided insights into the methodological framework’s stability under varying residential project conditions. The artifact was subsequently refined by adjusting information flows, activity parameterization, and line-of-balance structuring. This procedure extended beyond technical verification, incorporating a comprehensive evaluation of methodological coherence in real planning and control scenarios. As a result, the validation process consolidated an integrated framework combining digital modeling, structured scheduling, and performance analytics, establishing a robust methodological framework for strategic workforce planning in building construction projects. To improve financial clarity and facilitate interpretation by international readers, the project values in Table 4 are reported in both Colombian pesos (COP), as the local currency of the case-study context, and US dollars (USD). Since the framework is applied during the planning stage, these values are referred to as estimated project budgets rather than project costs, following the terminology commonly used in project management for planned financial baselines. The USD equivalents were calculated using the official Colombian Representative Market Exchange Rate (TRM) certified for 24 April 2026: 1 USD = COP 3560.62.

The two case studies were completed residential building projects; therefore, the framework was applied retrospectively as a planning-stage desktop implementation using available project documentation, BIM models, planning records, and reconstructed workflow data. In this context, validation should not be interpreted as field validation during live construction execution or as evidence that the framework directly changed decisions on site. Rather, the validation focused on verifying the operational feasibility, internal consistency, data traceability, and reproducibility of the proposed BIM–LOB workflow under real project conditions. The retrospective application made it possible to test whether the framework could reproduce the type of workforce-planning decisions that project teams would make before baseline approval, including quantity extraction, productivity assignment, duration estimation, crew sizing, crew allocation, sequencing verification, and identification of spatial–temporal inconsistencies. The case studies therefore validated the methodological logic and planning applicability of the framework, while future research should examine its prospective use during active project planning and subsequent monitoring and controlling phases.

3.5. Feedback on the Framework

The proposed framework was evaluated through an expert-judgment process to assess its technical relevance and its capacity to generate value in residential building projects. This phase identified structural deficiencies in workforce allocation and planning practices that the framework seeks to address by systematically integrating traditional management processes, BIM, and space–time analysis. Validation followed an in-person presentation that formally introduced the conceptual foundations, artifact design, and preliminary case study findings. Participating experts had demonstrated experience in project management and BIM implementation, ensuring that assessments were grounded in operational and strategic criteria. To quantitatively analyze the collected perceptions, the Relative Utility Index (RUI) methodology was used in line with established analytical procedures reported in prior studies [98,99,100,101]. This approach converted ordinal evaluations into normalized indicators of perceived utility, enabling structured comparisons across assessed dimensions. The analysis was restricted to residential building projects to maintain consistency with the empirical validation scope defined for the artifact.

The expert panel was configured through purposive sampling, as the objective of this phase was to obtain technically informed judgments from professionals capable of assessing the applicability, clarity, and practical relevance of the proposed BIM–LOB framework. The selection was not intended to produce a statistically representative sample of the construction sector, but rather to ensure that the evaluators had sufficient domain knowledge to judge the framework from planning, technical, managerial, and operational perspectives. The inclusion criteria were defined before the evaluation process and considered four aspects: professional background, experience in the construction industry, familiarity with project planning or BIM-supported management, and capacity to evaluate workforce planning decisions in building projects (see

Table 5. Expert selection criteria for framework evaluation.

Criterion	Operational Definition	Purpose Within the Evaluation
Professional background	Professionals or academics related to civil engineering, architecture, construction management, design, supervision, consultancy, construction execution, or ownership	To ensure technical understanding of building project planning and management
Professional experience	Minimum of three years of experience in the construction industry	To ensure sufficient exposure to planning, coordination, and resource-management problems in practice
Technical familiarity	Experience or direct familiarity with at least one of the following areas: BIM-supported project management, scheduling, workforce planning, resource estimation, construction-site coordination, or residential building projects	To ensure that the experts could evaluate the practical applicability of the BIM–LOB framework
Evaluation perspective	Inclusion of builders, academics/researchers, consultants/supervisors, designers, and one owner representative	To obtain feedback from multiple decision-making and operational viewpoints involved in building projects
Scope alignment	Experience related to building construction or planning processes applicable to residential projects	To maintain consistency with the empirical scope of the case studies and the proposed framework

). Specifically, experts were invited when they had a professional or academic background related to civil engineering, architecture, construction management, project supervision, design, consultancy, or ownership; at least three years of experience in the construction industry; and direct familiarity with at least one of the following areas: project planning and scheduling, BIM implementation, resource estimation, workforce coordination, construction-site management, or residential building projects. These criteria were established to ensure that the panel could assess not only the conceptual coherence of the framework, but also its operational feasibility in planning environments where work packages, quantities, productivity assumptions, crew sizing, and location-based sequencing must be coordinated.

The assessment instrument was designed to collect expert judgments on the perceived utility of the proposed BIM–LOB framework for mitigating recurrent deficiencies in workforce planning and allocation. Before completing the instrument, participants attended a structured presentation in which the research problem, the methodological logic of the framework, the BIM–LOB workflow, the two case-study applications, and the expected planning outputs were explained. This step was included to ensure that all experts evaluated the same artifact under a common interpretive basis. The instrument was then organized into two main sections. The first section collected professional profile information, including professional role, years of experience, and relationship with construction project planning, BIM-supported management, design, supervision, or construction execution. This information was used to characterize the expert panel and to verify the technical relevance of the responses. The second section asked experts to assess the extent to which the framework could mitigate a set of recurrent deficiencies associated with workforce planning and allocation in building projects.

The deficiencies included in the instrument were derived from the literature review, the traditional planning processes identified in this study, and the implementation problems observed during the development of the BIM–LOB workflow. The evaluated items addressed four dimensions: input reliability, planning and sequencing consistency, resource allocation feasibility, and digital-process automation. Input reliability included deficiencies related to the incorrect estimation of work quantities, productivity-rate allocation, and quantification of human resources. Planning and sequencing consistency included inadequate definition of precedence relationships, poor critical-path analysis, deficiencies in time and budget baseline planning, and scheduling inaccuracies caused by limited visual methods. Resource allocation feasibility included overallocation of work crews and inadequate crew assignment during the planning phase. Digital-process automation included low automation in human resource planning and allocation, poor automation of planning activities, and limited integration of emerging technologies and methodologies. Each item was evaluated using a five-point Likert scale, where 1 represented very low perceived mitigation capacity and 5 represented very high perceived mitigation capacity. The responses were processed using the Relative Utility Index (RUI), allowing the ordinal evaluations to be normalized and ranked according to the perceived contribution of the framework.

The assessment instrument was distributed to a selected group of 31 professionals with experience in BIM-supported project management, ensuring that responses reflected sector-specific expertise. All participants had at least three years of experience in the construction industry. Demographic analysis revealed that 39% of participants reported more than five years of professional experience, while 61% indicated three to five years in the construction industry (see

Table 6. Experience of the experts consulted.

Experience	Number of Professionals	Percentage
3 to 5 years	19	61%
5 to 10 years	8	26%
10 to 15 years	3	10%
More than 15 years	1	3%
Total	31	100%

). This distribution reflects a sample of mid-level and experienced practitioners, strengthening the robustness of interpretive conclusions. Regarding professional roles, 45% identified as builders, 19% as academics or researchers, 13% as consultants or inspectors, 19% as designers, and 4% as owners (see

Table 7. Role of the Experts Consulted.

Role	Number of Professionals	Percentage
Builder	14	45%
Academic/Researcher	6	19%
Consultant/Supervisor	4	13%
Designer	6	19%
Owner	1	4%
Total	31	100%

). This professional diversity allowed the framework to be evaluated from multiple operational and decision-making perspectives. Overall, the RUI application enabled quantification of the framework’s perceived utility in practical planning environments, providing empirical evidence supporting its operational feasibility in strategic human resource management for residential construction projects.

To reduce the risk of biased interpretations derived from the composition of the expert panel, three methodological precautions were adopted. First, the evaluation was based on a purposive expert-judgment strategy rather than on stakeholder representativeness. Therefore, participants were selected according to their technical capacity to assess workforce planning, BIM-supported management, scheduling, construction coordination, and residential building processes. This criterion explains the larger proportion of builders, designers, consultants/supervisors, and academics/researchers, as these professional groups are directly involved in defining work packages, estimating quantities, assigning productivity rates, structuring schedules, coordinating crews, and validating construction planning workflows. Second, all participants evaluated the framework using the same structured assessment instrument and the same Likert scale, which reduced variability associated with different interpretation criteria and enabled comparison through a normalized Relative Utility Index. Third, the expert survey was not used as the only source of evidence supporting the framework. The perceived-utility results were interpreted together with the implementation and calibration of the BIM–LOB workflow in two residential case studies, which provided project-based evidence regarding quantity takeoff, sequencing verification, crew allocation, and workflow traceability. However, the authors recognize that the participation of only one owner limits the extent to which the results can represent owner-side perceptions as an independent stakeholder group. For this reason, the owner’s response was retained as an additional decision-making perspective, but no separate owner-level inference was made. The findings are therefore interpreted as multi-professional expert judgments on the operational feasibility and perceived utility of the framework, with stronger representation from the technical and managerial actors most directly involved in workforce planning and allocation. Future studies should expand the owner-side sample and compare perceptions across stakeholder groups to further examine whether the perceived benefits of BIM–LOB integration vary according to contractual role, decision-making responsibility, or project governance structure.

4. Results

This section presents the proposed methodological framework for human resource planning in building construction by integrating BIM and LOB. First, the section describes the processes associated with human resource planning and allocation within the traditional methodology, establishing a baseline set of planning activities against which the contributions of BIM and LOB are articulated. Second, it defines the BIM uses considered most relevant to workforce planning and clarifies their implementation objectives, specifying how each use contributes to improving the estimation, allocation, and coordination of crews. Third, the section examines the relationship between the selected BIM uses and the traditional planning processes, highlighting where BIM can strengthen specific tasks, reduce manual effort, and improve the consistency of planning assumptions. Fourth, it presents the BIM–LOB workflows developed for human resource planning, detailing the sequence of actions, data exchanges, and intermediate outputs required to connect model-based quantities and productivity assumptions with location-based scheduling and crew assignment. Finally, the section integrates these elements into a general process map that combines traditional planning workflows with BIM and LOB procedures into a unified methodological framework for improving human resource allocation in construction projects. The framework is developed following a Design Science Research approach, in which the artifact is iteratively refined through a cyclic process of design, implementation, evaluation, and improvement. This cycle is operationalized through two case studies, presented in Section 5, which are used to validate and calibrate the proposed process map.

4.1. Process for Human Resource Planning and Allocation

A systematic literature review was conducted to formalize the processes required for human resource planning and allocation in construction projects. Starting from a corpus of 1617 documents, a three-stage screening procedure based on explicit inclusion and exclusion criteria reduced the sample to 29 documents for in-depth analysis. Evidence was collected through a detailed review of these 29 studies to extract process-level contributions that could be consolidated into a coherent planning logic. The distribution of research emphases evidences an important imbalance: 72.41% of the selected documents focus on human resource planning, 20.69% address allocation, and only 6.90% explicitly discuss the use of tools to support human resource management. This pattern indicates that, although planning principles are frequently discussed, operational allocation procedures and workflows supported by tools remain relatively underdeveloped, with only two studies offering detailed, practical descriptions of the planning and allocation processes.

Based on the synthesis of the extracted evidence and guided by the PMBOK Guide, Sixth Edition [44], as the reference baseline for traditional methodology processes, 23 processes were identified and organized into five categories (see

Table 8. Processes for human resource planning.

Id	Categories	Id	Processes	References
C1	Project scope and duration	P1	Define the project scope	[30]
		P2	Define the project duration	[30,37,51]
C2	Structuring and quantification	P3	Define the construction process	[44,52,78]
		P4	Define the project phases	[44,52]
		P5	Define the work packages	[30]
		P6	Define the activities within the work packages	[44,52]
		P7	Establish the attributes of each activity	[30,37,51]
		P8	Define the work breakdown structure (WBS)	[44,52]
		P9	Establish predecessors and successors	[30,37,51]
		P10	Quantify the work volumes	[30,37,51]
C3	Human Resource estimation and allocation	P11	Identify human resource requirements	[30,37,51]
		P12	Identify human resources	[30,37,51]
		P13	Estimate and quantify the required human resources	[44,52,78]
		P14	Formulate work crews	[30,37,51]
		P15	Assign work crews to each activity	[30,37,51]
C4	Schedule Baseline	P16	Estimate activity durations	[30,37,51]
		P17	Verify human resource assignments	[44,52,78]
		P18	Identify and analyze the project’s critical path	[30]
		P19	Estimate the schedule baseline	[30]
		P20	Consolidate human resources by activity	[30,51]
		P21	Compress the schedule	[30]
C5	Cost Baseline	P22	Unit price analysis (human resources + Equipment + Materials)	[30]
		P23	General project budget	[30]

). The first category, project scope and duration, includes defining project scope and duration as the boundary conditions for staffing. The second category, structuring and quantification, comprises processes that translate scope into executable work, including defining the construction process and phases, establishing work packages and activities, specifying activity attributes, defining the WBS, establishing predecessor–successor relationships, and quantifying work volumes. The third category, human resource estimation and allocation, captures the core workforce planning decisions: identifying human resource requirements and resources, estimating and quantifying required labor, formulating crews, and assigning crews to activities. The fourth category, schedule baseline, consolidates time-based planning and verification, including estimating activity durations, verifying human resource assignments, analyzing the critical path, establishing the schedule baseline, consolidating human resources by activity, and compressing the schedule when required. Finally, the cost baseline category includes unit price analysis and the general project budget, providing the link between staffing decisions and cost performance. The 23 processes presented in Table 8 correspond to consolidated process labels derived from the coding and synthesis protocol described in Section 3.1.

Table 8 should be interpreted as the result of an integrated synthesis between PMBOK-based logic and construction-specific evidence extracted from the systematic literature review. The PMBOK Guide provided the general process architecture and standardized terminology for traditional project management, whereas the reviewed studies provided operational evidence on how workforce planning and allocation are performed in construction contexts. Some processes were mainly inherited from PMBOK logic, particularly those associated with work structuring, resource estimation, duration estimation, and schedule or budget baselines. Other processes were mainly observed in the construction literature, especially those related to work-volume quantification, crew formulation, crew assignment, location-based sequencing, and verification of workforce continuity. A third group of processes was classified as hybrid because their terminology was aligned with PMBOK, but their operational content was specified using evidence from construction-planning studies.

4.2. BIM Uses and Implementation Goals in Human Resources Planning

A total of 25 BIM uses were first identified from the Pennsylvania BIM Guide [96], and their utility for human resource planning and allocation in building projects was assessed through expert judgment (n = 20) using the Relative Utility Index (RUI). The resulting ranking (see

Table 9. Relative Utility Index (RUI) for BIM uses in human resource planning.

Id	Categories	Id	BIM Uses	RUI	Rank
C1	Planning	U3	Phase Planning	0.88	1
C1	Planning	U4	Programming	0.88	1
C3	Construction	U16	Site Utilization Planning	0.80	3
C1	Planning	U2	Cost Estimation	0.79	4
C1	Planning	U5	Site Analysis	0.78	5
C3	Construction	U17	Construction System Design	0.78	5
C3	Construction	U19	3D Control and Planning	0.77	7
C4	Operation	U21	Maintenance Scheduling	0.67	8
C4	Operation	U24	Space Management/Tracking	0.64	9
C3	Construction	U15	3D Coordination	0.62	10
C3	Construction	U18	Digital Fabrication	0.56	11
C1	Planning	U1	Existing Conditions Modeling	0.55	12
C4	Operation	U22	Building System Analysis	0.53	13
C4	Operation	U20	Record Model	0.52	14
C2	Design	U7	Design Authoring	0.50	15
C4	Operation	U23	Asset Management	0.50	15
C2	Design	U6	Design Reviews	0.46	17
C2	Design	U14	Code Evaluation	0.46	17
C4	Operation	U25	Disaster Planning	0.45	19
C2	Design	U9	Structural Analysis	0.44	20
C2	Design	U12	Other Engineering Analysis	0.41	21
C2	Design	U13	LEED Evaluation	0.41	21
C2	Design	U8	Energy Analysis	0.40	23
C2	Design	U11	Mechanical Analysis	0.38	24
C2	Design	U10	Lighting Analysis	0.37	25

) shows a clear concentration of higher RUI scores in planning and construction-oriented uses, suggesting that experts primarily associate BIM-based human resource planning with (1) front-end definition and coordination of work packages and (2) the production-oriented control of execution. Specifically, Phase Planning and Programming obtained the highest RUI values (0.88), followed by Site Utilization Planning (0.80), Cost Estimation (0.79), Site Analysis (0.78), Construction System Design (0.78), and 3D Control and Planning (0.77). These results indicate that BIM’s perceived value for human resource planning is strongly linked to its capacity to structure work in executable phases, generate reliable quantity and cost information, and provide a shared digital representation that supports production control.

Building on this prioritization, the study focused on the BIM uses positioned in the upper quartile, operationalized as RUI > 0.75, and aligned them with the requirements of BIM–LOB integration for workforce planning. Accordingly, seven BIM uses were selected and re-coded for implementation within the proposed framework: Phase Planning (U₁), Scheduling (U₂), Site Utilization Planning (U₃), Site Analysis (U₄), Cost Estimation (U₅), Construction System Design (U₆), and 3D Control and Planning (U₇). This selection was then translated into five implementation objectives that guide how each BIM use contributes to human resource planning and allocation.

Table 10. Relationship between the proposed objectives and the selected BIM uses.

Id	BIM Implementation Objectives	BIM Uses *
		U1	U2	U3	U4	U5	U6	U7
Obj1	Promote collaborative workflows during planning and human resource allocation in construction projects	√	√					√
Obj2	Integrate visual tools (BIM-LOB) for planning and human resource allocation in construction projects	√	√					√
Obj3	Improve communication and coordination among professionals responsible for planning and human resource allocation	√	√				√	√
Obj4	Improve accuracy in estimating resources and activity durations during project planning		√			√		√
Obj5	Increase productivity in mid-rise building construction through a framework integrating BIM and LOB	√	√	√	√	√	√	√

* U₁: Phase Planning; U₂: Scheduling; U₃: Site Utilization Planning; U₄: Site Analysis; U₅: Cost Estimation; U₆: Construction System Design; U₇: 3D Control and Planning.

formalizes this alignment by mapping each objective to the BIM uses that enable it. Objective 1 (promoting collaborative workflows) and Objective 2 (integrating visual tools through BIM–LOB) rely primarily on the combined application of Phase Planning, Scheduling, and 3D Control and Planning, as these uses create a shared planning environment and provide the time-linked structure required for translating model-based information into LOB visualizations. Objective 3 (improving communication and coordination) expands this set by incorporating Construction System Design, reinforcing early constructability-oriented decisions, and reducing coordination losses that typically propagate into workforce disruptions. Objective 4 (improving accuracy in resource and duration estimation) is supported by Scheduling, Cost Estimation, and 3D Control and Planning, reflecting the central role of time- and cost-linked model information in producing defensible crew sizing and duration assumptions. Finally, Objective 5 (increasing productivity in mid-rise building construction) integrates all seven BIM uses, consolidating them into a single implementation logic in which tools such as Revit, Dynamo, Project, and Line of Balance jointly support process automation, resource management, and more consistent allocation of work crews.

4.3. Integration Between BIM Uses and Planning Processes

Figure 3 presents the crosswalk between the BIM uses prioritized through the RUI assessment and the traditional processes identified as candidates for replacement or supplementation within human resource planning and allocation. These traditional processes were derived from the systematic literature review and complemented by the PMBOK Guide (Sixth Edition) to capture the core steps required to plan, estimate, and allocate labor in building projects. The resulting mapping makes explicit how BIM adoption can move HR planning from document-based interpretations toward model-based decision support by linking each process to the BIM use that is most capable of improving its inputs, outputs, or verification logic. In this study, the selection of processes to be replaced or supplemented followed the criterion of functional fit and technological applicability, recognizing that BIM may fully automate some activities while only strengthening others through improved visualization, coordination, and data traceability.

4.4. BIM Workflows for Human Resource Planning

Workflows are structured sequences of interrelated activities that organize and coordinate operational processes within a production system. In construction environments, precise workflow definitions are essential for aligning planning, scheduling, and execution under a coherent managerial structure. This study developed a workflow specifically oriented toward human resource planning in building construction projects, with the aim of strengthening decision-making during the early project stages. The approach recognizes that inefficient on-site labor distribution is a critical source of productivity loss and schedule deviations. Consequently, the proposed model introduces process enhancements during the planning phase by systematically integrating digital methodologies and space–time analytical tools. The integration of Building Information Modeling (BIM) and LOB consolidates geometric, quantitative, and temporal data within a unified operational framework. Although each tool performs robustly when implemented independently, their coordinated application within a common methodological framework expands their analytical and managerial capabilities. This integration improves crew estimation accuracy, enhances activity synchronization, and clarifies responsibility assignment. From a strategic standpoint, the developed workflow aims to establish a more predictable planning environment, thereby reducing uncertainty and enhancing organizational efficiency in residential construction projects.

4.4.1. LOB Workflows

The implementation of the LOB methodology in construction project planning requires a rigorously structured procedure grounded in evidence from systematic academic inquiry. Drawing on a comprehensive literature review across specialized databases, a set of eight interdependent procedural stages was identified to guide the operational deployment of visual scheduling mechanisms in building projects [61,102,103]. These stages were not treated as isolated technical actions; instead, they were conceptualized as components of an integrated planning logic that strengthens temporal coherence and spatial continuity across repetitive construction activities. The sequential organization of these processes was embedded within the broader traditional planning structure and strategically aligned with selected BIM uses, thereby establishing a consolidated methodological architecture. Through this integration, LOB transcends its conventional role as a graphical scheduling technique and becomes a decision-support instrument that reveals systemic inefficiencies in execution strategies. Incorporating these structured steps into the process map reinforces planning transparency, enhances coordination between crews, and supports proactive identification of performance deviations. Consequently, consolidating LOB within the integrated framework contributes to a more analytically robust scheduling environment aligned with contemporary digital planning paradigms in construction management.

Figure 4 outlines the operational sequence for implementing LOB within building construction scheduling and illustrates the logical progression of the proposed methodology. The process begins with the explicit definition of project zones or production phases, establishing the spatial backbone for organizing repetitive activities. Next, tasks are formalized and parameterized, enabling the configuration of balance-line criteria tailored to project characteristics. Quantitative estimates of work volumes then provide the analytical basis for determining optimal crew sizes and resource distributions. Using these inputs, activity durations are calculated in line with assigned labor capacities, ensuring temporal feasibility and production continuity. The workflow then proceeds to the graphical visualization of the scheduling lines, facilitating spatiotemporal interpretation of activity interactions. Finally, the methodology incorporates a structured inconsistency analysis stage to detect discontinuities, overlapping sequences, or logic conflicts embedded within the schedule. This systematic progression enables planners to refine production strategies and adjust labor allocations before execution begins, thereby ensuring reliable workforce deployment and strengthening operational predictability in building projects.

4.4.2. LOB for Error Identification

The incorporation of the LOB methodology into scheduling environments, including Microsoft Project, extends beyond conventional Gantt-based representations by introducing a space–time perspective that exposes sequencing inconsistencies often undetected in traditional schedules [104]. The integration developed in this study, supported by an add-in that exports structured data in .csv format for subsequent visualization, enables a more rigorous examination of construction logic by linking activity duration, spatial progression, and crew productivity through the slope of each line. In the first case study, a two-story single-family dwelling, the LOB representation clearly highlights interference points among crews, abrupt slope variations that reflect productivity shifts, and temporal gaps between predecessor and successor activities. In the first case study, a two-story single-family dwelling, the workforce plan was structured into five work crews comprising a total of ten workers, with two workers assigned to each crew. This configuration was used to represent the minimum operational units required to execute the planned structural sequence while preserving continuity across the two project phases. Each crew was linked to specific activities in the schedule and then represented in the LOB diagram through a differentiated production line, allowing the analysis to connect crew size, type of work, activity duration, and spatial progression.

In the first case study, a two-story single-family dwelling, the workforce plan was structured into five work crews comprising a total of ten workers, with two workers assigned to each crew. The crews were defined according to the main work packages represented in the planning model. This configuration was used to represent the minimum operational units required to execute the planned structural sequence while preserving continuity across the two project phases. Each crew was linked to specific activities in the schedule and then represented in the LOB diagram through a differentiated production line, allowing the analysis to connect crew size, type of work, activity duration, and spatial progression. Under this configuration, the LOB representation highlighted interference points among crews, abrupt slope variations that reflected changes in production rates, and temporal gaps between predecessor and successor activities. Dividing the project into two phases and assigning five explicitly defined crews to concurrent work fronts enabled the identification of how minor adjustments to precedence logic could generate overlaps, idle periods, or incompatible crew movements that disrupt workflow continuity (see Figure 5). Therefore, the contribution of the LOB analysis was not limited to schedule visualization; it also provided a mechanism to verify whether the proposed crew structure was feasible in both time and space before finalizing the workforce plan (see Figure 5). This outcome challenges the adequacy of linear scheduling approaches in capturing actual production dynamics.

The second case study, conducted in an eight-story multifamily building with a basement and nine active crews, increases the level of complexity and further validates the strategic value of LOB as a diagnostic instrument (see Figure 6). The graphical output exposes convergence and divergence patterns among production lines that would remain concealed under task aggregation in conventional schedules. The coexistence of multiple concurrent work fronts underscores the need to synchronize execution rates to prevent unproductive accumulations or idle periods across crews. In this context, line slopes take on comparative significance, allowing the evaluation of coherence between planned durations and actual operational capacity, thereby identifying deviations before they translate into contractual delays. Using the add-in to generate structured datasets strengthens analytical traceability by directly linking the WBS, precedence relationships, and productivity parameters to the space–time representation. Consequently, the implemented methodology does not merely provide an alternative visualization; it reframes the planning process by embedding a diagnostic mechanism that integrates scheduling logic, resource deployment, and construction sequencing within a unified analytical structure. The evidence indicates that integrating LOB with digital tools represents a meaningful methodological advancement for residential projects of varying scales, particularly in environments where operational variability demands more sensitive and anticipatory control mechanisms [105].

4.4.3. BIM-LOB Workflows

Implementing BIM–LOB workflows for human resource planning in construction projects starts by structuring the project into execution phases and selecting BIM uses that can strengthen the definition, sequencing, and verification of the work. Phase planning provides the organizational backbone for workforce decisions by framing the project into manageable production stages and by enabling early identification of interfaces among trades and work fronts. On that basis, scheduling consolidates the definition of project activities and supports the systematic estimation of durations, which are later refined through productivity assumptions and crew configurations. Site utilization planning and cost estimation play a complementary role by connecting spatial constraints and quantifiable scope to resource requirements. In practice, these uses support the extraction and consolidation of work quantities, the specification of productivity rates, and the iterative adjustment of crew sizes and crew counts until allocation is consistent with the production logic and the available work fronts. Throughout the workflow, the combined visualization of the schedule and the Line of Balance provides a verification layer that makes crew continuity, parallelization feasibility, and potential interferences explicit. This graphical analysis enables early detection of inconsistencies that typically remain opaque in conventional schedules, supporting corrective adjustments before baselines are finalized. As a result, the BIM–LOB approach strengthens the reliability of workforce planning by improving the coherence among quantities, durations, sequencing, and crew deployment, with direct implications for reducing planning-driven delays and cost deviations.

Figure 7 shows that each selected BIM use is linked to specific processes within the traditional methodology and also contributes to an integrated planning logic. BIM Use 1, Phase Planning, is applied to processes for defining project phases and structuring the work. Yet, its influence extends across the workflow because phase boundaries condition activity packaging, sequencing decisions, and the aggregation of resource requirements. BIM Use 2, Scheduling, supports the development and graphical representation of the project schedule and provides the temporal structure required to generate and interpret the LOB visualization. BIM Use 3, Site Utilization Planning, strengthens the establishment and verification of precedence relationships by incorporating workspace availability, access, and logistical constraints into sequencing decisions that directly affect crew deployment. BIM Use 4, Site Analysis, informs the definition of LOB parameters by supporting the identification of work locations, repetitive units, and spatial segmentation needed to represent production flow. BIM Use 5, Cost Estimation, links model-based quantities and resource assumptions to cost baselines, enabling the financial implications of staffing decisions to be evaluated consistently with the schedule. BIM Use 6, Design of Construction Systems, supports the definition of phases, scheduling logic, and precedence structuring because it is closely connected to constructability and to the production methods that determine productivity rates and crew composition. BIM Use 7, 3D Control and Planning, is not treated as a primary driver of the planning workflow but rather as a validation and calibration mechanism for the process map, supporting the cyclical improvement logic embedded in the Design Science Research methodology.

4.5. Methodological Framework for Human Resource Allocation

Figure 8 synthesizes the proposed methodological framework for human resource planning and allocation in residential building projects as a general process map that integrates traditional project management practices with BIM- and LOB-enabled procedures. The framework is conceived as an end-to-end planning logic that connects upstream project definition with downstream workforce decisions through traceable information packages and explicit decision points. Its starting point is a consolidated set of inputs typically available at early stages, including general project information, requirements, and initial studies, design documentation, technical specifications, identified risks, and preliminary schedules. These inputs are progressively transformed into planning artifacts that support labor estimation and allocation, ensuring that workforce decisions are grounded in consistent scope interpretation and in the execution logic embedded in the work breakdown structure. On the map, the workflow begins by defining the project scope and establishing approximate phase durations, which provide the temporal and organizational boundaries for workforce planning. The framework then incorporates a set of BIM uses oriented to planning and constructability, enabling the structured definition of phases and work packages, and strengthening the specification of predecessor–successor relationships required for coherent sequencing. A key differentiator of the framework is the operational link between model-based quantification and workforce estimation. Dynamo-supported routines extract quantities from the BIM model and assign productivity-related information back to model elements via shared parameters, supporting standardized duration estimation and reducing errors associated with manual data handling. Human resource estimation and allocation are carried out by verifying whether proposed crews and staffing levels satisfy production requirements and schedule constraints.

LOB is then introduced as a complementary layer to refine and verify the schedule through flow-based visualization, supporting early detection of inconsistencies such as discontinuities in crew progression, overlaps in work fronts, and infeasible parallelization. This visual verification strengthens planning robustness before baselines are finalized and helps prevent downstream corrective actions that typically increase cost and disrupt productivity. In the final portion of the process map, schedule and human resource outputs are consolidated with cost analyses to produce an integrated baseline that supports budget formation and managerial decision-making. Each phase of the framework concludes with clearly defined deliverables, reinforcing transparency and implementation readiness. The framework was developed using a Design Science Research approach, allowing iterative refinement of the process map through continuous improvement cycles and its validation and calibration through two real-world residential case studies.

4.5.1. Category I: Project Scope and Duration

This category establishes the boundary conditions under which human resource planning decisions are made (see Figure 9). It consolidates general project information and client requirements on scope, cost, and time, along with initial studies and designs, technical and economic information, and the main project risks. By formalizing these inputs at the outset, the framework ensures that workforce planning is anchored in an agreed scope and realistic constraints, rather than optimistic assumptions that later trigger rework in staffing decisions. The category concludes by defining the project scope and objectives and setting approximate durations for the major phases, providing a feasibility envelope that guides subsequent estimation and allocation activities and will be verified and refined through the baseline development stages.

4.5.2. Category II: Structuring and Quantification

In this category, the project scope is translated into executable work definitions and measurable quantities that can be used to derive labor demand. The framework structures the construction process into phases, defines work packages and activities, and specifies activity attributes in a manner compatible with both scheduling and resource estimation (see Figure 10). A key step is developing a work breakdown structure that links contractual requirements to work packages, enabling traceability between what is required, what is planned, and what must be staffed. Predecessor–successor relationships are established to reflect the intended production logic, and quantities are quantified at the level needed to support duration calculation and workforce estimation. BIM use in this category strengthens consistency and transparency in how work is structured and quantified, supporting phase planning, scheduling, and construction system design as a means to anticipate constructability-driven adjustments before execution and to reduce ambiguity in the inputs that will later define crew sizing and allocation.

4.5.3. Category III: Resource Estimation and Allocation

This category operationalizes human resource planning by transforming quantified work into labor requirements and assignable crew structures. The framework begins by identifying the workforce requirements implied by the WBS and by defining the human resources needed to execute the planned activities (see Figure 11). It then estimates and quantifies the necessary labor and forms work crews with a level of granularity appropriate for allocation and control. A verification point is embedded to confirm that the proposed resources are compatible with the project and contractual requirements before crews are assigned to specific activities. BIM supports this category by integrating spatial and site-related constraints into allocation decisions through site utilization planning, site analysis, and phase planning, improving the realism of crew deployment assumptions and reducing the likelihood of infeasible assignments caused by work-face limitations, access constraints, or concurrent operations in restricted areas.

4.5.4. Category IV: Schedule Baseline

The schedule baseline category consolidates time-feasible workforce planning by linking activity durations, sequencing logic, and crew assignments into a baseline that can be monitored and controlled (see Figure 12). Activity durations are estimated using base crew configurations derived in the previous category, and human resource assignments are verified to ensure coherence between staffing and sequencing decisions. The framework then incorporates critical path identification and analysis to detect schedule drivers and to assess whether the planned workforce distribution creates bottlenecks or unrealistic concurrency. A schedule baseline is established and, if needed, schedule compression is applied to meet project time requirements. Line of Balance is integrated as a complementary visual verification layer that tests flow continuity and exposes inconsistencies that may remain hidden in conventional schedules, such as discontinuities in crew progression, overlaps in work fronts, and infeasible parallelization across repetitive units. By iteratively refining the schedule using both baseline logic and LOB visualization, the framework enhances the reliability of human resource allocation, supports a smoother production flow, and reduces the likelihood that workforce plans will require disruptive corrections during execution.

4.5.5. Category V: Cost Baseline

This category consolidates the financial implications of the planned workforce configuration by integrating labor decisions with equipment and material requirements into a coherent cost baseline. The framework performs unit price analysis to reflect the cost structure of construction execution (see Figure 13). Then it consolidates this information into the general project budget, ensuring that the staffing strategy derived from quantities, productivity assumptions, and schedule constraints is consistent with financial constraints. BIM-based cost estimation supports traceability between model-derived quantities, planned durations, and labor costs, improving the reliability of the cost baseline and facilitating subsequent monitoring and control. As a result, the framework produces a cost-ready representation of the workforce plan, enabling decision-makers to evaluate staffing options not only in terms of schedule feasibility and flow continuity but also in terms of their budget impact and cost performance risk.

It is important to clarify that the proposed framework is primarily intended for the planning phase of building construction projects. Its main contribution is to generate a structured, traceable, and visually verifiable workforce plan before execution begins. In this sense, the framework does not yet operate as a closed-loop monitoring and controlling system. However, it provides the planning baseline required for future planned-versus-actual comparisons. The BIM–LOB workflow produces planned values for work quantities, productivity assumptions, activity durations, crew sizes, crew assignments, work-front sequencing, and location-based production flow. These planned outputs can serve as reference values against which actual field data could be compared during execution. Therefore, although the current study focuses on planning, the proposed artifact establishes the information structure needed to identify future deviations in labor use, production rhythm, crew continuity, and schedule performance during the monitoring and controlling phase.

4.5.6. Algorithmic Formulation and Data Schema of the BIM–LOB Workflow

To improve the operational reproducibility of the proposed framework, the BIM–LOB workflow was formalized as an algorithmic procedure supported by a structured data schema. The algorithm converts BIM-derived quantities, productivity assumptions, crew configurations, and precedence relationships into a workforce-planning baseline that can be verified through location-based visualization. The procedure is organized into seven steps. First, BIM elements are classified according to WBS code, activity, construction phase, and location or work front. Second, quantities are extracted from the BIM model and aggregated by activity and location. Third, productivity rates are assigned to each activity based on the corresponding work package and production assumptions. Fourth, activity durations are estimated by relating quantities, productivity rates, and crew size. Fifth, crews are assigned to activities according to resource availability, minimum crew composition, and work-front constraints. Sixth, the schedule baseline is generated by incorporating activity durations, precedence relationships, lags, calendars, and resource assignments. Seventh, the Line of Balance representation is used to verify crew continuity, production rhythm, spatial progression, and potential sequencing conflicts. If overallocations, discontinuities, or infeasible work-front overlaps are detected, the workflow returns to the crew-sizing and scheduling steps until a coherent planning baseline is obtained.

The computational logic can be summarized as follows. For each activity $a$ and location $l$, the work quantity $Q_{a,l}$ is obtained by aggregating the quantities of all BIM elements linked to the same WBS code, activity, and location. If productivity is expressed as output per worker-day, activity duration is estimated as $D_{a,l}=Q_{a,l}/(P{R}_a\times N_{a,l})$, where $P{R}_a$ is the productivity rate of activity $a$ and $N_{a,l}$ is the assigned crew size. If productivity is expressed as crew output per day, duration is estimated as $D_{a,l}=Q_{a,l}/P{R}_a$. The resulting duration is then checked against precedence relationships, calendar constraints, resource availability, and LOB continuity requirements. A feasible workforce plan is obtained when no precedence violation, crew overallocation, or incompatible location-based overlap remains after the iterative adjustment process.

The data schema used to operationalize the workflow includes seven core data tables: BIM elements, activities, locations, productivity rates, crews, precedence relationships, and planning outputs. Each BIM element record contains an element identifier, category, quantity, unit, WBS code, activity identifier, location identifier, and construction phase. Each activity record contains the activity identifier, work package, activity description, unit of measurement, and associated BIM use. Productivity records contain the activity identifier, productivity rate, unit, source of assumption, and applicable crew type. Crew records contain the crew identifier, trade or work type, crew size, labor composition, and availability. Precedence records contain predecessor activity, successor activity, lag, and relationship type. The output table stores calculated quantities, durations, crew assignments, start and finish dates, LOB sequence, detected conflicts, and performance indicators. This schema ensures traceability between model elements, planning assumptions, crew allocation decisions, and the resulting LOB-based verification outputs.

4.5.7. Formal Input–Transformation–Output Structure of the BIM–LOB Framework

To improve the operational reproducibility of the proposed framework, its input–transformation–output structure was formalized. The framework receives project information, BIM model data, productivity assumptions, scheduling logic, and location-based constraints as inputs. These inputs are transformed through a sequence of rules that connect BIM-derived quantities, WBS coding, activity duration estimation, crew sizing, crew assignment, schedule structuring, and LOB-based verification. The outputs correspond to a workforce planning baseline composed of coded quantities, activity durations, crew configurations, resource allocation tables, LOB diagrams, detected sequencing conflicts, overallocation events, and planning-stage performance indicators.

The transformation logic follows a sequential and iterative procedure. First, each BIM element is associated with a WBS code, activity, and location or work front. Second, quantities are extracted from the BIM model and aggregated by activity and location. Third, productivity assumptions are linked to each activity to estimate duration and labor demand. Fourth, crew sizes and crew assignments are defined according to the required work quantity, expected production capacity, available work fronts, and project duration constraints. Fifth, the resulting activities, durations, precedence relationships, and crew assignments are exported to the scheduling environment. Sixth, the LOB representation is generated to verify whether the planned crew flow is feasible in time and space. When the LOB visualization reveals sequencing conflicts, resource overallocations, discontinuities, or incompatible work-front overlaps, the schedule and crew assignments are adjusted iteratively until a coherent planning baseline is obtained.

In computational terms, for each activity $a$ and location $l$, the framework links the work quantity $Q_{a,l}$, the productivity rate $P{R}_a$, and the assigned crew size $N_{a,l}$ to estimate activity duration. When productivity is expressed per worker-day, duration is estimated as a function of $Q_{a,l}/(P{R}_a\times N_{a,l})$. When productivity is expressed as crew production per day, duration is estimated as a function of $Q_{a,l}/P{R}_a$. The resulting duration is then checked against precedence relationships, calendar constraints, crew availability, and location-based continuity requirements. This logic ensures that the proposed workforce plan is not only quantity-based but also constrained by sequencing feasibility and spatial progression.

4.5.8. Data Architecture and Workflow Logic of the BIM–LOB Framework

The data structure includes seven core information groups: project structure, BIM elements, activities, locations or work fronts, productivity assumptions, crews, and precedence relationships. Each information group contains the minimum parameters required to reproduce the workflow. Project structure includes the WBS, work packages, construction phases, and planning calendar. BIM elements include element ID, category, quantity, unit of measurement, WBS code, activity code, location code, and phase. Activities include activity ID, description, unit of measurement, associated work package, and required BIM use. Productivity assumptions include productivity rate, productivity unit, source of assumption, applicable activity, and crew type. Crew records include crew ID, trade or work type, number of workers, labor composition, and availability. Precedence relationships include predecessor, successor, lag, and relationship type. The output records include quantities by activity and location, calculated durations, crew assignments, start and finish dates, LOB sequence, detected conflicts, overallocation events, and performance indicators.

The transformation logic follows a sequential and iterative procedure. First, BIM elements are coded according to WBS, activity, phase, and location. Second, quantities are extracted from the model and aggregated by activity and location. Third, productivity rates are assigned to the corresponding activities and linked to crew composition. Fourth, activity durations are calculated from work quantities, productivity assumptions, and crew capacity. Fifth, crews are assigned to activities according to labor availability, work-front constraints, and planned sequence. Sixth, the schedule baseline is generated using activity durations, precedence relationships, lags, and calendars. Seventh, the Line of Balance representation is used to verify spatial–temporal coherence, crew continuity, production rhythm, and potential sequencing conflicts. When conflicts, overallocation events, idle periods, or infeasible work-front overlaps are identified, the workflow returns to the crew-sizing and scheduling steps until a coherent planning baseline is obtained. This formalized structure ensures traceability from BIM model elements to workforce-planning outputs and allows the framework to be reproduced using equivalent project data.

5. Implementation and Calibration of the Framework

The implementation of the framework in the two case studies was conducted retrospectively. The projects had already been completed at the time of the study; therefore, the BIM–LOB workflow did not influence field execution decisions, contractual commitments, or real-time site management. Instead, the case studies were used as real project environments to test the planning logic of the proposed artifact. The implementation reconstructed the planning workflow from available project information and evaluated whether BIM-derived quantities, productivity assumptions, activity durations, crew configurations, and LOB-based sequencing could be integrated into a coherent workforce plan. Under this scope, the case studies validated the internal consistency and practical applicability of the workflow rather than the final construction performance of the completed projects. The proposed methodological framework was implemented and calibrated through a staged application in two residential building projects, following a progressive approach that prioritizes functional verification before scalability testing. The initial implementation focused on a two-floor single-family house model developed for a project located in Cajicá, Colombia. The building has a construction area of 295 m² and features a reinforced concrete frame structural system, with an estimated construction budget of COP 97,087,426, equivalent to USD 27,267 using the official Colombian Representative Market Exchange Rate certified for 24 April 2026. To ensure methodological consistency and concentrate the analysis on the most labor-intensive and critical production activities, the application was limited to the reinforced concrete and steel structural components. Figure 14 presents the structural model used as the baseline for testing the automation routines and verifying the overall feasibility of the proposed workflow. This workflow includes extracting model-based quantities, structuring work packages, and generating the necessary intermediate outputs for scheduling and crew planning. This first case study served as a controlled setting to identify discrepancies between modeled elements and planning breakdowns, refine parameterization rules, and adjust the data structures needed for subsequent integration with location-based representations.

The second implementation focused on a medium-rise residential project in Bogotá, Colombia (see Figure 15). The estimated project budget for this case was COP 7,381,873,823, equivalent to USD 2,073,199 using the same exchange rate. The BIM model used in this case features a higher level of architectural and construction detail, which facilitated a more rigorous assessment of information consistency and planning traceability. This application of the framework allowed for the identification of planning aspects that could benefit from better alignment between model-based information, sequencing logic, and workforce allocation decisions. Specifically, the implementation suggested improvements in the precision of scheduling inputs, the transparency of coordination among construction management and human resource planning roles, and the quality of decision-making by enabling assumptions to be evaluated against a shared digital representation. The specific outcomes of these implementations, along with the refinements made during calibration, are discussed in the following subsections.

5.1. Automation in Quantity Estimation

In the single-family case study, the automation of quantity estimation was implemented using two complementary Dynamo scripts developed in Revit. One script was designed to extract element attributes and quantities, while the other was used to import planning data back into the model. The extraction script (see Figure 16) retrieves structured information from selected model categories and exports it directly to an Excel database. This process facilitates the rapid generation of a traceable quantity takeoff, eliminating the need for manual transcription. The workflow was set up to capture essential parameters for downstream workforce planning, including element identifiers and descriptive fields that enable filtering by trade, location, and level. In the other project, the exported fields included the element ID, category, name, reference level, mark, and geometric measures such as area and length. This approach ensured that quantity records were firmly tied to unique model elements, thereby reducing common sources of inconsistency that can arise when quantities are interpreted differently across various disciplines and planning tools.

A second Dynamo routine was developed to complete the process by importing planning inputs from Excel into Revit (see Figure 17). This step is important for enabling model-based duration calculations, which in turn support crew sizing and workforce quantification. The imported values were assigned to shared parameters created within the model to maintain interoperability and avoid reliance on non-standard project parameters. In this implementation, the shared parameter “Performance” was used to store productivity-related information, facilitating the calculation of activity durations at the element level. This approach preserves traceability between model quantities, productivity assumptions, and the resulting time estimates. Additionally, a Work Breakdown Structure (WBS) code parameter was imported to link each element with the work packaging structure used for scheduling and resource allocation. This bidirectional exchange allowed the framework to (1) generate quantities from the model with minimal manual effort and (2) reintroduce standardized planning attributes back into the model. This ensures that duration and resource calculations are executed consistently, using the same information base for both visualization and coordination.

The observed benefits are consistent with prior evidence on Dynamo-enabled workflows for automated quantity takeoff and parameter enrichment. Shi et al. [106] showed that Dynamo scripts can support detailed quantity computation and database export for production-oriented applications, reinforcing the value of scripted extraction to improve accuracy and repeatability. Alothaimeen et al. [107] similarly highlight the need to incorporate additional parameters to enable precise extraction and management of information for decision-making, with automated transfers to spreadsheets improving data organization and analysis. In the present study, these capabilities translated into a practical planning advantage: once quantities and parameters were standardized and linked to element IDs, the framework reduced the time required to generate inputs for duration estimation and improved the consistency of subsequent crew allocation decisions across the case studies.

5.2. Automation and Improvements in Resource Quantity Estimation Accuracy

After extracting the work quantities associated with each activity in the project Work Breakdown Structure (WBS) from the BIM model using the Dynamo routine outlined in Section 5.1, the dataset was exported to a structured Microsoft Excel spreadsheet. This format allowed for controlled manipulation and traceable revisions of the planning inputs. The spreadsheet environment enabled the systematic definition and adjustment of productivity rates at the appropriate work-package level, thereby reducing the variability that typically emerges when quantities and production assumptions are managed through unstructured documents. Once validated, the productivity data were reintroduced into the Revit model via a dedicated Dynamo import script, enabling automated loading and assignment of values to each element via shared parameters. This bidirectional workflow reduced the need for manual entry, improved data consistency, and minimized assignment errors that frequently propagate into duration estimation and crew sizing decisions. With quantities and productivity rates stored at the element level and linked to WBS codes, activity durations could be computed in a standardized manner, after which precedence relationships were established to reflect the intended construction logic.

The productivity rates used in the case studies were treated as planning assumptions rather than as productivity measurements collected during live execution. These rates were obtained from project planning records, unit-price analyses, and historical production assumptions available for the analyzed work packages. When the same activity could be associated with more than one productivity reference, the selected value was reviewed according to three criteria: consistency with the work-package definition, compatibility with the crew composition assigned in Microsoft Project, and coherence with the overall duration constraints of each case study. The values were then reviewed by the research team and incorporated into the BIM environment through the shared parameter “Performance,” which enabled traceability between each model element, its associated WBS code, the productivity assumption, and the resulting duration estimate. Accordingly, the framework does not claim to replace productivity measurement on site; rather, it provides a structured mechanism to make productivity assumptions explicit, traceable, editable, and verifiable before the schedule baseline is finalized.

The duration and workforce estimates were obtained by linking three planning inputs: BIM-derived work quantities, productivity rates, and feasible crew configurations. Activity duration was calculated by relating the quantity of work to the expected production capacity of the assigned crew. The resulting activity durations were then evaluated in Microsoft Project and through LOB visualization to verify whether the proposed crew sizes and number of crews were consistent with the planned sequence, project duration, and available work fronts. This iterative process allowed the research team to adjust crew configurations before finalizing the workforce plan, particularly when the first allocation generated overallocations, idle time, or spatial–temporal conflicts. Therefore, the reported workforce outputs should be understood as calibrated planning results produced by the BIM–LOB workflow, not as measured as-built labor consumption values.

The sensitivity analysis shows that the workforce outputs are highly dependent on the productivity assumptions used during planning (see

Table 11. Sensitivity analysis of workforce outputs under productivity-rate variation.

Scenario	Productivity-Rate Assumption	Case #1: Equivalent Workforce Requirement	Case #2: Equivalent Workforce Requirement	Interpretation *
Conservative productivity	Base productivity × 0.80	13 workers	90 workers	Lower productivity increases the labor capacity required to maintain the planned duration
Moderately conservative productivity	Base productivity × 0.90	12 workers	80 workers	A small reduction in productivity increases the required workforce and may intensify crew-overlap risks
BIM–LOB base estimate	Base productivity × 1.00	10 workers	72 workers	Workforce output produced by the calibrated BIM–LOB workflow
Moderately optimistic productivity	Base productivity × 1.10	10 workers	66 workers	Higher productivity reduces the equivalent labor requirement or creates schedule float
Optimistic productivity	Base productivity × 1.20	9 workers	60 workers	Higher productivity substantially reduces equivalent labor demand, although final crew configurations must still respect minimum crew-composition constraints

* Note: Workforce values in the sensitivity scenarios are rounded upward to the nearest whole worker and should be interpreted as equivalent labor-capacity requirements rather than final contractual crew configurations. The base scenario corresponds to the workforce outputs obtained through the BIM–LOB workflow. The sensitivity analysis was included as a benchmarking exercise to show how strongly workforce outputs depend on productivity assumptions.

). In the single-family case, the base BIM–LOB configuration required ten workers, but a 20% reduction in productivity would increase the equivalent workforce requirement to 13 workers to maintain the planned duration. In the multifamily case, the base estimate of 72 workers would increase to 90 workers under the same conservative productivity scenario. Conversely, higher productivity assumptions reduce the equivalent labor-capacity requirement or create time buffers that could be used to stabilize workflow continuity. This analysis does not constitute field validation of actual productivity; rather, it provides a benchmark for interpreting the robustness of the workforce estimates and for identifying the range within which project managers should review crew-size decisions when productivity assumptions vary.

The subsequent resource planning stage was executed in Microsoft Project, where labor resources were assigned to each activity, and an iterative procedure was used to converge on feasible crew configurations. In practice, the iteration consisted of adjusting crew size and the number of crews while verifying the resulting durations against the planned sequence and the overall time constraints, to avoid workforce overallocation and minimize idle time due to underallocation. This step operationalizes the framework’s core contribution: resource estimation is not treated as a single deterministic calculation but as a controlled calibration process in which quantities, productivity assumptions, and schedule feasibility are reconciled through explicit decision rules supported by BIM-enabled traceability and location-based visualization. These results align with and extend prior automation-oriented scheduling research. Altun and Akcamete [108] show how 4D modeling can link 3D models and schedules via Dynamo to support spatiotemporal simulation and improve planning efficiency. The present framework complements that contribution by incorporating the Line of Balance as an additional layer of verification that makes production flow discontinuities and crew interference more visible, thereby supporting earlier detection of inconsistencies that directly affect labor allocation. Similarly, Wang and Azar [109] propose automated scheduling for concrete structures using BIM-derived work packages and productivity-based duration estimation, with outputs organized in project management software. The approach presented here expands this logic by embedding LOB-based visual programming into the workflow, enabling planners to evaluate whether the generated schedule is not only technically consistent but also operationally feasible in terms of crew continuity and location-based execution.

The case study applications illustrate the magnitude of the workforce estimates produced through this integrated procedure. In the single-family house, the framework resulted in a requirement of ten workers organized into five crews, each consisting of one lead worker and one assistant. In the medium-rise residential project, the resulting estimate was 72 workers organized into 9 crews, each composed of 2 lead workers and 6 assistants. These configurations were adjusted to reflect the scale and complexity of each project. They were derived through the same sequence of model-based quantity extraction, productivity assignment, duration calculation, scheduling, and iterative allocation.

5.3. Visualization of Human Resource Allocation

During the implementation of the proposed framework, workforce allocation was not treated solely as a numerical output but as a spatial–temporal configuration that must be verifiable in the production environment. For both case studies, resources were assigned to enable crews to operate in parallel while avoiding spatial conflicts, work-face congestion, and unintended overlaps in repetitive units. To support this verification, each crew or work group was assigned a distinctive color that remained consistent across the 3D BIM environment and the corresponding Line of Balance representations. In the single-family case (Figure 18) and the multifamily case (Figure 19), the color-coding scheme enabled rapid identification of crew locations within the model, improving the interpretability of allocation decisions for planners and site stakeholders. The same colors were then replicated in the LOB charts for the activities associated with each crew, creating a common visual language that connected location-based sequencing with the model-based representation of the work. This linkage strengthened traceability between planned crew deployment and the physical work fronts, allowing inconsistencies to be detected earlier than would be possible through tabular schedules alone.

From a managerial perspective, the visualization strategy provided two practical advantages. First, it improved communication and coordination by allowing stakeholders to understand, at a glance, which crews were expected to work in each area and when, reducing ambiguity in work-face assignments. Second, it supported an explicit verification of feasibility by making potential interferences visible in both the BIM model and the LOB chart, which is particularly relevant when multiple crews execute activities concurrently across units. This is consistent with the general premise that visual planning reduces cognitive load and increases decision reliability in resource-intensive environments, especially when schedules must be adjusted iteratively as productivity assumptions or constraints evolve.

5.4. Sequencing of Project Activities

During the application of the proposed framework, activity sequencing was operationalized as a combined logic-and-flow problem, in which precedence relationships defined in the WBS were continuously verified against workforce feasibility and location-based execution. To support this verification in both case studies, each crew responsible for executing WBS activities was assigned a distinctive color, and the same coding was used to represent the corresponding activities in the LOB charts. This ensured consistent task identification across the 3D model, the 4D simulation, and the visual schedule, enabling planners to track where and when each crew was expected to operate. Beyond communication benefits, the color-consistent representation supported systematic detection of sequencing inconsistencies that typically remain hidden in conventional Gantt schedules. In particular, it facilitated the identification of workforce overallocation, where a crew or trade would be implicitly required to work in parallel on incompatible work fronts, and underallocation, where crews would become idle due to gaps created by misaligned precedence logic. By making these conditions visible, the framework strengthened workflow continuity and supported simultaneous crew deployment without interferences, thereby improving the reliability of the planned production flow.

This sequencing strategy is consistent with the evidence reported by Bohórquez et al. [39], who emphasize that visual identification of crews supports supervision and improves understanding of labor participation across construction activities. In the present study, however, the use of crew-based visual coding extends beyond supervision toward an explicit planning function: it becomes a mechanism to validate whether the proposed sequence can sustain the intended crew paths across time and location. As a result, sequencing decisions were not evaluated solely on logical correctness but also on their implications for crew continuity and resource utilization, which are central to preventing planning-driven productivity losses. Figure 20 illustrates five representative moments from the 4D BIM simulation for the single-family housing case study, showing the evolution of structural works, including reinforcement placement, foundations, and the reinforced-concrete frame. These moments correspond to days 20, 40, 70, and 100 after activity initiation, with the associated calendar dates also indicated in the figure. The LOB charts use the same time slices and color scheme, making it easy to compare BIM’s spatial view with LOB’s flow representation. This combined view supports a clearer interpretation of the sequencing logic, highlighting how the planned order of activities translates into spatial progression and crew continuity across work fronts.

A similar approach was adopted in the multifamily housing case study, where crew-based color identification was applied to both structural and architectural activities. Figure 21 presents six moments from the 4D simulation at different stages of execution in months 10, 14, 18, and 22. A salient observation in this case is that the architectural phase begins in month 18. In contrast, the structural phase reaches its final stage, creating a transitional period during which parallel trades operate simultaneously. The BIM–LOB integration made this transition explicit by showing the overlap of structural and architectural flows and by visualizing the corresponding crew assignments for each phase. This integration enabled a more comprehensive assessment of sequencing feasibility by revealing potential work-face congestion and resource conflicts during phase overlap, while also supporting informed adjustments to maintain continuity, minimize idle time, and sustain the planned production rhythm across the building’s repetitive units.

6. Evaluation and Refinement of the Proposed Framework

6.1. Performance Indicators: Traditional Methodology vs. BIM–LOB Framework

Table 12. Performance indicator—Traditional methodology vs. BIM-LOB.

Performance Indicators—Traditional Methodology vs. BIM–LOB Framework
Performance Indicator	Case Study #1 (295 m2—3 Months—5 Crews)			Case Study #2 (3660 m2—22 Months—9 Crews)			Impact
	Traditional Methodology	BIM-LOB	Reduction (%)	Traditional Methodology	BIM-LOB	Reduction (%)
E1—Time for quantity takeoff (man-hours)	8.0	1.0	87.5%	40.0	4.0	90.0%	High
E2—Errors in productivity parameters (N°)	6	1	83.3%	18	3	83.3%	High
E3—Sequencing conflicts detected—LOB vs. Gantt (N°)	0	3	—	0	7	—	High
E4—Crew overallocation events (N°)	4	1	75.0%	12	2	83.3%	High
E5—Total workforce plan generation time (man-hours)	24.0	10.0	58.3%	120.0	48.0	60.0%	Medium
E6—Plan reproducibility (%deviation)	22%	5%	77.3%	25%	6%	76.0%	High

presents a comparative evaluation of six performance indicators (E₁–E₆) obtained from the application of the traditional methodology and the integrated BIM–LOB approach across the two case studies analyzed. Reference values for the traditional methodology were established through expert judgment with a panel of 31 professionals with more than three years of experience in residential building construction, while BIM–LOB values were measured directly during implementation. For indicator E₁, the time required for quantity takeoff decreased from 40.0 to 4.0 man-hours, transforming a procedure that under traditional practice can extend up to five working days into a near-immediate task enabled by automation through Dynamo scripts.

Indicator E₂ shows a reduction in errors associated with productivity parameters from 18 to 3, which is relevant because each uncorrected error generates an incorrect production rate that propagates to all activity durations and crew sizes dependent on that parameter; this situation is mitigated through standardized linkage to shared parameters in Revit, eliminating reliance on individual judgment in each assignment. E₃ reports the detection of 7 sequencing conflicts through LOB visualization before baseline finalization, compared to zero conflicts identified under Gantt charts, not due to their absence but to the limited capacity of bar chart representations to expose spatiotemporal crew interferences; each conflict not identified at early stages can translate into unproductive time and emergency reassignments during execution.

Indicator E₄ shows a reduction in overallocation events from 12 to 2, where each unresolved event implies the simultaneous assignment of a crew to incompatible work fronts, a condition that on site typically leads to improvised personnel redistribution and loss of productive continuity, corrected through iterative schedule calibration and BIM–LOB spatial verification. E₅ reflects a 60% reduction in the total time required to generate a complete workforce plan, meaning that a process that previously could extend up to fifteen working days can be completed in a few days through the automated workflow across Revit, Dynamo, Excel, Microsoft Project, and the LOB add-in. Finally, indicator E₆ evaluates plan reproducibility as the percentage deviation between two versions of the same process applied to identical data: under the traditional methodology this deviation reaches 25%, implying that different planners can obtain distinct crew configurations, activity durations, and sequencing decisions, while BIM–LOB integration reduces this variability to 6% through shared parameters and automated routines that guarantee consistent results regardless of who executes the process.

Based on the formal input–transformation–output structure defined in Section 4.5, the evaluation of the framework was expanded beyond expert-perceived utility. A controlled planning-stage comparison was conducted using the same project data under two approaches: the traditional planning methodology and the proposed BIM–LOB workflow. The purpose was not to perform full field validation or inferential statistical testing, but to assess whether the formalized workflow produced measurable differences in planning efficiency, information consistency, sequencing verification, resource allocation, and reproducibility. Six performance indicators were therefore defined: quantity takeoff time, errors in productivity parameters, sequencing conflicts detected, crew overallocation events, total workforce-plan generation time, and plan reproducibility.

In addition to the productivity-sensitivity analysis reported in Section 5.2 provides a controlled comparison between the traditional planning approach and the BIM–LOB workflow across six performance indicators. This comparison was included to benchmark the planning outputs of the framework against a non-integrated baseline and to clarify the type of improvement that can be assessed at the planning stage.

6.2. Identified Deficiencies Mitigated by the Framework

The proposed framework was presented to a group of 31 experts with more than three years of experience in BIM methodology and construction to evaluate its effectiveness. The discussion focused on common industry shortcomings that the framework aims to address. Subsequently, a survey was administered to the same professionals to assess how this approach could mitigate the identified deficiencies. The results of this survey, which involved 31 experts, are presented in

Table 13. Survey to assess deficiencies addressed by the framework.

Id	Identified Deficiencies Mitigated by the Framework	RUI	Rank
D1	Incorrect estimation of work quantities and allocation of productivity rates	0.90	1
D2	Inaccurate quantification of human resources and productivity rates	0.88	2
D3	Inadequate definition of precedence relationships between activities	0.86	3
D4	Poor analysis of the critical path in human resource management and allocation	0.86	3
D5	Low automation in human resource management and allocation	0.86	3
D6	Deficiencies in time and cost baseline planning	0.85	6
D7	Inaccuracies in project scheduling due to a lack of visual methods	0.85	6
D8	Unclear or incomplete definition of work packages (WBS)	0.84	8
D9	Overallocation of human resources (work crews) in the project schedule	0.84	8
D10	Inadequate allocation of work crews during the project planning phase	0.81	10
D11	Lack of integration of emerging technologies and methodologies	0.80	11
D12	Poor automation of human resource planning activities	0.79	12

RUI: Relative Utility Index.

and were analyzed using the Relative Utility Index (RUI) methodology.

Table 13 reports the perceived utility of the proposed BIM–LOB framework for mitigating recurrent shortcomings in human resource planning, based on a survey of thirty-one experts and analyzed using the Relative Utility Index. The RUI scores are consistently high, ranging from 0.79 to 0.90, suggesting broad agreement that the framework can address multiple weaknesses that typically compromise crew sizing, allocation, and schedule feasibility. The two highest-ranked deficiencies are directly linked to input accuracy: incorrect estimation of work quantities and assignment of productivity rates (RUI = 0.90) and inaccurate quantification of human resources and productivity rates (RUI = 0.88). This pattern indicates that experts attribute the greatest value to the approach’s capacity to strengthen the quantitative basis of planning, where BIM-based quantity takeoff and model-informed parameterization can reduce reliance on manual approximations and improve consistency among scope, production assumptions, and staffing requirements.

A second cluster of highly rated deficiencies highlights gaps in logic, control, and automation: inadequate definition of precedence relationships (RUI = 0.86), poor critical path analysis in human resource management and allocation (RUI = 0.86), and low automation in human resource management and allocation (RUI = 0.86). These results suggest that experts perceive the framework’s main operational contribution as improving the coherence of sequencing and the transparency of trade flows, which are central premises of LOB when combined with a structured schedule environment. Similarly, shortcomings associated with baseline quality and the absence of visual methods remain salient, including deficiencies in time- and cost-based baseline planning (RUI = 0.85) and inaccuracies in project scheduling due to the lack of visual methods (RUI = 0.85). Importantly, even the lowest-ranked issues still receive strong support, including unclear or incomplete WBS definition and resource overallocation (both RUI = 0.84), inadequate crew allocation during planning (RUI = 0.81), lack of integration of emerging technologies (RUI = 0.80), and poor automation of planning activities (RUI = 0.79).

7. Conclusions

This study presents three main theoretical contributions. First, it identifies 23 processes related to the traditional methodology for planning and allocating human resources in building construction projects. These processes are categorized into five main areas: project scope and duration (C₁), structuring and quantification (C₂), resource estimation and allocation (C₃), schedule baseline (C₄), and cost baseline (C₅). These categories encompass workflows essential for defining the activities required for project development and for estimating resources, costs, and execution times in construction projects using the traditional approach. Second, the study identifies the most beneficial BIM uses for human resource allocation in construction projects, as measured by the Relative Utility Index (RUI). This index was calculated from a survey of 20 experts in the field. Seven BIM uses were identified to have a utility exceeding 75%: phase planning (U₁), scheduling (U₂), site use planning (U₃), site analysis (U₄), cost estimation (U₅), construction systems design (U₆), and 3D control and planning (U₇). These uses were integrated with the processes required to implement the LOB method, thereby facilitating the creation of a BIM-LOB workflow. Third, a process map was developed to merge the identified processes from the traditional methodology with the selected BIM uses and the steps involved in applying the Line of Balance method. This theoretical integration helps fill a knowledge gap regarding the planning, estimation, and scheduling of human resources in building construction projects by combining BIM and Lean methodologies.

The practical contributions of the developed methodological framework were demonstrated through its application in two case studies. First, the framework improves the accuracy of estimating work quantities and the human resources needed for project activities. This methodology involves exporting data from a Revit 3D model using a Dynamo script and importing it into a Microsoft Excel spreadsheet. This approach enhances the estimation and calculation of work quantities, yielding more precise results, reducing common errors, and automating the extraction of quantities. Second, applying the methodological framework in construction projects enables integrating traditional methodologies with technological tools such as BIM and LOB. This integration enhances productivity in planning, estimating, scheduling, and human resource allocation. The framework addresses key deficiencies, including: (1) inaccurate estimates of work quantities and productivity rates, (2) imprecise quantification of human resources and their performance, (3) inadequate definition of precedence relationships between activities, (4) poor critical path analysis in resource management and allocation, (5) low automation in human resource management, (6) deficiencies in time and cost baseline planning, and (7) inaccuracies in project scheduling due to a lack of visual methods. The proposed framework is effective in mitigating these issues, resulting in improvements in planning, technological integration, and the automation of essential processes in construction projects. Additionally, it addresses the disorganized initial allocation of crews during planning phases, contributing to more efficient resource management. Third, the methodological framework enhances collaborative workflows in planning and allocating human resources for construction projects, allowing for better control over activity execution and resource management. By implementing the LOB method, crew allocation improves through the identification of scheduling inconsistencies and the more efficient distribution of resources. This increases productivity by preventing errors that could cause delays or cost overruns. Additionally, the combined use of BIM and LOB equips managers with superior decision-making tools. Visual aids, using a color-coded scheme, highlight the appropriate number of crews needed for each project phase. This approach enables evaluation of the effects of assigning more crews simultaneously to reduce project duration, fewer crews to extend the timeline, and the associated cost efficiency. The visual analysis provided by BIM and LOB ensures precise control from the early stages of the project, further increasing productivity by identifying errors and improving decision-making to mitigate potential delays and cost overruns.

This study helps bridge the knowledge gap regarding the implementation of BIM and LOB methodologies; however, it has limitations that offer opportunities for future research. (1) The methodological framework was applied only to two building types: a two-story single-family house and an eight-story multifamily residential building with a basement. This limitation restricts the generalization of the results to other building types or larger-scale projects. Future research could explore applying the framework to more complex projects, such as buildings taller than eight stories. (2) The proposed BIM–LOB framework is mainly theoretical–methodological in nature and is most suitable for the planning phase of the project. The framework was designed to structure workforce planning before execution by integrating BIM-derived quantities, productivity assumptions, crew sizing, activity sequencing, and LOB-based visualization. Therefore, it should not be interpreted as a fully developed monitoring and controlling system. However, the framework generates a structured planning baseline that could support future planned-versus-actual comparisons. Future research should extend the proposed artifact toward the monitoring and controlling phase by integrating actual field data, such as daily crew records, actual labor hours, completed quantities, activity start and finish dates, progress reports, location-based production records, and digital progress-capture technologies. (3) The framework’s use was confined to building construction projects, but it could be adapted for use in road infrastructure, bridges, or maintenance and renovation projects. This presents new research opportunities to extend its applicability to other sectors, with appropriate adjustments for each case. (4) The framework could be enhanced by incorporating artificial intelligence (AI) algorithms to support decision-making, thereby improving predictive capabilities and optimizing resource allocation. Future studies could investigate AI-driven solutions for crew assignment within scheduling software, automating crew-size and personnel-allocation scenarios to streamline decision-making and reduce manual effort. (5) This framework could also be adapted to incorporate additional BIM dimensions, such as 9D, which are directly associated with Lean Construction. Such an adaptation would enable the integration of additional Lean tools beyond LOB, thereby broadening its application to human resource management within construction projects. (6) Currently, the framework overlooks the influence of other key elements, such as materials and equipment, by focusing exclusively on human resource management. These factors significantly impact early-stage project planning. Future research could integrate the management of all necessary resources, providing a more comprehensive and robust view of the construction process by adapting the framework to encompass all resource types. (7) Another limitation concerns the composition of the expert panel used to evaluate the perceived utility of the framework. Although the panel included professionals from different technical and managerial roles, only one owner representative participated. Consequently, the results should not be interpreted as statistically representative of owner-side perceptions. Future research should expand the participation of owners and developers to compare how the perceived value of BIM–LOB integration varies among clients, designers, builders, supervisors, and project managers. (8) The evaluation strategy should be interpreted within the methodological and retrospective scope of the study. Although inferential statistical analysis was not conducted due to the limited number of case studies and the purposive nature of the expert samples, the revised manuscript incorporates descriptive statistics, dispersion measures for expert ratings, and controlled planning-stage performance indicators.

A further limitation concerns the level of mathematical formalization of the proposed framework. Although this study formalizes the BIM–LOB workflow through an input–transformation–output structure, a data schema, transformation rules, and planning-stage performance indicators, it does not develop a full mathematical optimization model with objective functions, decision variables, constraints, or proof of optimality. This decision is consistent with the methodological scope of the study, which focuses on developing and evaluating a reproducible planning framework rather than proposing an optimization algorithm. Future research should translate the BIM–LOB workflow into a mathematical formulation capable of optimizing workforce planning and allocation. Such a formulation could define decision variables related to crew size, crew assignment, activity start dates, work-front sequence, and resource availability, as well as objective functions aimed at minimizing resource peaks, reducing idle time, avoiding crew overallocation, improving workflow continuity, and maintaining schedule feasibility.