Analysis of Government-Led OSC Industrialization Index: Focusing on Singapore’s Buildability Score

Abstract

The global construction industry faces persistent challenges of low productivity and labor shortages, positioning Off-Site Construction (OSC) as a critical solution. However, standardized industrialization indices for objectively evaluating OSC adoption remain underdeveloped, particularly in emerging markets. This study aims to identify a benchmark policy model and derive design principles for future indices. Specifically, this study focuses on ‘policy-driven markets’ where strong government intervention is essential for initial ecosystem formation, excluding mature market-driven economies where the ecosystem is already established (e.g., USA, Sweden, Japan). To identify an optimal benchmark, a comparative assessment was conducted on five institutional frameworks across four countries (UK, Malaysia, Singapore, and China). Notably, within China, Hong Kong SAR was analyzed as a distinct regulatory jurisdiction separate from Mainland China due to its unique construction governance system. This assessment was based on five key policy dimensions: Legal Mandate, Scope, Indicator Composition, Enforcement Mechanism, and Sustainability. The analysis identified Singapore’s ‘Buildability Score’ as the most comprehensive model in terms of systemic completeness and practical efficacy. A virtual project simulation demonstrated that the scoring system functions as a powerful regulatory mechanism, effectively driving the adoption of standardized, dry-process, and modularized high-productivity methods from the earliest design stages. While Singapore’s system serves as an effective policy tool for OSC proliferation, it exhibits clear limitations regarding reduced architectural design flexibility and insufficient sustainability integration. Consequently, future industrialization indices must evolve to balance productivity with architectural design diversity and integrate sustainability criteria while reflecting specific regional construction ecosystems.

1. Introduction

1.1. Background

The global construction industry is facing persistent challenges, including low productivity, labor shortages, an aging workforce, and difficulties in quality management [1,2]. In many countries, the aging of the construction workforce is particularly severe, with a shortage of younger workers to replace retiring skilled professionals [3,4]. These structural issues lead to project delays and cost overruns, identified as major factors hindering the sustainable growth of the industry [5]. As a transformative alternative to address these circumstances, Off-Site Construction (OSC) is gaining prominence. This method involves prefabricating major building components and modules in a controlled factory environment for subsequent assembly on-site. By enabling parallel work streams between on-site and off-site activities, OSC can reduce overall project timelines by 30–50%, minimize material waste, and ensure consistent quality [1,6,7]. Furthermore, operations in a controlled environment contribute to a reduction in on-site safety accidents [8]. Recent life cycle assessments have further demonstrated that modular construction can achieve significant environmental benefits compared to conventional methods [9].

1.2. Research Objectives and Scope

With the global proliferation of OSC, various nations have developed ‘Industrialization Indices’ to objectively measure OSC adoption and drive productivity improvements through policy mandates. For instance, the UK government recommends a minimum Pre-Manufactured Value (PMV) through procurement guidance for central government projects [10]. Similarly, Malaysia utilizes the Industrialised Building System (IBS) Score to incentivize private sector participation [11]. However, these national frameworks vary significantly in their methodological approaches, legal mandates, and enforceability.

Crucially, this study explicitly excludes mature market-driven economies where the OSC ecosystem is already fully established (e.g., USA, Sweden). Instead, it targets ‘policy-driven markets’ where strong government intervention is essential for initial ecosystem formation. Consequently, this study conducts a comparative assessment of five distinct institutional frameworks across four nations (UK, Malaysia, Singapore, and China)—regions where comparative studies have identified significant variations in OSC practices [12].

Notably, within the national framework of China, this study analyzes ‘Hong Kong SAR’ and ‘Mainland China’ as separate regulatory jurisdictions. While Hong Kong is an integral part of China, it operates under the ‘One Country, Two Systems’ principle, maintaining independent construction laws and technical codes (e.g., British-based standards) that differ from the National Standards of Mainland China. Therefore, treating them as distinct cases is a methodological necessity for accurately comparing divergent policy approaches. Based on this scope, the study evaluates the five frameworks using five key policy dimensions: Legal Mandate, Scope, Indicator Composition, Enforcement Mechanism, and Sustainability [13,14].

Through this analysis, Singapore’s Buildability Score was identified as the most comprehensive model in terms of systemic completeness and regulatory effectiveness [15,16]. Therefore, this study conducts an in-depth analysis of Singapore’s Buildability Score to examine its institutional structure and policy impacts. Ultimately, this research aims to derive key implications for developing advanced industrialization indices tailored to nations seeking to accelerate their construction modernization. Specifically, this study focuses on ‘policy-driven markets’ where government intervention is essential for market creation, thus distinguishing its scope from market-driven approaches. Unlike previous studies that focused on individual project-level efficiency, this study distinguishes itself by proposing a macro-level ‘Policy Maturity Framework’ to evaluate government-led industrialization strategies. This paper is organized as follows: Section 2 reviews the relevant literature on OSC and policy evaluation frameworks. Section 3 presents the research methodology and analytical framework. Section 4 provides comparative analysis results across the five institutional frameworks. Section 5 conducts an in-depth analysis of Singapore’s Buildability Score through virtual project simulation, and Section 6 discusses the findings and presents conclusions.

2. Literature Review

2.1. Theoretical Background and Terminological Framework

To ensure clarity in comparative analysis, this study establishes a comprehensive terminological framework. This section examines the evolution from ‘Industrialized Construction’ to ‘Off-Site Construction (OSC)’, integrates regional terminologies under the OSC umbrella, and justifies the selection of OSC as the primary scope of this research.

2.1.1. Evolution: From Industrialized Construction to OSC

The concept of Industrialized Construction emerged in the 1950s and 1960s to address global housing shortages by adopting manufacturing principles [17]. Academically, it was defined as a system enhancing productivity through the rationalization, standardization, and mechanization of the construction process. Goodier and Gibb broadly define Off-Site Construction as the manufacture and pre-assembly of building components, elements, or modules before installation into their final location [17]. In the 2010s, with the advent of Industry 4.0, this concept evolved into modern OSC. While sharing the foundational principles of industrialization, contemporary OSC places greater emphasis on ‘digital processes’ and ‘supply chain integration’. The National Institute of Building Sciences (NIBS) defines OSC as the planning, design, fabrication, and assembly of building elements at a location other than their final installed location [18]. Unlike early industrialized construction, which focused on physical mass production, modern OSC represents a paradigm shift that integrates information management across all phases, leveraging digital technologies such as BIM(Building Information Modeling) [3,19]. This digital transformation has also facilitated the integration of building services within modular construction, enabling more sophisticated MEP(Mechanical, Electrical, and Plumbing) coordination [20].

2.1.2. Regional Terminologies Under the OSC Umbrella

Under the overarching concept of OSC, various terms are used globally, reflecting specific regional policy contexts.

• Off-Site Manufacturing (OSM): Often used interchangeably with OSC in academic literature, emphasizing the manufacturing aspect of the process [17].
• Modern Methods of Construction (MMC): Widely used in the UK, this term refers to a broad range of innovative techniques, including both off-site manufacturing and on-site efficiency improvements [10,21].
• Industrialised Building System (IBS): Predominantly used in Malaysia, emphasizing the systematic industrialization of the process through standardized components [11].
• Modular Integrated Construction (MiC): Adopted in Hong Kong SAR, specifically referring to high-level volumetric 3D modular construction [22,23].
• Design for Manufacturing and Assembly (DfMA): In Singapore, utilized as a guiding design philosophy to maximize buildability and productivity [16,24].

Despite these regional nuances, they share the common objective of shifting construction activities from the site to a controlled environment. Therefore, this study adopts OSC as the umbrella term to encompass all these variations.

2.1.3. Justification for Focusing on OSC

This study explicitly targets OSC as the scope of research rather than limiting it to a specific method like ‘Modular Construction’. This is because the industrialization indices operated by various governments (e.g., PMV, Buildability Score) evaluate the entire spectrum of prefabrication—ranging from simple components (2D) to fully volumetric units (3D)—rather than a single construction method. For instance, a policy might award points for both ‘Precast Concrete (PC) panels’ and ‘Modular Units’. Therefore, to accurately compare the effectiveness of national policies, it is essential to analyze the comprehensive OSC Industrialization Indices that cover diverse prefabrication levels and technologies, rather than focusing solely on modular construction.

2.2. Benefits and Challenges: The Justification for Policy Intervention

2.2.1. Potential Benefits of OSC

Academic literature consistently demonstrates that OSC offers significant advantages over traditional on-site construction. By shifting production to a controlled factory environment, OSC enables concurrent engineering of site work and manufacturing, potentially reducing project timelines by 30–50% [1,6]. Additionally, it enhances quality control through standardized production, reduces material waste, and significantly improves on-site safety by minimizing work at height and exposure to hazardous environments [7,8]. These benefits position OSC as a promising solution to the chronic labor shortages and productivity stagnation facing the global construction industry. Furthermore, comparative studies have shown that modular prefabrication can deliver superior sustainable performance during the construction phase [25], and environmental cost–benefit analyses confirm the efficiency of prefabricated approaches for public housing [26].

2.2.2. Market Barriers and the ‘Chicken and Egg’ Dilemma

Despite these clear theoretical benefits, voluntary OSC adoption by the private sector remains limited in many countries. This is primarily due to significant economic and structural barriers: high initial capital investment for factory setup and machinery; uncertain return on investment without guaranteed project pipelines; and a fragmented supply chain that hinders early design-construction integration [6,27]. Industry experts emphasize that this situation creates a “vicious cycle” or a “chicken and egg” dilemma. Companies cannot achieve economies of scale without a large market volume, but the market cannot grow without the cost competitiveness that comes from those economies of scale. In regions where the OSC market is not yet mature, private enterprises alone cannot bear the risks associated with this initial investment [6,28]. These barriers have been extensively documented across different national contexts. In England, Agapiou [29] found that cost-related barriers are perceived as the most significant obstacles among housing associations. Studies in Portugal [30] and India [31] using interpretive structural modeling have revealed similar hierarchical barrier structures, with lack of knowledge and high initial costs consistently identified as fundamental constraints. In China, developers cite uncertain return on investment and fragmented supply chains as primary concerns [32], while cost–benefit analyses reveal that the economic barriers are compounded by inadequate policy support [33].

2.2.3. The Necessity of Government-Led Policy

This market failure provides the justification for government intervention. In ‘Market-Driven Markets’ (e.g., Sweden, Japan), high labor costs and a long history of prefabrication have already established a stable ecosystem where the private sector leads innovation [34]. However, in ‘Policy-Driven Markets’ (e.g., Singapore, Hong Kong SAR), the ecosystem is still nascent. Therefore, strong government leadership is essential to break the vicious cycle. By implementing mandatory ‘Industrialization Indices’ and linking them to incentives or penalties, governments can create the ‘guaranteed market demand’ that experts identify as a prerequisite for private investment [15]. This creates a stable environment where companies can confidently invest in technology and facilities, eventually leading to cost reductions and self-sustaining growth. Research using game theory has further demonstrated that well-designed incentive policies can effectively balance the interests of government, developers, and consumers to accelerate prefabricated construction adoption [35].

2.3. Theoretical Framework for Policy Evaluation

To objectively assess the effectiveness of government-led industrialization indices, it is essential to establish a robust theoretical framework. This study draws upon the ‘Policy Instruments Theory’ and the ‘Policy Maturity Model’ to derive the evaluation criteria used in the comparative analysis.

2.3.1. Policy Instruments Theory: Carrots, Sticks, and Sermons

Bemelmans-Videc et al. (1998) classified government policy instruments into three categories: Regulations (Sticks), Economic Means (Carrots), and Information (Sermons) [13].

• Regulations (Legal Mandate & Scope): These are mandatory rules that define the ‘scope’ of compliance and the legal ‘binding force’. In the context of OSC, this corresponds to whether the index is legally required for building permits.
• Economic Means (Incentives): These involve financial inducements or penalties to guide behavior. This aligns with the ‘Enforcement Mechanism’ of the index (e.g., GFA bonuses or levy exemptions).
• Information (Indicator Composition): This refers to the transfer of knowledge. A sophisticated ‘Indicator’ serves as a standard for information, guiding the industry on how to implement OSC effectively.

2.3.2. Policy Maturity and Effectiveness

However, the mere existence of policy instruments does not guarantee effectiveness. The ‘Policy Maturity Model’ suggests that a policy evolves from a voluntary/abstract stage to a mandatory/specific stage [14].

• Maturity of Mandate: A mature policy shifts from ‘voluntary recommendations’ to ‘mandatory obligations’.
• Maturity of Specificity: A mature index moves from ‘simple quantitative volume checks’ to ‘qualitative evaluations of technological levels’ (e.g., distinguishing between simple PC and advanced DfMA).

Based on these theories, this study derives four core dimensions for the analytical framework: Legal Mandate, Scope, Indicator Composition, and Enforcement Mechanism. Additionally, given the growing emphasis on environmental performance in contemporary construction policy, Sustainability is included as a fifth dimension to assess the future-orientation of each index [8,25]. Recent studies have also emphasized the importance of integrating Design for Manufacturing and Assembly (DfMA) considerations into policy frameworks to maximize productivity gains [24,36,37]

3. Materials and Methods

3.1. Analysis Framework: Policy Maturity Assessment

Building upon the theoretical foundations established in Section 2.3 (Policy Instruments Theory and Policy Maturity Model), this study designed a comprehensive evaluation framework to comparatively analyze OSC industrialization indices. The framework comprises five key dimensions formulated to systematically analyze the core requirements for a policy system to function successfully:

• A. Legal Mandate (Regulation/Stick): Measures the ‘enforceability’ of the policy.
• B. Scope (Regulation/Stick): Evaluates the ‘comprehensiveness’ of the evaluation target.
• C. Indicator Composition (Information/Sermon): Assesses the ‘qualitative level’ and technological specificity of the system.
• D. Enforcement Mechanism (Incentive/Carrot): Analyzes the means that secure its ‘effectiveness’ through penalties or incentives.
• E. Sustainability (Future-orientation): Evaluates the integration of environmental values.

To ensure objectivity and reliability in comparative analysis, a structured Scoring Rubric (1-to-5 point scale) was applied to minimize subjectivity. This rubric quantifies the ‘maturity’ of each policy, where Level 1 represents a voluntary or basic stage, and Level 5 represents a mandatory or highly advanced stage [14]. This framework enables a systematic comparison of how each country’s index has evolved in terms of policy maturity and institutional completeness. The detailed evaluation criteria for each level are presented in

Table 1. Evaluation Rubric for Industrialization Indices (Levels 1, 3, and 5).

Criteria	Level 1 (Low/Voluntary)	Level 3 (Moderate/Mixed)	Level 5 (High/Mandatory)
A. Legal Mandate	Voluntary guideline or recommendation only.	Mandatory for specific public projects.	Strictly mandatory for both public & private sectors; linked to building permits.
B. Scope	Evaluates a single aspect (e.g., structure only).	Covers structure and partial architectural finishes.	Comprehensive evaluation (Structure + Architecture + MEP) covering the entire building system.
C. Indicator Composition	Simple quantitative metric (e.g., % of components).	Hybrid of quantitative and qualitative measures.	Advanced metric with weighted scores for technological complexity (e.g., DfMA) and innovation.
D. Enforcement Mechanism	No specific enforcement or simple promotion.	Incentive-based (Bonus GFA, subsidies) or Penalty-based only.	Dual mechanism combining strong penalties (permit denial) with financial incentives.
E. Sustainability	No consideration of environmental values.	Indirect environmental benefits mentioned.	Integrated evaluation where productivity scores are linked to environmental ratings (e.g., Green Mark).

. For clarity, Table 1 presents the criteria for Levels 1, 3, and 5, with Levels 2 and 4 representing intermediate stages.

3.2. Case Selection Criteria and Data Collection

To ensure the derivation of meaningful policy implications for nations aiming to industrialize their construction sectors through government intervention, this study applied strict inclusion and exclusion criteria for case selection.

3.2.1. Exclusion Criteria: Market-Driven Markets

Crucially, this study excludes ‘Market-Driven Markets’ where the OSC ecosystem is already fully matured through voluntary private sector demand. Representative examples include Sweden, the United States, and Japan, where prefabrication has been established through decades of private sector-led development.

• Reason for Exclusion: In these regions, high labor costs, harsh climatic conditions, and established supply chains naturally drive OSC adoption without the need for strong regulatory mandates or specific ‘Industrialization Indices’. As the primary mechanism for OSC adoption here is endogenous market demand rather than exogenous government policy, they are unsuitable for analyzing the effectiveness of “government-led industrialization indices,” which is the core objective of this study.

3.2.2. Inclusion Criteria: Policy-Driven Markets

Instead, this study targets ‘Policy-Driven Markets’ where the government actively intervenes to overcome market inertia and the ‘chicken and egg’ dilemma identified in Section 2.2.

• Reason for Selection: These are markets where the ecosystem is nascent or developing, and the government utilizes specific ‘Industrialization Indices’ as core policy instruments to mandate or incentivize productivity improvements.
  Selected Cases: Based on this criterion, five representative institutional frameworks across four nations were selected

  −

  United Kingdom: Utilizing Pre-Manufactured Value (PMV) to drive modernization in public infrastructure.

  −

  Malaysia: Implementing the IBS Score to systematically industrialize its developing construction sector.

  −

  Hong Kong SAR (China): Promoting MiC (Modular Integrated Construction) as a distinct regulatory jurisdiction to address high-density housing needs.

  −

  Mainland China: Enforcing the Assembly Rate to manage massive urbanization and environmental goals.

  −

  Singapore: Operating the Buildability Score to overcome severe reliance on foreign labor.

These five institutional cases represent a diverse spectrum of economic contexts and policy approaches, making them suitable subjects for comparative analysis using the proposed policy maturity framework.

3.2.3. Data Collection

For the comparative analysis, primary data were collected from official government documents, including legislative codes, technical manuals, and policy roadmaps for each index (e.g., BCA Code of Practice, CIDB IBS Manual, IPA Roadmap). Secondary data were derived from relevant academic literature and government reports published between 2015 and 2024 to ensure currency. This multi-source approach enhances the validity of the comparative assessment by triangulating policy documents with academic interpretations.

4. Comparative Analysis Results

4.1. Overview of National Industrialization Indices

Based on the selection criteria established in Section 3.2, this section provides a technical overview of the operational mechanisms and calculation methods for the five selected institutional indices.

4.1.1. United Kingdom: Pre-Manufactured Value (PMV)

The UK government actively promotes productivity improvements in public sector projects through procurement guidance such as ‘The Construction Playbook’. The central performance metric is the Pre-Manufactured Value (PMV). It is important to note that PMV is not a legal mandate but rather a recommended target within government procurement frameworks. PMV is defined as “the financial proportion of a project’s gross construction cost derived from pre-manufacturing in a controlled factory environment”.

Calculation Method: It is a clear quantitative metric calculated based on the ‘cost’ of the prefabricated portion relative to the total construction cost.

$$PMV\left(\%\right)={\displaystyle \frac{Total Cost of Pre-Manufactured Elements}{Total Construction Cost}}\times 100$$

Here, the numerator represents the total cost of pre-manufactured elements, including materials, labor, transportation, and factory overheads. This formula focuses on the financial value of off-site work rather than the physical complexity of the technology [10]. This approach aligns with the government’s broader infrastructure transformation strategy outlined in the Roadmap to 2030 [38]. The Farmer Review highlighted the critical need for such modernization, warning that the industry must ‘modernise or die’ in the face of chronic productivity challenges [21]. Recent case studies of UK housing projects have documented practical implementation experiences with these methods [39].

4.1.2. Malaysia: Industrialised Building System (IBS) Score

Malaysia operates the IBS Score to modernize its construction industry. It is a scoring system that quantitatively evaluates the proportion of IBS components used in a project. Calculation Method: The IBS Score (out of 100) is the sum of points from three main parts

• Part 1: Structural Systems (Max 50 points): Points awarded based on the area percentage of IBS structural elements (e.g., precast concrete, steel).
• Part 2: Wall Systems (Max 20 points): Points awarded based on the area percentage of IBS wall systems (e.g., precast walls, drywalls).
• Part 3: Other Simplified Solutions (Max 30 points): Points for standardized components like prefabricated staircases or roof trusses [11].

Research has identified key drivers, barriers, and critical success factors for IBS adoption among Malaysian contractors [40].

4.1.3. Hong Kong SAR (China): Modular Integrated Construction (MiC)

Hong Kong SAR promotes Modular Integrated Construction (MiC) to address high population density and labor shortages. MiC represents the most advanced form of OSC, involving 3D modules with finishes and services installed in factories. Evaluation Method: Rather than a complex calculation formula, Hong Kong SAR utilizes a policy incentive based on ‘Adoption’. Projects are evaluated on whether they meet technical MiC requirements (e.g., factory-integrated structure and finishes). Qualifying projects receive a 6% Gross Floor Area (GFA) bonus, serving as a powerful financial motivator [22,23]. Recent research has explored how digital technologies can support smart construction through MiC implementation [41], while studies using Total Interpretive Structural Modelling have identified key drivers for MiC adoption in affordable sustainable housing [42]. The COVID-19 pandemic demonstrated the potential of modular construction for rapid facility deployment; Hong Kong SAR successfully utilized MiC methods to construct quarantine and treatment facilities with 106% improved time efficiency compared to conventional methods [43]. Schedule risk analyses using social network approaches have further highlighted the complex stakeholder interactions in prefabrication housing production [44].

4.1.4. Mainland China: Prefabrication Assembly Rate

Mainland China uses the Assembly Rate, defined by the national standard GB/T 51129-2017 [45]. It evaluates the degree of prefabrication across the building system. Calculation Method: The score is derived from the ratio of pre-assembled components in four categories: (1) Main Structure, (2) Exterior Wall System, (3) Internal Wall/Ceiling System, and (4) Finishes and MEP.

Final Score = Basic Item Score(0–100) + Bonus Item Score(0–20)

Bonus points are awarded for smart technologies like BIM. Certification thresholds vary by province; while many municipalities require assembly rates exceeding 50% for ‘Prefabricated Building’ certification, specific requirements and calculation methods differ significantly across regions [45,46]. However, the implementation of assembly rate calculations varies significantly across Chinese provinces. Li et al. [46] conducted a comparative analysis of prefabrication rate calculation methods across 28 provincial-level administrative regions, revealing substantial differences in evaluation approaches. Provincial governments have developed their own supporting policies with varying emphases [47]. Jiang et al. [48] investigated the effectiveness of prefabrication incentive policies, finding that policies promulgated after 2015 led to significant increases in adoption rates. Research has also examined the efficiency of the Chinese prefabricated building industry [49] and developed policy frameworks specifically tailored for underdeveloped areas [50]. Cost–benefit analyses have documented the economic barriers to promoting prefabricated construction [33], while developers cite uncertain returns and supply chain fragmentation as primary concerns [32].

4.1.5. Singapore: Buildability Score (B-Score)

Singapore operates the Buildability Score, a mandatory metric evaluating how efficiently a design can be constructed. Calculation Method: The score is calculated across three systems—Structural, Architectural, and MEP. Historically, the system utilized a Labor Saving Index (LSI) coefficient to weight different construction methods. The current 2022 Edition has evolved to use ‘Allocated Points’ (SN, AN, MN), which assign specific point values to each method based on labor-saving potential, awarding higher points to advanced technologies (e.g., PPVC/Modular) over simple precast components.

• Structural System: Points for labor-saving methods (e.g., steel, precast).
• Architectural System: Points for standardized components (e.g., drywalls, unitized windows).
• MEP System: Points for integrated modules like Prefabricated Bathroom Units (PBUs).

This system is unique in that it mandates a minimum score for building plan approval, directly linking productivity to the permitting process [16].

4.2. Comparative Assessment Results

Using the ‘Policy Maturity Assessment’ framework and evaluation rubric (Table 1) defined in Section 3.1, the five national indices were systematically evaluated across the five policy dimensions. The results are summarized in

Table 2. Comparative Scoring Matrix.

Region (Index Name)	A. Legal Mandate	B. Scope	C. Indicator Composition	D. Enforcement Mechanism	E. Sustainability	Total Score
Singapore (Buildability Score)	5	5	5	5	3	23
Main land China (Assembly Rate)	4	4	3	3	2	16
Hong Kong SAR (MiC Acceptance)	3	4	4	3	2	16
Malaysia (IBS Score)	3	3	2	4	1	13
UK (PMV)	3	2	1	2	1	9

Level 1 (Low) to Level 5 (High) based on the Policy Maturity Assessment framework. A. Mandate: 5 (Universal mandatory) vs. 1 (Voluntary). B. Scope: 5 (Full System Integration) vs. 1 (Component-based). C. Composition: 5 (Qualitative/Advanced tech favored) vs. 1 (Simple cost/volume). D. Enforcement: 5 (Dual Carrot & Stick) vs. 1 (None). E. Sustainability: 5 (Fully Integrated) vs. 1 (No Linkage).

and visualized in Figure 1.

Analysis by Policy Dimension

A. Legal Mandate (Enforceability)

• Singapore (Level 5): Uniquely, the Code of Practice on Buildability mandates a minimum score for all private and public projects exceeding 5000 m² [16]. Compliance is a prerequisite for building plan approval, making it a strictly binding obligation [16].
• Mainland China (Level 4): While the central government sets ambitious targets, mandates are often enforced through regional regulations (e.g., Shanghai, Beijing) rather than a single unified national law for all private projects [45,46].
• UK (Level 3): PMV targets are recommended in The Construction Playbook as procurement guidance, but this does not constitute a legal mandate; it is limited to central government procurement and does not legally bind the private housing market [10].
• Malaysia (Level 3): The use of IBS is mandatory for government projects worth over RM 10 million, requiring a minimum IBS Score of 70, but private sector adoption remains largely voluntary or incentive-based [11].

B. Scope (Comprehensiveness)

• Singapore (Level 5): The index evaluates the entire building system, comprehensively covering Structural, Architectural, and MEP systems under a unified framework defined in the legislation [16].
• Mainland China (Level 4): The Assessment Standard (GB/T 51129) covers structure, enclosure, and fit-out, but the evaluation is often criticized for focusing on the physical volume of components rather than system integration [45].
• Malaysia (Level 3): The IBS Manual heavily skews points towards Structural (50 pts) and Wall systems (20 pts), with limited emphasis on complex MEP integration compared to Singapore [11].
• UK (Level 2): PMV measures the financial value (cost). As noted in the PMV Guidance, high-cost items can inflate the score regardless of the technical extent of prefabrication, limiting its technical comprehensiveness [10].

C. Indicator Composition (Qualitative Level)

• Singapore (Level 5): The system has evolved from the original Labor Saving Index (LSI) coefficient to the current ‘Allocated Points’ system (2022 Edition), which structurally favors advanced technologies. For example, PPVC (Modular) receives significantly higher allocated points than simple precast components, promoting qualitative technological advancement [16].
• Hong Kong SAR (Level 4): Although voluntary, the policy definition of MiC requires high-level 3D volumetric integration (structure + finish + services), ensuring that adopted projects meet high qualitative standards [22,23].
• Mainland China (Level 3): It employs a hybrid approach but relies heavily on quantitative volume ratios (e.g., precast concrete volume > 50%), which does not necessarily distinguish between simple and advanced manufacturing techniques [45].
• UK (Level 1): It relies on a single quantitative metric based on financial cost (%). This can be influenced by market price fluctuations (e.g., rising material costs) rather than actual productivity gains [10].

D. Enforcement Mechanism (Effectiveness)

• Singapore (Level 5): It operates a robust dual mechanism. The ‘Stick’ (permit denial under the Building Control Act) ensures the baseline, while the ‘Carrot’ (GLS land sales requirements and funding) drives innovation [15,16].
• Malaysia (Level 4): It utilizes a levy exemption mechanism under the CIDB Act, where achieving a certain IBS score waives the construction levy, serving as a powerful financial motivator for the private sector [11,40].
• Hong Kong SAR (Level 3): The primary driver is the 6% GFA concession. While effective, it lacks a punitive mechanism for projects that do not adopt MiC [22,23].

E. Sustainability (Future-orientation)

• Singapore (Level 3): The Code of Practice links productivity scores with the Green Mark certification. High buildability scores can contribute to achieving higher environmental ratings, demonstrating partial policy integration [16].
• Mainland China & Hong Kong SAR (Level 2): Environmental benefits (e.g., waste reduction) are mentioned as policy goals in national roadmaps but are not directly integrated into the calculation formulas of the indices [23,45].
• UK & Malaysia (Level 1): These indices focus purely on productivity (cost or volume) with no direct linkage to sustainability metrics in their calculation manuals [10,11].
• Environmental cost–benefit analyses of prefabricated public housing have demonstrated that productivity improvements can be achieved alongside environmental benefits [26].

4.3. Selection of In-Depth Case Study

The comparative analysis identifies Singapore’s Buildability Score as the most mature and comprehensive policy model. It represents a successful case where government intervention effectively created a market for high-tech OSC methods. Therefore, Section 5 will conduct an in-depth simulation of the Singaporean model to understand how its scoring mechanism practically influences design and construction behaviors. Specifically, the simulation will examine how the LSI-based scoring structure influences method selection and quantify the score impact of adopting or abandoning key DfMA technologies.

5. Analysis of Singapore’s Buildability Appraisal System

5.1. Introduction and Evolution of the System for Productivity Innovation

Singapore’s Buildability system is the product of a long-term, systematically designed policy that has evolved to address the chronic productivity issues in its construction industry. In the 1980s, amidst rapid economic growth, Singapore’s construction sector was grappling with the dual challenges of low productivity and high dependency on foreign labor [1,2]. The national recession in 1985 made fundamental improvements to the industry a pressing national priority, and as a solution, the concept of Buildability was developed, benchmarking the advanced evaluation system of Japan’s Takenaka Corporation [15]. In 2001, the Building and Construction Authority (BCA) first introduced the buildability legislation with the objective of raising productivity right from the design stage and reducing the industry’s reliance on foreign workers [16]. The initial system focused on encouraging architects and engineers to deliver buildable designs. Subsequently, to ensure that the potential in the design was realized on-site, constructability requirements were added in 2011 to incentivize builders to adopt more labor-efficient technologies and methods [16]. This established a comprehensive evaluation framework connecting the upstream design phase to the downstream construction phase. Since 2014, the BCA has continued to strengthen the system by progressively raising the minimum required scores and, notably, by stipulating the adoption of specific productive technologies on sites sold under the Government Land Sales (GLS) Programme [15,16]. The most recent 2022 Edition further evolves the system by integrating Design for Manufacturing and Assembly (DfMA) components into the three main disciplines of Structural, Architectural, and MEP, and by introducing ‘outcome-based solutions’ to encourage innovative designs [16].

5.2. Analysis of Buildable Design and Constructability Score Systems

Singapore’s Buildability system is composed of two core pillars: the Buildable Design Score (B-Score), which assesses the potential of the design phase, and the Constructability Score (C-Score), which evaluates the level of implementation during the construction phase. These two systems work in a complementary manner to drive high productivity throughout the entire project lifecycle, from design to construction [16].

5.2.1. Calculation Method for Buildable Design Score (B-Score)

The B-Score evaluates the inherent productivity potential during the design phase and is a mandatory requirement for building plan approval [16]. Under the Code of Practice (2022 Edition), the B-Score comprises three main systems—Structural, Architectural, and Mechanical, Electrical, and Plumbing (MEP)—along with an Innovation category. The score for each system is calculated by multiplying the Allocated Points assigned to specific methods by their Percentage of Coverage [16].

Step 1: Calculation of System Scores

Methods with higher labor-saving potential are assigned higher allocated points (S_N, A_N, M_N), which are weighted by their application ratio (area or length) across the building [16].

• Structural System: For instance, in a residential project, applying ‘Structural Steel’ (allocated 35 points) to 80% of the total floor area would yield approximately 28 points [16].
• Architectural System: Points are awarded based on the usage of high-productivity methods like drywalls or curtain walls. For example, using dry construction methods for 90% of the total wall length can secure a high score of over 36 points [16].
• MEP System: Unlike previous versions, the MEP system is now a reinforced independent assessment category. Points are earned by adopting prefabricated vertical/horizontal modules or prefabricated pump skids [16].

Step 2: Innovation and Bonus Points

• In addition to the basic systems, projects can secure up to 20 additional points under the ‘Innovation and Others’ category by adopting technologies that significantly enhance productivity. Bonus points are awarded for modular components like Prefabricated Kitchen Units (PKU) and Prefabricated Common Toilets (PCT), or for new technologies that achieve at least 20% manpower savings. Advanced DfMA technologies, such as PPVC, are critical for maximizing the total score, as they garner high points across Structural, Architectural, and MEP systems, as well as in the Innovation category [16].

Step 3: Final B-Score Calculation

• The final B-Score is the summation of the scores from these four categories (Structural + Architectural + MEP + Innovation), and the total score is capped at 120 points [16].

The maximum points allocated to each system (Structural, Architectural, MEP) are not uniform across all building types; rather, they are differentially weighted to reflect the manpower consumption patterns specific to each development category.

• Residential Projects: Due to the high volume of partition walls, finishes, and interior works, the Architectural System (A_N) carries the highest weightage (up to 40 points). Conversely, MEP weighting (15–20 points) is relatively low due to the repetitive nature of standard units.
• Commercial Projects: Given the necessity for complex HVAC, fire protection, and IT infrastructure, the weightage for the MEP System (M_N) is set at 35 points—more than double that of residential projects. This highlights the critical impact of MEP modularization on overall productivity in commercial buildings.
• Industrial Projects: Characterized by high ceilings, long spans, and heavy loading requirements, the Structural System (S_N) is allocated 50 points, accounting for nearly half of the total score, thereby prioritizing productivity in structural works.

This weighted system represents a strategic approach designed to intensively manage the critical trades that consume the most labor for each building type, rather than applying a “one-size-fits-all” standard [16].

5.2.2. Calculation Method for Constructability Score (C-Score)

The C-Score evaluates how proactively a builder adopts labor-efficient technologies and methods on-site [16]. Unlike the B-Score’s weighted average method, the C-Score is calculated using a point-scoring system, where a set number of points is earned for each evaluation item [16]. The evaluation consists of three main categories [16]:

• Structural System: Points are awarded for using technologies and equipment that increase on-site productivity, such as system formwork, automated rebar fabrication, and high-strength concrete [16].
• Architectural, Mechanical, Electrical, and Plumbing (AMEP) Systems: Points are given for applying advanced technologies like prefabricated components, on-site automation equipment (e.g., painting robots), and non-destructive testing (NDT) [16].
• Good Industry Practices: Additional points are awarded for the level of smart construction technology adoption, including process management using BIM (Building Information Modeling), on-site surveying with drones and laser scanning, and the application of Integrated Digital Delivery (IDD) platforms [16].

As such, the B-Score evaluates the inherent productivity of a design, while the C-Score assesses the advanced technologies and methods adopted by the builder on-site via a checklist, thereby complementarily driving productivity improvement throughout the entire construction process.

5.3. Virtual Data Simulation

This section conducts virtual simulations for two representative building types to specifically analyze the impact of the Buildability Score system on construction method selection in actual projects. The simulation was performed strictly following the calculation methods and Allocated Points (S_N, A_N, M_N) specified in the BCA’s Code of Practice on Buildability (2022 Edition) [16]. The current Allocated Points system represents an evolution from the earlier LSI coefficient methodology, maintaining the same productivity-incentivizing logic while providing more granular point assignments.

5.3.1. Simulation Overview and Allocated Points

Two virtual projects representing the characteristics of the Singapore construction market were established, as shown in Table 3. The scale (GFA, number of stories) for each project was assumed based on representative levels commonly found in Singapore to ensure realism.

• Project A (Public Housing, HDB): Accounts for a large proportion of Singapore’s housing supply and is characterized by high standardization. The baseline assumes the adoption of the highly productive Advanced Precast Concrete System (APCS), drywalls, and Prefabricated Bathroom Units (PBUs), which are strongly recommended by government policy.
• Project B (Office Building): Represents a private commercial building where diverse methods are mixed for design flexibility. The baseline assumes a Steel Frame and Curtain Wall for the main structure and envelope, but incorporates traditional wet-trade blockwork for internal partitions, reflecting common practices.

Table 3. Overview of Virtual Simulation Projects.

Category	Project A: Public Housing (HDB)	Project B: Office Building
Category & Weight	Public Residential (S1: 45, A1: 40, M1: 15)	Commercial (S3, A3, M3) (S3: 35, A3: 30, M3: 35)
Scale	GFA 15,000 m2, 20 stories	GFA 20,000 m2, 15 stories
Baseline Methods (Assumed)	- Structure: APCS (80%), Cast-in situ (20%) - Arch: Precast/Drywall (75%), PBU Wall (25%) - MEP: Prefab Modules (Vertical/Horizontal, 50%) - PBU Adoption: 80% of total bathrooms - Feature: High standardization & PBU	- Structure: Steel Frame (80%), Cast-in situ (20%) - Arch: Curtain Wall (50%), Blockwall (50%) - MEP: Conventional (On-site, 100%) - Feature: Flexible design & Mixed wet trades

Table 3. Overview of Virtual Simulation Projects.

Category	Project A: Public Housing (HDB)	Project B: Office Building
Category & Weight	Public Residential (S1: 45, A1: 40, M1: 15)	Commercial (S3, A3, M3) (S3: 35, A3: 30, M3: 35)
Scale	GFA 15,000 m2, 20 stories	GFA 20,000 m2, 15 stories
Baseline Methods (Assumed)	- Structure: APCS (80%), Cast-in situ (20%) - Arch: Precast/Drywall (75%), PBU Wall (25%) - MEP: Prefab Modules (Vertical/Horizontal, 50%) - PBU Adoption: 80% of total bathrooms - Feature: High standardization & PBU	- Structure: Steel Frame (80%), Cast-in situ (20%) - Arch: Curtain Wall (50%), Blockwall (50%) - MEP: Conventional (On-site, 100%) - Feature: Flexible design & Mixed wet trades

The allocated points (S_N, A_N, M_N) for the key construction methods applied in the simulation are derived from the Code of Practice (2022 Edition) and are summarized in

Table 4. Allocated Points for Methods Applied in Simulation (Based on COP 2022) [16].

System	Specific Method	Allocated Points (HDB)	Allocated Points (Office)
Structural (SN)	- APCS (Advanced Precast Concrete)	39 (S1)	N/A
	- Structural Steel	N/A	33 (S3)
	- Cast-in situ Concrete	10 (S1)	15 (S3)
Architectural (AN)	- Precast/Drywall	26 (A1)	N/A
	- Curtain Wall	N/A	16 (A3)
	- PBU (Prefabricated Bathroom)	28 (A1)	N/A
	- Brick/Blockwall (Wet)	0 (A1)	0 (A3)
MEP (MN)	- Prefab Modules	12 (M1)	20 (M3)
	- Conventional (On-site)	0	0

. Note that points are assigned differently depending on the development category (Residential vs. Commercial).

5.3.2. Baseline Score Calculation

The estimated B-Scores for each project were calculated based on the established baseline methods and allocated points. The calculation formula involves multiplying the allocated points of each system by its coverage ratio (area or length). The detailed calculation processes are shown in

Table 5. Baseline B-Score Calculation Example for Project A (Public Housing).

Evaluation Component	Specific Method	Application Ratio	Allocated Points	Calculation	Score
Structural (max 45)	APCS	80%	39	0.8 × 39	31.2
	Cast-in situ	20%	10	0.2 × 10	2.0
Architectural (max 40)	Precast/ Drywall	75%	26	0.75 × 26	19.5
	PBU (Bathroom)	25%	28	0.25 × 28	7.0
MEP System (max 15)	Prefab Modules	50%	12	0.5 × 12	6.0
Others	Innovation/ Bonus	-	-	(Assumed)	+15.0
Total Score					80.7

and

Table 6. Baseline B-Score Calculation Example for Project B (Office Building).

Evaluation Component	Specific Method	Application Ratio	Allocated Points	Calculation	Score
Structural (max 35)	Steel Frame	80%	33	0.8 × 33	26.4
	Cast-in situ	20%	15	0.2 × 15	3.0
Architectural (max 30)	Curtain Wall	50%	16	0.5 × 16	8
	Blockwall (Wet)	50%	0	0.5 × 0	0.0
MEP System (max 35)	Conventional	100%	0	1.0 × 0	0.0
Others	Innovation/ Bonus	-	-	(Assumed)	+5.0
Total Score					42.4

.

The Application Ratio for PBU (25%) refers to wall length coverage for scoring purposes. Separately, the project adopts PBU for 80% of total bathrooms, meeting the mandatory threshold and qualifying for Industry Standard PBU (+2.5) and PBU Repetition (+4.0) bonus points. The remaining bonus points (+8.5) include Industry Standard Precast HS and other buildable features.

The summary of the results is presented in

Table 7. Summary of Baseline B-Score Calculation Results.

Category	Project A (HDB)	Project B (Office)
Structural Score	33.2 (max 45)	29.4 (max 35)
Architectural Score MEP Score	26.5 (max 40) 6.0 (max 15)	8.0 (max 30) 0.0 (max 35)
Other Bonus	15.0 (incl. PBU bonus 6.5)	5.0
Total Estimated Score	80.7	42.4

. Project A (HDB), which actively adopted high-productivity methods (APCS, PBU), achieved near-maximum scores in both structural and architectural categories. Notably, the 80% PBU adoption rate qualifies for additional bonus points under Industry Standardization and Repetition categories. In contrast, Project B (Office), which incorporated traditional wet trades, recorded relatively lower scores.

5.3.3. Sensitivity Analysis: Method Change Scenarios

Baseline scores alone may not clearly reveal the impact of individual method choices on the score. Therefore, this section analyzes how sensitively the B-Score changes when key high-productivity methods are intentionally replaced with lower-productivity alternatives. This aims to verify the system’s mechanism for incentivizing specific technological choices.

The rationale for the method changes in each scenario is as follows:

• Scenario A-1 (Structure): Changing APCS (modern precast) to traditional Cast-in situ concrete to verify the impact of structural system selection.
• Scenario A-2 (Integrated): Replacing PBUs (prefabricated bathrooms) with conventional on-site wet bathrooms. This change not only reduces the direct coverage score but also disqualifies the project from PBU-related bonus points (Industry Standard PBU and PBU Repetition), demonstrating the compounding impact of DfMA technologies.
• Scenario B-1 (Architecture): Switching from Curtain Wall (dry) to Brickwall (wet) to measure the sensitivity of the score to the dry-process adoption in facade works.

The analysis results are presented in

Table 8. Sensitivity Analysis Simulation Results (Method Change). ▼ indicates score decrease from baseline.

Scenario	Change Description (High Pts→Low Pts)	Resulting Component Score	Change in Total Score
A-1: HDB Structure	APCS (39 pts)→Cast-in situ (10 pts)	Structure: 33.2→10.0	▼ 23.2 pts
A-2: HDB PBU	PBU (28 pts)→(loses 80% PBU adoption)	Arch: 26.5→19.5, Bonus:15.0→8.5	▼ 13.5 pts
B-1: Office Facade	Curtain Wall (16 pts)→Brick (0 pts)	Arch: 8.0→0.0	▼ 8.0 pts

.

The simulation results demonstrate that abandoning high-productivity methods leads to a drastic drop in the total score, ranging from 8 to over 23 points. Notably, Scenario A-2 shows that eliminating PBU results in a 13.5-point loss—comprising 7.0 points from direct coverage and 6.5 points from forfeited bonus points—illustrating how the scoring system creates compounding penalties for abandoning DfMA technologies. In particular, reverting to wet trades (masonry, plastering), which have zero allocated points, could make it impossible to meet the BCA’s minimum score requirements. This proves that the Buildability Score operates as a powerful mechanism compelling designers to exclude wet trades and select dry and prefabricated methods from the earliest design stages.

5.4. Analysis of Design and Construction Behaviors Induced by the Buildability Score System

The preceding simulation results clearly demonstrate that the Buildability Score system functions not merely as an evaluation metric but as a powerful policy tool that guides the behaviors of construction project participants in specific directions. Through the structure of the scoring system and the sensitivity analysis, the specific behavioral changes intended by the BCA at the design (Buildable Design) and construction (Constructability) stages can be identified as follows.

5.4.1. Behaviors Induced in the Design Stage (Impact on Design)

As the B-Score is directly linked to building plan approval [16], designers are compelled to consider buildability as a core design parameter from the project’s outset.

• Preference for Standardized and Modularized Designs: Achieving a high B-Score necessitates the use of standardized components that carry high allocated points (S_N, A_N, M_N) [16]. This incentivizes designers to favor simple, grid-based layouts over complex, irregular forms. Furthermore, adopting ‘Industry Standard Dimensions’ for components such as windows, doors, and PBUs grants additional bonus points, compelling designers to prioritize standardized specifications over bespoke designs [16].
• Emphasis on ‘Dry’ Construction Methods: In the architectural system evaluation, traditional on-site ‘wet’ trades like brickwork and plastering are assigned zero or very low allocated points [45]. As confirmed by the simulation results (Scenario B-1), adopting wet methods results in a failure to secure points. Consequently, designers naturally gravitate towards ‘dry’ construction methods with high allocated points, such as drywall partitions and prefabricated panels, which are manufactured off-site and installed quickly on-site.
• Proactive Integration of DfMA Technologies: The ‘Innovation’ category of the B-Score strongly motivates the integration of high-value DfMA technologies, such as Prefabricated Bathroom Units (PBUs), modular MEP systems, and PPVC, into the design from the early stages. The significant score difference of over 13 points observed in the simulation (Scenario A-2) depending on PBU adoption—comprising both the direct coverage score (7.0 points) and the associated bonus points for industry standardization and repetition (6.5 points)—highlights that these technologies function almost as mandatory requirements rather than mere options [16].

5.4.2. Behaviors Induced in the Construction Stage (Impact on Construction)

The C-Score directly evaluates the builder’s technology adoption, while the B-Score indirectly influences construction stage behaviors by providing designs that are easier for contractors to build.

• Maximization of Off-site Production: The precast concrete, steel components, and various prefabricated modules adopted during the design phase to achieve a high B-Score inevitably increase the proportion of work carried out in factories by the builder. This serves as a key driver for shifting the construction process from traditional on-site work towards a model centered on ‘off-site manufacturing + site assembly’.
• Simplification of On-site Work and Adoption of Smart Technologies: When precisely manufactured components arrive on-site, the role of the construction site shifts away from complex processing and fabrication towards simpler tasks focused on erection and assembly. Concurrently, the C-Score encourages builders to actively invest in smart construction technologies such as System Formwork, BIM-based process management, Integrated Digital Delivery (IDD), and other Good Industry Practices, thereby enhancing both on-site productivity and safety [16].

6. Discussion and Conclusions

6.1. Summary of Key Findings

This study conducted a comparative analysis of government-led OSC industrialization indices across five institutional frameworks in policy-driven markets to identify an effective benchmark model. The comparative assessment utilizing the Policy Maturity Framework revealed significant variations in policy design. Singapore’s Buildability Score achieved the highest maturity level (23/25), distinguished by its comprehensive scope, technology-weighted scoring, and robust dual enforcement mechanism [15,16]. In contrast, other indices showed limitations: the UK’s PMV relies on financial metrics rather than technical complexity [10]; however, this reflects the UK’s ‘Market-led’ approach rather than a policy failure. Therefore, the lower score of the UK in this framework indicates a ‘Lower Level of Intervention Intensity,’ rather than a lack of policy effectiveness. Since the UK adopts a ‘Market-Led’ approach, voluntary guidelines are preferred over mandatory regulations in this mature ecosystem. It is also worth noting that efforts to develop sophisticated indices are expanding globally, as seen in the recent ‘OptimizIA’ index by the Chilean Chamber of Construction [51]. Malaysia’s IBS Score is heavily weighted toward structural components [11], and China’s Assembly Rate primarily measures volume without distinguishing technological sophistication [36]. Crucially, the in-depth simulation of Singapore’s system empirically verified that the Buildability Score functions as a powerful “behavioral steering mechanism” rather than merely a passive evaluation metric. The sensitivity analysis revealed that abandoning high-productivity methods results in score reductions ranging from 8 to over 23 points. Notably, the elimination of PBU resulted in a 13.5-point loss—comprising both direct coverage scores and forfeited bonus points. This illustrates how the system creates compounding penalties that effectively compel designers to adopt DfMA technologies from the earliest design stages, thereby validating the system’s regulatory effectiveness. Cost analyses based on multiple case studies in China further support this finding, demonstrating that government support is essential to offset the initial cost premiums associated with off-site construction [52].

6.2. Effectiveness of Government-Led Policy Intervention

The findings provide empirical support for the necessity of government-led intervention in nascent OSC markets. As discussed in Section 2, private sector adoption faces significant barriers—high initial capital investment, uncertain ROI, and fragmented supply chains—creating a ‘chicken and egg’ dilemma that market forces alone cannot resolve [6,28]. Singapore’s experience demonstrates that a well-designed intervention can break this vicious cycle. By mandating minimum buildability scores for permit approval, the government artificially created the ‘guaranteed market demand’ that experts identify as a prerequisite for private investment [15,16]. The simulation confirms that this mandatory framework creates a compelling economic logic for adopting DfMA technologies; the 13.5-point penalty for abandoning PBU translates directly into permit approval risk, which overrides conventional cost–benefit calculations. This contrasts with market-driven economies, where OSC adoption evolved organically. In policy-driven markets lacking established ecosystems, government intervention serves as a catalyst. However, the analysis reveals that merely establishing an index is insufficient; the index must incorporate sophisticated mechanisms—specifically technology weighting and dual enforcement—to genuinely steer market behavior. This approach aligns with DfMA principles that emphasize early-stage design decisions for manufacturing and assembly efficiency [24,37].

6.3. Design Principles for Effective OSC Industrialization Indices

Based on the analysis of Singapore’s benchmark model [16], this study derives four design principles for effective OSC industrialization indices, addressing the critique that such metrics often measure “component usage” rather than “productivity.”

• Dual Enforcement Mechanism: Combining ‘sticks’ (mandatory baseline for permit approval) and ‘carrots’ (incentives like GLS requirements) is essential to ensure universal compliance while driving innovation.
• Technology-Weighted Scoring (Value over Volume): This is critical for promoting qualitative advancement. Unlike simple volume-based metrics (e.g., prefabrication percentage) that treat all precast components equally, Singapore’s allocated points system assigns significantly higher weights to advanced DfMA technologies (e.g., PPVC, PBU). This structural preference ensures the metric measures “Labor Saving Value” rather than just physical volume.
• Differentiated Weightings by Building Type: Allocating different maximum points to Structural, Architectural, and MEP systems based on the specific manpower consumption patterns of each building type allows for targeted management of critical labor-intensive trades.
• Integration with Industry Standardization: Bonus points for industry standard dimensions and components promote supply chain efficiency and reduce customization costs, creating a virtuous cycle for manufacturers and developers.

6.4. Limitations and Future Research

This study has limitations that should be acknowledged. First, the comparative analysis of five institutional frameworks was designed as a screening mechanism to identify the most mature policy model. Countries with market-driven OSC ecosystems (e.g., Sweden, USA) were intentionally excluded to focus on government-led interventions, limiting the generalizability of findings to policy-driven contexts. Second, the simulation relied on virtual projects based on the BCA Code of Practice (2022) [16]. Fourth, regarding validation, this study focused on the ‘maturity of policy design’ using quantitative indicators. Future research should incorporate qualitative validation methods, such as Focus Group Discussions (FGD), to capture professional insights. Additionally, empirical validation using actual project data (e.g., construction cost, schedule performance) is necessary to verify whether higher policy scores translate into tangible on-site productivity gains. Finally, economic trade-offs, such as potential cost increases due to mandatory adoption, were not fully covered in this scope. While this validated the scoring logic, real-world projects may exhibit different patterns due to site-specific constraints. Third, this study focused on productivity-enhancing mechanisms without fully examining the trade-offs. As noted in the literature, mandatory buildability requirements may constrain architectural flexibility and design innovation [53]. Future research should address these limitations through empirical validation using actual project data and by investigating the correlation between productivity scores and actual construction costs/duration. Future research should also examine the application of modular construction in high-rise buildings [54], which presents unique structural and logistical challenges. Studies have highlighted both opportunities and challenges of modular methods in dense urban environments [55], suggesting that context-specific adaptations may be necessary. Additionally, comprehensive life cycle assessments of modular buildings [9] should be integrated into policy evaluation frameworks to ensure that productivity gains do not come at the expense of long-term environmental performance.

Furthermore, this study defines ‘Mandatory’ policies as the highest maturity level (Level 5). This reflects the specific context of ‘Government-Led’ markets where strong intervention is required to overcome initial market inertia. Therefore, this scoring logic may not be directly applicable to mature markets (e.g., UK, USA) where voluntary, market-driven mechanisms are more effective. Additionally, the simulation in this study focuses on the mechanism of score deduction and policy compliance. It does not include a quantitative Cost–Benefit Analysis (CBA). Future research should verify the economic trade-offs between obtaining higher policy scores and the actual construction costs or schedule benefits.

6.5. Conclusions

This study identified Singapore’s Buildability Score as the most comprehensive policy model among five policy-driven markets. Through systematic comparison and in-depth simulation, the research demonstrated how well-designed industrialization indices can effectively steer design and construction behaviors toward high-productivity methods. The key contribution of this study lies in deriving evidence-based design principles—specifically the shift from volume-based to value-based evaluation—to overcome the ‘chicken and egg’ dilemma in nascent OSC markets. These principles provide an actionable framework for policymakers seeking to develop or refine their own industrialization indices.