ISSN 1392-3730 / eISSN 1822-3605

2024

JOURNAL of CIVIL ENGINEERING
and MANAGEMENT

Volume 30
Issue 3
Pages 234–247
https://doi.org/10.3846/jcem.2024.20953

MIVES MULTI-CRITERIA FRAMEWORK TO SUSTAINABILITY
INDEX OF DESIGN FOR MANUFACTURE AND ASSEMBLY
Tan TAN1, Lang ZHENG1, Fan XUE1, Zhikang BAO2,
Zigeng FANG3, Xiaohu LIU4
1 Department Real Estate and Construction, The University of Hong Kong, Hong Kong SAR, China

2 School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, United Kingdom
3 Department of Civil Engineering, University of Nottingham, Nottingham, United Kingdom

4 Department of Architecture, Huazhong University of Science and Technology, Wuhan, China

Article History:
■ received 10 November 2022
■ accepted 24 November 2023

Abstract. Embracing sustainable strategies that consider Design for Manufacture and Assembly (DfMA) has become a
rapidly growing trend in urban development. Continued uncertainty on the sustainability assessment of design could
drive a series of indecisive decision-making among design alternatives, further disrupting the potential opportunities
toward sustainable DfMA. However, there is a lack of research on sustainable design assessments for DfMA and establishing a sustainable index. This research establishes an integrated value model for the sustainability assessment framework and DfMA sustainability index to address this challenge. This model integrates Building Information Modelling
(BIM) with MIVES, a customisable Multi-Criteria Decision Making (MCDM) tool. The pilot case of this framework is the
retrofit of a commercial building’s façade system, which demonstrated the capability of the proposed framework. Data
collection and analysis include the comparisons between five design alternatives. This research furthers previous studies
and has three-fold significance: 1) Establishing reasonable multi-criteria for the sustainable DfMA indices; 2) Adapting
the MIVES approach for comparative analysis across three building phases to make it compatible with DfMA; 3) developing a quantitative analysis method for sustainable design assessment of DfMA in the construction industry.

Keywords: DfMA, sustainability index, sustainability assessment, MIVES, sustainable design.
Corresponding author. E-mail: liuxiaohu@hust.edu.cn

1. Introduction
Global sustainable construction faces several challenges. A
primary concern is the growing demand for resources and
energy, driven by rapid urbanisation and the increasing
global population (Bao, 2023; Desa, 2018). Another challenge is the significant contribution of the Architectural
Engineering and Construction (AEC) industry to greenhouse gas emissions, accounting for nearly 27% of global
CO2 emissions (Hamilton, 2023). Additionally, sustainable
construction faces the complexities of balancing social,
economic, and environmental factors to achieve longterm resilience and adaptability in architectural designs
(Kibert, 2016). To achieve sustainability, it is significant for
architects and engineers to evaluate and optimise design
solutions by considering both sustainability performance
and buildability. However, as sustainability is a multifaceted concept that cannot be fully captured by computer
algorithms alone, it is necessary to involve human intel-

ligence in comparing the sustainability of multiple design
alternatives, particularly for the generation of parametric buildings with irregular shapes and non-standardised
components.
As an emerging and promising design solution, Design
for Manufacture and Assembly (DfMA) provides enormous
opportunities for the AEC industry to embrace sustainability (Lu et al., 2021; Tan et al., 2020). DfMA aims to address
some of the potential downstream manufacturing and assembly issues by evaluating and improving the early product’s design process to achieve control over the total cost
of ownership (Boothroyd, 2005; Dewhurst, 2010). Theoretically, DfMA consists of two core components, design for
manufacture (DfM: which focuses on the manufacture of
individual components) and design for assembly (DfA: an
approach to the assembly of components) (Bogue, 2012).
In the words of Luiten and Fischer (1998), DfMA transforms

Copyright © 2024 The Author(s). Published by Vilnius Gediminas Technical University
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Journal of Civil Engineering and Management, 2024, 30(3), 234–247

the traditional sequential process of construction into one,
in which design and manufacture are interdependent. Although the body of research on DfMA has been growing
across regions, how to integrate the concept of DfMA into
sustainable design remains an unmatured area. Specifically, a knowledge gap is the decision analysis and making
of opting for the best-fit sustainability design in the DfMA
process.
The pressing need for a “Sustainability Index” in DfMA
stems from the increasing global demand for environmentally conscious buildings and resource-efficient construction processes. A sustainability index is a tool used
to measure and assess the environmental, social, and economic performance of building projects (Ariaratnam et al.,
2013). It combines various sustainability indicators to create a comprehensive picture of a design’s commitment
to sustainable practices and long-term value creation. By
evaluating diverse design factors, a sustainability index allows investors, stakeholders, and policymakers to make informed decisions and promote sustainable development
in building design. Developing a comprehensive sustainability index would enhance DfMA by enabling the systematic evaluation and optimisation of building designs, ultimately promoting resource conservation, waste reduction,
and environmental stewardship. However, current DfMA
methodologies largely neglect the incorporation of quantifying sustainability metrics, thus presenting a significant
research gap in developing the DfMA sustainability index.
Multi-Criteria Decision Making (MCDM) is a decision
analysis and decision-making methodology that utilises
available information, explores stakeholders’ perceptions
and demands, and evaluates the performances of various
alternatives (Tan et al., 2021). In the AEC industry, MCDM
has also been widely applied to supporting decision-making in building design due to its universality and capability
for abstract concepts defined by a set of indicators, like
“sustainability” (Fallahpour et al., 2020; Šaparauskas, 2003).
Information acquisition is critical in facilitating strategic
decisions, especially information about these indicators for
sustainability assessment. In addition, MCDM and Building Information Modelling (BIM) technology are widely
regarded as complementary systems. MCDM can thus be
better stimulated to organise various decision-making criteria more efficiently and rigorously with the assistance of
BIM. In turn, BIM capabilities can also be amplified with
the synergy of MCDM when dealing with several uncertainties and risks in construction projects (Tan et al., 2021).
In the initial design stage of a construction project, designers can benefit from BIM-enabled integration and dynamic
information exchanges to make better evidence-based solutions (Al Hattab & Hamzeh, 2018).
MIVES (Spanish acronym: Modelo Integrado de Valor
para una Evaluación Sostenible, in English: Integrated Value Model for Sustainability Assessment) is a customisable
MCDM tool that allows decision-makers to evaluate and
quantify the parameters (i.e. requirements, criteria and indicators) by using Analytic Hierarchy Process (AHP) and

235
the value function (Cots et al., 2022). This concept enables the indirect measurement of the satisfaction grade
of various stakeholders involved in the decision-making
procedure. In addition, MIVES performs well in uncertainty
analysis and calibration problems. It can be employed in
different locations with varying characteristics without being constrained by the present conjuncture (Gilani et al.,
2019, 2022). These main characteristics make MIVES
unique among other competing methods for developing
the DfMA sustainability index. However, MIVES usually
only considers the “triple bottom line” sustainability (i.e.,
Economic, Environmental, Social) for the assessment dimensions. The factors that mutually exclude each other are
incorporated into these three dimensions, making it difficult to compute complex situations where certain factors
may influence multiple dimensions simultaneously. In addition, there are no specific MIVES frameworks for DfMA.
This research aims to establish a MIVES multi-criteria
framework for the DfMA sustainability index by modifying
and advancing the MIVES technique. The second section
provides a literature review of sustainable assessment in
DfMA and the development of MIVES in the construction industry. The third section explains the MIVES multicriteria framework proposed in this research. The fourth
section demonstrates the process and applicability of this
approach by presenting a case study on developing the
DfMA sustainability index. This research adopts an empirical case of a façade retrofit project in Wuhan, China, comparing five design alternatives. The fifth section discusses
the research’s main contributions, including the novelty of
the proposed approach and how it fills the research gap in
the field. Finally, the sixth section summarises the research.
Overall, this research presents a valuable contribution to
the field of sustainable assessment in DfMA, by proposing
a novel approach that takes into account multiple criteria
and offers a systematic process for evaluating the sustainability index.

2. Literature review
Advancing assessment tools is a significant challenge in
the construction industry. Ness et al. (2007) elevated the
understanding of sustainability assessment to a broader
interpretation of sustainability than just focusing on environmentally-focused areas. Early building assessment
systems, such as Building Research Establishment Environmental Assessment Method (BREEAM), Leadership in
Energy and Environmental Design (LEED) and Green Star,
have been developed globally. These evaluation systems
are usually only relevant to the site’s environmental characteristics (Kaur & Garg, 2019; Retzlaff, 2008). However, the
requirement for methods to assess building sustainability
is now increasing. And it is not limited to the assessment
of building components or the built environment (Berardi,
2011; Haapio, 2012). Building sustainability assessment requires quantitative and qualitative indicators at different
spatial scales – from material selection, energy and indoor

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T. Tan et al. Mives multi-criteria framework to sustainability index of design for manufacture and assembly

air quality analysis to whole building assessment. The integration of sustainability factors and knowledge at different
building stages (e.g., manufacturing, assembly, operation
and maintenance) also needs to be considered to reduce
the adverse environmental impacts of building production processes (Ding, 2008; Tan et al., 2020; Wasim et al.,
2022). Various sustainability issues are intertwined and
involve conflicting aspects to assess. Hence, several life
cycle assessment tools have been developed specifically
to address the building as a whole, such as Eco-Quantum,
EcoEffect, ENVEST, BEES, ATHENA and LCA House (Bragança et al., 2010). These systems vary in development history, strategic choices, assessment structures, assessment
criteria and local benchmarking (Kaur & Garg, 2019). They
all include a list of criteria organised into major categories,
e.g., site planning, resource conservation, infrastructure
optimisation, waste management, and recognised innovative technologies. Weighting or percentages are applied
to score across the criteria. Building sustainability assessment methods and tools, whether performance-based, environmental or life-cycle-based, are constantly evolving to
improve their capabilities. Current research is still aiming
to find a suitable method to achieve the most appropriate
balance between the variety of sustainability dimensions
and the flexibility for different building types (Bragança
et al., 2010). Most tools focus on projects involving traditional construction methods. However, specialised methods for assessing the sustainability of designs for off-site
construction projects are still in their nascent stages.
Several sustainable assessments for DfMA have been
developed, but they are predominantly focused on manufacturing rather than construction. For example, Suresh
et al. (2016) proposed integrating DfMA and sustainability
assessment to ensure sustainable product design for an
automotive component, evaluating environmental impact
using SolidWorks software and various inputs of CAD
model, material, manufacturing process, manufacturing
location, and distribution. Yang et al. (2017) proposed a
sustainability analysis framework for DfMA that involves
additive manufacturing-enabled part consolidation and
applied it to evaluate a floor attachment component for
an underground train. Peruzzini and Pellicciari (2018) developed a user experience model to estimate the manufacturing sustainability of DfMA, optimising product and
process design for improved sustainable manufacturing.
They applied the model to an industrial case study on redesigning a machine for automated drug management,
resolving issues such as high costs and stressful assembly
phases for workers. Han et al. (2021) developed a sustainability assessment framework for DfMA during the
conceptual stage. The framework includes four metrics:
material, production, use, and end of life, and was applied
to evaluate the sustainability of two portable blender design concepts. It can be observed that there is a gap in
that sustainability assessments of DfMA largely originate
from the manufacturing sector, which fundamentally differs from the construction industry. The manufacturing

sector focuses on the mass production of standardised
products, whereas the construction industry is typically
project-based (Lu et al., 2021). Therefore, existing manufacturing research on DfMA cannot be directly applied to
the evaluation of architectural design in the construction
industry. The sustainability assessment of DfMA within
the construction sector remains an area that has not been
thoroughly researched and discussed. Besides, DfMA sustainability assessment is a complex and multidimensional
issue requiring a holistic approach. The review suggests
that DfMA can provide significant sustainability benefits,
such as reduced carbon footprint, improved safety performance, and enhanced cost-effectiveness. However, further research is needed to validate and refine the existing
models and tools and to address the practical challenges
of implementing DfMA in different settings and industries.
The literature review highlights that one of the main challenges of DfMA sustainability assessment is the lack of
a standardised methodology in the construction industry.
MIVES has been demonstrated to offer a significant
and representative measurement of sustainability across
various domains, though there is potential for further refinement. Some studies have explored the use of MIVES
for sustainability assessment regarding building design
(see Table 1). MIVES has been used frequently as a specialised and holistic sustainability assessment model for
obtaining global sustainability indices (Pons et al., 2016).
Compared with other MCDM techniques, Pons et al. (2016)
argued that MIVES can define complete, objective and
easy-to-apply sustainability assessment methods for most
samples within the construction sector. However, utilising
MIVES in these studies encounters some challenges when
facing holistic sustainability considering various building
stages. The determination of sets of multi-criteria in these
studies might not be suitable for new scenarios, such as
off-site construction and DfMA. The transformation of
construction methods significantly impacts the determination of various indicators’ weightings, which urges proposing emerging key sustainability indicators and removing
unimportant ones. In addition, the “triple bottom line” (i.e.,
economic, environmental, social) was widely employed as
the three dimensions’ requirements (see Table 1). Thereafter, most studies categorised indicators into these three
dimensions, although a few studies tried to involve some
other dimensions, like the technological dimension (PonsValladares et al., 2023) and the functional dimension (Gilani et al., 2022). However, by dividing the indicators in this
way, namely 3–4 dimensions to constructing sustainability,
contradictions may arise. That is, certain indicators may
have an impact in at least two dimensions. For example,
waste may have economic and environmental impacts,
while safety impacts both social and economic. The assessment may be imprecise when these factors are classified into only one dimension. In general, there is potential
for MIVES to integrate with DfMA, thus providing a pathway for establishing a DfMA sustainability index, which is
currently a research gap. In addition, an essential gap for

Journal of Civil Engineering and Management, 2024, 30(3), 234–247

237

Table 1. MIVES-based sustainability assessment to building design
Authors
Gilani et al.
(2017)
Gilani et al.
(2019)
Gilani
(2020)

Requirements

Construction, Maintenance, End of life;
Consumption, Waste, Reusability, Emission; Safety,
Comfort, Aesthetics, Added value.

Façade alternatives – 3D sandwich
panels

Economic;
Environmental; Social.

Cost, Consumption, Emission, Waste, Safety, Labour
availability, Added comfort, Aesthetics.

Five residential façade systems
commonly used in Barcelona, two
real buildings

Economic;
Environmental; Social.

Economic;
Environmental; Social.

Lozano
Economic;
et al. (2023) Environmental; Social.

Cost, Consumption, Waste, Emission,
Safety, Comfort, Aesthetics.

Façade systems in contemporary
residential buildings in Barcelona

Cost, Fabrication & Assembling, Use, End of Life,
Production & Assembling added value, User added
value.

Five Intelligent Façade Layers
alternatives and three case studies
at schools in Barcelona

Costs, Return on investment, Material used, Emissions,
Disturbance created, Inconvenience for owners,
Comfort and health, Architectural heritage, Aesthetic.
Cost, Time, Emissions, Resources, Social, Safety.

PonsEconomic; Environmental; Cost, Emissions, Resource consumption, Innovation,
Valladares
Social; Technological.
Working conditions, Third-party effects, Adaptability,
et al. (2023)
Availability.
Maleki et al. Economic;
(2023)
Environmental; Social.
Gilani et al.
(2022)

Case study

Economic;
Environmental;
Social.

Habibi et al. Economic;
(2020)
Environmental; Social.
Egiluz et al.
(2021)

Criteria (Indicators)

Functional;
Economic;
Environmental; Social.

Consumption, Waste, Emission, Cost, Safety, Sense of
belonging to place, Comfort, Aesthetics.

A real residential building in
Bilbao

A bridge (Las Arenas Viaduct)
Castilla-La Mancha park footbridge
in Alcobendas, Madrid
Nine residential skyscrapers in
Dubai

Constructive solutions, Security, Condensation; Cost;
Six most common residential
Consumption, Emission, Waste; Safety, Constructability, façade systems in Barcelona
User added comfort, Aesthetics.

enhancement lies in advancing variants of the MIVES to account for mutually conflicting factors or influences across
multiple dimensions, thereby yielding a MIVES framework
that is more adept at accommodating a diverse array of
requirements.

3. The proposed framework
The proposed framework, depicted in Figure 1, is composed of five distinct phases. The initial phase utilises BIM
to define the various design alternatives. Subsequently,
academic literature and the Delphi technique are employed in the second phase to establish the requirements
tree. The remaining three phases (3–5) rely on the MIVES
methodology to determine the weights for all parameters
in the tree, create the value function for each parameter,
and ultimately determine the DfMA SI for each alternative. The detailed implementation process for each phase
is elaborated on in the subsequent sections.

3.1. Phase 1: Defining the design alternatives
and the problem to be solved
Phase 1 of the proposed framework serves the purpose
of identifying design alternatives that can be evaluated.
During this phase, the design team undertakes the task of
identifying and analysing various design alternatives that
have the potential to address the sustainability challenges
or goals at hand. Such challenges could be focused on
minimising construction waste and energy consumption,
or improving manufacturability and assemblability. These
alternatives may include different materials, manufacturing

processes, assembly methods, and other design considerations. The team then proceeds to evaluate each alternative
against the established sustainability criteria for the project. By defining the problem at this early stage, the design
process can remain focused on the project’s sustainability
goals, ensuring that the final design solution is effective
and aligned with the intended objectives.

3.2. Establishing the requirement tree
The procedure for establishing the requirements tree involved several steps. Initially, a preliminary list of parameters was compiled based on the available literature. For
example, this research builds on the work by Tan et al.
(2020) in which we have reviewed and discussed relevant
DfMA guidelines. Thereafter, the Delphi technique was
employed, involving two questionnaire iterations, to solicit responses from an expert panel regarding two specific
questions in this research. The first question pertained to
identifying the most critical parameters contributing to
refining the DfMA concept when assessing sustainability.
The second question sought pairwise comparisons of the
importance of parameters at the same hierarchy.
To ensure a panel of highly qualified experts for this
research, potential candidates were required to meet at
least one of the following criteria: 1) senior practitioners
in sustainable architectural design or 2) experts in building sustainability assessment. Following the first questionnaire iteration, a set of final responses from six experts
was used to construct an authoritative requirements tree
(as shown in Table 2) that only included the most significant parameters for DfMA sustainability assessment.

T. Tan et al. Mives multi-criteria framework to sustainability index of design for manufacture and assembly

238

Figure 1. Proposed framework for DfMA sustainability index
Table 2. Requirements tree
Requirements
R1.
Manufacture

Criteria

Indicators

C1.
I1. Reduced number of molds
Manufacturing quantity I . Reduced number of part counts
2
I3. Use off-the shelf components

R 2.
Assembly

R 3.
Operation &
Maintenance

C2.
I4. Standardised parts
Manufacturing difficulty I . Simplified parts
5
C3.
Assembly quantity

C4.
Assembly difficulty

C5.
Performance

I6. Reduced connections and connectors

Sustainability aspects
T1. Economic T2. Environmental T3. Social
√

√

√

√

√
√
√
√

I7. lightened material and components

√

I9. Assembly error tolerance

√

I8. Standardised connectors

I10. Multifunctional and multi-use parts

I11. Environmentally friendly building forms

√
√

I12. Environmentally friendly indoor space
I14. Low operation energy consumption

I17. Reduced fragile parts

√

I19. Safety and resilience

√

I18. Easy replacement of building components
and materials
I20. Reduced cleaning requirements

√
√
√
√
√

√

√

√

√

√

√

I15. Contextual compatibility

C6.
Maintainability

√

√

I13. Environmentally friendly materials

I16. Visual quality

√

√

√

√
√

√

√

√
√

Journal of Civil Engineering and Management, 2024, 30(3), 234–247

239

This requirements tree comprises three main sustainability
requirements, namely R1 Manufacture, R2 Assembly, and
R3 Operation & Maintenance. These three requirements
are further subdivided into six criteria and 20 indicators.
The parameters were evaluated based on their impact on
the “triple bottom line”, ensuring that the sustainability
assessment of the DfMA concept was comprehensive and
well-rounded.

3.3. Establishing weights of
all parameters in the tree

Table 3. Parameter comparison matrices (Left) and scale of
relative importance (Right)

 1 I12  I1n 


I
1  I2n 
A =  21
    
I I

 n1 n2  1 

Definition

1

Equal importance

5

Strong importance

3
7
9

2,4,6,8

n
RI

1

2

0

3

0

4

5

6

7

8

Moderate importance
Very strong or
demonstrated importance
Extreme importance
Intermediate values

10

(2)

After CR < 0.1 is met, the parameter comparison matrices data can be input to AHP and calculated for the
weights for each requirement, criterion and indicator.

3.4. Establishing the value
function for each indicator
As depicted in Figure 2, the MIVES methodology involves
calculating the score of the value function for each indicator related to each alternative using Eqn (3):
Vi = A + B[1 – exp(–Ki×(|Xind – Xmin|/Ci)Pi],

(3)

where A is the response value to Xmin, generally A = 0;
Variable B is the factor that ensures the score of the value
function falls within the range 0 to 1, obtained with Equations (4); Xind is the abscissa value of alternative being
evaluated with respect to the indicator i under consideration; Xmin and Xmax are the value of the indicator i at the
minimum and maximum satisfaction, respectively; Pi is the
shape factor that dictates whether the value function curve
for the indicator i is concave (Pi < 1), straight (Pi ≈ 1),
convex or S-shaped (Pi > 1) (Figure 2); Ci is the abscissa
value for the inflexion point in curves with Pi > 1; Ki is the
ordinate value to the point Ci:
(4)

To simplify the calculation of the value function, the
specific data of objective indicators in this research were
remapped to a range from 1 to 10. The data of subjective
indicators were also on a scale of 1 to 10, and the value
function shapes for each indicator were generated by a
group of diverse stakeholders. In cases where stakeholders
S-Shape
Concave
V(Xi)
1
Lineal

(1)

where λmax is the parameter comparison matrix’s largest
eigenvalue and the size. The larger the CI, the more inconsistent the entire matrix is, and when the CI is 0 it is the
ideal consistent matrix. To judge whether the calculated
consistency index is acceptable for the study, the ratio of
the CI to the random index (RI) needs to be calculated to

9

0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49

B = [1 – exp(–Ki×(|Xmax – Xmin|/Ci)Pi]–1.

The parameter comparison results obtained by the
Delphi technique may still be logically inconsistent. Therefore, to quantitatively evaluate the magnitude of this inconsistency, a consistency index (CI) should be calculated
prior to the weights calculation:
CI = (λmax – n) / (n – 1),

Table 4. The Random Index (RI)

CR = CI / RI.

Once the requirements tree has been defined, the next
step involves calculating the weights of the requirements,
followed by the calculation of the weights of the criteria
included in each requirement, and finally the weights of
the indicators included in each criterion. These weights
reflect the relative importance and strength of each parameter. In the MIVES methodology, experts use the AHP
to determine the weights.
To determine the weights of the parameters in this
research, parameter comparison matrices and a scale of
relative importance were developed (as shown in Table 3).
These were used in conjunction with the Delphi technique
to address research question 2 and eliminate the subjectivity of the AHP method. By conducting a second iteration
of the questionnaire, the expert panel was able to provide
their most consistent judgment on the pairwise importance of the parameters. This approach ensured that the
weights were based on a rigorous and reliable process, ultimately contributing to the robustness of the framework.

Intensity of
importance

obtain the consistency ratio (CR) (Table 4). If CR < 0.1, then
the consistency of the parameter comparison matrix can
be accepted; otherwise, the expert panel needs to revise
the parameter comparison matrix again.

Convex

Indicator i

0
Xmin

Xmax

Figure 2. Different value function shapes

T. Tan et al. Mives multi-criteria framework to sustainability index of design for manufacture and assembly

240

held differing views, the minimum squares approach was
used to reconcile the differing perspectives and arrive at a
consensus. This ensured that the value functions accurately
reflected the stakeholders’ perspectives and contributed
to a more comprehensive evaluation of the alternatives.

3.5. Determining the sustainability
index of each alternative
The final phase of the proposed MIVES-based method involves evaluating the sustainability index of each alternative using Eqn (5):
=
SI

i =N

∑  ⋅  ⋅  ⋅V ( S ) ,
i =1

i

i

i

i

i⋅x

(5)

where ai, bi, and gi are the weights of each requirement,
criterion, and indicator, respectively; Vi(Si×x) is the value
function score of the alternative x with respect to the indicator i under consideration; N is the total number of
indicators. To determine the most sustainable alternative,
the index of T1 Economic, T2 Environmental, and T3 Social can be calculated based on the sum of corresponding indicators’ results (i.e., VIi). This index provides a comprehensive evaluation of each alternative’s performance
across economic, environmental, and social parameters,
highlighting areas of strength and weakness. Ultimately,
the stakeholders involved in the decision-making process
can use the sustainability index to select the most suitable
design alternative. This can be identified as the alternative
with the highest SI, or the one that achieves a balanced
performance across all parameters. By considering the
sustainability index, stakeholders can make well-informed
decisions, ensuring that the final design solution is aligned
with the project’s sustainability goals and objectives.

4. Case study
4.1. Context and background
A façade retrofit project was used to test the proposed
framework. Embracing sustainable retrofit strategies for
existing buildings has been a rapidly growing trend in the
a)

age of urban regeneration. Retrofitting existing buildings,
such as their façade systems, can bring enormous benefits for the achievement of sustainable cities and society
(Moghtadernejad et al., 2021). The case is a façade retrofit
project located in Wuhan, China. There are eight design
alternatives completed in December 2019. All design alternatives have the same building floors, which decreases
the negative impacts from other factors and contributes
to the focus of the façade retrofit design. There are some
drawbacks to the existing old façade system. For example, the design style is outdated, has poor visual quality,
and has low visual identification (see Figure 3). In addition,
some site conditions, such as nearby transportation systems, parking systems and site landscape, impact the new
design. This case represents a typical practice in China’s
building retrofit projects. A significant problem is evaluating these design alternatives, especially regarding sustainability. This research adopted five design alternatives with
a rich data set selected for the sustainability assessment,
as shown in Figure 4. The alternatives involved different
building materials and structural forms, where all the façade systems were manufactured off-site and assembled
on-site. Thus, the project is an appropriate empirical setting to test the proposed framework and establish the
DfMA indices. The parameterised special-shaped façade in
some design schemes had brought significant challenges
to manufacturing and assembly, especially in terms of sustainability assessment. BIM Level 3 models were then used
for data collection and analysis by following the framework proposed in Section 3.

4.2. Case study results
In the second phase, the weights of each parameter were
generated through the Delphi technique and AHP (see
Table 5). Both R1 Manufacture and R2 Assembly hold the
same 0.20 weights, while R3 Operation & Maintenance
dominants 0.60 weights. In off-site construction, both
these two stages share the same importance rather than
solely the construction stage. It can be observed C5 Performance accounts for the most ratio among all criteria,
as it has long-standing effects on building sustainability.
b)

Figure 3. Existing façade system

Journal of Civil Engineering and Management, 2024, 30(3), 234–247

241

a)

b)

c)

d)

e)

Figure 4. Alternative façade designs
Table 5. Requirements tree with calculated weights
Requirements
R1.
Manufacture (0.20)

R2.
Assembly (0.20)

Criteria
C1. Manufacturing quantity (0.25)

Indicators
I1. Reduced number of moulds (0.26)

I2. Reduced number of part counts (0.10)
I3. Use off-the shelf components (0.64)

C2. Manufacturing difficulty (0.75)

I4. Standardised parts (0.75)

C3. Assembly quantity (0.25)

I6. Reduced connections and connectors (1.00)

C4. Assembly difficulty (0.75)

I5. Simplified parts (0.25)

I7. lightened material and components (0.10)
I8. Standardised connectors (0.37)

I9. Assembly error tolerance (0.16)
R3.
Operation&
Maintenance (0.60)

C5. Performance (0.75)

I10. Multifunctional and multi-use parts (0.37)

I11. Environmentally friendly building forms (0.13)

I12. Environmentally friendly indoor space (0.08)
I13. Environmentally friendly materials (0.05)

I14. Low operation energy consumption (0.32)
I15. Contextual compatibility (0.20)
C6. Maintainability (0.25)

I16. Visual quality (0.22)

I17. Reduced fragile parts (0.26)

I18. Easy replacement of building components and materials (0.06)
I19. Safety and resilience (0.17)

I20. Reduced cleaning requirements (0.51)

T. Tan et al. Mives multi-criteria framework to sustainability index of design for manufacture and assembly

242

In the next phase, the value function for each indicator
was defined. The specific data of objective indicators were
obtained directly from the BIM (see Figure 5). Data of I1,
I3–I8, I10, I14 and I17 can be observed or calculated through
the schedules and quantities function in Revit, which provides statistics on the various available fields (e.g., function, type, count, supplier, manufacturer, assembly code
and description, material density, volume, operation and
cost, etc.) of each part. The data of I2 is directly displayed
on the user interface (see Figure 5). Afterwards, the obtained data of each objective indicator from experts’ ratings were remapped. For example, the common divisor of
I2 data is taken as 330, so the remainder of I2 data divided
by the common divisor is minimised to reduce the error of
subsequent calculations. The result of the division operation is rounded to obtain the remapped value of I2 data,
see Table 7. The finalised data for each indicator value
function was obtained by combining the opinions of the
different experts, which are presented in Table 7. Then, the
value function for each indicator related to each alternative was calculated. The SI, the values of requirement (VRi),
criterion (VCi), and indicator (VIi) related to each alternative
are presented in Table 8.
Table 6. Remapped the specific data of I2 into the range from
1 to 10
(a)

(b)

(c)

(d)

(e)

2208

1657

1340

2189

715

7

5

4

7

2

Table 7. Data of each indicator value function related to each
alternative
Alternatives
Indicators (a) (b) (c) (d) (e)

Types of value function curves
Types

C

K

P

I1

8

9

6

7

4

DL

10

0.01

1

I3

3

3

7

4

8

ICVX

5.5

0.2

1.5

9

IL

1

I2
I4
I5
I6

I7
I8

I9

I10

I11

I12
I13
I14
I15

I16
I17
I18

I19

I20

7
1
3
3
9
3
4
5
7
5
9
6
8
8
4
9
8
4

5
1
4
8
9
3
3
3
9
5
9
7
8
9
4
6
8
4

4
5
4
5
8
6
5
4
4
5
6
6
5
2
6
6
7
6

7
3
4
5
5
5
6
3
4
5
4
5
4
2
5
5
7
9

2
9
4

DL
IL

DL

10
1

0.01

10

0.01

1

0.01

1

0.01

5

0.8

2

DCCV

7.25

8

ICCV

4.25

8

IS

6.5

7
4
5
3

IL
IL

IS

ICCV

0.01

0.01

0.8

0.85

0.8

3.1

4.5

0.95

0.75

6

0.2

1.7

6.25

3

ICVX

5.5

0.01

1

0.01

7.75

0.7

5

DCVX

9

ICCV

6
9

IL

DCCV

1

1

0.85

DCCV
ICVX

1

0.65

4
6

1

4.5
7.5

0.6

0.01
0.6

1
1

3

0.93
2.3
3

1

0.35

0.65

Note: DL – Decreasing Lineal; ICVX – Increasing Convexly; IL –
Increasing Lineal; DCCV – Decreasing Concavely; ICCV – Increasing
Concavely; IS – Increasing Convexly to Concavely; DCVX –
Decreasing Convexly.

Figure 5. Exported data of from BIM

Journal of Civil Engineering and Management, 2024, 30(3), 234–247

243

Table 8. Values of SI, VRi, VCi, and VIi for each of the five alternatives
Alternatives

SI

(a)

0.48

0.09

0.47

0.62

0.17

0.06

0.78

0.37

0.59

0.71

(c)

0.42

0.46

0.50

0.38

0.57

0.43

0.56

0.48

0.32

0.56

(b)
(d)
(e)

Alternatives

0.47

0.35

0.57
VI1

VR1

0.11

0.26

0.85
VI2

VR2

0.25

0.48

0.63
VI3

VR3

0.66

0.34

0.46
VI4

VC1

0.17

0.27

0.73
VI5

VC2

0.09

0.26

0.89
VI6

VC3

0.22

0.56

0.67
VI7

VC4

0.26

0.45

0.61
VI8

VC5

0.65

0.32

0.48

0.23

0.13

0.00

0.23

0.78

0.21

0.23

0.57

(c)

0.45

0.67

0.60

0.46

0.34

0.56

0.36

0.57

0.68

(d)
(e)

Alternatives
(a)

(b)
(c)

(d)
(e)

0.33

0.67

VI11
0.52

0.88

0.08

0.08

0.71

0.33

0.89

VI12
0.34

0.34

0.34

0.34

0.34

0.13

0.23

0.73

VI13
0.96

0.96

0.81

0.63

0.51

0.00

0.23

0.89

VI14
0.57

0.46

0.57

0.68

0.77

The results of the evaluation are presented in Figure 6
and Figure 7, indicating that alternative (e) has the highest SI, while alternative (c) achieves the most balanced
performance across all parameters. Additionally, alternatives (a) and (b) stand out in terms of their performance
in operation. To compare the “triple bottom line” performance, which includes economic, environmental, and social performance, Figure 8 presents the performance index
for each alternative. The data indicates that alternative (e)
performs the best in terms of both the economy and the
environment, while alternative (b) has the highest performance index on the social level. Based on these findings,
stakeholders can select the design alternative that best
aligns with their sustainability goals and priorities.

5. Discussion
MIVES has been used in several studies to conduct sustainability assessments of buildings, especially façade design (Egiluz et al., 2021; Gilani et al., 2017, 2019, 2022;
Habibi et al., 2020). Several studies have adopted the
MIVES technique in the AEC industry. They have demonstrated the merits of this model for sustainability assessment, especially in terms of application agility, user-friendliness, and flexibility and customisation provided by the
value functions (Lizarralde et al., 2022). However, none of
these MIVES approaches focuses on DfMA. This research
proposes a MIVES multi-criteria framework for the DfMA
sustainability index involving five phases: defining the design alternatives and problem to be solved, establishing
the requirements tree, establishing weights for all parameters in the tree, establishing the value function for each
indicator, and determining the sustainability index of each
alternative.

0.34

0.34

0.89

VI15
0.70

0.70

0.29

0.18

0.42

0.22

0.56

0.67

VI16
0.56

0.77

0.01

0.01

0.03

0.21

0.70

0.93

VI17
0.30

0.30

0.09

0.18

0.18

0.23

0.46

0.68

VI18
0.89

0.57

0.57

0.46

0.57

0.40

0.44

0.33

0.56

0.39

VI10

0.22

0.11

0.70

VI9

(a)

(b)

VC6

0.77

0.90

VI19
0.94

0.94

0.90

0.90

0.97

0.46

0.34

0.23

0.34

VI20
0.83

0.83

0.68

0.31

0.31

The framework also captures the complexity of the
design process by using a multi-level structure to evaluate the sustainability of design alternatives, from requirements to criteria to indicators. This structure allows for a
comprehensive evaluation of design alternatives, considering multiple factors that contribute to sustainability. The
framework involves human intelligence for the assessment
and quantification of the parameters. Stakeholders can use
the data obtained to select the design alternative with
which they are most satisfied. The pilot results confirm
that the MIVES framework can provide a comprehensive
and integrated approach to evaluating the sustainability
of DfMA. Like the two sides of a coin, the drawbacks of
this approach are that it relies on expert judgment and input to determine parameter weights, which can introduce
subjectivity and potential biases into the assessment. Additionally, the framework may not encompass all aspects
of sustainability relevant to a particular project, necessitating customisation to ensure a comprehensive evaluation.
Compared with other DfMA sustainability tools, those
approaches only focus on the manufacturing industry, such
as the work developed by Yang et al. (2017) and Eastwood
and Haapala (2015), while there are no such DfMA tools in
the construction industry. This research presents a method
that integrates the concepts of MIVES and DfMA in the
construction industry context. In contrast to most MIVES
approaches in construction, such as the tool developed by
Pons et al. (2016) and Sánchez-Garrido et al. (2022), which
only focuses on the comparison of the “Triple Bottom
Line”, this research modified MIVES for shifting to a comparison across six dimensions of DfMA, including manufacturing quantity, manufacturing difficulty, assembly quantity, assembly difficulty, performance, and maintainability.

T. Tan et al. Mives multi-criteria framework to sustainability index of design for manufacture and assembly

244
1.0
0.9

Manufacturing quantity
Assembly difficulty

Manufacturing difficulty
Performance

Assembly quantity
Maintainability

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Alternative (a)

Alternative (b)

Alternative (c)

Alternative (d)

Alternative (e)

Figure 6. Criteria performance for each alternative
0.9
0.8

SI

Manufacture

Assembly

Operation& Maintenance

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Alternative (a)

Alternative (b)

Alternative (c)

Alternative (d)

Alternative (e)

Figure 7. Sustainability and requirements performance for each alternative
12

Economic

Environmental

Social

10
8
6
4
2
0

Alternative (a)

Alternative (b)

Alternative (c)

Alternative (d)

Alternative (e)

Figure 8. Triple bottom line performance for each alternative

Based on the performance in these six aspects, design alternatives can further be compared from economic, environmental, and social perspectives. Consequently, this
method offers a more building design technical-focus
approach specifically to DfMA for conducting sustainable
comparisons. When sufficient and accessible objective
data are available, the data influencing these six indicators can be obtained from BIM models. Simultaneously,
this method allows the establishment of input data for the
evaluation system through expert subjective scoring when
objective data are lacking. This flexible operability lays the
foundation for the widespread application of this method.
For theoretical implications, this research furthers previous studies in DfMA. By proposing a MIVES multi-criteria

framework, the way to establish DfMA sustainability index
is developed. Design evaluation is an essential aspect of
the concept of DfMA (Gao et al., 2020), and the research
fills the gap of the lack of DfMA sustainability index by
adapting the MIVES approach with the considerations
from the manufacturing, assembly, and operations &
maintenance phases. This research furthers previous research about DfMA guidelines (Bao et al., 2022; Tan et al.,
2020) and constructability assessment (Qi et al., 2021) to
an overall index. A feasible approach to quantify DfMA
qualitative guidelines is established. In addition, this research broadens the scope of DfMA application cases, as
previous studies mainly focus more on new buildings (Tan
et al., 2020) or interior projects (Bao et al., 2022). The 20

Journal of Civil Engineering and Management, 2024, 30(3), 234–247

indicators proposed in this research can help practitioners
start from these aspects to improve retrofit projects’ capabilities and sustainable development using DfMA.
As for the implications for the industry, the retrofit of
existing buildings is an important part of urban regeneration in the use case. How to continue the style and
culture of the city through retrofit design and achieve the
ultimate goal of sustainable development is one of the
problems faced by the practitioners. The demolition of
existing buildings, the transportation of waste materials
and the input equipment are all consumption of resources.
At the same time, the dust generated during the demolition of buildings can also cause environmental pollution
problems, which is even more contradictory to sustainable development. The DfMA and MIVES methods can be
used together to deal with these challenges. In general,
the MIVES framework can be used by architects, engineers,
and other stakeholders to evaluate the sustainability of
DfMA in different building projects. The weights assigned
to these indicators do not represent a one-size-fits-all setting; instead, users can adjust the relevant weights according to their specific needs. This flexibility enables targeted
evaluation of projects using these indicators, making it a
valuable tool for identifying and addressing sustainability
issues in a range of contexts. This research contributes to
the theoretical aspect of DfMA as a design strategy and
philosophy and the practical aspect of DfMA as a design
evaluation tool.

6. Conclusions
The MIVES multi-criteria framework demonstrates the ability to assess the DfMA sustainability index by comparing
five design alternatives for a façade retrofit project. Specifically, this research contributes to the adoption of the
multi-criteria MIVES approach. This research furthers previous studies and has three-fold significance: 1) Establishing
reasonable multi-criteria for the sustainable DfMA indices;
2) Adapting the MIVES approach for comparative analysis
across three building phases to make it compatible with
DfMA; 3) developing a quantitative analysis method for
sustainable design assessment of DfMA in the construction
industry. Specifically, this research goes beyond previous
MIVES-based studies for the “triple bottom line”-informed
requirements setting. During the assessment, various life
cycles can be considered by replacing the “triple bottom
line” through R1 Manufacture, R2 Assembly, and R3 Operation & Maintenance. Especially with the transformation of
off-site construction, the new scenario challenges brought
by the manufacture and assembly stages induce essential
changes for the requirements of design evaluation, especially in terms of sustainability.
Another implication is that after this replacement, the
previous indicators classified under the “triple bottom
line” and had to do an either-or-choice would no longer
be the only option. Therefore, indicators affecting two or
three aspects of the “triple bottom line” can be included

245
in the evaluation, making the assessment more accurate.
In addition, this research sheds light on the application of
BIM for approach development and bridges the synergy
approaches between MCDM and BIM. The expert-based
rating system is established using objective BIM data and
BIM functionalities, combining subjective and objective
data advantages. The framework can evaluate the façade
system retrofit mentioned in the case and can be applied
to various other types of building projects after corresponding adjustment of the indicators and weightings
based on project requirements.
This research also has limitations. In the phase of
scheme design, professionals frequently encounter difficulties in executing high-precision BIM modelling for all
possible design alternatives. As a result, variations in the
depth of data between various design options frequently
occur, which may affect the evaluation and results. In addition, the weighting and scoring system among the indicators of the proposed method is based on the expert
team. The advantage is that scoring based on experts can
provide more flexibility and enforceability in practice, but
it may lack data objectivity, which, however, requires higher accuracy of the BIM model data. The quantitative data
obtained through BIM in the methodology of this research
merely serves as a foundation for the expert evaluators to
assign scores and does not assume an overarching, deterministic role in determining the final outcome.
Subsequent research can take three divergent paths.
Firstly, it can create specific metrics tailored to different
building types, such as new design projects, retrofit projects, and interior projects, to derive their corresponding
DfMA indices. Secondly, future research can develop more
advanced value function models that can better capture
the complexity of sustainability criteria and indicators.
Thirdly, it can strive to establish an automated scoring
calculation process based on BIM techniques for select indicators, thus enhancing the method’s objectivity. Overall,
this research represents a valuable contribution to the field
of sustainability assessment and DfMA, as it has expanded
the horizons of DfMA-based design evaluation development and advanced the discourse surrounding the DfMA
sustainability index.

Funding
This study is supported by the National Natural Science
Foundation of China (51978295), and Hong Kong Research
Grants Council Collaborative Research Fund (C7080-22GF).

Author contributions
T. T. conceived the study and was responsible for the design and development of the data analysis. T. T. L. Z. and
X. L. were responsible for data collection and analysis.
T. T. were responsible for data interpretation. L. Z. Z. B. Z.
F. wrote the first draft of the article. F. X. reviewed and
edited the final draft. X. L. was responsible for the funding.

246

T. Tan et al. Mives multi-criteria framework to sustainability index of design for manufacture and assembly

Disclosure statement
Authors of this article declare no competing financial, professional, or personal interests from other parties.

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