FACULTY OF ARCHITECTURE, PLANNING AND SURVEYING
UNIVERSITY TEKNOLOGI MARA
SHAH ALAM
MASTERS OF SCIENCE INTEGRATED CONSTRUCTION PROJECT MANAGEMENT
(AP776)
Real Estate Big Data Analytics
(EMS715)
INDIVIDUAL ASSIGNMENT:
Identifying Indicators For Three Criteria Of Sustainable Construction Such As
Economic, Environmental, And Social, And Prioritise Them According To Their Level
Of Significance For Sustainable Supplier Selection
PREPARED BY:
BAQI TRYA YULIAN PUTRI (2022822484)
PREPARED FOR:
PROF SR TS DR HJ ABDUL HADI HJ NAWAWI
SUBMISSION DATELINE
16 DECEMBER 2022
i
Contents
Abstract ............................................................................................................................... iii
1. Introduction ........................................................................................................................... 1
2. Literature Review .................................................................................................................. 2
2.1 The Parameter of Sustainable Supplier Selection ............................................................ 2
2.2 Best-Worst Method Approach ......................................................................................... 3
3. Study Methodology ............................................................................................................... 4
4. Findings ................................................................................................................................. 8
5. Conclusion........................................................................................................................... 10
References…...…………………………………………………………………….…………13
Table Contents
Table 1: The respondent’s details……………………………………………………………..5
Table 2: best-worst data……………………………………………………………………….6
Table 3: Data of the best criterion over other criteria…………………………………………7
Table 4: Data of The other criterion over the worst criterion…………………………………7
Table 5: Consistency of Data…………………………………………………………………..8
Table 6: Data Ranking………………………………………………………………………....8
Figure Content
Figure 1: Best-Worst Chart……………………………………………………………………7
ii
Abstract
propose: To help the construction practitioners easily find their prioritisation to evaluate
sustainable supplier selection among all the big data input. in this term, the Sustainable
Supplier will be evaluated based on three fundamental components of Environment, Economic,
and Social using the Best-Worst Method which is a method used to rank or list a collected big
data to analyse the importance of the big data input.
Concept: This study uses using Best-Worst Method to select a sustainable supplier. BWM will
identify and rank the importance of all the big data based on Decision-Maker input. Finally, it
will be known that input is the most important to the least important of the big data.
Findings: The analysis showed that the most important component was the environmental side,
followed by Economic and Social. The selection parameters that mostly took effect were: first,
for the environmental side with top three ranked are "the usage of reused and recyclable
materials and equipment on construction proses", "addressing the green technology and
renewable resource", "waste management", and "the usage of nontoxic materials". Second, The
Economic side's top three ranked are; "efficiency, cost, and flexibility". And third, the social
side's top three ranked are; the "Healthy and Safety program", "Information disclosure", the
"child labour awareness".
Study Implications: to identify the crucial environmental, economic, and social parameters to
evaluate supplier performance on sustainable construction supply chains. To specify the most
important aspect to increase The supplier's performance in the construction industry. To
facilitate managers to understand the unimportant and not-useful list to evaluate the supplier's
performance easily. To help construction industry practitioners and managers to review their
evaluation strategy on various parameters connected to the supplier's performance selection.
iii
1.
Introduction
If we think back, the sustainable design also increases the economic effect. The first
example is when we use renewable energy such as collar power plants, wind, and water power
plants, it means we reduce the electricity expenses, imagine how long the construction will take
the time we could keep using renewable electricity for the whole time construction project, and
we even could keep using it until the project finish. This example could affect not only the
economy but also society. We could reduce the fuel energy usage which causes much pollution
and gas emission. The National Technology Policy embodies elements of economic,
environmental, and social policies, as reflected in the five (5) objectives as follows: "to
minimise the growth of energy consumption while enhancing economic development", "to
facilitate the growth of the green technology industry and enhance its contribution to the
national economy", "to increase national capability and capacity for innovation in green
technology development and enhance Malaysia's competitiveness in green technology in the
global arena", "to ensure sustainable development and conserve the environment for future
generations" and "to enhance public education and awareness on green technology and
encourage its widespread use". The National Green Technology Policy is built on four pillars:
"Energy, seek to attain energy independence and promote efficient utilisation", "Environment,
conserve and minimise the impact on the environment", "Economy, Enhance the national
economic development through the use of technology" and "social, improve the quality of life
for all" www.malaysia.gov.my. Dennis (2007) suggested that the first aim of addressing
sustainable development is to encourage product quality (recyclable materials) and minimise
the cost and time delay risk by adapting waste management and minimising waste product
usage. Environment, Economic and social are large information, and to synthesise this
information, this study will use Bes-Worst Method.
Best-Worst Method is a Multi-Criteria Decision Making (MCDM). It is worked by
sorting the information of the big data into the class number, evaluating the alternative from
large information of the big data, ranking all the information, listing the importance, and
checking the outcome validity with the interaction between Decision Maker and the Analyst.
This method can formulate the parameters that objective metrics can not evaluate. It will find
the importance of big data to analyse the solution to gain the main goals. All the big data from
the internet, journals, organisations databases, Company databases, etc., will first be ranked
from the Best (most important) to the Worst (Least Important) using the decision maker. Then
they are formulated and solved to identify the weights of different parameters. The final scores
are addressed by merging the weights of different criteria settings and alternatives based on the
1
selected best alternative. A consistency ratio is aimed at the BWM to check the reliability and
validity, velocity, variety, veracity, and value of the comparison. BWM presents more reliable
final results than similar methods (Rezaei, J. (2015). Best-Worst Method is used to rank or list
collected big data to analyse the importance of the input data. It helps the practitioners easily
find their prioritisation of all the big data.
2.
2.1
Literature Review
The Parameter of Sustainable Supplier Selection
This study focus on three fundamental components of sustainable development, which
are environmental, Economic, and Social. In economic criteria, parameters that are mostly
addressed are risk management, product cost, time management, flexibility, turnover, services,
product quality, and delivery time (Tong et al.,2022; Hoseini et al., 2021; Cui et al.,2021;
Alamroshanet al., 2022). Alamroshanet al.(2022) stated that several major aspects of the
environmental assessment are; non-toxic materials, reused materials, easily recycled, green
warehousing and technology, renewable energy usage, and pollution control. (Demirkol,
2021). Orji and Ojadi (2021) suggested that evaluating the sustainable supplier selection needs
four criteria environmental, economic, social, and the newest category of a pandemic response
strategy to anticipate the pandemic consequences. This study concludes with 9 component
parameters for each Economic, Environment, and Social parameter to evaluate supplier
selection. First is Economic parameters consisting of Efficiency (information sharing, delivery
reliability, ease of communication, responsiveness); cost (including product rejection cost,
operational and transaction purchase); flexibility and scalability (such as changing demand,
modification, etc.); on-time delivery; quality; quick response; financial strength; technology
skill; and risk management. Second is the Environmental parameter consisting of reused and
recyclable materials usage, green product and technology usage, regular environmental audits,
providing the facilities training, waste management, environmental standard and regulation
attention, energy consumption awareness and product recycling, pollution control, and
exchangeable packaging used. Third is Social parameters consisting of workplace safety
protocols, information disclosure, Child labour awareness, Pandemic response strategy
parameter (wearing personal protective equipment, well educated in any information about the
pandemic, following new regulations, and addressing economic recovery programs), human
rights, product responsibility, employment procedure transparency, rules and regulations
obedient, and social responsibility.
2
2.2
Best-Worst Method Approach
The significance and importance list for each main parameter given is evaluated based
on its weight number, specified by the best-worst method (BWM) approach, one of the multicriteria decision-making (MCDM) methods. Further, the results will be shown after the ranking
accuracy check (based on optimal weights). A dynamic decision support system (DSS) was
used for sustainable supplier selection in the supply chain loop, adapting BWM connected to a
fuzzy inference system (FIS) (Alavi et al.,2021). To model the obscurity in the schemes, fuzzy
BWM evaluation followed by a two-stage FIS was used in the sustainable supplier selection
process on various sustainability Industries. Grey systems theory (GST) was adopted for the
problem avoidance associated with various parameter ranks in the ordinal priority approach
(OPA) for megaproject sustainable suppliers selection (Mahmoudi et al.,2021). For the
Environmental, Economic, and Social suppliers evaluation, this study used fuzzy grey
relational analysis (FGRA), entropy weight method (EWM) of cloud computing, and Failure
mode and effects analysis (FMEA). The output of the methods was integrated with the
decision-making trial result and evaluation laboratory (DEMATEL) for effective decisionmaking (Wet al., 2021). involving uncertainty study of the supplier selection process, fuzzy
was used to compute the weights and interval parameters (Zandkarimkhani et al.,2022). An
innovative method according to spherical fuzzy was also used in the chemical industry supplier
selection (Wang et al.,2022).
Rezaei, J. (2016), in his paper, presented that "The Best Worst Method (BWM) is a
multi-criteria decision-making method that uses two vectors of pairwise comparisons to
determine the weights of criteria among big data input. First, the decision-maker identifies the
best (e.g. most desirable, most important) and the worst (e.g. least desirable, least important)
criteria, after which the best criterion is compared to the other criteria and the other criteria to
the worst criterion. A nonlinear minimax model is then used to identify the weights such that
the maximum absolute difference between the weight ratios and their corresponding
comparisons is minimised. The minimax problem may result in multiple optimal solutions.
Although in some cases, decision-makers prefer to have multiple optimal solutions, in other
cases, they prefer to have a unique solution". Liang, F., Brunelli, M., Rezaei, J. (2020)
Explained the BWM method that "The Best-Worst Method (BWM) uses ratios of the relative
importance of criteria in pairs based on the assessment done by decision-makers. When a
decision-maker provides the pairwise comparisons in BWM, checking the acceptable
inconsistency to ensure the validity, velocity, and rationality of the assessments, is an important
step". "The best-worst method (BWM) is a multicriteria decision-making (MCDM) method to
3
deliver the relative importance (weight) of a set of criteria used to evaluate a set of alternatives"
Liang, Y., Ju, Y., Tu, Y., & Rezaei, J. (2022). Brunelli, M., Rezaei, J. (2019) examined that
"the best–worst method for multi-criteria decision making produces a more mathematical
perspective. The central part of this manuscript introduces the best–worst method. This
alternative does not change the original idea behind the best–worst method. Yet, it can be
shown that it is mathematically more sound and leads to an optimisation problem that can be
linearised and thus solved".
3.
Study Methodology
This study used a case-based methodology that a Malaysian construction company has
carried out. The company will use a performance evaluation system to evaluate sustainable
suppliers. The management will evaluate the supplier's performance parameters, especially on
the capability to mitigate the dangerous consequences inflicted on the environment. Thus, the
organisation’s objective alignment, this study focuses on identifying the parameters of
performance to evaluate sustainable performance. A step-by-step approach to calculating the
interval of each parameter was accomplished using the BWM method.
In this study, the 27 performance parameters data were collected from construction
organisations (the construction practitioners, managers, and procurement management) in
Malaysia by surveying the construction project site, collecting the previous project observation
from construction organisations, and collecting from the study case journal. Uncovering the
information needed, like correlations, market trends, sustainable regulation, and customer
preferences, could help the construction organisation make the decision. The 27 collected data
consisted of 9 environmental, nine economic, and nine social parameters, called the "subparameters". Next, the collected data (the 27 performance parameters on supplier selection)
will be listed using the BWM method, which utilises the suppliers ranking based on sustainable
performance context (from the most important to the least important among the 27 collected
parameters).
The methodology contains six steps. First, identify the major parameters of the
sustainable performance evaluation (the data collected from the construction organisation will
be listed). Second, identify construction industry-specific parameters from the first step by
systematically analysing and deleting the irrelevant ones. Steps 1 and 2 identified 27
parameters as a consequence. The third step was determining a suitable technique to show the
parameter's prioritisation ranking, BWM method resolved the analysis purpose, and BWM
generated the weights of the parameters. In the last step, the results were analysed.
4
The data collected from the construction organisations give a way to analyse the data
setting and information for better construction sustainability performance. The collected data
contain the most needed and crucial parameters for the successful construction industry in
sustainability. Nowadays, sustainability has become one of the most government visions to
gain the Sustainable Development Goals by 2030. The data could improve the construction
practitioner's business-related outcomes, effectively more marketing and customer
personalisation, and increase operational efficiency.
Several respondents of the construction practitioners addressed the 27 performance
parameters. After the data is collected and stored, the data will be analysed and listed from the
most important to the least important (best to worst). The construction industry respondents
collected all the data by surveying the construction project site, collecting the previous project
observation from construction reorganisations from study cases of the journals, internet and
Construction organisation databases, and all other related data. The organiser of the listed data
was called the "Decision-Maker". This "Decision-Maker" will conclude 27 parameters among
all the data and then will list the 27 parameters from the best (most important) to the worst
(least important).
Table 1: The respondent’s details
Best-Worst Method
Step 1: prioritise the sustainable supplier selection and involve the sustainable supplier
parameters determination. The data defined by the consultation of decision-makers panels
5
identified and completed the parameter criteria {C1, C2,..., Cn}. Next, the data will be listed
from the best (the most important) to the worst (the least important). The top-ranked parameter
from the list could help the organisation evaluate the sustainable supplier, improve the
construction's service quality, and meet future needs.
Step 2: Specify the best and worst criteria. The best-worst criteria were specified by the
decision-makers (the construction practitioners). The most important criterion is
environmental, while the worst, which is the least important in preference, is social. Next, the
environment, economy, and Social subparameters will be listed.
Step 3: The decision-makers utilised interval numbers from one to nine (1 represents the lowest
importance rating while 9 represents the highest importance rating). Where :
1 = equally important
2 = weakly important
3 = moderately important
4 = moderately plus important
5 = strongly important
6 = strongly plus important
7 = very strongly important
8 = very strongly plus important
9 = extremely important
Step 4: Specify the worst criterion over other criteria by comparing numbers 1 to 9.
Most important criteria(Best) and least important criteria (Worst).
Table 2: best-worst data
The data above state that all decision makers rated environmental as the best while social as
the worst among the three.
AB = (aB1, aB2, … , aBj)
ABj presents the best criterion (B) over other criteria (j)
6
Table 3: Data of the best criterion over other criteria
AW = (a1W, a2W, … , ajW)
The other criterion (j) over the worst criterion (W) is presented by ajW.
Table 4: Data of The other criterion over the worst criterion
Figure 1: Best-Worst Chart
7
The optimal weights value. The consistency ratio (Ksi*) shows the velocity, Validity, and
veracity. Nearer to 0 indicate a consistent result.
Table 5: Consistency of Data
All the data above is evidence that the environment is the most important, followed by the
economy, while the least is social criteria.
Table 6: Data Ranking
4.
Findings
Therefore, to efficiently chose suppliers based on sustainable performance, the
environmental criteria should get the most attention from construction organisations. The
attempts should be aimed at understanding suppliers' environmental criteria in the construction
8
supply chains. Received inputs from the decision-makers specifying all the subcategories and
the weight.
For the environmental criteria, the "reused and recyclable materials usage"(ENV3) ;
(0.252819) was indicated as the most important parameter of the supplier's sustainability
performance in the construction industry. This should be carried out by the working culture
using easily recycled materials. The second-ranked parameter on the environmental side is the
"green product and technology usage" (ENV 4) ; (0.172956). Aligned with the increasing
environmental protection awareness, suppliers have a procedural rule and obligation to switch
to green production techniques wherever and whenever (Sukmawati and Setiawan,2022). The
parameter of "regular environmental audits" (ENV8) ; (0.093915) showed the third rank in the
environmental criteria, which is indicated that regular monitoring is needed to ensure the
supplier's compliance with the healthy environmental protocols conducted. Regular
environmental audit is important to sensitise the vendors and other stakeholders about the
importance of environmental awareness behaviour in an organisation. It also could increase the
supplier's responsibility to reduce environmental issues. Next is the fourth-ranked, which is
"providing the facilities training about environment protection" (ENV9) ; (0.089457).
Improving employee skills in the environment could increase environmental protection and
maintenance. This behaviour could increase profit, where environmental performance is
becoming famous and the first evaluation of the construction industry. Then the rank followed
by the "management of waste products" (ENV7) ; (0.088809), the "environmental regulation
attention" (ENV6) ; (0.086511), the "energy consumption awareness and product recycling"
(ENV1) ; (0.086186), the "pollution control" (ENV2) ; (0.074308), and the "exchangeable
packaging used" (ENV5) ; (0.055038).
The next criterion is economic criteria. The first rank is "Efficiency" (ECO8) ;
(0.239602) contained information sharing, delivery reliability, ease of communication, and
responsiveness was indicated as the best or the most important parameter for sustainable
supplier selection. The second-ranked parameter on economic criteria is the "Cost" (ECO1) ;
(0.187031). The cost parameter commonly consisted of product rejection cost, operational and
transaction purchase, purchase price, and taxes. The third-ranked is "flexibility and scalability"
(ECO4) ; (0.09092) consisted of changing demand, modification, and the ability to adapt the
urgent need while maintaining accuracy. Suppliers must be flexible, especially in an
unpredictable situation like a pandemic, etc. And then the rank followed by The parameter of
"On time delivery" (ECO 3) ; (0.090008), "Quality" (ECO 2) ; (0.087204), "quick response"
9
(ECO 6) ; (0.084721), the "Financial strength" (ECO 7) ; (0.084237), "Technology skill" (ECO
5) ; (0.073329) and the "Risk Management" (ECO 9) ; (0.062948).
The last criterion is the Social criteria. The "workplace safety protocols" (SOC 3) ;
(0.215403). Addressing strict safety management, healthy behaviour, and identifying and
managing hazards in organisations are keys to employee productivity. The second-ranked
parameter is the "Information disclosure" (SOC 4) ; (0.123486). Supplier management should
give as detailed information as possible about the workplace, such as some relevant areas of
information to anticipate the risks. Some important information types that should be known are
the key items supplied, the contact details, certifications, detailed references, and the contract.
Certain data connected to the production, key performance parameters, customer satisfaction
reference, data related to delivery, etc. Supplier financial performance information also should
be shared, including modern slavery status, health and safety management adopted, and human
rights issues. The third-ranked parameter is "child labour awareness" (SOC 8) ; (0.11124).
Involving child labour is also connected to human rights regulation. It could affect the
construction process license. Therefore it should address good transparency in the supply chain.
The fourth rank is the
"Pandemic response strategy parameter" (SOC2) ; (0.111047).
Contained wearing personal protective equipment, the well-educated in any information about
the pandemic, following new regulations, and addressing economic recovery programs. Next
lower rank is followed by the "human rights" (SOC7) ; (0.102654), the "product responsibility"
(SOC6) ; (0.100757), the "employment procedure transparency" (SOC5) ; (0.096211), the
"rules and regulations obedient" (SOC1) ; (0.089951) and the "social responsibility" (SOC9) ;
(0.045248).
5.
Conclusion
Study Implication and Application
1.
The study identifies the crucial environmental, economic, and social parameters to
evaluate supplier performance in sustainable construction supply chains. These could
help the practitioners and managers focus on the important aspect list.
The suppliers and vendors in the construction industry can take advantage of this study
to specify the most important aspect to increase their performance, making them more
competitive and guiding them to build a strong relationship with the client. A supplier
can't pass the best performance list based on practical cases. Therefore, this study shows
major parameters. The suppliers could analyse their weaknesses and strengthen the
existing ones.
10
2.
Although the environmental parameter was presented as the most important supplier
performance parameter, other criteria should still be considered. Many other criteria
could be client preceptive focus. The suppliers at least should pass the top three ranked
of each environmental, economic and social criterion.
This study also facilitates managers to understand the unimportant and not-useful list
to evaluate the supplier's performance easily. Thus, the managers could choose the least
important indicators affecting the construction process.
3.
The study helps construction industry practitioners and managers to review their
evaluation strategy on various parameters connected to the supplier's performance
selection. However, this should be considered a partial performance decision
framework. The study presents the issues handled by the relevant management
organisations and suppliers for continuous consideration to improve the capability.
11
References
Ahmad, S., Abdullah, A., & Talib, F. (2021). Lean-green performance management in
Indian SMEs: a novel perspective using the best-worst method approach.
Benchmarking, 28(2), 737–765. https://doi.org/10.1108/BIJ-05-2020-0255
Alamroshan, F., Lai, M. and Yahya, M. (2022), "The green-agile supplier selection
problem for the medical devices: a hybrid fuzzy decision-making approach",
Environmental Science and Pollution Research, Vol. 29 No. 5, pp. 6793-6811
Alavi, B., Tavana, M. and Mina, H. (2021), "A dynamic decision support system for
sustainable supplier selection in a circular economy", Sustainable Production and
Consumption, Vol. 27, pp. 905-920.
Brunelli, M., Rezaei, J. (2019). A multiplicative best-worst method for multi-criteria
decision making, Operations Research Letters, 47(1), 12-15.
Demirkol,_I. (2021), "International supplier selection using AHP method", Third Sector
Social EconomicReview, Vol. 56 No. 1, pp. 353-370
Kumar, A., Shrivastav, S., Adlakha, A., & Vishwakarma, N. K. (2022). Appropriation of
sustainability priorities to gain a strategic advantage in a supply chain. International
Journal of Productivity and Performance Management, 71(1), 125–155.
https://doi.org/10.1108/IJPPM-06-2020-0298
Lau, J. L., Hashim, A. H., Samah, A. A., & Salim, A. S. S. (2016). Understanding the
environmental worldviews of Malaysian project managers. Smart and Sustainable
Built Environment, 5(4), 307–324. https://doi.org/10.1108/SASBE-06-2016-0012
Liang, F., Brunelli, M., Rezaei, J. (2020). Consistency issues in the Best Worst Method:
Measurements and thresholds, Omega, 96, pp. 102175.
Liang, Y., Ju, Y., Tu, Y., & Rezaei, J. (2022). Nonadditive best-worst method:
Incorporating criteria interaction using the Choquet integral. Journal of the
Operational Research Society, 1-12
Mahmoudi, A., Deng, X., Ahmed Javed, S. and Zhang, N. (2021), "Sustainable supplier
selection in megaprojects: grey ordinal priority approach", Business Strategy and The
Environment, Vol. 30No. 1, pp. 318-339
Orji, I.J. and Ojadi, F. (2021)," Investigating the COVID-19 pandemic's impact on
sustainable supplier selection in the Nigerian Manufacturing Sector", Computers &
Industrial Engineering, Vol. 160, pp. 1-19
Rezaei, J. (2015). Best-worst multi-criteria decision-making method, Omega, 53, pp. 4957.
12
Singh, A., Kumar, V., Verma, P., & Ramtiyal, B. (2022). Can suppliers be sustainable in
construction supply chains? Evidence from a construction company using the best
worst approach. Management of Environmental Quality: An International Journal.
https://doi.org/10.1108/MEQ-03-2022-0057
Sukmawati, M. and Setiawan, A.D. (2022), "A conceptual model of green supplier
selection in the manufacturing industry using AHP and TOPSIS methods",2022 7th
International Conference on Business and Industrial Research (ICBIR), IEEE, pp.
659-664, May
Tabatabaee, S., Ashour, M., Sadeghi, H., Hoseini, S. A., Mohandes, S. R., Mahdiyar, A.,
Ismail, S., & Hosseini, M. R. (2022). Towards adopting the most suitable green walls
within sustainable buildings using interval type-2 fuzzy best-worst method and
TOPSIS technique. Engineering, Construction, and Architectural Management.
https://doi.org/10.1108/ECAM-06-2022-0551
Wang, C.N., Chou, C.C., Dang, T.T., Nguyen, H.P. and Nguyen, N.A.T. (2022),
"Integrating triple-bottom line in sustainable chemical supplier selection: a
compromise decision-making-based spherical fuzzy approach", Processes, Vol. 10
No. 5, pp. 1-23
Wu, C., Lin, Y. and Barnes, D. (2021), "An integrated decision-making approach for
sustainable supplier selection in the chemical industry", Expert Systems with
Applications, Vol. 184, pp. 1-16.
Zandkarimkhani, S., Amiri, M. and Mousavi, S.M.H. (2022), "A hybrid multi-criteria
decision-making method for sustainable supplier selection: a case study",
International Journal of Management and Decision Making, Vol. 21 No. 2, pp. 113128.
13