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AHP–PROMETHEE Integrated Assessment of Circular Economy
Strategies for Progression toward Sustainability
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AHP–PROMETHEE Integrated Assessment of Circular Economy Strategies
for Progression toward Sustainability
M Shilpa, M R Shivakumar, Hamritha S, Tirunagiri Lekisha, Guna Deepika, Priyadarshan V
Ramaiah Institute of Technology, Bangalore, Karnataka, India
1.INTRODUCTION
The Circular Economy (CE) mainly focuses on the reuse, repair, remanufacturing, and
recycling of materials and energy (D Bourguignon, 2014). CE aims in promoting sustainable development through elimination of waste and pollution , this can be achieved by
disassociating economic growth and resource consumption. According to Ellen MacArthur Foundation, Circular Economy is an industrial model that is designed to be regenABSTRACT:
In manufacturing industries, the conventional linear economy is now slowly moving towards circular economy, which fundamentally aims at reducing unsustainable consumption trends. This work presents the determination of factor weights that affect
the circular economy in manufacturing industries, using the Analytic Hierarchy Process.
The weights of these factors are then used to rank the industries which apply the circular economy concepts. For this purpose, Preference Ranking Organization Method for
Enrichment Evaluations has been used. A survey questionnaire regarding the practice
of circular economy with respect to the identified critical factors is formulated and survey responses from ten manufacturing industries are analyzed using Preference Ranking Organization Method for Enrichment Evaluations and these industries are ranked
for their circular economy practice. Sensitivity analysis is conducted for the obtained
results and robustness in the ranks are ensured.
Keywords: Circular Economy, Sustainability, Analytic Hierarchy Process, Preference Ranking Organization
Method for Enrichment Evaluations, Manufacturing
erative and restorative. It is built upon three core principles (Ellen MacArthur, 2013) designing out waste and pollution, keeping products and resources in use and regenerating natural systems. The CE model not only addresses crucial environmental concerns
but also plays a significant role in fostering innovation across various sectors, boosting
economic opportunities for communities, and fortifying the resilience of supply chains
against unforeseen disruptions (Ellen MacArthur, 2013). India, as a growing economy, is
facing enormous stress on its natural resources due to population growth, urbanization, and industrial development (Rizos Vasileios, 2016). Policy frameworks and demand for recycled goods are also significant determinants of the success of recycling
activities in the textile industry (Małgorzata Koszewska,2018). CE implementation challenges must be addressed by widespread measures, including policy (Juan F. VelascoMuñoz,2021). There are also social challenges in addition to that, such as low public
awareness and support with CE conduct (F Khan, 2022). Market-based barriers include
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difficulties in finding high-quality green raw material and tough competition in the market, which can deter companies from implementing circular models (F Khan,2022).
Transitioning to a worldwide circular economy will intensify through action taken by
nations and organizations on primary issues. In particular, pressing issues include climate change, resource Shortfall, and economic resilience (Julian Kirchherr,2018).
2.LITERATURE REVIEW
The adoption of a CE by the manufacturing industry is confronted by a number of key
challenges. Monetary constraints in the form of high initial investment (Bjoern Jaeger,
2020) and restricted availability of funds present main barriers against the implementation of sustainable practices (Rizos Vasileios,2016). To achieve these multifaceted barriers requires a holistic approach by means of investment (Sofia Ritzén, 2017), technological innovation, organizational commitment, public involvement, and facilitative
regulatory frameworks. Small and Medium Enterprises (SME) tend to face difficulty in
defending factory retooling costs or establishing reverse logistics systems due to lack of
adequate finances (Andrea Cantú, 2021). These additional expenses make recycled
products less competitive in the market, posing a significant barrier for SMEs to implement CE strategies (C Bohringer, 2015). Investment must be targeted in the form of infrastructure and collaboration with logistics partners to maximize reverse logistics operations (Kannan Govindan,2018) The technological barriers include product
complexity and innovation diffusion. The complexity found in new product designs, especially those which incorporate mixed materials, significantly prevents the feasibility
of recycling and remanufacturing processes (Shubhangini Rajput, 2021). The intricacy
poses challenges to sustainability attempts within CE systems, as the intricate design of
these products makes it difficult to disassemble and recover materials. The Cultural
and Organizational Barriers include risks due to business models and hierarchical rigidities. Many SMEs exhibit a preference for traditional linear business models due to familiarity and a perception that these models entail lower risk. This risk aversion can impede the adoption of innovative CE practices, as the transition to circular models is
often viewed as a departure from established, low-risk operations. Overcoming this
barrier requires demonstrating the long-term benefits and sustainability of CE initiatives to shift organizational mind set toward embracing change (Noora Piila, 2022) Organizational structures characterized by rigid hierarchies can limit flexibility and stifle
innovation, both of which are crucial for the successful transition to CE. In such environments, decision-making is often centralized, and employees may feel constrained in
proposing or implementing new ideas (Noora Piila, 2022) Promoting a more flexible
and open organizational culture is essential to foster the creativity and responsiveness
needed for effective CE adoption. From the available literature, it can be inferred that
while individual organizations can implement circular practices, systemic change requires coordinated efforts among businesses, governments, academia, and civil society
(Gianmarco Bressanelli,2019) Cross-Sector Collaborations (CSCs) are pivotal for pooling
resources, expertise, and innovation to create scalable circular solutions (Sunil Luthra,
2022) The circular economy encompasses diverse sectors such as manufacturing, energy, agriculture, and waste management (Elodie Suzanne,2020). Collaborations imbue
a sense of shared mutual benefit and responsibility (Rizos Vasileios,2022) Global value
chains include various actors from regions and industries. Circular economy demands a
multi-dimensional solution to handle resources flows, recycling, and reuse, thereby
making CSCs inevitable to deal with the complexity of operations globally (Vikas Kumar,2019). For this purpose, the most appropriate variables of CSCs impacting circular
economy are obtained from literature as well as managers' discussions in different industries. Stakeholder Engagement (SE) is an important variable. Stakeholder engagement between different stakeholders. Effective communication and engagement mechanisms are needed for aligning goals and interests (Nadine Leder, 2023). Technology
Integration (TI) - Technological adoption such as recycling technology, digital platforms,
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and artificial intelligence to monitor the utilization of resources can greatly improve the
effectiveness of circular practice (Erika Grabocka, 2024). Resource Sharing (RS) - Physical, financial, and intellectual resources shared across sectors is critical to improve the
use of materials and generate of low waste. It can encompass the sharing of logistic
networks or R&D operations (A Supanut,2024). Business Models (BM) - The decision
on business models, e.g., product-as-a-service, remanufacturing, or take-back, determines how circularity is realized and how various sectors cooperate (Nadine Leder,
2023). Supply Chain Interactions (SCI) - Successful cooperation among supply chain actors (e.g., raw material producers, manufacturers, recyclers) in order to achieve the circle and mitigate environmental impacts is a key characteristic of circular economy objectives (Sunil Luthra, 2022). Consumer Behaviour (CB) - consumer uptake of circular
activities such as product return schemes, recycling, and purchasing remanufactured
products can render circular practices successful (Stuart Danvers, 2023) Financial Incentives and Investment (FI) - Availability of finance and financial incentives towards
green projects or circular business models can facilitate inter-sector collaboration (Sunil
Luthra, 2022) Market Demand (MD) - Market and consumer demand for green products and processes can render collaboration initiatives between manufacturing sectors
and other industries (e.g., retail, distribution) projects (Stuart Danvers, 2023)
3.DATA COLLECTION AND ANALYSIS
Data collection for the Analytic Hierarchy Process (AHP) (Saaty, 1980) and Preference Ranking
Organization Method for Enrichment Evaluations (PROMETHEE) methods primarily involves
gathering expert judgments through structured questionnaires and surveys, facilitating pairwise
comparisons of alternatives across various criteria (Vahid Balali,2014). AHP has been extensively
used in numerous fields, such as health care, strategic planning, and budgeting (Katharina
Schmidt,2015). In the Analytic Hierarchy Process (AHP), pairwise comparison matrix is derived
by having decision-makers match every criterion against all the other criteria and give numerical
values corresponding to their relative significance (Augustinas Maceika,2021), and are presented
in Table 1. For the purpose of data collection, a survey questionnaire was prepared by having
interactions with industry professionals, plant managers and consultants. Based on the discussions, important areas of focus, such as waste reduction, energy efficiency, and materials reuse,
were identified and questions around these focus areas were formulated using Likert scale. The
survey questionnaire was made more relevant by considering international frameworks like the
Ellen MacArthur Foundation's circularity indicators (Ellen MacArthur, 2013). The survey questionnaire was rolled out using Google forms and direct outreach. Follow up mail and phone calls
were made to obtain more number of responses. This survey was conducted for two months
during November and December 2024 and about 215 responses were obtained from managers
and middle level employees from various manufacturing industries all over India. The survey
responses were analysed for missing responses and a total of 207 valid survey responses were
considered for further analysis. Based on this, the pairwise comparison matrix for the CSC variables was constructed and is shown in table 1. The normalized matrix (Nazanin Vafaei, 2016)
along with priority weights of the various criteria / factors are presented in table 2.
Table 1: Pairwise Comparison Matrix for the CSC variables
Criteria
SE
TI
RS
BM
SCI
CB
FI
MD
SE
1.000 0.333
0.200
0.333
0.250
0.167
0.200
0.250
TI
3.000 1.000
0.333
0.500
0.333
0.200
0.250
0.333
RS
5.000 3.000
1.000
0.500
0.333
0.250
0.250
0.333
BM
3.000 2.000
2.000
1.000
0.333
0.250
0.250
0.333
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SCI
4.000 3.000
3.000
3.000
1.000
0.333
0.250
0.333
CB
6.000 5.000
4.000
4.000
3.000
1.000
0.333
0.500
FI
5.000 4.000
4.000
4.000
4.000
3.000
1.000
0.333
MD
4.000 3.000
3.000
3.000
3.000
2.000
3.000
1.000
Table 2: Priority Weights for the CSC variables
Criteria
SE
TI
RS
BM
SCI
CB
FI
MD
SE
TI
RS
BM
SCI
CB
FI
MD
0.032
0.097
0.161
0.097
0.129
0.194
0.161
0.129
0.016
0.047
0.141
0.094
0.141
0.234
0.188
0.141
0.011
0.019
0.057
0.114
0.171
0.228
0.228
0.171
0.020
0.031
0.031
0.061
0.184
0.245
0.245
0.184
0.020
0.027
0.027
0.027
0.082
0.245
0.327
0.245
0.023
0.028
0.035
0.035
0.046
0.139
0.417
0.278
0.036
0.045
0.045
0.045
0.045
0.060
0.181
0.542
0.073
0.098
0.098
0.098
0.098
0.146
0.098
0.293
Priority
Weights
0.029
0.049
0.074
0.071
0.112
0.186
0.230
0.248
Consistency index (CI) are then computed using the formula given in equation 1. A CI close to
zero implies high consistency of judgments. (John David, 2007). Table 3 shows the Eigen values
for all seven criteria. The Consistency Ratio obtained is 0.099, which is less than 0.1 and hence
the pairwise comparison matrix is consistent. This matrix can be made use of for further analysis.
λ
−𝑛
CI = 𝑚𝑎𝑥
𝑛−1
Eq. (1)
where, 𝑛 is the number of criteria.
Table 3: Eigen values for the CSC variables
Criteria
SE
TI
RS
BM
SCI
CB
FI
MD
λmax
Eigen value
λ
8.636
8.415
8.428
8.819
9.062
9.248
9.735
9.474
8.977
Consistency Ratio CR = CI / RI
CR = 0.099
Where RI is the random Index
…… Eq.(2)
3.1 PROMETHEE
PROMETHEE is a multi-criteria decision-making technique that facilitates the ranking and selection of alternatives based on multiple criteria. A decision matrix is developed where each alternative is evaluated against each criterion. The responses of the survey questionnaire were further analysed to obtain the decision matrix, as shown in table 4.
Table 4: Decision matrix
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IND 1 IND 2 IND 3 IND 4 IND 5 IND 6 IND 7 IND 8 IND 9 IND 10
74.34 73.03 68.34 68.63 72.56 73.44 69.43 65.21 60.55 77.46
82.75 84.47 80.16 83.61 86.29 81.85 80.87 76.79 73.28 92.84
88.76 94.66 89.55 79.87 92.83 87.86 91.06 86.00 70.10 78.36
87.92 81.16 91.94 57.72 74.73 87.02 77.56 88.34 51.27 79.89
75.71 75.20 91.05 85.77 89.72 74.81 71.60 87.47 75.11 85.62
67.86 70.08 68.21 71.17 67.98 66.96 66.48 65.08 62.70 72.33
88.76 94.66 89.55 91.17 85.47 87.86 91.06 86.00 79.70 91.92
72.95 72.21 68.71 69.85 75.57 72.05 68.61 65.57 61.58 80.83
The normalization is performed using equation 3 (Krzysztof Palczewski,2019). The normalized
weighted matrix for the alternative industries is shown in table 5.
Criteria
SE
TI
RS
BM
SCI
CB
FI
MD
Normalized Value =
𝐴𝑐𝑡𝑢𝑎𝑙 𝑉𝑎𝑙𝑢𝑒 − 𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑡ℎ𝑒 𝐶𝑟𝑖𝑡𝑒𝑟𝑖𝑜𝑛
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑡ℎ𝑒 𝐶𝑟𝑖𝑡𝑒𝑟𝑖𝑜𝑛 − 𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑡ℎ𝑒 𝐶𝑟𝑖𝑡𝑒𝑟𝑖𝑜𝑛
…… Eq.(3)
Table 5: Normalized Weighted Matrix
Alternative
IND 1
IND 2
IND 3
IND 4
IND 5
IND 6
IND 7
IND 8
IND 9
IND 10
SE
0.82
0.74
0.46
0.48
0.71
0.76
0.53
0.28
0.00
1.00
TI
0.48
0.57
0.35
0.53
0.67
0.44
0.39
0.18
0.00
1.00
RS
0.76
1.00
0.79
0.40
0.93
0.72
0.85
0.65
0.00
0.34
BM
0.90
0.73
1.00
0.16
0.58
0.88
0.65
0.91
0.00
0.70
SCI
0.21
0.19
1.00
0.73
0.93
0.17
0.00
0.82
0.18
0.72
CB
0.54
0.77
0.57
0.88
0.55
0.44
0.39
0.25
0.00
1.00
FI
0.61
1.00
0.66
0.77
0.39
0.55
0.76
0.42
0.00
0.82
MD
0.59
0.55
0.37
0.43
0.73
0.54
0.37
0.21
0.00
1.00
The simplest form of the preference function is linear:
P (𝑥ᵢ, 𝑥ⱼ) =
(
𝑥ᵢ−𝑥ⱼ
)
…… Eq.(4)
max (𝑥ᵢ−𝑥ⱼ)
Where 𝑥ᵢ, 𝑥ⱼ are the scores of two alternatives for a specific criterion.
These functions transform the differences in performance into preference degrees (Irik Mukhametzyanov,2018), and are shown in table 6.
Table 6: Preference function
Alternative
IND 1
IND 2
IND 3
IND 4
IND 5
IND 6
IND 7
IND 8
IND 9
IND 10
IND IND IND IND IND IND IND IND IND IND
1
2
3
4
5
6
7
8
9
10
0.00 0.03 0.07 0.13 0.08 0.06 0.14 0.23 0.56 0.05
0.16 0.00 0.19 0.18 0.20 0.20 0.22 0.37 0.69 0.09
0.12 0.11 0.00 0.12 0.10 0.16 0.17 0.21 0.60 0.09
0.16 0.08 0.11 0.00 0.15 0.20 0.20 0.28 0.59 0.01
0.14 0.13 0.12 0.18 0.00 0.18 0.25 0.25 0.62 0.07
0.00 0.01 0.06 0.11 0.06 0.00 0.10 0.18 0.50 0.04
0.04 0.00 0.03 0.07 0.09 0.06 0.00 0.18 0.48 0.04
0.07 0.08 0.00 0.08 0.03 0.08 0.11 0.00 0.39 0.05
0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00
0.32 0.24 0.32 0.25 0.28 0.38 0.41 0.49 0.84 0.00
Alternatives are then ranked based on their net flow values, facilitating a comprehensive evaluation that accounts for all criteria are presented in table 7. These ranking assists decision makers
in identifying the most preferred alternatives (Shankha Shubhra Goswami, 2020).
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Table 7: Net flow and Rank for alternatives
Alternative
IND 10
IND 2
IND 5
IND 4
IND 3
IND 6
IND 7
IND 8
IND 9
IND 1
Positive
Flow (Φ⁺)
0.35
0.23
0.19
0.18
0.17
0.11
0.10
0.09
0.00
0.13
Negative
Flow (Φ⁻)
0.04
0.07
0.10
0.11
0.09
0.13
0.16
0.22
0.53
0.10
Net Flow
(Φ)
0.31
0.16
0.09
0.06
0.08
-0.02
-0.06
-0.13
-0.52
0.03
Rank
1
2
3
4
5
6
7
8
9
10
4. RESULTS AND DISCUSSION:
IND 10 has the highest Net Flow (Φ = 0.31), meaning it is the most preferred alternative among the 10 options. IND 9, despite having a very high Negative Flow (Φ⁻ = 0.53),
is ranked last due to its low preference relative to other alternatives. Alternatives with
high positive flows (such as IND 10, IND 2) indicate strong performance and favorable
ranking. On the other hand, those with high negative flows (such as IND 9) are considered less preferred, often due to consistently poor performance in certain criteria,
making them less competitive in the decision-making process. IND 10 appears to be
the best choice based on the PROMETHEE analysis, while IND 9 should be avoided due
to its significant negative performance across the criteria. Sensitivity analysis has been
performed by increasing and decreasing the weights in steps of 10% for each criterion
and the ranking based on PROMETHEE results are presented in figure 1. Tables 8 and 9
show the ranking stability and sensitivity scores for each of the alternatives. From this,
it can be noticed that IND 10 remains consistently at the top, suggesting it is the most
preferred alternative regardless of weight changes. IND 6 also shows strong stability
but with slight variations. Alternatives that show small fluctuations but do not experience drastic ranking changes. These alternatives are reliable choices but might be affected by minor changes in preferences. IND 2, IND 5, and IND 7 fall into this category.
IND 3 and IND 8 show frequent ranking changes, suggesting that their suitability depends heavily on how much weight is assigned to different criteria. Since IND 10 remains at the top ranking across all variations, it is a safe and strong choice, especially
when decision-makers seek stability.
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Fig 1: Results of Sensitivity Analysis
Table 8: Ranking Stability of alternatives
Alternative
IND 1
IND 2
IND 3
IND 4
IND 5
IND 6
IND 7
IND 8
IND 9
IND 10
Best
Rank
1
2
3
4
4
5
5
6
7
8
Worst Ranking
Standard
Rank
Change
Deviation
2
1 Very Low
4
2 Low
6
3 Moderate
7
3 Moderate
9
5 High
8
3 Moderate
10
5 High
9
3 Moderate
10
3 Moderate
10
2 Low
Table 9: Sensitivity scores of alternatives
Alternative
IND 1
IND 2
IND 3
IND 4
IND 5
IND 6
IND 7
IND 8
IND 9
IND 10
Sensitivity
Score
0
1
1
1
1.25
1.2
1.25
1.5
1.5
1.25
Interpretation
Extremely Stable
Very Stable
Moderately Stable
Moderately Stable
High Sensitivity
Moderate Sensitivity
High Sensitivity
Moderate Sensitivity
Moderate Sensitivity
Low Sensitivity
5.CONCLUSION
This study applied AHP) and PROMETHEE to evaluate and rank alternatives in the context of cross-sectoral collaborations for a circular economy. The results from both
methodologies provide a structured and systematic approach to decision-making, ensuring logical consistency and effective ranking of alternatives based on multiple criteria. The pairwise comparison matrix allowed for a structured evaluation of criteria, ensuring the relative importance of each was quantified. The consistency ratio (CR =
0.099) was below the threshold of 0.1, confirming the reliability of the judgments
made in the comparison process. The highest priority weight was assigned to MD
(0.248), indicating its crucial role in cross-sectoral collaborations, followed by FI (0.230)
and CB (0.186). Findings from PROMETHEE indicate that IND 10 emerged as the most
preferred alternative with the highest net flow (Φ = 0.31), demonstrating superior performance across multiple criteria. IND 9 had the lowest net flow (-0.52), indicating it
performed poorly relative to other alternatives. The ranking outcomes align with practical decision-making needs by identifying the most effective alternatives. After performing the sensitivity analysis, Stability was observed for IND 10, which consistently
remained the top alternative under various weight adjustments. IND 6 showed some
fluctuation but retained a strong position overall; IND 3 and IND 8 were highly sensitive
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to weight changes, suggesting their rankings depended heavily on specific criteria. The
analysis confirmed that IND 10 is a robust choice with minimal susceptibility to weight
variations. From the sensitivity analysis, it can be observed that if stability is the most
critical factor, then IND 10 is the best choice. If slight flexibility is acceptable, then IND
6 is a good alternative. If a balance between sensitivity and performance is required,
then IND 2, IND 5, and IND 7 should be considered. If weight preferences are still uncertain, it is better to avoid highly sensitive alternatives like IND 3 and IND 8.
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