Evaluation and ranking of solutions to mitigate sustainable remanufacturing supply chain risks: a hybrid fuzzy SWARA-fuzzy COPRAS framework approach

Figures & data

Table 1. Summary of research works on remanufacturing supply chain

Reference	Focus of the study	Solution methodology/technique used	Sector focussed	Country/Region
Ijomah (2009)	Key decision issues that impede remanufacturing operations	Case study	Aerospace, Automotive industry	United Kingdom
Mukherjee and Mondal (2009)	Explore the issues in remanufacturing process	Interpretive Structural Modelling (ISM)	Remanufacturing industry	India
Kuo et al. (2010)	Barriers in maintenance and remanufacturing	ISM	Copy machine	Taiwan
Alimoradi, Yussuf, and Zulkifli (2011)	Select the best remanufacturing facility location	Fuzzy TOPSIS	n.a.	Malaysia
Ghazalli and Murata (2011)	Best product EOL strategy selection for remanufacturing	AHP-Case based reasoning	Electronic	Japan
Zhang et al. (2011)	Crucial problems to be faced before, during and after remanufacturing	Analysis based on past published research articles	Automotive	China
Kannan, Diabat, and Madan Shankar (2014)	Analyse drivers of EOL product	ISM	Tire manufacturing	India
Das and Chaudhari (2015)	Identify the motivating and impeding factors of reverse supply chain	Empirical analysis (Survey)	Electronic	India
Xia, Govindan, and Zhu (2015)	Analyses of remanufacturing internal barriers	Grey-Decision Making Trial and Evaluation Laboratory (DEMATEL)	Automotive	China
Wei et al. (2015)	Drivers and barriers for remanufacturing	Empirical analysis (Survey)	Car part and machinery remanufacturers	China
Widera and Seliger (2015)	Explore the barriers and potentials of remanufacturing	Business model canvas	Medical	Germany
Zhu, Sarkis, and Lai (2015)	Barriers for RSC	Grey-DEMATEL	Automotive	China
Govindan, Shankar, and Kannan (2016)	Main barriers faced by auto parts remanufacturer	ISM-AHP	Automotive	India
Matsumoto et al. (2016)	Drivers and barriers for remanufacturing	Analysis based on past published research articles	n.a.	Japan
Sharma, Garg, and Sharma (2016a)	Identifies the remanufacturing initiatives	Empirical analysis (Survey)	Remanufacturing industry	India
Chakraborty, Mondal, and Mukherjee (2017)	Identify product design criteria to enhance remanufacturability	Fuzzy AHP-Axiomatic Design approach	Automotive	India
Ilgin (2017)	Selection of appropriate used product for remanufacturing	AHP- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-Simulation	n.a.	Turkey
Karvonen et al. (2017)	Identifies the barriers and solutions to overcome them	Empirical analysis (Survey+Interview+Workshop)	Remanufacturing industry	Europe
Sinha et al. (2017)	Explore the issues restricting remanufacturing growth	Empirical analysis (Survey)	Automobile	India
Zhang, Yang, and Chen (2017)	Development status of remanufacturing and barriers investigation	Empirical analysis (Survey)	Automotive	China
Bhatia, Rajiv, and Srivastava (2018)	External barriers of remanufacturing	Grey-DEMATEL	Electronic	India
Matsumoto, Chinen, and Endo (2018)	Investigate the factors that impact consumer’s intention to purchase remanufactured product	Empirical analysis (Survey)	Automotive	Asia
Ansari, Kant, and Shankar (2019)	Prioritise the performance outcomes due to adoption of remanufacturing practices	Fuzzy AHP-fuzzy TOPSIS	Remanufacturing industry	India
Toke and Kalpande (2019)	Prioritise the green manufacturing CSFs to achieve sustainability	AHP	Manufacturing industry	India

Table 2. List of common sustainable remanufacturing supply chain risks

Sustainable RSC main risks	Sub-criteria and code	Description	Reference	Type of criterion	Experts opinion regarding minimise criteria or maximise criteria
Economic risks	Cannibalisation risk (ER1)	Launching of a remanufactured product by the original manufacturer would impact the sales of new product	Terkar, Vasudevan, and Kalamkar (2013)	Min	OEMs need to reduce the risk associated with product cannibalisation and formulate strategies to increase the productivity and overall profitability of the system
	Pricing policy risk (ER2)	Finalising the price of new product and remanufactured product is a complex and crucial task	Sinha et al. (2017)	Min	Uncertainty related to optimal pricing of a newly developed product and remanufactured product should be minimised to increase the market share and customer trust
	International trade policy risk (ER3)	Import restrictions and high tariff rates on imported used product cores for remanufacturing increases the cost of the remanufactured product, thus resulting in less profit	Das and Chaudhari (2015)	Min	The government is imposing high tariffs on imported used products as it has the fear that the nation would turn into a dump yard. However, easing the trade barriers and involving multinational companies would stimulate remanufacturing
	Initial investment support risk (ER4)	In developing countries, small manufacturers do not get sufficient financial support needed to set up remanufacturing facilities or expand production scales	Das and Chaudhari (2015)	Max	Financial institutions need to provide maximum economic support to small manufacturing units or informal sectors to set up remanufacturing/refurbishing facilities and procure additional equipment’s to increase production
	Returned cores quality level risk (ER5)	The quality level of returned product cores is uncertain. Uncertainty in the quality level of the returned product significantly affects the inventory planning	Saavedra et al. (2013)	Max	Organisations need to develop an efficient reverse logistics system and sorting system for the used product to maximise the return rate of good quality cores
	Supply risk (ER6)	The supply base is unorganised in developing countries, so the sources of raw material for the OEMs is uncertain. Hence, production planning of the organisational supply chain is significantly affected	Sinha et al. (2017)	Max	There is a need to increase the supply source to the remanufacturers to reduce the uncertainty associated with supply and enable better production planning
	Process failure risk (ER7)	Remanufacturing is a complicated process and several issues such as in-service failure costs, costs involved in lost production and material wastage can affect the organisational performance	Parkinson and Thompson (2004)	Min	Any possible failure in the process needs to be minimised as any deficiency in the processes would result in a poor quality of the product and increase in wastage of material
	Product end-of-life recoverability risk (ER8)	The collection rate of used products entirely depends on the end consumer returns which is not deterministic, in fact highly uncertain. Risk pertaining to recoverability can affect the supply chain remanufacturing	Abdulrahman et al. (2015)	Max	For effective and meaningful engagement in remanufacturing business, attention needs to be focussed to maximise the recoverability of used cores
	Instability in currency exchange rates and taxes (ER9)	Frequent variations in the currency exchange rates and taxes due to global reasons would result in a financial crisis and would affect the RSC effectiveness	Mangla, Kumar, and Barua (2015); Govindan and Chaudhuri (2016)	Min	Government bodies need to take robust steps to strengthen the economy and minimise the risk associated with currency exchange rates and tax issues
	Economic benefits related risk (special subsidies, concessions) from the government (ER10)	Remanufacturers supply environmentally friendly products to consumers but also bear the pressure of high cost and taxes. Hence, the issues in providing subsidies and tax reliefs from government bodies would impact supply chain remanufacturing implementation	Matsumoto et al. (2016); Zhang, Yang, and Chen (2017)	Max	Maximum economic benefits in terms of special subsidies and concession should be offered by the government institutions to encourage remanufacturing practices in supply chain
Social risks	Consumer acceptability risk (SR1)	Consumer belief that remanufactured products are second-hand products and of low quality compared to the new product, restricts the marketability of the remanufactured product	Rathore, Kota, and Chakrabarti (2011)	Max	Acceptability rate of remanufactured product among the consumers’ needs to be maximised to enhance the profitability of the entire system
	Political instability risk (SR2)	Unstable ruling government in the nation would impact the remanufacturing business due to frequent economic policy changes	Govindan and Chaudhuri (2016)	Min	This risk factor needs to be minimised as an unstable government would result in frequent changes in economic policy thereby impacting the supply chain
	Consumer negligence risk (SR3)	Throw away attitude of consumer after product use and not returning it to the collection centres or manufacturer impacts the return rate	Bhatia, Rajiv, and Srivastava (2018)	Min	There is need that customers minimise their negligence towards handling of the used product at its end-of-life and contribute towards sustainable development
	Conflicting objectives of manufacturer and government (SR4)	The government intends to enhance waste management practices resulting in environmental benefits, while organisations intend to generate economic benefits. Distinguished objectives of both parties will impact effective implementation	Expert Opinion	Min	Both manufacturer and government should keep aside their differences and try to minimise the conflicts amongst them for effective implementation of supply chain remanufacturing
	Workers technology know-how risk (SR5)	Due to innovation and globalisation, technologies are constantly changing and hence lack of technical training to workers will impact the operational performance	Saavedra et al. (2013); Sharma, Garg and Sharma (2016b)	Max	Knowledge of the workers about the new tools and technologies needs to be maximised by conducting continuous training programmes
	Organisational integration risk (SR6)	This corresponds to the issues related to the integration of suppliers, consumers and joint ventures with competitors that impacts supply chain remanufacturing effectiveness	Abdulrahman et al. (2015)	Max	Efforts should be made to maximise the integration between all system partners for better coordination and order management
Environmental risks	Government environmental policy related risk (ENVR1)	In the Indian policy document, there is no law that defines waste as part of a cycle of production-consumption-recovery or recognises the issue of waste. Supply chain remanufacturing related laws are still evolving which would hamper its implementation	Rathore, Kota, and Chakrabarti (2011); Saavedra et al. (2013)	Max	Government institutions need to document structured and clear policies/laws related to remanufacturing for better handling of used products which is usually a waste for the consumer
	Risk related to handling of end-of-life products (ENVR2)	In developing countries like India, the EOL activities (reuse, recycling, remanufacturing) is mostly carried out by informal sectors. Hence, there is a high risk regarding damage to environment as well as human health	Rathore, Kota, and Chakrabarti (2011); Sharma, Garg, and Sharma (2016b)	Max	Management of end-of-life products (used products) needs to be maximised to reduce the processing of EOL products at small and informal sectors in an informal manner damaging the environment to a large extent
	Disposition risk (ENVR3)	During dismantling of the product for remanufacturing, a large quantity of components accumulate which cannot be either reused or recycled. Environmentally disposal of hazardous waste is crucial and essentiality of RSC	Sharma, Garg, and Sharma (2016b)	Max	There is a need to maximise the disposal of inferior quality hazardous components in an environmentally friendly manner to move towards sustainability
	Consumers environmental awareness risk (ENVR4)	Consumers have little knowledge about the environmental benefits accrued due to the use of remanufactured product	Sharma, Garg, and Sharma (2016a)	Max	Consumers knowledge regarding the environmental benefits of using a remanufactured product needs to be maximised to boost sustainable remanufacturing business
	Packaging risk (ENVR5)	Collecting used products and transporting back to the manufacturing unit is in fact the first process in remanufacturing. Hence, waste generated due to packaging material can cause environmental damage	Govindan and Chaudhuri (2016)	Min	RSC involves a large amount of packaging material. Hence, the risk involved with this factor needs to be minimised by the use of material that can be recycled or reused resulting in least packaging waste
	Suppliers green ability risk (ENVR6)	The selection of an appropriate supplier is an important criteria for supply chain success. In a globalised competitive market like today risks related to supplier ability to deliver green raw materials is a crucial issue	Rostamzadeh et al. (2018)	Max	Suppliers ability to provide green materials needs to be maximised to enhance the reusability and recyclability which ultimately leads to improvement in the environmental performance of RSC
	Cleaner production capability (ENVR7)	Organisation inability to improve productivity, establish environmental management systems and environmental accounting of the manufacturing process can significantly impact RSC effectiveness	Experts opinion	Max	The organisational remanufacturing production process need to move towards cleaner methods to improve the social-economical-environmental performance of the supply chain
	Organisation interest in environmental improvements (ENVR8)	Supply chain remanufacturing results in environmental benefits but organisations need to collect used products, re-process and remarket it, thereby making the traditional supply chain complex. Hence, lack of interest by organisations to go for remanufacturing is an important issue	Experts opinion	Max	There is a need that organisations become more responsible by taking initiatives and modify their traditional supply chain to induct remanufacturing business with the objective to maximise the environmental benefits

Note: To determine the type of criterion (minimise criteria or maximise criteria), the experts panel group was provided with a Questionnaire form (Appendix I)

Table 3. Solutions to overcome the sustainable remanufacturing supply chain risks

Code	Solution	Description	Reference
S1	Sustainable product design	Sustainable product design enables efficient disassembly of the product, thus improving the recovery rate of the cores. Also, the use of environmentally friendly materials and sustainable manufacturing process would result in supply chain remanufacturing effectiveness	Sharma, Garg, and Sharma (2016a); Sinha et al. (2017)
S2	Initiatives from management and modification in organisation goals	Supply chain remanufacturing activities can be intensified by management through the development of appropriate strategies, separate funds allocation, and exclusive marketing	Expert opinion
S3	Technological up-gradation	Up-gradation in technology will result in improving the functionality/performance of the remanufactured product, thereby improving the product quality and product features	Sharma, Garg, and Sharma (2016a)
S4	Government, manufacturer and distributor collaboration to develop action plans	It means that all supply chain remanufacturing stakeholders collaborate and formulate strategies to infuse remanufacturing concept, create awareness, and effective collection of used product	Sharma, Garg, and Sharma (2016a), 2016b)
S5	Promotional strategies	Promoting that remanufactured products are ‘new-like’ product at cheaper rates and green products, would not only attract the environmentally conscious consumers but also a large segment of low-income consumers	Mukherjee and Mondal (2009)
S6	Setting up international standards	Adopting the best remanufacturing practices after consultation with brand remanufacturers, developing standards, implementing inspection certification of remanufactured products, and formulation of guarantee policy would protect consumers interest	Wei et al. (2015); Zhang, Yang, and Chen (2017)
S7	Develop structured channels to collect EOL products	Designing proper channels to collect used product will help reduce the uncertainty in timing and quantity of EOL products, hence improving the production planning and control activities	Sharma, Garg, and Sharma (2016b); Bhatia, Rajiv, and Srivastava (2018)
S8	Motivate consumers to take initiatives regarding environmental protection	Consumers should be made responsible so that they also contribute to environmental protection. Reward mechanism and incentives should be provided to motivate the consumers	Bhatia, Rajiv, and Srivastava (2018)
S9	Relaxation in current legislative restriction	Government need to modify the legislative policies such as law relevant to imported used products and taxes on them, to promote supply chain remanufacturing, and hence sustainability	Wei et al. (2015)
S10	Structured guidelines for product remanufacturing	Supply chain remanufacturing is highly organised in nature, especially in developing nations. Formulating a structured framework and drafting new guidelines would give a boost to supply chain remanufacturing industry	Rathore, Kota, and Chakrabarti (2011); Zhang, Yang, and Chen (2017)
S11	Integration of lean principles	Lean remanufacturing is a newly evolved manufacturing process that enables effective utilisation of available energy and resources, while reducing wastes in the process and thereby increasing efficiency	Vasanthakumar, Vinodh, and Ramesh (2016); Expert Opinion
S12	Ease to provide financial assistance	Allocation of funds through financial institutions to the organisations willing to adopt remanufacturing practices should be made simpler to propagate its implementation	Expert Opinion

Table 4. The fuzzy scale

Linguistic scale	Triangular fuzzy number
Much less important	(0.222, 0.25, 0.286)
Very less important	(0.286, 0.333, 0.4)
Less important	(0.4, 0.5, 0.667)
Moderately important	(0.667, 1, 1.5)
Equally important	(1, 1, 1)

Source: Chang (1996)

Table 5. Alteration for fuzzy membership functions

Linguistic scale	Triangular fuzzy numbers
Very low (VL)	(0222, 0.25, 0.286)
Low (L)	(0.286, 0.333, 0.4)
Medium (M)	(0.4, 0.5, 0.667)
High (H)	(0.667, 1, 1.5)
Very high (VH)	(1.5, 2, 2.5)

Source: Chang (1996)

Figure 1. Flow diagram of the proposed method

Figure 2. Structural decision hierarchy to rank sustainable RSC risk solutions

Table 6. Final weight of main risk factors

Criteria	${\tilde{s}}_j$			${\tilde{k}}_j$			${\tilde{q}}_j$			${\tilde{w}}_j$
Economic				1.000	1.000	1.000	1.000	1.000	1.000	0.478	0.526	0.584
Environmental	0.667	1.000	1.500	1.667	2.000	2.500	0.400	0.500	0.600	0.191	0.263	0.351
Social	0.222	0.250	0.286	1.222	1.250	1.286	0.311	0.400	0.491	0.149	0.211	0.287

Table 7. Local weight and final weight of economic risks sub-criteria

Criteria	${\tilde{s}}_j$			${\tilde{k}}_j$			${\tilde{q}}_j$			Local weight ${\tilde{w}}_j$			Global weight ${\tilde{w}}_j$			Non-fuzzy $w_j$
ER8: Product end-of-life recoverability risk				1.000	1.000	1.000	1.000	1.000	1.000	0.396	0.443	0.496	0.189	0.233	0.289	0.237
ER5: Returned cores quality level	1.000	1.000	1.000	2.000	2.000	2.000	0.500	0.500	0.500	0.198	0.221	0.248	0.095	0.116	0.145	0.119
ER4: Initial investment support risk	0.667	1.000	1.500	1.667	2.000	2.500	0.200	0.250	0.300	0.079	0.111	0.149	0.038	0.058	0.087	0.061
ER3: International trade policy risk	0.400	0.500	0.667	1.400	1.500	1.667	0.120	0.167	0.214	0.047	0.074	0.106	0.023	0.039	0.062	0.041
ER10: Economic benefits (special subsidies, concessions) related risk	0.400	0.500	0.667	1.400	1.500	1.667	0.072	0.111	0.153	0.028	0.049	0.076	0.014	0.026	0.044	0.028
ER6: Supply risk	0.400	0.500	0.667	1.400	1.500	1.667	0.043	0.074	0.109	0.017	0.033	0.054	0.008	0.017	0.032	0.019
ER7: Process failure risk	0.286	0.333	0.400	1.286	1.333	1.400	0.031	0.056	0.085	0.012	0.025	0.042	0.006	0.013	0.025	0.014
ER2: Pricing policy risk	0.286	0.333	0.400	1.286	1.333	1.400	0.022	0.042	0.066	0.009	0.018	0.033	0.004	0.010	0.019	0.011
ER9: Instability in currency exchange rates and taxes	0.222	0.250	0.286	1.222	1.250	1.286	0.017	0.033	0.054	0.007	0.015	0.027	0.003	0.008	0.016	0.009
ER1: Cannibalisation risk	0.222	0.250	0.286	1.222	1.250	1.286	0.013	0.027	0.044	0.005	0.012	0.022	0.003	0.006	0.013	0.007

Table 8. Local weight and final weight of social risks sub-criteria

Criteria	${\tilde{s}}_j$			${\tilde{k}}_j$			${\tilde{q}}_j$			Local weight ${\tilde{w}}_j$			Global weight ${\tilde{w}}_j$			Non-fuzzy $w_j$
SR1: Consumer acceptability risk				1.000	1.000	1.000	1.000	1.000	1.000	0.388	0.467	0.562	0.058	0.099	0.161	0.106
SR3: Consumer negligence risk	0.667	1.000	1.500	1.667	2.000	2.500	0.400	0.500	0.600	0.155	0.233	0.337	0.023	0.049	0.097	0.056
SR5: Workers technology know-how risk	0.667	1.000	1.500	1.667	2.000	2.500	0.160	0.250	0.360	0.062	0.117	0.202	0.009	0.025	0.058	0.031
SR4: Conflicting objectives of manufacturer and government	0.400	0.500	0.667	1.400	1.500	1.667	0.096	0.167	0.257	0.037	0.078	0.145	0.006	0.016	0.041	0.021
SR6: Organisational integration risk	0.286	0.333	0.400	1.286	1.333	1.400	0.069	0.125	0.200	0.027	0.058	0.112	0.004	0.012	0.032	0.016
SR2: Political instability risk	0.222	0.250	0.286	1.222	1.250	1.286	0.053	0.100	0.164	0.021	0.047	0.092	0.003	0.010	0.026	0.013

Table 9. Local weight and final weight of environmental risks sub-criteria

Criteria	${\tilde{s}}_j$			${\tilde{k}}_j$			${\tilde{q}}_j$			Local weight ${\tilde{w}}_j$			Global weight ${\tilde{w}}_j$			Non-fuzzy $w_j$
ENVR1: Government environmental policy				1.000	1.000	1.000	1.000	1.000	1.000	0.429	0.479	0.528	0.082	0.126	0.185	0.131
ENVR2: Risk related to handling of end-of-life products	1.000	1.000	1.000	2.000	2.000	2.000	0.500	0.500	0.500	0.215	0.239	0.264	0.041	0.063	0.093	0.066
ENVR4: Consumers environmental awareness risk	0.667	1.000	1.500	1.667	2.000	2.500	0.200	0.250	0.300	0.086	0.120	0.158	0.016	0.031	0.056	0.034
ENVR3: Disposition risk	0.667	1.000	1.500	1.667	2.000	2.500	0.080	0.125	0.180	0.034	0.060	0.095	0.007	0.016	0.033	0.019
ENVR8: Organisation interest in environmental improvements	0.400	0.500	0.667	1.400	1.500	1.667	0.048	0.083	0.129	0.021	0.040	0.068	0.004	0.010	0.024	0.013
ENVR7: Cleaner production capability	0.400	0.500	0.667	1.400	1.500	1.667	0.029	0.056	0.092	0.012	0.027	0.048	0.002	0.007	0.017	0.009
ENVR6: Suppliers green ability risk	0.286	0.333	0.400	1.286	1.333	1.400	0.021	0.042	0.071	0.009	0.020	0.038	0.002	0.005	0.013	0.007
ENVR5: Packaging risk	0.222	0.250	0.286	1.222	1.250	1.286	0.016	0.033	0.058	0.007	0.016	0.031	0.001	0.004	0.011	0.005

Table 10. Fuzzy decision matrix between alternative and criteria (Expert 1)

	Criteria
Solutions (Alternatives)	ER1	ER2	ER3	….	… .	ENVR6	ENVR7	ENVR8
S1	(0.286,0.0.333,0.4)	(0.286,0.333,0.4)	(0.222,0.25,0.286)	… .	… .	(0.667,1,1.5)	(1.5,2,2.5)	(0.4,0.5,0.667)
S2	(0.4,0.5,0.667)	(0.4,0.5,0.667)	(0.286,0.333,0.4)	… .	… .	(0.667,1,1.5)	(1.5,2,2.5)	(1.5,2,2.5)
S3	(0.4,0.5,0.667)	(0.286,0.333,0.4)	(0.286,0.333,0.4)	… .	… .	(0.667,1,1.5)	(1.5,2,2.5)	(0.667,1,1.5)
… .	… .	… .	… .	… .	… .	… .	… .	… .
… .	… .	… .	… .	… .	… .	… .	… .	… .
S10	(0.286,0.333,0.4)	(0.4,0.5,0.667)	(0.4,0.5,0.667)	… .	… .	(0.667,1,1.5)	(0.667,1,1.5)	(0.667,1,1.5)
S11	(0.4,0.5,0.667)	(0.286,0.333,0.4)	(0.286,0.333,0.4)	… .	… .	(1.5,2,2.5)	(1.5,2,2.5)	(0.667,1,1.5)
S12	(0.286,0.333,0.4)	(0.286,0.333,0.4)	(0.667,1,1.5)	… .	… .	(1.5,2,2.5)	(1.5,2,2.5)	(1.5,2,2.5)

Table 11. Aggregate fuzzy decision matrix between alternatives and criteria

	Criteria
Solutions (Alternatives)	ER1	ER2	ER3	… .	ENVR6	ENVR7	ENVR8
S1	(0.2647, 0.3053, 0.3620)	(0.2647, 0.3053, 0.3620)	(0.2433, 0.2777, 0.3240)	… .	(0.5780, 0.8333, 1.2223)	(1.2223, 1.6667, 2.1667)	(0.3620, 0.4443, 0.5780)
S2	(0.3620, 0.4443, 0.5780)	(0.3620, 0.4443, 0.5780)	(0.2647, 0.3053, 0.3620)	… .	(0.5780, 0.8333, 1.2223)	(1.2223, 1.6667, 2.1667)	(1.2223, 1.6667, 2.1667)
S3	(0.3620, 0.4443, 0.5780)	(0.2647, 0.3053, 0.3620)	(0.2647, 0.3053, 0.3620)	… .	(0.5780, 0.8333, 1.2223)	(1.2223, 1.6667, 2.1667)	(0.5780, 0.8333, 1.2223))
… .	… .	… .	… .	… .	… .	… .	… .
… .	… .	… .	… .	… .	… .	… .	… .
S10	(0.2647, 0.3053, 0.3620)	(0.3620, 0.4443, 0.5780)	(0.3620, 0.4443, 0.5780)	… .	(0.5780, 0.8333, 1.2223)	(0.5780, 0.8333, 1.2223)	(0.5400, 0.7777, 1.1333)
S11	(0.3620, 0.4443, 0.5780)	(0.2433, 0.2777, 0.3240)	(0.2647, 0.3053, 0.3620)	… .	(1.2223, 1.6667, 2.1667)	(1.2223, 1.6667, 2.1667)	(0.5400, 0.7777, 1.1333)
S12	(0.2647, 0.3053, 0.3620)	(0.2433, 0.2777, 0.3240)	(0.5780, 0.8333, 1.2223)	… .	(1.2223, 1.6667, 2.1667)	(1.2223, 1.6667, 2.1667)	(0.9447, 1.3333, 1.8333)

Table 12. Normalised fuzzy decision matrix between alternatives and criteria

	Criteria
Solutions (Alternatives)	ER1	ER2	ER3	… .	ENVR6	ENVR7	ENVR8
S1	(0.0860, 0.0993, 0.1177)	(0.1258, 0.1452, 0.1721)	(0.0491, 0.0560, 0.0654)	… .	(0.0879, 0.1267, 0.1858)	(0.1403, 0.1913, 0.2487)	(0.0544, 0.0668, 0.0869)
S2	(0.1177, 0.1445, 0.1879)	(0.1721, 0.2112, 0.2748)	(0.0534, 0.0616, 0.0731)	… .	(0.0879, 0.1267, 0.1858)	(0.1403, 0.1913, 0.2487)	(0.1837, 0.2505, 0.3256)
S3	(0.1177, 0.1445, 0.1879)	(0.1258, 0.1452, 0.1721)	(0.0534, 0.0616, 0.0731)	… .	(0.0879, 0.1267, 0.1858)	(0.1403, 0.1913, 0.2487)	(0.0869, 0.1252, 0.1837)
… .	… .	… .	… .	… .	… .	… .	… .
… .	… .	… .	… .	… .	… .	… .	… .
S10	(0.0860, 0.0993, 0.1177)	(0.1721, 0.2112, 0.2748)	(0.0731, 0.0897, 0.1167)	… .	(0.0879, 0.1267, 0.1858)	(0.0664, 0.0957, 0.1403)	(0.0812, 0.1169, 0.1703)
S11	(0.1177, 0.1445, 0.1879)	(0.1157, 0.1320, 0.1540)	(0.0534, 0.0616, 0.0731)	… .	(0.1858, 0.2533, 0.3293)	(0.1403, 0.1913, 0.2487)	(0.0812, 0.1169, 0.1703)
S12	(0.0860, 0.0993, 0.1177)	(0.1157, 0.1320, 0.1540)	(0.1167, 0.1682, 0.2467)	… .	(0.1858, 0.2533, 0.3293)	(0.1403, 0.1913, 0.2487)	(0.1420, 0.2004,0.2755)

Table 13. Weighted normalised fuzzy decision matrix between alternatives and criteria

	Criteria
Solutions (Alternatives)	ER1	ER2	ER3	… .	ENVR6	ENVR7	ENVR8
S1	(0.0006, 0.0007, 0.0008)	(0.0014, 0.0016, 0.0019)	(0.0020, 0.0023, 0.0027)	… .	(0.0006, 0.0009, 0.0013)	(0.0013, 0.0017, 0.0022)	(0.0007, 0.0009, 0.0011)
S2	(0.0008, 0.0010, 0.0013)	(0.0019, 0.0023, 0.0030)	(0.0022, 0.0025, 0.0030)	… .	(0.0006, 0.0009, 0.0013)	(0.0013, 0.0017, 0.0022)	(0.0024, 0.0033, 0.0042)
S3	(0.0008, 0.0010, 0.0013)	(0.0014, 0.0016, 0.0019)	(0.0022, 0.0025, 0.0030)	… .	(0.0006, 0.0009, 0.0013)	(0.0013, 0.0017, 0.0022)	(0.0011, 0.0016, 0.0024)
… .	… .	… .	… .	… .	… .	… .	… .
… .	… .	… .	… .	… .	… .	… .	… .
S10	(0.0006, 0.0007, 0.0008)	(0.0019, 0.0023, 0.0030)	(0.0030, 0.0037, 0.0048)	… .	(0.0006, 0.0009, 0.0013)	(0.0006, 0.0009, 0.0013)	(0.0011, 0.0015, 0.0022)
S11	(0.0008, 0.0010, 0.0013)	(0.0013, 0.0015, 0.0017)	(0.0022, 0.0025, 0.0030)	… .	(0.0013, 0.0018, 0.0023)	(0.0013, 0.0017, 0.0022)	(0.0011, 0.0015, 0.0022)
S12	(0.0006, 0.0007, 0.0008)	(0.0013, 0.0015, 0.0017	(0.0048, 0.0069, 0.0101)	… .	(0.0013, 0.0018, 0.0023)	(0.0013, 0.0017, 0.0022)	(0.0018, 0.0026, 0.0036)

Table 14. Final results and rank of the solutions (alternatives)

Solutions	$P_j$			$R_j$			De-fuzzy $P_j$	De-fuzzy $R_j$	$Q_j$	$N_j$ (%)	Rank
S1	0.0946	0.1294	0.1743	0.0154	0.0307	0.0494	0.1328	0.0319	0.1812	83.83	5
S2	0.1267	0.1739	0.2316	0.0212	0.0387	0.0595	0.1774	0.0398	0.2161	100.00	1
S3	0.0961	0.1296	0.1710	0.0188	0.0721	0.1165	0.1322	0.0691	0.1545	71.51	9
S4	0.1257	0.1710	0.2248	0.0306	0.0520	0.0776	0.1738	0.0534	0.2027	93.78	2
S5	0.0760	0.1005	0.1319	0.0176	0.0261	0.0344	0.1028	0.0260	0.1620	74.97	6
S6	0.1201	0.1632	0.2144	0.0159	0.0687	0.1137	0.1659	0.0661	0.1892	87.57	3
S7	0.0945	0.1251	0.1611	0.0174	0.0258	0.0340	0.1269	0.0257	0.1869	86.46	4
S8	0.0703	0.0935	0.1236	0.0173	0.0234	0.0298	0.0958	0.0235	0.1614	74.66	7
S9	0.0633	0.0817	0.1070	0.0211	0.0289	0.0369	0.0840	0.0290	0.1372	63.47	12
S10	0.0814	0.1131	0.1580	0.0236	0.0418	0.0633	0.1175	0.0429	0.1534	71.00	10
S11	0.0970	0.1315	0.1756	0.0136	0.0651	0.1079	0.1347	0.0622	0.1595	73.79	8
S12	0.0758	0.1045	0.1457	0.0171	0.0335	0.0539	0.1087	0.0348	0.1529	70.75	11

Table 15. Sensitivity analysis

		$N_j$
Expt. No.	Experiment conditions	S1	S2	S3	S4	S5	S6	S7	S8	S9	S10	S11	S12
1	ER1 = 0.015; ER2-ENVR8 = 0.6	79.71	100.00	81.29	97.50	74.12	95.89	83.31	71.99	62.79	71.58	96.23	90.11
2	ER2 = 0.015; ER1 = 0.6, ER3-ENVR8 = 0.6	80.00	100.00	80.03	97.03	68.05	91.35	83.28	72.10	62.87	72.45	94.37	89.65
3	ER3 = 0.015; ER1-ER2 = 0.6, ER4-ENVR8 = 0.6	79.38	98.96	79.98	100.00	67.60	93.14	82.84	71.57	70.79	72.07	94.55	93.00
4	ER4 = 0.6; ER1-ER3, ER5-ENVR8 = 0.015	33.41	63.98	29.11	100.00	37.28	31.59	39.85	40.26	43.79	32.82	31.51	59.76
5	ER5 = 0.6; ER1-ER4, ER6-ENVR8 = 0.015	63.50	100.00	87.50	50.32	50.61	90.99	50.13	49.55	44.10	60.81	90.87	49.63
6	ER6 = 0.6; ER1-ER5, ER7-ENVR8 = 0.015	52.80	71.07	57.08	100.00	69.92	91.39	65.04	63.56	99.70	93.32	91.25	68.82
7	ER8 = 0.6; ER1-ER7; ER9-ENVR8 = 0.015	89.93	95.05	81.14	93.58	83.09	68.10	100.00	89.82	51.25	59.21	67.97	65.08
8	ER10 = 0.6; ER1-ER9, SR1-ENVR8 = 0.015	37.23	47.59	29.47	100.00	40.16	33.60	44.66	43.86	64.50	41.61	40.10	99.43
9	SR1 = 0.6; ER1-ER10, SR2-ENVR8 = 0.015	95.01	100.00	55.89	68.03	98.88	90.22	60.84	93.54	54.29	61.55	59.55	67.28
10	SR5 = 0.6; ER1-SR4, SR6-ENVR8 = 0.015	45.78	100.00	48.12	48.85	42.98	91.56	48.10	47.45	42.45	60.25	91.45	65.19
11	SR6 = 0.6; ER1-SR5, ENVR1-ENVR8 = 0.015	43.73	100.00	30.70	40.00	40.24	57.91	51.26	90.69	39.78	41.49	57.81	63.73
12	ENVR2 = 0.6; ER1-ENVR1, ENVR2-ENVR8 = 0.015	61.71	95.29	53.34	93.90	50.67	85.75	100.00	57.07	50.08	58.86	85.63	81.97
13	ENVR3 = 0.6; ER1-ENVR2, ENVR4-ENVR8 = 0.015	89.51	94.84	80.35	93.31	52.89	84.39	100.00	59.90	52.25	86.39	84.26	92.51
14	ENVR4 = 0.6; ER1-ENVR3, ENVR5-ENVR8 = 0.015	60.72	65.24	82.79	93.78	94.06	56.39	54.22	100.00	42.73	42.69	56.27	47.88
15	ENVR6 = 0.6; ER1-ENVR5, ENVR6-ENVR8 = 0.015	64.30	68.95	56.32	100.00	67.91	59.84	56.74	51.26	44.59	61.58	92.11	99.30
16	ENVR7 = 0.6; ER1-ENVR6, ENVR8 = 0.015	89.76	94.96	80.82	93.48	52.28	84.77	100.00	54.00	51.65	59.32	84.64	92.69
17	ENVR8 = 0.6; ER1-ENVR7 = 0.015	45.10	95.72	53.23	94.46	48.54	40.86	100.00	48.66	61.42	55.94	54.17	81.91
18	ER1-ER4 = 0; ER5-ENVR8 = 0.3	78.54	100.00	79.31	95.54	72.56	97.96	82.38	69.66	68.39	72.55	95.24	89.94
19	ER1-ENVR8 = 0.03	84.87	100.00	68.80	96.23	84.03	79.92	99.98	94.57	79.86	76.12	80.19	93.30

Figure 3. Results of sensitivity analysis ($N_j$ scores)

Table

Sub-criteria and Code	Whether minimise criteria or maximise criteria	Reason for minimise or maximise criteria
Cannibalisation risk (ER1)
Pricing policy risk (ER2)
International trade policy risk (ER3)
Initial investment support risk (ER4)
Returned cores quality level risk (ER5)
Supply risk (ER6)
…
…
Disposition risk (ENVR3)
Consumers environmental awareness risk (ENVR4)
Packaging risk (ENVR5)
Suppliers green ability risk (ENVR6)
Cleaner production capability (ENVR7)
Organisation interest in environmental improvements (ENVR8)

Table

Criteria	Arranging the criteria from most important to least important	Assigning the importance weight
		Much less important (0.222, 0.25, 0.286)
		Very less important (0.286, 0.333, 0.4)
		Less important (0.4, 0.5, 0.667)
		Moderately important (0.667, 1, 1.5)
		Equally important (1, 1, 1)
Economic
Social
Environmental
Sub-criteria	Arranging the criteria from most important to least important	Assigning the importance weight
		Much less important (0.222, 0.25, 0.286)
		Very less important (0.286, 0.333, 0.4)
		Less important (0.4, 0.5, 0.667)
		Moderately important (0.667, 1, 1.5)
		Equally important (1, 1, 1)
Cannibalisation risk
Pricing policy risk
International trade policy risk
Initial investment support risk
Returned cores quality level risk
Supply risk
Process failure risk
Product end-of-life recoverability risk
Instability in currency exchange rates and taxes risk
Economic benefits related risk (special subsidies, concessions) from the government

Table

	Criteria
Alternatives	ER1	ER2	ER3	….	ENVR6	ENVR7	ENVR8
	Very low (0222, 0.25, 0.286)	Very low (0222, 0.25, 0.286)	Very low (0222, 0.25, 0.286)	….	Very low (0222, 0.25, 0.286)	Very low (0222, 0.25, 0.286)	Very low (0222, 0.25, 0.286)
	Low (0.286, 0.333, 0.4)	Low (0.286, 0.333, 0.4)	Low (0.286, 0.333, 0.4)	….	Low (0.286, 0.333, 0.4)	Low (0.286, 0.333, 0.4)	Low (0.286, 0.333, 0.4)
	Medium (0.4, 0.5, 0.667)	Medium (0.4, 0.5, 0.667)	Medium (0.4, 0.5, 0.667)	….	Medium (0.4, 0.5, 0.667)	Medium (0.4, 0.5, 0.667)	Medium (0.4, 0.5, 0.667)
	High (0.667, 1, 1.5)	High (0.667, 1, 1.5)	High (0.667, 1, 1.5)	….	High (0.667, 1, 1.5)	High (0.667, 1, 1.5)	High (0.667, 1, 1.5)
	Very high (1.5, 2, 2.5)	Very high (1.5, 2, 2.5)	Very high (1.5, 2, 2.5)	….	Very high (1.5, 2, 2.5)	Very high (1.5, 2, 2.5)	Very high (1.5, 2, 2.5)
S1
S2
S3
….
….
S10
S11
S12

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