How to Assess Reverse Logistics of e-Waste Considering a Multicriteria Perspective? A Model Proposition
Abstract
:1. Introduction
2. Literature Review
2.1. Reverse Logistics of e-Waste
2.2. Multiple Criteria Decision Aid (MCDA)
- Identification and structuring of the problem. Before any analysis begins, the various stakeholders including facilitators and technical analysts, need to develop a common understanding of the problem, the objectives, the decisions to be made, and the criteria by which those decisions are judged and evaluated;
- Construction and use of the model. A primary feature of the multicriteria approach is the development of formal models of decision-maker preferences, valuing trade-offs, goals, among others, to allow those alternatives and policies or actions under consideration to be compared and related to each systematically and transparently; and
- Development of action plans. The analysis does not solve the decision problem. All management science, and multicriteria decision analysis in particular, also concerns the implementation of results, translating analysis into specific action plans.
3. Methods and Techniques
3.1. Systematic Literature Review
- -
- IF is the impact factor, which is divided by 1000 (one thousand), aiming to normalize its value concerning the other criteria;
- -
- α is a weighting factor ranging from 1 to 10, to be attributed by the researcher. The closer the number is to one, the lower the importance the researcher will attribute to the criterion year, while the closer to 10, the higher is the importance;
- -
- ResearchYear is the year in which the research was developed;
- -
- PublishYear is the year in which the paper was published, and
- -
- Ci is the number of times the paper has been cited [10].
3.2. Multicriteria Modeling
4. Presentation of Results of Systematic Literature Review (SLR)
4.1. Analysis of the Papers Found in Systematic Literature Review (SLR)
4.2. The Model Proposed to Assess the Implementation of the Reverse Logistics of e-Waste in the Earlier Stages
- (a)
- (b)
- This context of decision-making comprehends several stakeholders such as government, citizens, waste manager, industries of electrical electronic equipment (EEE), recycling industries of e-waste, third-party logistics providers, small, medium or large size retailers, waste pickers and, non-governmental organizations, and others [3,4,36];
- (c)
- (d)
- (e)
- In realistic situations, decision-makers may have difficulties to decide due to (i) there is subjective or insufficient data; (ii) the analyst may not know the preferences of the decision-makers; and (iii) it is hard, or even arbitrary to assign weights or exact values to the criteria chosen to evaluate the options of actions (alternatives) [36]; and
- (f)
- (1)
- Identifying the decision-makers (active participants to be involved in decision making). These people should be selected for their knowledge and experience about various functions in the company, and they do not have the same competence. Since conflicting judgments are unlikely to be resolved by consensus, stakeholders can be weighed according to their power to influence results.
- (2)
- Validating the set of decision criteria. Identify the significant factors involved in the decision process based on the criteria included in the model (obtained from the literature review). This can be done with brainstorming sessions involving several individuals, performing different functions, and to obtain different points of view. Some of these factors may not be significant in order to achieve the overall objective, so they must be filtered out; if there are conflicts, a group leader must resolve them.
- (3)
- Identifying the alternatives. The alternatives are the options of action aimed at the implementation of reverse logistics—if any of them is unfeasible, it must be eliminated before applying the method—by mutual agreement between the stakeholders involved;
- (4)
- Eliciting the preferences of the decision-makers. Assess the alternatives (options of action) through objective/direct values, or, still, through ordinal scales, obtain the evaluation of each action option according to the criteria validated by the decision-makers. if there are different measures, it is necessary to normalize the data before applying the method (dividing the value of each cell in the matrix by the total of the column of which it is part);
- (5)
- Applying the PCP method. To probabilistically compose the preferences of the decision-makers and prioritize the actions that need immediate actions to improve the process. The proposed methodology seeks to optimize according to all criteria and considers the probabilities of maximization;
- (6)
- Delivering results. The results can be presented in a ranking organized from the best to worst, or the worst to best, demonstrating what alternatives should be improved in order to reach the objective of the implementation of the reverse logistics of e-waste [p1]; and
- (7)
- Providing Feedback. Based on the results, it is possible for decision-makers to decide whether to maintain the course of action or whether to implement changes to improve the performance of indicators in a timely and accurate manner.
Numerical Example to Illustrate the Use of the Model Proposed
- A1—Door-to-door collection by truck;
- A2—Door-to-door collection by waste pickers;
- A3—Delivery at EEE shops collection;
- A4—Delivery at metro stations;
- A5—Delivery at delivery points;
- A6—Refurbishment by social program;
- A7—Informal dismantling
- A8—Informal recycling;
- A9—Adequately recycled regionally;
- A10—Dismantled by recycling company.
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Authors | No. of Articles | Author | No. of Articles Fractionalized |
---|---|---|---|
Ahmed S | 2 | WAGNER TP | 1.333 |
Aras N | 2 | Barua Mk | 1.000 |
Barua Mk | 2 | Gobbi C | 1.000 |
Buyukozkan G | 2 | Krikke H | 1.000 |
Erol I | 2 | Prakash C | 1.000 |
Guarnieri P | 2 | Ravi V | 1.000 |
Korugan A | 2 | Srivastava Sk | 1.000 |
Prakash C | 2 | Subramanian N | 0.833 |
Serifoglu Fs | 2 | Ahmed S | 0.667 |
Subramanian N | 2 | Guarnieri P | 0.583 |
Paper | Total Citations (TC) | TC per Year |
---|---|---|
Srivastava SK, 2008, Omega-International Journal Of Management Sci | 211 | 19.18 |
Chi X, 2011, Waste Management | 169 | 21.12 |
Geyer R, 2010, International Journal of Advanced Manufacturing Technology | 98 | 10.89 |
Rahman S, 2012, International Journal Of Production Economics | 74 | 10.57 |
Abdulrahman MD, 2014, International Journal Of Production Economics | 63 | 12.60 |
Barker TJ, 2011, Omega- International Journal Of Management Sci | 62 | 7.75 |
Govindan K, 2014, European Journal of Operational Research | 56 | 11.20 |
Bernon M, 2011, International Journal Physical Distribution Logistics Management | 55 | 6.88 |
Zoeteman BCJ, 2010, International Journal Of Advanced Manufacturing Technology | 54 | 6.00 |
Ozceylan E, 2014, Transportation Res Pt E-Logistics Transportation Review | 42 | 8.40 |
Sources | No. of Articles |
---|---|
Journal of Cleaner Production | 7 |
Resources Conservation and Recycling | 5 |
Waste Management | 5 |
International Journal of Production Economics | 4 |
International Journal of Advanced Manufacturing Technology | 3 |
Omega-International Journal of Management Science | 3 |
European Journal of Operational Research | 2 |
International Journal of Logistics Management | 2 |
International Journal of Physical Distribution & Logistics Management | 2 |
Applied Soft Computing | 1 |
Author Keywords (DE) | No. of Articles | Keywords-Plus (ID) | No. of Articles |
---|---|---|---|
Reverse Logistics | 17 | Management | 16 |
Recycling | 6 | Returns | 9 |
WEEE | 6 | Model | 8 |
e Waste | 5 | Performance | 7 |
Supply Chain Management | 5 | Product Recovery | 7 |
Electronic Waste | 3 | Recovery | 7 |
Sustainability | 3 | Electronic Waste | 6 |
AHP | 2 | Reverse Logistics | 6 |
Barriers | 2 | Uncertainty | 6 |
Electronics Industry | 2 | Design | 5 |
Cited References | Citations |
---|---|
Fleischmann M, 2001, Prod Oper Manag, V10, P156 | 8 |
Jayaraman V, 1999, J Oper Res Soc, V50, P497, Doi 10.1057/Palgrave.Jors.2600716. | 8 |
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Cited First Author | Citations |
---|---|
Guide Vdr | 33 |
Fleischmann M | 28 |
Jayaraman V | 14 |
Ravi V | 14 |
Anonymous | 11 |
Ilgin Ma | 10 |
Pishvaee Ms | 10 |
Carter Cr | 9 |
Guarnieri P | 9 |
Ongondo Fo | 9 |
COUNTRIES | TC | TC per Year |
---|---|---|
India | 302 | 50.3 |
USA | 253 | 36.1 |
Australia | 243 | 121.5 |
United Kingdom | 125 | 25 |
The Netherlands | 120 | 40 |
China | 95 | 47.5 |
Denmark | 83 | 41.5 |
Turkey | 58 | 19.3 |
Brazil | 41 | 8.2 |
Spain | 17 | 17 |
AUTHORS | CRITERIA/VARIABLE CONSIDERED | METHODS/MODELS | TECHNICAL PROCEDURE |
---|---|---|---|
[40] | No one identified | No one identified | Survey |
[41] | LCA criteria: Door-to-door collection by truck, Door-to-door collection by pickers, Delivery at electrical electronic equipment (EEE) shops collection Delivery at metro stations, Delivery at delivery points, Dismantled by recycling company, Refurbishment by social program, Informal dismantling Informal recycling, Adequately recycled regionally, Social; Enterprises/cooperatives, PWB recycling exported overseas, Landfill; Economic criteria: System feasibility System efficiency, Population awareness and adhesion to reverse logistics, Innovation and generation of new economic activities, Competitiveness of formal products in regard to informal ones; Social criteria: Social inclusion, Formal employment Generation of income, Opportunity for professional development, Health risks and working, Conditions, Access to healthcare, Access to education. | PCP | Modeling |
[42] | No one identified | No one identified | Survey |
[3] | No one identified | No one identified | Interview |
[43] | Legislation; customer demand; strategic cost/benefit; environmental concern; volume and quality; incentive, resource, integration and coordination | Decision making trial and evaluation laboratory (DEMATEL) | Modeling |
[44] | Sources, recyclers, smelters, illegal dumping, offshoring, landfills, demand markets | Multitiered e-cycling network model | Modeling |
[45] | CRT recycling: plastics recycling, glass-to-lead recycling; Plastics recycling: glass-to-glass recycling, chemical recycling, thermal recycling. | MAGIQ technique | Modeling |
[4] | Strategic: General enforcement of the law, qualification of the companies, partnerships, Incentives; Environmental: Promotion of environmental education, creation of natural resources savings policies, creation of policies for the correct disposal of e-waste by companies; Economic: Creation of policies for materials reutilization; savings through the use of recycled materials; revenues from the sales of e-waste; creation of advertising campaigns, installation of collection points for e-waste, creation of tax incentives for companies operating RL of e-waste; Social: Creation of policies for public awareness; creation of employment policies, creation of digital inclusion policies and, creation of qualification policies. | Strategic Options Development Analysis (SODA) | Structuring problem |
[46] | Electrical fault, Mechanical fault, Electrical and mechanical faults | No one identified | Interviews |
[47] | Reuse, recycling, return incentive, collection and shipping, inspection, sorting, average cost, average revenue | No one identified | Documental analysis |
[48] | Clear return policies, recognition of reverse logistics as a factor in creating competitive advantage, performance management system, information technology support, forecasting and planning. | No one identified | Survey and interviews |
[49] | Economic viability, environmental soundness, transport issues, | No one identified | Documental analysis |
[50] | Economic: Operating cost (transportation and labor); Fixed cost: (capital investment, Operating time); Operational: Handling system capacity, Human resources (number of staff involved); Strategic: Accessibility (proximity to the customer/distance), Flexibility and responsiveness (product flexibility, type of products accepted) System response (time); Social: Stakeholders’ participation/willingness to cooperate. | Fuzzy Analytic Hierarchy Process (AHP) | Modeling |
[51] | No one identified | No one identified | Documental analysis |
[52] | Knowledge requirements: Accessibility of information, What and how to sort/segregate, Where to bring materials, How to get to drop-off site, When (days and hours) to go, Acceptance requirements, Drop-off procedures, Drop-off fees; Proximity to a collection site: Distance walking, Distance vehicular, Degree of traffic congestion/control, Access to public transportation, Timing of public transportation; Opportunity to drop-off materials: Days open, Hours open, Authorization to drop-off; Draw of the collection site: Retail centers, Non-retail services, EOL fees, Cleanliness, security; Ease of the process: Sorting/segregation requirements, Storage requirements, Processing/cleaning, Storage needs, Collection container type, Physical effort, Physical access, Staff interactions and assistance, Access to vehicle, Drop-off procedure. | No one identified | Documental analysis |
[53] | No one identified | No one identified | Survey |
[54] | Channel conflict, investment, Closed-loop chains unlikely, control over designs/materials, Decouples design, contracting and oversight, Processes fast and flexible, Allows for recovery of valuable modules, Higher recovery rates, Long-term contracts, Higher variable costs, Closed-loop supply chains, levels of material recovery and learning, access to scarce materials | No one identified | Documental analysis |
[55] | Total cost, number of disposed items, material sales revenue, and customers’ satisfaction level | ARTODTO model, linear physical programming model | Modeling |
[56] | The types, numbers and locations of storage sites, The types, numbers and locations of recycling facilities, The quantity of product categories to be allocated to storage sites, The quantity of product categories to be allocated to recycling facilities, The network flow of product categories through storage sites, recycling facilities and secondary market, The total cost of the reverse logistics system. | Mixed integer linear programming (MILP) | Modeling |
[57] | Specification of a new product, sales quantity, marketing implementation feasibility, production capacity, technical information, information related to quality control, | No one identified | Case study |
[58] | Firm performance, resources capacity, service delivery, reverse logistics operation, communication and information technology systems, geographical location, reputation and experience | AHP-VIKOR approach | Modeling and case study |
[59] | Convenience, Store type, Store Status, Location, Hours | Regression analysis | Documental analysis |
[60] | Profit, quantity, quality and timing of returns on decisions, customer convenience distance constraints and the per unit transportation costs | Mixed Integer Linear Programming (MILP) | Modeling |
[61] | Cost savings (Recycled product; Testing; Scrap shipped; Original facility) and business relations (Proprietary knowledge and control; Customer interactions and direct relationships) | AHP | Modeling |
[62] | Time delays on asset recovery opportunities, cost of transportation from a collection point to a central processing location, capacitated returns processing center, information sharing between the collection point and the central processing facility, | No one identified | Modeling |
[63] | Costs, profits, and requirements for developing the reverse supply chains infrastructure for collecting and recycling end-of-life electronic | MILP | Modeling |
[64] | Sales quantity of new devices realized by the retailer during one period; recovery rate; selling price of refurbished devices; refurbishing/remanufacturing cost; inspection/sorting cost; disposal cost for devices not selected for refurbishment; recycling fee to distributor | Analytical model | Modeling |
[65] | Mass balance, GER value per type of material, Energy use on the process level, Energy use on the facility level, Product volume and capacity use, Capacity of equipment, Ton-kilometers and fuel consumption trucks WMS of LSP/truck owner, Degree of substitution per recovery option, Conversion factors CO2 | MILP | Modeling |
[66] | Capacity Criteria (CC); Financial Ability (FA) IT System (IT) Service Quality (SQ); RL Activities (RA) Geographical Location (GL), Partner Image & Experience (PE) | Fuzzy AHP TOPSIS | Modeling |
[67] | Management, financial, policy and infrastructure | Factor Analysis | Documental analysis |
[68] | Profit | Game Theory | Modeling |
[46] | Quality; cost | No one identified | Survey and interviews |
[69] | Internal barriers and external barriers | No one identified | Interviews |
[70] | Investment; installing capacity; collection network; quantity of discarded products. | MILP | Modeling |
[71] | Components of the TPB, socio-demographic and socio-economic variables, the degree of awareness towards the problem, and the personal assessment of the environmental situation of Brazil as predictors of e-waste recycling | Theory of Planned Behavior (TPB); (ANOVA), logit regression | Survey |
[72] | Barriers for GSCM Implementation; Drivers for GSCM Implementation; Environmental Performance; Internal Environmental Management; Green Purchasing; Life Cycle Assessment; Eco-Design; Waste Management; Reverse Logistics; Green Manufacturing and Remanufacturing | No one identified | Interview |
[73] | Costs of transportation, purchasing, refurbishing, and operating the disassembly workstations | Nonlinear mixed integer programming | Modeling |
[74] | Total cost of the network | Nonlinear mixed integer programming | Modeling |
[75] | Operational performance, organizational integration and management reporting and control | No one identified | Documental analysis |
[76] | Level of expertise; service cost, service capacity; facility capacity; people employed; information system | No one identified | Documental analysis |
[77] | Credibility; Confirmability; Control; Transferability | No one identified | Documental analysis |
[78] | Primary focus; Recovery process focus; Lead time focus | No one identified | Interviews |
[79] | Total profit; Total revenue; Total cost; Total emission cost; Total collection cost; Total transportation Cost; Total fixed facility cost; Total disposal cost; Total processing cost | MILP | Modeling |
Group | Criteria |
---|---|
Economic | Costs of transportation, Service cost, Cost of refurbishing and operating the disassembly workstations; System feasibility; System efficiency, Innovation and generation of new economic activities, Competitiveness of formal products in regard to informal ones; Sales quantity; Costs with maintenance of equipment; Costs with workforce; Costs with infrastructure; Strategic cost/benefit; Total cost of the network; Total profit; Total revenue; Total cost; Total emission cost; Total collection cost; Total fixed facility cost; Total disposal cost; Total processing cost; Total Investment; Sales quantity of new devices realized by the retailer during one period; Recovery rate; Selling price of refurbished devices; Refurbishing/remanufacturing cost; Inspection/sorting cost; Disposal cost for devices not selected for refurbishment; Recycling fee to distributor; Creation of policies for materials reutilization; Savings through the use of recycled materials; Revenues from the sales of e-waste; Costs with advertising campaigns; Costs of installation of collection points for e-waste; Incentives for customers return e-waste; |
Technical and Managerial | Specification of a new product; Marketing implementation feasibility, Production capacity; Technical information; Quality control; Level of expertise; Facility capacity; Information system; Credibility; Confirmability; Control; Transferability; Sorting/segregation requirements; Storage requirements; Processing/cleaning; Storage needs; Collection container type; Physical effort, Physical access; Staff interactions and assistance; Access to vehicle; Drop-off procedure; Integration and coordination; Volume and quality of waste; Installing capacity; Collection network; Quantity of discarded products; Handling system capacity, Human resources (number of staff involved); |
Environmental | Compliance with environmental legislation; Environmental concern from consumers; Population awareness and adhesion to reverse logistics; Promotion of environmental education; Natural resources savings; Policies for the correct disposal of e-waste by customers; Mass balance; Energy use on the process level; Energy use on the facility level; Product volume and capacity use; Ton-kilometers and fuel consumption trucks; Degree of substitution per recovery options; Conversion factors CO2; Carbon footprint; Water footprint; |
Social | Social inclusion, Formal employment/Jobs; Generation of income; Opportunity for professional development; Health & Safety; Labor Conditions; Access to healthcare; Access to education; Digital Inclusion; Gender Issues; Inclusion of vulnerable workers; Diversity issues; Socio-demographic variables; Socio-economic variables; Stakeholders’ participation/willingness to cooperate; Engagement in community; Qualification/training programs. |
Economic | Technical & Management | Environmental | Social | ||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Alternative | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | C16 | C17 | C18 | C19 | C20 | C21 | C22 | C23 | C24 | |
A1 | Door-to-door collection by truck | 2 | 4 | 3 | 2 | 4 | 5 | 4 | 4 | 5 | 4 | 4 | 3 | 3 | 3 | 3 | 4 | 5 | 5 | 4 | 3 | 3 | 2 | 2 | 3 |
A2 | Door-to-door collection by waste pickers | 4 | 4 | 3 | 4 | 4 | 3 | 3 | 3 | 3 | 4 | 3 | 3 | 4 | 4 | 4 | 5 | 4 | 3 | 5 | 5 | 4 | 4 | 4 | 3 |
A3 | Delivery at EEE shops collection | 3 | 3 | 4 | 3 | 4 | 3 | 4 | 2 | 3 | 2 | 2 | 3 | 4 | 3 | 3 | 4 | 3 | 4 | 3 | 2 | 3 | 3 | 3 | 3 |
A4 | Delivery at metro stations | 4 | 3 | 3 | 3 | 3 | 4 | 3 | 4 | 3 | 4 | 3 | 4 | 3 | 4 | 4 | 4 | 4 | 4 | 4 | 3 | 4 | 3 | 4 | 4 |
A5 | Delivery at delivery points | 4 | 3 | 4 | 4 | 3 | 3 | 3 | 3 | 4 | 3 | 3 | 4 | 4 | 4 | 5 | 4 | 3 | 5 | 5 | 4 | 4 | 4 | 3 | 4 |
A6 | Refurbishment by social program | 3 | 3 | 4 | 3 | 4 | 3 | 4 | 2 | 3 | 2 | 2 | 3 | 4 | 3 | 3 | 4 | 3 | 4 | 3 | 2 | 3 | 3 | 3 | 3 |
A7 | Informal dismantling | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 2 | 3 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 3 | 3 | 3 | 2 | 3 | 2 | 2 |
A8 | Informal recycling | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 2 | 3 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 3 | 3 | 3 | 2 | 3 | 2 | 2 |
A9 | Adequately recycled regionally | 4 | 4 | 3 | 4 | 4 | 3 | 3 | 3 | 3 | 4 | 3 | 3 | 4 | 4 | 4 | 5 | 4 | 3 | 5 | 5 | 4 | 4 | 4 | 3 |
A10 | Dismantled by recycling company; | 4 | 4 | 3 | 4 | 4 | 3 | 3 | 3 | 3 | 4 | 3 | 3 | 4 | 4 | 4 | 5 | 4 | 3 | 5 | 5 | 4 | 4 | 4 | 3 |
Optimistic | Pessimistic |
It considers satisfactory to meet at least one criterion. | It seeks optimization according to all criteria. |
Progressive | Conservative |
Consider the probabilities of maximizing preferences. | Consider the concern only in avoiding negative extremes; chances of not minimizing preferences. |
Ranking | |||||
---|---|---|---|---|---|
Beta-PERT | Triangular Distribution | ||||
1° | A10 | 9,23081e−92 | 1° | A10 | 6,3226e−28 |
2° | A9 | 2,28793e−60 | 2° | A9 | 1,7923e−25 |
3° | A5 | 5,669e−113 | 3° | A5 | 2,8322e−32 |
4° | A2 | 1,92132e−68 | 4° | A2 | 1,9254e−27 |
5° | A4 | 3,45752e−55 | 5° | A4 | 1,106e−24 |
6° | A1 | 6,6985e−106 | 6° | A1 | 4,3885e−31 |
7° | A6 | 3,1517e−194 | 7° | A6 | 3,6666e−39 |
8° | A8 | 4,3651e−217 | 8° | A8 | 2,7404e−40 |
9° | A7 | 3,55103e−51 | 9° | A7 | 9,2334e−24 |
10° | A8 | 6,27103e−37 | 10° | A8 | 9,3223e−21 |
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Guarnieri, P.; Camara e Silva, L.; Vieira, B.d.O. How to Assess Reverse Logistics of e-Waste Considering a Multicriteria Perspective? A Model Proposition. Logistics 2020, 4, 25. https://doi.org/10.3390/logistics4040025
Guarnieri P, Camara e Silva L, Vieira BdO. How to Assess Reverse Logistics of e-Waste Considering a Multicriteria Perspective? A Model Proposition. Logistics. 2020; 4(4):25. https://doi.org/10.3390/logistics4040025
Chicago/Turabian StyleGuarnieri, Patricia, Lucio Camara e Silva, and Bárbara de Oliveira Vieira. 2020. "How to Assess Reverse Logistics of e-Waste Considering a Multicriteria Perspective? A Model Proposition" Logistics 4, no. 4: 25. https://doi.org/10.3390/logistics4040025