Short answer
When selecting third-party reverse logistics providers, implement a decision-making framework that quantifies and weighs sustainability criteria (economic, social, environmental) using methods that can handle data uncertainty, such as fuzzy logic.
- Field
- Resource Management
- Source
- Journal of Enterprise Information Management (2021)
- Method
- Hybrid Decision-Making Approach (Entropy Method + Projection Model)
- Evidence
- Strong effect
A hybrid decision-making approach incorporating fuzzy logic and projection modeling can effectively evaluate and rank third-party reverse logistics providers based on sustainability criteria, even with uncertain data. This resource management research insight is drawn from a 2021 study published in Journal of Enterprise Information Management. Using Hybrid decision-making approach (entropy method + projection model), researchers explored how this design variable affects real-world outcomes. The key design takeaway: When selecting third-party reverse logistics providers, implement a decision-making framework that quantifies and weighs sustainability criteria (economic, social, environmental) using methods that can handle data uncertainty, such as fuzzy logic.
Prioritizing sustainable third-party reverse logistics providers using a novel fuzzy-projection model
A hybrid decision-making approach incorporating fuzzy logic and projection modeling can effectively evaluate and rank third-party reverse logistics providers based on sustainability criteria, even with uncertain data.
Journal of Enterprise Information Management · 2021
Key Findings
- 01A hybrid decision-making approach using interval-valued intuitionistic fuzzy sets, entropy, and projection models can effectively handle uncertainty in qualitative data for provider selection.
- 02The proposed methodology allows for the assessment of criteria and the rating of alternatives simultaneously, providing a comprehensive evaluation framework.
- 03A case study demonstrated the efficiency and viability of the introduced hybrid method for selecting 3PRLPs in the manufacturing sector.
Application
Design takeaway
When selecting third-party reverse logistics providers, implement a decision-making framework that quantifies and weighs sustainability criteria (economic, social, environmental) using methods that can handle data uncertainty, such as fuzzy logic.
How to apply
When designing or redesigning reverse logistics systems, use this fuzzy-projection model to objectively compare and select third-party providers, ensuring alignment with circular economy principles and sustainability targets.
Project actions
- 01When researching potential suppliers or partners, consider using fuzzy logic to handle subjective or uncertain evaluation criteria.
- 02Structure your evaluation criteria around the three pillars of sustainability: economic, social, and environmental.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Addresses the complex issue of provider selection under uncertainty.
- +Integrates multiple sustainability dimensions (economic, social, environmental).
Limitations
Gathering reliable expert opinions for fuzzy inputs can be challenging. The model's complexity might require specialized software or advanced mathematical understanding.
Reliability & validity
The study's validity is supported by the use of expert interviews and literature review for criteria selection, and the application of sensitivity analysis and comparison processes. Reliability is enhanced through the systematic application of the proposed hybrid decision-making model.
Think critically
How might the 'uncertainty' in the fuzzy logic approach be further quantified or validated in a practical design project?
Design Principles
"Employ multi-criteria decision-making (MCDM) techniques that account for uncertainty when evaluating complex operational choices, particularly in sustainability-focused initiatives."
In the context of the circular economy, selecting the right partners for reverse logistics is crucial for resource recovery and waste reduction. This research provides a robust method for assessing providers, ensuring that economic, social, and environmental factors are holistically considered, leading to more sustainable supply chain operations.
What This Means for Your Design
This study shows a smart way to pick companies that help manage returned products, making sure they are good for the environment, society, and the economy, even when you don't have perfect information.
How to use in your project
- 1.Reference this study when discussing the methodology for evaluating and selecting components or systems, particularly if your design project involves sustainability or supply chain considerations.
- 2.Use the identified criteria (economic, social, environmental) as a framework for your own evaluation process.
Add to My Project
Quick Cite
Paragraph starter
This research by Chen et al. (2021) provides a robust methodology for selecting third-party reverse logistics providers based on sustainability criteria, utilizing a hybrid approach of fuzzy logic and projection modeling to manage data uncertainty. The study identified key economic, social, and environmental factors crucial for circular economy initiatives, offering a valuable framework for evaluating partners in sustainable supply chains.
Source
Journal of Enterprise Information Management
Sustainable third-party reverse logistics provider selection to promote circular economy using new uncertain interval-valued intuitionistic fuzzy-projection model
journal · 2021
View sourceQuestions About This Research
- What does the research say about prioritizing sustainable third-party reverse logistics providers using a novel fuzzy-projection model?
- When selecting third-party reverse logistics providers, implement a decision-making framework that quantifies and weighs sustainability criteria (economic, social, environmental) using methods that can handle data uncertainty, such as fuzzy logic. Evidence: Journal of Enterprise Information Management (2021).
- Why does "Prioritizing sustainable third-party reverse logistics providers using a novel fuzzy-projection model" matter for design?
- In the context of the circular economy, selecting the right partners for reverse logistics is crucial for resource recovery and waste reduction. This research provides a robust method for assessing providers, ensuring that economic, social, and environmental factors are holistically considered, leading to more sustainable supply chain operations.
- How can designers apply this research?
- When selecting third-party reverse logistics providers, implement a decision-making framework that quantifies and weighs sustainability criteria (economic, social, environmental) using methods that can handle data uncertainty, such as fuzzy logic.
- What were the main findings?
- A hybrid decision-making approach using interval-valued intuitionistic fuzzy sets, entropy, and projection models can effectively handle uncertainty in qualitative data for provider selection.. The proposed methodology allows for the assessment of criteria and the rating of alternatives simultaneously, providing a comprehensive evaluation framework.. A case study demonstrated the efficiency and viability of the introduced hybrid method for selecting 3PRLPs in the manufacturing sector.
- What research method was used?
- Hybrid Decision-Making Approach (Entropy Method + Projection Model).
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2021 journal from Journal of Enterprise Information Management.
- What should I do differently in my next project?
- When designing or redesigning reverse logistics systems, use this fuzzy-projection model to objectively compare and select third-party providers, ensuring alignment with circular economy principles and sustainability targets.
- What are the limitations?
- The effectiveness of the model relies on the quality and availability of expert knowledge and data for the fuzzy set inputs. The specific criteria identified may need adaptation for different industries or regions.