Prioritizing sustainable third-party reverse logistics providers using a novel fuzzy-projection model

Why It Matters

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.

Key Finding

The study developed and validated a new decision-making tool that uses fuzzy logic and projection modeling to select the best third-party reverse logistics providers based on sustainability goals, even when information is incomplete or uncertain.

Key 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.

Research Evidence

Aim: How can a hybrid decision-making approach using interval-valued intuitionistic fuzzy sets, the entropy method, and a projection model be developed to effectively select and rank third-party reverse logistics providers based on sustainability criteria within manufacturing companies?

Method: Hybrid Decision-Making Approach (Entropy Method + Projection Model)

Procedure: Literature review and expert interviews were conducted to identify 16 key criteria for evaluating third-party reverse logistics providers (3PRLPs), categorized by economic, social, and environmental sustainability. The entropy method was used to determine the weights of these criteria, and a projection model under interval-valued intuitionistic fuzzy sets was applied to rank the 3PRLPs. Sensitivity analysis and comparison were performed to validate the model.

Context: Manufacturing Industry

Design Principle

Employ multi-criteria decision-making (MCDM) techniques that account for uncertainty when evaluating complex operational choices, particularly in sustainability-focused initiatives.

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.

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.

Student Guide (IB Design Technology)

Simple Explanation: 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.

Why This Matters: Understanding how to evaluate complex choices with uncertain information is key for any design project that involves selecting materials, suppliers, or manufacturing processes, especially when sustainability is a goal.

Critical Thinking: How might the 'uncertainty' in the fuzzy logic approach be further quantified or validated in a practical design project?

IA-Ready Paragraph: 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.

Project Tips

• When researching potential suppliers or partners, consider using fuzzy logic to handle subjective or uncertain evaluation criteria.
• Structure your evaluation criteria around the three pillars of sustainability: economic, social, and environmental.

How to Use in IA

• 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.
• Use the identified criteria (economic, social, environmental) as a framework for your own evaluation process.

Examiner Tips

• Demonstrate an understanding of how to apply decision-making models to real-world problems, especially those involving sustainability.
• Be prepared to justify the selection of your evaluation criteria and the methods used to weigh them.

Independent Variable: Sustainability criteria (economic, social, environmental) and their associated weights.

Dependent Variable: Rankings of third-party reverse logistics providers.

Controlled Variables: The set of 16 identified evaluation criteria, the application of the entropy method for weighting, and the projection model for ranking.

Strengths

• Addresses the complex issue of provider selection under uncertainty.
• Integrates multiple sustainability dimensions (economic, social, environmental).

Critical Questions

• To what extent do the identified 16 criteria generalize across different manufacturing sectors?
• How sensitive is the final provider ranking to variations in the expert judgments used to define the fuzzy sets?

Extended Essay Application

• An Extended Essay could explore the application of this fuzzy-projection model to a specific product's end-of-life management, evaluating potential recycling or refurbishment partners.
• Investigate how different cultural contexts might influence the weighting of social and environmental criteria in provider selection.

Source

Sustainable third-party reverse logistics provider selection to promote circular economy using new uncertain interval-valued intuitionistic fuzzy-projection model · Journal of Enterprise Information Management · 2021 · 10.1108/jeim-02-2021-0066