Fuzzy FMEA enhances risk assessment for plastic waste reverse logistics

Why It Matters

Effective risk management is crucial for the success of circular economy initiatives, particularly in complex supply chains like plastic recycling. This approach allows designers and operations managers to proactively address potential failures, ensuring greater efficiency and environmental impact.

Key Finding

A new method using fuzzy logic and multiple factors (like cost and business impact) helps better identify and rank the risks involved in collecting and processing plastic waste for recycling.

Key Findings

• The proposed hybrid framework effectively identifies and prioritizes risks in plastic waste reverse logistics.
• Incorporating criteria beyond traditional FMEA (severity, occurrence, detection) such as cost of failure, complexity of resolution, and business impact significantly enhances risk assessment.
• The use of trapezoidal fuzzy sets accounts for the inherent uncertainty in expert judgments and decision-making.

Research Evidence

Aim: How can a hybrid risk assessment framework, incorporating fuzzy logic and expanded criteria, improve the identification and prioritization of risks in plastic waste reverse logistics?

Method: Hybrid Decision Model

Procedure: A novel framework was developed by combining Failure Mode and Effects Analysis (FMEA) with Analytic Hierarchy Process (AHP), LOgarithmic Percentage Change-driven Objective Weighting (LOPCOW), and Additive Ratio Assessment (ARAS) under trapezoidal fuzzy sets. This framework was applied to a case study of a waste plastic recycling manufacturer.

Context: Waste plastic recycling industry, reverse logistics

Design Principle

Proactive risk mitigation through multi-criteria, fuzzy-logic-enhanced analysis is essential for sustainable reverse logistics.

How to Apply

When planning a reverse logistics system for recycled materials, use FMEA but expand the criteria to include cost, complexity, and business impact, and use fuzzy logic to handle subjective assessments.

Limitations

The framework's effectiveness is dependent on the quality of expert input and the specific context of the case study.

Student Guide (IB Design Technology)

Simple Explanation: This study shows a smarter way to figure out what could go wrong when trying to get waste plastic back for recycling. It uses a special math technique (fuzzy logic) and looks at more than just how likely a problem is or how bad it is; it also considers how much it costs to fix and how it affects the business.

Why This Matters: Understanding and managing risks is key to creating successful and sustainable products and systems. This research provides a robust method for tackling the complexities of waste management and recycling.

Critical Thinking: How might the subjective nature of fuzzy sets introduce bias into the risk assessment, and what steps could be taken to mitigate this?

IA-Ready Paragraph: The study by Sumrit and Keeratibhubordee (2024) offers a valuable framework for risk assessment in reverse logistics, particularly for waste management. Their hybrid approach, integrating FMEA with fuzzy logic and additional criteria like cost of failure and business impact, provides a more comprehensive analysis than traditional methods. This can be applied to identify and prioritize potential risks in the development of sustainable systems, ensuring greater resilience and effectiveness.

Project Tips

• When assessing risks for your design project, think beyond the obvious failure modes.
• Consider using qualitative data and fuzzy logic if your risk assessment involves subjective expert opinions.

How to Use in IA

• This research can inform the risk assessment section of your design project, demonstrating a sophisticated approach to identifying potential issues in your chosen system.

Examiner Tips

• Demonstrate an understanding of how uncertainty and subjective judgment can be managed in risk assessment.

Independent Variable: Risk criteria (severity, occurrence, detection, cost of failure, complexity of failure resolution, impact on business), fuzzy set parameters, weighting methods (AHP, LOPCOW).

Dependent Variable: Prioritized list of failure modes, risk assessment scores.

Controlled Variables: Case study context (waste plastic recycling manufacturer in Thailand), number of failure modes identified, specific fuzzy set type (trapezoidal).

Strengths

• Novel integration of multiple decision-making techniques.
• Inclusion of practical, business-relevant risk criteria.
• Application of fuzzy logic to handle uncertainty.

Critical Questions

• To what extent can the results of this framework be generalized to other types of waste or reverse logistics operations?
• How sensitive is the final risk prioritization to changes in the weighting of different criteria?

Extended Essay Application

• An Extended Essay could explore the application of this framework to a specific environmental challenge, such as e-waste or textile recycling, comparing the identified risks and mitigation strategies.

Source

Risk Assessment Framework for Reverse Logistics in Waste Plastic Recycle Industry: A Hybrid Approach Incorporating FMEA Decision Model with AHP-LOPCOW- ARAS Under Trapezoidal Fuzzy Set · Decision Making Applications in Management and Engineering · 2024 · 10.31181/dmame812025984