Document Type : Research Article
Authors
Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
10.30473/coam.2026.76472.1359
Abstract
This study develops a mathematically informed optimization framework for decision-making in reverse supply chain management, with an application to Apple’s MacBook product line. The proposed framework integrates Failure Mode and Effects Analysis (FMEA) with deep learning, based sentiment analysis in a multi-stage structure designed to quantify risk factors and predict consumer-driven outcomes. The dataset consists of 91 days of Twitter user feedback on Apple notebooks, processed using supervised learning algorithms to extract sentiment scores and thematic indicators of product performance. The analysis identifies “power and battery” and “storage” as the most critical components contributing to user dissatisfaction and elevated risk severity. These data-driven insights are incorporated into an optimization model that supports decisions on product recycling, refurbishment, and reuse. The hybrid framework enhances decision stability and accuracy compared with conventional reverse logistics models, while improving operational efficiency and environmental performance. The results demonstrate the model’s suitability as a scalable, machine-learning-supported optimization tool for reverse supply chain systems.
Highlights
- Proposed a hybrid, mathematically grounded decision-support framework that integrates Failure Mode and Effects Analysis (FMEA) with deep learning–based sentiment analysis to optimize reverse supply chain strategies for Apple MacBook products.
- Conducted large-scale supervised sentiment analysis on 91 days of Twitter data (over 43 million tweets), achieving an accuracy of 89.9% in classifying consumer satisfaction (Happy/Unhappy) across 13 critical product attributes.
- Identified Power & Battery and Storage as the most critical failure components through FMEA, with the highest Risk Priority Numbers (RPN = 48 and 36), collectively contributing approximately one-third of total reverse supply chain risk and informing recovery prioritization.
- Introduced an Optimization Rate metric to quantitatively assess shifts from default reuse policies toward data-driven recovery decisions, demonstrating that newer MacBook models (e.g., 2024 M3 Pro/Max) support reuse rates exceeding 90%, compared to below 50% for legacy 2015 models.
- Demonstrated superior decision accuracy, robustness, and stability relative to conventional reverse logistics models, establishing a scalable, consumer-centric optimization tool that enhances sustainability, reduces material waste, and improves economic performance.
Keywords
Main Subjects
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