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Abstract This article investigates the design decision facing original equipment manufacturers (OEMs) in determining the optimal degree of repairability by considering market dynamics. The article develops a game-theoretic model to optimize the degree of product repairability for smartphones in a market in which an OEM and a coalition of independent service providers compete in offering repair services. A survey is conducted to estimate the consumer-related parameters of the game theory model by considering factors such as repair cost, prior repair experience of customers, and the quality of repair services offered by the OEM and independent repair service providers. The findings reveal that regardless of the repairability level, the OEM's repair profits are maximized when a significant disparity in the quality of repair services between the OEM and their competitors exists. On the other hand, independent repair service providers' profits are maximized when there is a low disparity in the quality of repair services. Also, the results show why the adoption of a fully repairable device is not the optimal strategy adopted by OEMs. Instead, a sufficiently large degree of repairability can be the strategic choice, as it maximizes the total OEM's profits derived from both the sale of future products and the provision of repair services for previously sold devices. At the same time, this strategy can encourage repair practices among consumers toward a more sustainable society. 

This paper aims to introduce an Artificial Intelligence (AI) guided computational framework for the automatic identification, inspection, assessment, and remanufacturing of end-of-use products. The proposed framework consists of three main steps: (1) developing computer vision and image processing algorithms for analyzing product teardown images, (2) quantifying the economic and environmental value of remanufacturing from product images, and (3) developing recommender algorithms to identify the best recovery decision for each device. The paper discusses the importance of advancing object detection, image segmentation, and machine learning algorithms to automatically compute the value embedded in discarded items and developing recommendation systems to determine remanufacturing operations from product configurations. The main focus of the paper is on the value assessment and remanufacturing of electronic waste (e-waste). The paper emphasizes the need for developing object detection for identifying small objects (e.g., screws, bolts, snaps) and overlapped components (e.g., cables, printed circuit boards) standard in the design of consumer electronics by incorporating product shapes and features. The proposed value assessment framework has applications beyond remanufacturing and can be used in take-back programs and other business models that benefit from product serialization and assessment of individual devices. 

Accurate prediction of repair durations is a challenge in product maintenance due to its implications for resource allocation, customer satisfaction, and operational performance. This study aims to develop a deep learning framework to help fleet repair shops accurately categorize repair time given product historical data. The study uses an automobile repair and maintenance dataset and creates an end-to-end predictive framework by employing a multi-head attention network designed for tabular data. The developed framework combines categorical information, transformed through embeddings and attention mechanisms, with numerical historical data to facilitate integration and learning from diverse data features. A weighted loss function is introduced to overcome class imbalance issues in large datasets. Moreover, an online learning strategy is used for continuous incremental model updates to maintain predictive accuracy in evolving operational environments. Our empirical findings demonstrate that the multi-head attention mechanism extracts meaningful interactions between vehicle identifiers and repair types compared to a feed-forward neural network. Also, combining historical maintenance data with an online learning strategy facilitates real-time adjustments to changing patterns and increases the model’s predictive performance on new data. The model is tested on real-world repair data spanning 2013 to 2020 and achieves an accuracy of 78%, with attention weight analyses illustrating feature interactions. 

The increasing volume of electronic waste (e-waste) creates significant environmental and economic challenges which demands practical management strategies. Life Cycle Assessment (LCA) has been known as a principal tool for evaluating the environmental impact of e-waste recycling and disposal methods. However, its application is hampered by inconsistencies in methodology, data limitations, and variations in system boundaries. This study provides a review of current LCA tools used in e-waste analysis and identifies gaps and opportunities for improvement. It categorizes studies into three groups: studies that applied LCA to product and process optimization, impact evaluation, and policy development. Findings reveal that LCA has been helpful in assessing the sustainability of different recycling strategies. However, significant variations exist in methodological approaches and data accuracy. Challenges such as the lack of standardized LCA protocols, the limited availability of regionspecific impact data, and inconsistencies in assessment methodologies are still barriers to its widespread adoption. Finally, the study discusses emerging trends in LCA aimed at addressing current gaps, including the incorporation of machine learning and artificial intelligence for predictive modeling, dynamic impact assessment frameworks, and the role of real-time data collection via IoT-based sensors.