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Products often experience different failure and repair needs during their lifespan. Prediction of the type of failure is crucial to the maintenance team for various reasons, such as realizing the device performance, creating standard strategies for repair, and analyzing the trade-off between cost and profit of repair. This study aims to apply machine learning tools to forecast failure types of medical devices and help the maintenance team properly decides on repair strategies based on a limited dataset. Two types of medical devices are used as the case study. The main challenge resides in using the limited attributes of the dataset to forecast product failure type. First, a multilayer perceptron (MLP) algorithm is used as a regression model to forecast three attributes, including the time of next failure, repair time, and repair time z-scores. Then, eight classification models, including Naïve Bayes with Bernoulli (NB-Bernoulli), Gaussian (NB-Gaussian), Multinomial (NB-Multinomial) model, Support Vector Machine with linear (SVM-Linear), polynomial (SVM-Poly), sigmoid (SVM-Sigmoid), and radical basis (SVM-RBF) function, and K-Nearest Neighbors (KNN) are used to forecast the failure type. Finally, Gaussian Mixture Model (GMM) is used to identify maintenance conditions for each product. The results reveal that the classification models could forecast failure type with similar performance, although the attributes of the dataset were limited. 

Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large dataset of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely random failure and physical damage. A frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the number of failures, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations. 

Abstract Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large data set of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely, random failure and physical damage. Frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the failure count, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations. 

Since its emergence, the cloud manufacturing concept has been transforming the manufacturing and remanufacturing industry into a big data and service-oriented environment. The aggressive push toward data collection in cloud-based and cyber-physical systems provides both challenges and opportunities for predictive analytics. One of the key applications of predictive analytics in such domains is predictive quality management that aims to fully exploit the potentials provided by the enormous data collected via cloud-based systems. As a case study, a data set of hard disk drives’ Self-Monitoring, Analysis and Reporting Technology (SMART) attributes from a cloud-storage service provider has been analyzed to derive some insights about the challenges and opportunities of using product lifecycle data. An analysis of time-to-failure monitoring of hard disk drives in real-time has been carried out and the corresponding challenges have been discussed. 

The remaining value within end‐of‐use/life hard disk drives (EoU/L HDDs) is often not optimally recovered. The improper collection and recovery of HDDs contribute not only to rising environmental and social concerns worldwide, but also to the transformation of the economy and a significant loss of value. Currently, the most preferred treatment option for used hard drives is to recover the metals with the highest recycling effectiveness, such as steel and aluminum, via a shredding‐based recycling process that results in both value and material leakages. The complexity of retrieving the remaining values within EoU/L HDDs demands a larger view of the global supply of HDDs available for recovery. The aim of this paper is to first identify the geographical patterns of transboundary global shipments of new and used HDDs between developing and developed regions, and then capture and quantify the value leakage by bringing several unique perspectives. Two analyses have been conducted. First, the loss of value due to the insufficient recovery of neodymium (Nd) at the global level is quantified. Second, the value leakage as a result of the delay on on‐time reuse of HDDs is captured. Furthermore, the central challenges toward proper recovery of HDDs, where consumer electronic industry can make significant contributions, have been identified. HDDs are well positioned to contribute important insights to the recovery of other electronic devices, so the findings from HDDs can be adopted for other types of electronics.