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Obsolescence is a challenge for Command, Control, Communications, Computers, Cyber, Intelligence, Surveillance and Reconnaissance (C5ISR) systems within the Army. These challenges include lower availability of components in the supply chain, schedule delays, and higher costs. Proactive obsolescence management in lieu of reactive obsolescence management is essential to assist with understanding and mitigating obsolescence challenges. This article presents a visualization tool for evaluating the risk of obsolescence to a system’s components. The tool was developed and deployed using the R statistical software program. The visualization tool focuses on the areas of component design life, procurement lead time, cost growth, and the number of manufacturers producing the component. These obsolescence properties are combined in the visualization tool to measure the level of obsolescence risk exposure from the supply availability of the components. The visualization tool was first demonstrated using simulations of 10 systems based on a mix of risk levels with respect to the obsolescence properties. The visualization tool allows a user to input component-level data for their system and compare it with two reference systems: the best case when all variables are low risk (i.e., proactive) and the worst case when all variables are high risk (i.e., reactive). The reference systems are included to help practitioners quickly understand their systems level of obsolescence risk from the supply availability of the components. The visualization tool was then validated with publicly available data for a drone and a user study that demonstrated improved accuracy, duration, and confidence in assessing obsolescence risk. [ABSTRACT FROM AUTHOR]

Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single model for a dataset. By contrast, the proposed algorithm first determines a mathematical cover of the dataset via unsupervised clustering and subsequently constructs multiple models, each of which is trained with the data in one cover. For each data point in the test dataset, an optimal model is selected for regression. Results of empirical experiments show that the proposed method improves the obsolescence prediction accuracy and accelerates the training procedure. A novelty of this study is that it demonstrates the effectiveness of unsupervised clustering methods for improving supervised regression algorithms. [ABSTRACT FROM AUTHOR]

Product obsolescence occurs in every production line in the industry as better-performance or cost-effective products become available. A proactive strategy for obsolescence allows firms to prepare for such events and reduces the manufacturing loss, which eventually leads to positive customer satisfaction. We propose a machine learning-based algorithm to forecast the obsolescence date of electronic diodes, which has a limitation on the amount of data available. The proposed algorithm overcomes these limitations in two ways. First, an unsupervised clustering algorithm is applied to group the data based on their similarity and build independent machine-learning models specialized for each group. Second, a hybrid method including several reliable techniques is constructed to improve the prediction accuracy and overcome the limitation of the lack of data. It is empirically confirmed that the prediction accuracy of the obsolescence date for the electrical component data is improved through the proposed clustering-based hybrid method. [ABSTRACT FROM AUTHOR]

Rapid changes in technology have led to an increasingly fast pace of product introductions. For long-life systems (e.g., planes, ships, and nuclear power plants), rapid changes help sustain useful life, but at the same time, present significant challenges associated with obsolescence management. Over the years, many approaches for forecasting obsolescence risk and product life cycle have been developed. However, gathering inputs required for forecasting is often subjective and laborious, causing inconsistencies in predictions. To address these issues, the objective of this research is to develop a machine learning-based methodology capable of forecasting obsolescence risk and product life cycle accurately while minimizing maintenance and upkeep of the forecasting system. Specifically, this new methodology enables prediction of both the obsolescence risk level and the date when a part becomes obsolete. A case study of the cell phone market is presented to demonstrate the effectiveness and efficiency of the new approach. Results have shown that machine learning algorithms (i.e., random forest, artificial neural networks, and support vector machines) can classify parts as active or obsolete with over 98% accuracy and predict obsolescence dates within a few months. [ABSTRACT FROM PUBLISHER]