Forecasting obsolescence risk using machine learning

With rapid innovation in the electronics industry, product obsolescence forecasting has become increasingly important. More accurate obsolescence forecasting would have cost reduction effects in product design and part procurement over a product's lifetime. Currently many obsolescence forecasting methods require manual input or perform market analysis on a part by part basis; practices that are not feasible for large bill of materials. In response, this paper introduces an obsolescence forecasting framework that is capable of being scaled to meet industry needs while remaining highly accurate. The framework utilizes machine learning to classify parts as active, in production, or obsolete and discontinued. This classification and labeling of parts can be useful in the design stage in part selection and during inventory management with evaluating the chance that suppliers might stop production. A case study utilizing the proposed framework is presented to demonstrate and validate the improved accuracy of obsolescence risk forecasting. As shown, the framework correctly identified active and obsolete products with an accuracy as high as 98.3%.