Any product can become obsolete. Products becoming obsolete causes increased costs to organizations due to the interruption of the usual flow of business. These interruptions can be seen in product and component shortages, stockpiling, and forced redesigns. To minimize the impact of product obsolescence, businesses must integrate forecasting methods into business processes to allow for proactive management. Proactive management enables organizations to maximize the time to make a decision and increases the number of viable decisions. This dissertation seeks to demonstrate new obsolescence forecasting methods and techniques, along with frameworks for applying these to business processes. There are four main types of product obsolescence: (1) technical, (2) functional, (3) style, and (4) legislative. In technical obsolescence, old products can no longer compete with the specifications of newer products. In functional obsolescence, products can no longer perform their original task due to aging factors. The third is style; many products no longer are appealing visually and become obsolete due to changes in fashion (e.g., wood paneling on cars). The last is legislative; if a government passes laws that forbid certain materials, components, chemicals in manufacturing process, or creates new requirements in a market, this can often lead to many products and designs becoming obsolete. The focus in this research is on technical and functional obsolescence. Technical obsolescence is especially prolific in highly competitive consumer electronic markets such as cell phones and digital cameras. This work seeks to apply traditional machine learning methods to predict obsolescence risk levels and assign a status of “discontinued” or “procurable” depending on the availability in the market at a given time. After these models have been investigated, a new framework is proposed to reduce the cost impact of mislabeled products in the forecasting process. Current models pick a threshold between the “discontinued” and “procurable” status that minimizes the number of errors rather than the error cost of the model. The new method proposes minimizing the average error cost of status misclassifications in the forecasting process. The method looks at the cost to the organization from false positives and false negatives; it varies the threshold between the statuses to reduce the number of more-costly errors by trading off less-costly errors using Receiver Operating Characteristic (ROC) analysis. This technique draws from practices in disease detection to decide the tradeoff between telling a healthy person they have a disease (false positive) and telling a sick person they do not have a disease (false negative). Early work and a case study using this method have demonstrated an average reduction of 6.46% in average misclassification cost in the cell phone market and a reduction of 20.27% in the camera market. Functional obsolescence management has grown with the increased connectivity of products, equipment, and infrastructure. In the alternative energy industry, as solar farms and wind turbines transform from emerging technologies to a fully developed one, these industries seek to better monitor the performance of aging equipment. Similar techniques to forecasting technical obsolescence is applied to predict whether equipment is functioning properly. A case study is used to predict functional obsolescence of wind turbines.Obsolescence forecasting models are quite useful when applied to aid business decisions. In business, the classic tradeoff is between longer life cycles and lower costs. This research seeks to create a generalized model to estimate the total life cycle cost of a component and the life cycle of a product before it becomes obsolete. These models will aid designers and manufacturers in better understanding how changes in usage and manufacturing factors shift the life cycle and total cost. A genetic optimization algorithm is applied to find the minimal cost given a desired life cycle. An alternative model is developed to find the maximum life cycle given a set cost level.