Forecasting Obsolescence Risk and Product Life Cycle With Machine Learning

Forecasting obsolescence risk and product lifecycle with machine learning by Connor

Rapid changes in technology have led to an increasingly fast pace of product introductions. New components offering added functionality, improved performance and quality are routinely available to a growing number of industry sectors (e.g., electronics, automotive, and defense industries). For long-life systems such as planes, ships, nuclear power plants, and more, these rapid changes help sust...

Obsolescence and Life Cycle Management for Avionics

An EPQ Model for Product Life Cycle (Maturity Stage) with Deteriorating Items and Shortages

  A product life cycle is the life span of a product in which the period begins with the initial product specification and ends with the withdrawal from the market of both the product and its support. A product life cycle can be divided into several stages characterized by the revenue generated by the product. This study investigates inventory control policies in a manufacturing system for a si...

Product Obsolete / Under Obsolescence

This application note describes a high-speed, reconfigurable, full-precision Transposed Form FIR filter design implemented in the VirtexTM and Virtex-II series and SpartanTM-II family of FPGAs. The VHDL reference design provided with this application note is easily modified to change filter parameters including coefficients and the number of taps. By illustrating a design methodology for digita...

Machine Learning Models for Housing Prices Forecasting using Registration Data

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...