Open AccessDemand Forecasting of Spare Parts of Automobiles using Gaussian Support Vector Machine
Author(s) -
R Kale Mayur,
B. Sakthi Kumar
Publication year - 2019
Publication title -
international journal online of sports technology and human engineering
Language(s) - English
Resource type - Journals
ISSN - 2349-0772
DOI - 10.24113/ojssports.v7i1.114
Subject(s) - spare part , remanufacturing , computer science , demand forecasting , operations research , industrial engineering , variety (cybernetics) , inventory control , process (computing) , production planning , stock (firearms) , manufacturing engineering , production (economics) , operations management , engineering , artificial intelligence , economics , mechanical engineering , macroeconomics , operating system
Reordering motor vehicle spare parts for the purposes of stock replenishment is an important function of the parts manager in the typical motor dealership. Meaningful reordering requires a reliable forecast of the future demand for items. Production planning and control in remanufacturing are more complex than those in traditional manufacturing. Developing a reliable forecasting process is the first step for optimization of the overall planning process. In remanufacturing, forecasting the timing of demands is one of the critical issues. The current article presents the result of examining the effectiveness of demand forecasting by time series analysis in auto parts remanufacturing. A variety of alternative forecasting techniques were evaluated for this purpose with the aim of selecting one optimal technique to be implemented in an automatic reordering module of a real time computerized inventory management system.