Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—This is the case-study of an automobile Industry in India. The prices of Critical Spare Parts (CSP) are range from tens to hundreds of thousand rupees. As the equipment’s operate, some critical spare parts need to be replaced due to wear and tear. If appropriate amount of critical spare parts are not prepared, machines may not be able to function, thus resulting in a waste of resources. However, estimation of the critical spare parts consumption is a complicated subject (Billinton and Ahllen, 1983). This investigation focuses on forecasting the critical spare parts and evaluating the prediction performance of different forecasting methods. Exponential smoothed model, Least Square method and moving average method (MA) are used to perform CSP demand prediction, so as to effectively predict the required number of CSP which can be provide as a reference of spare parts control. This investigation is verified by comparing the predicted demand and actual demand of critical spare parts in semiconductor factories
Index Terms—Critical spare parts, Exponential smoothed model, Least square method, Moving Average method (MA)
Cite: Nitin Yedmewar, Arun Kumar Sharma* and Vijay Choudhary, "A Case Study on Forecasting Csp in an Automobile Industry," International Journal of Mechanical Engineering and Robotics Research, Vol. 3, No. 3, pp. 91-94, July 2014.
Copyright © 2015-2023 International Journal of Mechanical Engineering and Robotics Research, All Rights Reserved