Forecasting System Monitoring under Non-normal Input Noise Distributions
Hoda Sabeti, Omar Al-Shebeeb and Majid Jaridi*
Department of Industrial Engineering, West Virginia University, Morgantown, West Virginia, USA
- *Corresponding Author:
- Jaridi M
Department of Industrial Engineering
West Virginia University, Morgantown, West Virginia, USA
Tel: +(304) 293-4099
E-mail: [email protected]
Received Date: June 17, 2016; Accepted Date: June 28, 2016; Published Date: June 30, 2016
Citation: Sabeti H, Al-Shebeeb O, Jaridi M (2016) Forecasting System Monitoring under Non-normal Input Noise Distributions. Ind Eng Manage 5:194. doi: 10.4172/2169-0316.1000194
Copyright: © 2016 Sabeti H, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In quantitative forecasting models and tracking signal methods, input noise is often assumed to be normally and independently distributed. The goal of this research was to study the distribution of tracking signal and build new monitoring schemes for when the input noise distribution is not necessarily normal. A demand process in the Wilson inventory model was simulated using several input noise distributions. The effectiveness of a proposed tracking signal model was evaluated and compared to existing methods using an inventory cost model. It was found that it is not realistic to assume a normal distribution for the tracking signal even when the noise is normal. Because of the dependency of tracking signal elements, and since there is no specific distribution for it, we used simulation to estimate the best value for the standard deviation and suggest ±3 íÂÂ íÂÂíÂÂ as the control limits. We compared this value with those suggested by other papers, and showed that the proposed limits work better when the process is under control and also when there are different amounts of shifts in mean demand. We also studied different values for the tracking signal smoothing parameter and analyzed the inventory costs for each.