Abstract
Sales forecasting is a starting point of supply chain management, and its accuracy influences business management significantly. In industries, how to improve forecasting accuracy such as sales, shipping is an important issue. In this paper, a hybrid method is introduced and plural methods are compared. Focusing that the equation of exponential smoothing method(ESM) is equivalent to (1,1) order ARMA model equation, a new method of estimation of smoothing constant in exponential smoothing method is proposed before by Takeyasu et.al. which satisfies minimum variance of forecasting error. Firstly, we make estimation of ARMA model parameter and then estimate smoothing constants. In this paper, combining the trend removing method with this method, we aim to improve forecasting accuracy. Trend removing by the combination of linear and 2nd order non-linear function and 3rd order non-linear function is carried out to the manufacturer’s data of sanitary materials. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.
Highlights
The needs for sales forecasting is prevailing among companies, but the contents of such needs are undergoing significant changes because of the rapid changes in the recent business environment
A mere application of exponential smoothing method (ESM) does not make good forecasting accuracy for the time series which has non-linear trend and/or trend by month
A new method to cope with this issue is required
Summary
The needs for sales forecasting is prevailing among companies, but the contents of such needs are undergoing significant changes because of the rapid changes in the recent business environment. Correct forecasting along with supply chain management is required that leads to the shortened lead time and less stocks. Time series analysis is often used in such themes as sales forecasting, stock market price forecasting etc. Sales forecasting is inevitable for Supply Chain Management. It is not well utilized in industries. It is because there are so many irregular incidents it becomes hard to make sales forecasting. In order to improve forecasting accuracy, we have devised trend removal methods as well as searching optimal parameters and obtained good results. Applied data are sales data, production data, shipping data, stock market price data, flight passenger data etc
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
