demand forecasting is inevitable in supply chain management. In industries, how
to improve forecasting accuracy such as sales, shipping is an important issue.
There are many researches made on this. 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 us which satisfies minimum variance of
forecasting error. Generally, smoothing constant is selected arbitrarily. But
in this paper, we utilize above stated theoretical solution. Firstly, we make
estimation of ARMA model parameter and then estimate smoothing constants. Thus
theoretical solution is derived in a simple way and it may be utilized in
various fields. A mere application of 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. In this paper, combining the
trend removing method with this method, we aim to improve forecasting accuracy.
An approach to this method is executed in the following method. Trend removing
by the combination of linear and 2nd order non-linear function and 3rd order non-linear function is carried
out to the sum total medical data of production and imports of the data of The
average daily number of patients for two cases (The total number of patients in
hospital, Outpatients number). The weights for these functions are set 0.5 for
two patterns at first and then varied by 0.01 increment for three patterns and
optimal weights are searched. For the comparison, monthly trend is removed
after that. Theoretical solution of smoothing constant of ESM is calculated for
both of the monthly trend removing data and the non-monthly trend removing
data. Then forecasting is executed on these data. 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.