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摘要:对于年度报告你有多少认识和了解?这篇文章不仅了解到现有年度报告一些局限性,也向作者学习学术性文章写作方法!
5.3 Potential Growth of Paint Market
Due to the ongoing recession and declined demand in real estate business, it is very tough to comment on the future growth of paint market. But from the past sales data and consultation with the experts, a potential growth of paint market is forecasted herewith. Three techniques are used for forecasting method. A projection of paint industry is publicized using all these three techniques in the successive sections. The assumptions on the basis of which the demand is projected are
1. The macroeconomic condition will remain unchanged
2. There will be no change in technology during the projected demand period
3. There will be no substitute for paints at this period
These forecasted demands are not actual demands. They are only predictions on the basis of previous sales and some external situation. Forecasting always involves error. Still, for better decision making, forecasting plays a very vital role. The Research Team suggests the authority to adopt a forecasting method with the least error.
Forecasting with Static Method
A static method assumes that the estimates of level, trend and seasonality within the systematic component do not vary as new demand is observed. In this case, we estimated each of these parameters based on historical data and then use the same values for all future forecasts. We assumed that, the systematic component of demand is mixed, that is,
Formula: Systematic component = (Level + Trend) X Seasonal Factor
With this method, the forecast in Period t for demand in Period t+l is given as follows:
Ft+l = {L+(t+l)T}St+l
Where,
L = Estimate of level at t=0 (the deseasonalized demand estimate during period t=0)
T= Estimate of trend (increase of decrease in demand per period)
St = Estimate of season factor of period t
Dt = Actual demand observed in period t
Ft = Forecast of demand for period t
Assuming 95% confidence interval level, the demand projection using this method till the year 2015 can be furnished in Table 5.4.
Forecasting with unconditional least square method
In this method, the estimates of level, trend and seasonality are updated after each demand observation. The forecast of period t+l in period t in this method is determined by,
Ft+l= (Lt +lTt) St+l,
Where, the terms follow their usual meaning
Demand forecasting using unconditional least square method at 95% confidence interval, a following projection can be obtained (Table 5.5).
This method is appropriate when demand is assumed to have a level and a trend in the systematic component but no seasonality. In this case,
Ft+1 = Lt +Tt,
Where, the terms carry their usual meaning.
Using this method, the projection of demand is given in Table 5.6.
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