# Supply Chain Forecast using Winter’s Model

The first step is to obtain initial estimates of level and trend using linear regression. We first run a linear regression (using the Excel tool Data | Data Analysis | Regression) between demand and time periods. The estimate of initial level L0 is obtained as the intercept coefficient, and the trend T0 is obtained as the X variable coefficient (or the slope).

L0 (Level) = 453780.0774 T0 (Trend) = 383.708

Therefore, calculation of DE seasonalized demand and seasonal factor is being carried out by sing the below formula.

DE Seasonalized demand = L0 + Time period * T0 Seasonal factor (SF) = Demand / De Seasonalized demand

The remaining Level, Trend and Forecasted load of period 1 is being calculated as follows

L1= 0.05 *(Demand / seasonal factor) + (1- 0.05) * ( L0 + T0) T1= 0.1*(L1-L0) + (1-0.1) * T0

Forecasted load = (L1 +T1) *SF1

# Methodology

The below table depicts the different Forecasting Methods and their Applicability, thus the problem is based on predicting the shipment load on festive seasons I have used winter’s model for forecasting.

TREND- AND SEASONALITY-CORRECTED EXPONENTIAL SMOOTHING (WINTER’S MODEL)

This method is appropriate when the systematic component of demand has a level, a trend, and a seasonal factor. In this case we have Assume periodicity of demand to be p. To begin, we need initial estimates of level (L0), trend (T0), and seasonal factors (S1, . . . , Sp). We obtain these estimates using the procedure for static forecasting described earlier.

In Period t, given estimates of level, Lt, trend, Tt, and seasonal factors, the forecast for future periods is given by

Ft+1 = 1Lt + Tt2St+1 and Ft+l = 1Lt + lTt2St+l

On observing demand for Period t + 1, we revise the estimates for level, trend, and seasonal factors as follows:

Lt+1 = a1Dt+1/St+12 + 11 – a21Lt + Tt2 Tt+1 = b1Lt+1 – Lt2 + 11 – b2Tt

# Results and discussion

Seasonal effect may occur within a year, month, week or day. To measure seasonal effect, we construct seasonal indexes, which attempt to gauge the degree to which the seasons differ from one another. One requirement for this method is that we have a time series sufficiently long to allow us to observe the variable over several seasons. The seasonal indexes, forecasted load, error, % error are computed as shown below. ##### You May Also Like ## ReSOLVE model for circular supply chain.

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