Correct Answer - Option 4 : The weighted moving average is usually more accurate than a simple moving average.
Weighted Moving Average Method:
In weighted moving average, the highest weightage is given to recent data & it decreases for older data points.
For n period weighted moving average, weightage is as follows:
\(\frac{n}{{{\rm{\Sigma }}n}},\;\frac{{n - 1}}{{{\rm{\Sigma }}n}},\;\frac{{n - 2}}{{{\rm{\Sigma }}n}}, \;- - - - - - - ,\frac{1}{{{\rm{\Sigma }}n}}\)
\({F_{n+1}} = \left[ ({{w_{1}} \times {D_{1}})\;+\;({w_{2}\times {D_{2}}})\;+\;..........+\;({w_{n}} \times {D_{n}}})\right]\)
The weighted moving average is usually more accurate than a simple moving average.
Simple Average Method:
In the simple moving average, we take the average of the past data points for future demand.
For 'n' period moving average forecast will be given by:
\({F_{n+1}} = \frac{{{D_1}\;+\;{D_2}\;+\;{D_3}\;+ \;{D_4}\;+\;\ldots \ldots \ldots \ldots \ldots \ldots \;+\;{D_n}}}{n}\)
Exponential forecasting:
\({F_T} = {F_{T - 1}} + α ({D_{T - 1}} - {F_{T - 1}})\)
where
FT is the forecast for the next period
\(({D_{T - 1}} - {F_{T - 1}})\) is the forecast error and
α is the smoothing constant.
