Sales Management Essays Example

Business

Introduction
The sales data is to be analyzed using a number of forecasting methods, in order to arrive at the most appropriate forecasting method.

Simple Moving Average

The simple moving average method would appropriate equal weightage to all prior data. The number of time periods in the past to be taken into account is not specified in this case. Hence, it is assumed that the entire historical data (year 2000 to 2009) would be taken for arriving at the simple moving average. The average of the entire data is arrived at by calculating (900+ 1400+ 1750+ 1900+ 1700+ 1500+ 2000+ 1950+ 2250+ 2300)/10 = 1765.

Thus, the forecast for the year 2010 using the simple moving average method is 1765.

Three Year Weighted Average
In a three year weighted average, the highest weightage of 3 would be given to data pertaining to the most recent year (2009), followed by a weight of 2 for data in the year 2008 and a weight of 1 for the data in the year 2007. The sum of the weights is 6. Accordingly, the forecast for the year 2010 is computed as: [(2300 x 3) + (2250 x 2) + (1950 x 1)]/ 6 = 2225.

Thus, the three year weighted average yields the forecast for 2010 as 2225.

Five Year Weighted Average
In the five year weighted average, a weight of 5 would be ascribed to the year 2009, followed by a weight of 4 for the year 2008, a weight of 3 for the year 2007, a weight of 2 for the year 2006 and a weight of 1 for the year 2005. The sum of weights is (5+4+3+2+1)=15. Accordingly, the forecast for the year 2010 is calculated as: [(2300x5) +(2250x4) + (1950x3) + (2000x2) + (1500x1)] / 15 = 2123.33.
Thus, the forecast for the year 2010 using the five year weighted average method is 2123.33.

Five Year Exponentially Smoothed Weighted Average

Exponential smoothing is a method where the weights to previous years reduce in an exponential manner. α is the smoothing parameter. For our example, we assume α to be 0.6, giving more weight to more recent observations. Our forecast for the year 2010 becomes:
0.6 x 2300 + 0.6(1-0.6) 2250 + 0.6(1-0.6)^2(1950)+ 0.6(1-0.6)^3(2000) +0.6(1-0.6)^4(1500) = 2207.04.
Thus, the forecast for 2010 using the five-year exponentially smoothed weighted average with a smoothing parameter α as 0.6 is 2207.04.

Most Accurate Forecasting Method

Given that the actual sales for the year 2010 were 2500, the three year weighted average method of forecasting proves to be the most accurate method in this case, as the output of the three year weighted average method is the closest to the actual data.