Individual Case Assignment: Cutting Edge Case Case Solution And Analysis, HBR Case Study Solution & Analysis of Harvard Case Studies

2b) Averaging

Harry had used the averaging method to average all the monthly call volumes in order to forecast the call volume of the current date. This meant that that value predictions were equal to the former data averages. This method can only be applied with the present and past data which is accessible easily. The MAD will have to be lesser than the last value method because all the calls would then be devised as trends however, the MAD for this method is 399 suggesting that this is the least accurate forecasting method of all.

2c) Moving Average (5 days)

This method is the middle ground method between the averaging and the last value method. This method makes use of the average sales (5 days) of the last month to forecast the sales of the current month. Therefore, the data of the month that is being used needs to be specified. The moving average method uses the sales of previous period for forecasting the sales of the next period. The MAD of this method is 104 suggesting the highest accuracy of this forecasting method among all the other methods.

2d) Exponential Smoothing (alpha = 0.1)

The exponential smoothing method makes use of the formula based on the weights of last observation and the preceding observation. This weight is called as the smoothing constant. It is evident that exponential smoothing method is a recursive relationship that gives most of the weight to the last observation and decreasing weights to the earlier observations. The MAD for this method is 184 which is higher than the last value and the moving average method. It gives a middle ground to this method in terms of accuracy based on a smoothing constant of 0.1.

2e) Exponential Smoothing (alpha = 0.5)

The exponential smoothing method makes use of the formula based on the weights of last observation and the preceding observation. This weight is called as the smoothing constant. It is evident that exponential smoothing method is a recursive relationship that gives most of the weight to the last observation and decreasing weights to the earlier observations. The MAD for this method is 116 which is the second lowest MAD among all the methods. It gives a second position to this method in terms of accuracy based on a smoothing constant of 0.5.

Question 3a: Prepare a forecast of call volume for July 2015 by applying Exponential Smoothing (with alpha = 0.5) to the prior 18 months of data. Use the appropriate Excel template from the Hillier text to prepare your forecast and assume that initial call volume is 24,000. Show your forecast below and attach the completed template.

Call Volume Forecast for July 2015 (Exponential Smoothing, alpha=0.5): 32303

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