7/28/2019 Exponential Smoothing Excercise

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CASE STUDY: VBW Catering

VBW Catering is a small company that makes and delivers lunchtime fast -food to local businesses. It has twoprincipal product ranges for each of which daily demand can be aggregated into common units for the purposes ofplanning. The first range consists of basic sandwich products sold to individual customers. The second rangecomprises pre-assembled light lunch menus ordered for example, for lunchtime business meetings. The owners of thebusiness currently have difficulty in forecasting demand on a day-to-day basis. This creates problems for them inplanning the purchase of materials and in the allocation of work to staff. They have been advised to consider using anexponentially weighted moving average technique in order to predict future demand. The idea being, that once asuitable model is selected, it can be used each evening to predict a demand figure for the following working day.

In order to investigate the suitability of this method of forecasting to their business and to develop an appropriatemodel, the owners have collected data on demand over a typical period of 20 days. This data is given in the followingtable. You are required to use this data to test two forecasting models - in the first, the value of the smoothing constant

() is to be 0.1 and in the second it is to be 0.5. In order to start the modelling process, in both cases, you may use aninitialising forecast value for working day 1 of 50 units.

Product range 1 (basic sandwiches) Product range 2 (pre-assembled lunch menus)

Working day number Demand Working day number Demand

1 50 1 78

2 47 2 81

3 48 3 91

4 55 4 22

5 51 5 10

6 46 6 82

7 52 7 15

8 47 8 18

9 57 9 28

10 50 10 79

11 44 11 72

12 52 12 87

13 48 13 16

14 47 14 20

15 44 15 86

16 46 16 22

17 56 17 89

18 42 18 76

19 55 19 1320 49 20 15

1. For each value of the smoothing constant () separately, use the method given below to calculate EWMA forecastfigures for days 2 to 20 for both product ranges. (This means that you should develop 4 different forecasts two eachfor each product range).

EWMA forecasting method:

The forecast figure for tomorrow = * demand today + (1 - ) * demand figure forecasted for today

NB. In carrying out this exercise, you need to remember that an EWMA forecast figure is calculated for the followingday each evening once the current days actual demand is known. At this point in time, the demand figure for thefollowing day is not known this is what you are forecasting!You have been given all the demand figures for days 1

to 20 at the start of the exercise, but, in order to simulate the actual process, you will need to work through the dataday-by-day ignoring future demand figures.

2. Plot the actual demand figures for days 1 to 20 for each product range against the forecasts that you havedeveloped using the two different models.

3. For each of the four forecasts, calculate the mean forecast error and the mean absolute deviation.

4. Comment on the results of the exercise that you have carried out and develop conclusions about:

the suitability of the EWMA approach to forecasting in this case

the most suitable model (value of) to use in this case

the influence of the value of on any EWMA forecast