EPM945 Optimization And Decision Making

EPM945 OPTIMIZATION AND DECISION MAKING - SET TASK 2019

Question 1

Read the following case study based on the Applegold Cider Company. You should then apply decision analysis to the problem facing Applegold. This will involve:

  1. Formulating the problem as a decision tree;
  2. using Bayesian analysis to update prior probabilities and nd the correct proba-bilities in the tree;
  3. discuss the strengths and limitations of your analysis.

Applegold is a major cider producer, producing draught cider for pubs and clubs, as well as bottles and cans, which has recently seen production and the number of outlets selling its products increase signi cantly.

The growth in draught cider has created some problems for the company’s managers. In particular, there is concern that when sales reach their peak in August, there might not be enough kegs (steel re-usable cider containers) available to meet demand. Applegold own about 100000 kegs, but it is felt by some managers that the stock should be increased.

The Operations Manager has proposed that 6000 new kegs be ordered immediately. The Accountant was not convinced. Kegs cost $70 each, so this would lead to an expenditure of $420000. They would be usable next year, but assuming 5% interest on capital, buying now rather than waiting until next year would cost $21000.

The Sales Manager proposes that the company wait until an accurate long range weather forecast is available for August, since demand depends heavily on the weather, with hot dry months leading to high demand. Such a forecast will be available in July. One problem is that it might be the case that other brewers had bought all of the available kegs by this time and the Operations Manager estimates a probability of 0.75 of getting the kegs if they wait until July.

The Sales Manager produces an interim forecast, with no knowledge of the weather, of how good sales are likely to be in August. She estimates that they will be at least 10% higher with probability 0.4, they will increase by a lower amount with probability 0.4, and there will be no increase with probability 0.2.

The Data Processing Manager suggests three possible strategies; buying 0, 3000 or 6000 kegs immediately. The associated change in pro t from these three strategies were estimated, based upon the assumption that with a 10% sales increase 6000 extra kegs will be used (if available), and for a lower increase 3000 extra kegs will be used (if available), with an associated pro t of $8 per keg. These are summarised in Table 1.

The Sales Manager suggests that it might still be better to wait for the weather forecast in July, and run the risk of the kegs not being available. Whether it is best to do so depends upon how accurate the forecasts are. The data in Table 2 give some data on the recent performance of the forecasts.

Number of kegs

Increase on last year

purchased

None

Up to 10 %

Over 10 %

0

0

0

0

3000

-10500

13500

13500

6000

-21000

3000

27000

Table 1: Predicted changes in pro t (in $) for combinations of di erent immediate purchasing strategies and sales increases.

Actual increase

Predicted increase

Total months

over the previous year

None

Up to 10 %

Over 10 %

10%

2

8

17

27

< 10%

8

24

8

40

0

16

8

5

29

Table 2: Sales over the last 96 months.

(20 marks)

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