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: a) Formulating the problem as a decision tree; b) using Bayesian analysis to update prior probabilities and find the correct proba- bilities in the tree; c) 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 significantly. The growth in draught cider has created some problems for the company's managers. In par ar, there is concern that when sales reach their peak in Augus 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 sholud be increased. The Operations Manager has propoased that 8000 new kegs be ordered immediately. The Accountant was not convinced. Kegs cost 60 each, so this would lead to an expenditure of 480000. They would be usable next year, but assuming 5% interest on capital, buying now rather than waiting until next year would cost 24000. 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 0.8 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 0.5, they will increase by a lower amount with probability 0.4, and there will be no increase with probability 0.1. The Data Processing Manager suggests three possible strategies; buying 0, 4000 or 8000 kegs immediately. The associated change in profit from these three strategies were estimated, based upon the assumption that with a 10% sales increase 8000 extra kegs will be used (if available), and for a lower increase 4000 extra kegs will be used (if available), with an associated profit of 7 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 purchased 0 4000 8000 None 0 -12000 -24000 Increase on last year Up to 10 % 0 16000 4000 Over 10 % 0 16000 32000 Table 1: Predicted changes in profit (in ) for combinations of different immediate purchasing strategies and sales increases. Total months Actual increase over the previous year None > 10% 2 10% 2
