Peter Omollo, director of operations at Harusi Corporation, was faced with some major decisions. The firm had been experiencing considerable difficulties in matching supply with demand; as a result, the company had been overproducing and had to sell the excess at a loss. At a recent board meeting, Asha Juma, the vice-president of marketing, reported on a new snowboard product, the Harusi Racer. She presented her rationale for introducing a new ski product at this time by highlighting the growth rate of ski equipment sales over the past five years. The board meeting concluded with the general manager tasking Peter to develop an analysis and report back his findings to the board the following week.

BACKGROUND

Harusi Corporation produced a variety of ski equipment including skis, bindings, poles and boots. The company marketed its products primarily over the Internet. Product demand fluctuated considerably prior to and during the ski season as a function of both the economy and the weather. In the past, Harusi management used historical data as a basis for developing a production strategy: this approach had proven to be less than satisfactory. Asha presented her rationale for introducing the Harusi Racer at this time by talking about the growth rate of ski equipment sales over the past five years. Peter was looking at several different production strategies for the upcoming winter season in conjunction with the Harusi Racer: she considered producing 10,000, 25,000 or 45,000 units. Harusi had one major manufacturing facility in UK; the company had the capability to employ either a batch flow or line flow manufacturing process. Peter had just completed an analysis on the costs associated with each process (see Exhibit 1). Asha had just provided Peter with the sales forecast for the Harusi Racer, which showed a 55 per cent probability of selling 35,000 units and a 45 per cent probability of selling 25,000 units. The selling price for the Harusi Racer was $125. Another factor that Peter needed to keep in mind was that Harusi had an outside vendor, Concord Inc., which could also produce the Racer to Harusi's specifications for a cost of $75 each. Harusi had a contract with its union workers which clearly specified that outside vendors could only be used in the event that demand exceeded production. Furthermore, unsold units could be sold at a clearance sale for $40. Given all of this information, Peter decided to use an analytics-based approach to determine the optimal production level. To further compound the situation, Peter had recently received a proposal from a local consulting firm named Utendo Forecasters (see Exhibit 2). Peter knew that this firm had a good track record for predicting upcoming weather conditions valuable information for a winter sports manufacturing company. The fee for developing a forecast was $20,000. Peter wanted to first figure out the maximum amount that he would be willing to pay for additional information under ideal conditions. He also realized that he would have to incorporate the consulting firm's cost and performance numbers into the analysis. After that, he could determine how much the sample information was worth so that he could decide whether or not to hire Utendo Forecasters. His first step was to decide on the best production process; he would need to determine the break-even volume for the two production options (batch and line). Once that task had been accomplished, he planned to determine the net income (revenue cost) for each of the six demand and production options (e.g. demand = 35,000 and production = 10,000, and so on) based on the most appropriate production process (batch or line). Having assembled the payoff table, he could then determine the optimal production strategy based on the production level that maximized expected value based on prior marketing probabilities.

These steps were reasonably clear; however, assessing the bid from Utendo Forecasters was another thing entirely. Sofia recalled a lecture from his graduate days involving a process for revising prior probabilities using additional information.

Exhibit 1

Cost / Process Batch Line Line

Fixed cost ($) 475,000 900,000

Variable cost ($/unit) 75 60

Exhibit 2

Actual / Predicted Predict Heavy Snow Predict Light Snow

Heavy snow 0.80 0.20

Light snow 0.25 0.75

ASSIGNMENT QUESTIONS

All calculations MUST be shown in detail.

1. Determine the break-even volume between batch flow and line flow production.

2. Determine the net incomes and expected value for each production level (10,000, 25,000 and 45,000) using a payoff table.

3. Identify the optimal production level based on maximizing expected value.

4. Determine the maximum amount that Harusi is willing to pay for perfect information.

5. Develop a decision tree for the problem facing Harusi Corporation without the probability values.

6. Compute the revised probabilities needed to complete the decision presented in the decision tree, and place these values in the decision tree. Show your probability calculations in tabular form.

7. Determine whether or not to hire the consultant using the decision tree. Explain why or why not. (Use the expected value as the decision criterion.)