This Forecasting Classroom Simulation is the 1st of our new classroom gaming exercises. It accompanies Chapter 4, Forecasting, and is free within our MyOMLab learning system.
Activity Brief
As an operations consultant, you have just signed a 2 year contract to provide monthly forecasts of customer demand for a new gas station. The gas station will sell 3 types of gas: Regular, MidGrade, and Premium. The gas station will have a total of 8 pumps offering all three types.
The gas station will also have a modest convenient store with a standard selection of snacks, beverages, and other miscellaneous items. However, the ownership group believes the station will attract business primarily due to its prime location near a major highway. Pricing for gas will be comparable to alternatives in the area and will predominantly be driven by market conditions relating to the price of crude oil per barrel.
The ability to forecast the next month forecast is critical for the station’s inventory management and other business planning. It will be necessary to gather various sources of information and ultimately analyze data in order to make the best forecast for each of the 24 months of the contract.
Your performance will be based on the collective mean absolute percentage error (MAPE) among the three types of gas. If you are able to forecast at less than or equal to 5% MAPE, you will receive a $10,000 bonus for your work. If your forecast are between 5% and 20% MAPE, you will not receive the bonus, but you will secure the position and receive a contract renewal. If the MAPE exceeds 20%, you will not receive a contract renewal.
Learning Objectives
- Understand and break down patterns of customer demand
- Generate forecasting models based on judgement, causal, time-series methods, or seasonal methods
- Evaluate the quality of a forecast model using error metrics (specifically mean absolute percentage error).
- Help students understand the distinction between the “signal” and the “noise” (Students are encouraged to also read The Signal and the Noise: Why So Many Predictions Fail, but Some Don’t. by Nate Silver). Many aspects of customer demand variation are explainable – the signal, but there needs to be an acceptance of unexplainable variation – the noise. In other words, students have to make a concession that their models will not predict customer demand with 100% accuracy.
Industry: Retail
