Instructions How to Write

Case Study: Forecasting Methods

Case Study Overview:
You have recently been assigned to the production planning department within your company. Your firm makes large blades for power generation windmills. The windmills are mostly used in the western part of the country and take about a week to build.

Your boss has provided you with the monthly demand for blades from the last year and asked that you produce a forecast for January so that he can work with the material planning and scheduling department to make sure that the required materials are on hand and schedule the workers hours.

By the end of the week, your boss has asked you to produce a professional video presentation SCRIPT with all of the needed data so that he can brief his boss on your findings.

Using the data provided, produce forecasting models using the following:
-Simple Moving Average (on a 4 month moving average)
-Weighted Moving Average (4 month moving average with weights = 0.4, 0.3, 0.2. 0.1)
-Exponential Smoothing (α = 0.3) (assume forecast for January is the same as the actual demand)

Provided Data for Forecasting Modules:
*Uploaded

Provided MGMT 440 Video- Forecasting Transcript:
*Uploaded

Questions:
Questions to be addressed
1. While the MAD can be used to determine which forecasting method is the most accurate, normally, the MAPE is a better measure. So, compare the MAPE of the three methods and determine which is the best model (e.g. the most accurate).
2. Base on the chosen model, what should you recommend to your boss as the forecasted demand for January?
3. Produce a line chart that shows all three of the forecast and the actual demand.
4. Review the chart and the data. Do you think that the forecasts have given you an accurate result? Do you notice any trends in the data that could affect the January forecast? If so, what would you recommend to your boss?

Forecasting Models
-Simple Moving Average (on a 4 month moving average)
-Weighted Moving Average (4 month moving average with weights = 0.4, 0.3, 0.2. 0.1)
-Exponential Smoothing (α = 0.3) (assume forecast for January is the same as the actual demand)

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