Beabsichtigte Lernergebnisse: Eine Übersicht über Vorhersageverfahren vorlegen. Vergangenheitsbasierte Verfahren für gleichbleibende Nachfrage und mit trendförmigem Verhalten erklären. Zukunftsbasierte Verfahren beschreiben. Aufzeigen, wie Vorhersagen in die Planung überführt werden können.
Section 10.2.3: Moving Average Forecast versus First-Order Exponential Smoothing Forecast
Intended learning outcomes: Disclose formulas for the relationship between α and n. Present the relationship between α and n in tabular form. Present an example of linear regression.
Exercise: Comparing Moving Average Forecast versus First-Order Exponential Smoothing Forecast
This exercise demonstrates the different types of demand forecast.
The first graph calculates the forecast using first-order exponential smoothing while the second is calculated by a method of your choice. Get used to the effects of N (the number of considered periods in the past) as well as the smoothing constant α, by chosing different values for these variables.
The initial setting marks the 11th and 12th month of the current yeat as unknown (="-"). You may also change these parameters.
Section 10.8.4 Scenario: Moving Average Forecast versus First-Order Exponential Smoothing Forecast
Intended learning outcomes: Differentiate between moving average forecast and first-order exponential smoothing using different numbers of observed values or smoothing constants α.
Comparing Moving Average Forecast versus First-Order Exponential Smoothing Forecast
This exercise demonstrates the different types of demand forecast.
The first graph calculates the forecast using first-order exponential smoothing while the second is calculated by a method of your choice. Play with the different parameters and use the calculate-button to see the according changes.
The initial setting marks the 11th and 12th month of the current yeat as unknown (="-"). You may also change these parameters.
In the red section at the top of the Web page, you can choose different values for the smoothing constant α . In the lower, green section you can choose either a different value for the smoothing constant a for comparison with the red curve or choose the number of values for the moving average forecast and compare the results of the technique with exponential smoothing (the red curve). Clicking on the “calculate” icon executes your input choice.
Case Study: Demand Forecasting
The level at which forecasting takes place (finished product, components or raw materials) is mainly dependent on the production environment. In a company pro-ducing for stock (Make to Stock) the finished products are forecast, and in a com-pany producing to customer order (Make to Order) the necessary raw materials, bought-in parts and production capacities must be forecast. A third variant is a pro-duction environment in which products are principially assembled the same way but the products are only fitted after the customer has made the order (Assemble to Order). In this case, frequently used components are forecast.
In this case study we will look more closely at the application of various forecasting methods.
German Version
Nachfrage und Bedarfsvorhersage PDF
Attachment
Nachfrage und Bedarfsvorhersage XLS
