Karteikarten
Operations: Forecasting
Karten in diesem Stapel (22)
- 1Strategic ForecastsMedium and long term forecasts for decisions related to strategy and aggregate demand.
- 2Tactical forecastsShort term forecasts used for making day to day decisions to meet demand.
- 34 Types of Forecasting1. Qualitative
2. time series
3. causal relationship
4. simulation - 4Moving AverageA forecast based on average past demand.
- 5Weighted Moving AverageA forecast made with past data where more recent data is given more significance than older data.
- 6Exponential SmoothingA time series forecasting technique using weightes that decrease exponentially (1 - alpha) for each past period.
- 7Smoothing Constant AlphaThe parameter in the exponential smoothing equation that controls the speed of reaction to differences between forecast and actual demand.
- 8Smoothing Constant DeltaAn additional parameter used in an exponential smoothing equation that includes an adjustment for trend.
- 9Linear Regression ForecastingA forecasting technique that fits a straight line to past demand data.
- 10DecompositionThe process of identifying and separating time series data into fundamental components such as trend and seasonality.
- 11Forecast ErrorDifference between actual demand and what was forecast.
- 12Mean absolute deviation (MAD)The average of the absolute value of the actual forecast error.
- 13Mean Absolute Percent Error (MAPE)The ave. error measured as a percentage of ave. demand.
MAPE = MAD/Ave. Demand - 14Tracking SignalA measure of whether the forecast is keeping pace with any genuine upward or downward changes in demand. This is used to detect forecast bias.
- 15Casual Relationship ForecastingForecasting using independent variables other than time to predict future demand.
- 16Components of Demand1. ave. demand for the period
2. a tend
3. a seasonal element
4. cyclical elements
5. random variation
6. autocorrelation. - 17Curve patterns of demand (Trend Lines)1. linear
2. S curve
3. asymptotic
4. exponential - 18Choosing a forecasting model depends on...1. Time horizon to forecast
2. Data availability
3. Accuracy required
4. Size of forecasting budget
5. Availability of qualified personnel - 19Time SeriesChronically ordered data that may contain one or more components of demand: trend, seasonal, cyclical, autocorrelation and random
- 20Additive seasonal variationAssumes that the seasonal amount is a constant no matter what the trend or average amount is.
- 21Multiplicative seasonal variationThe trend is multiplied by the seasonal factors.
- 22Decomposition: Using Least Squares RegressionDecompose the time series into components: a. Find seasonal component. b. Deseasonalize the demand. c. Find the trend component.
2. Forecast future values of each component. a. project trend component into the future. b. Multiply trend component by seasonal component.
Informationen
- Beschreibung
- Operations and Supply Chain (Jacobs and Chase), Chapt. 18
- Erstellt von
- akuma33 · Jan 2014
- Aktualisiert
- 19 Feb 2016
- Aufrufe
- 20
- Bewertung