SCM 301 1st Edition Lecture 4Outline of Last Lecture 1. Supplier Evaluation2. Strong Supplier partnerships3. Vendor Managed Inventories4. Third party logistics (3PL)5. Centralized purchasing vs. decentralized6. Demand forecasting7. Independent Demand: How a firm can manage it8. Forecasting Techniques9. Components of Demand10. Cyclical Component11. Random Component12. Qualitative Methods13. Quantitative Forecasting Methods14. Other Forecasting Methods15. Simple Moving Average FormulaOutline of Current Lecture I. Naïve ForecastingII. Simple moving average formulaIII. Measures of forecast accuracyCurrent LectureIV. Naïve Forecastinga. F(t+1)= A(t)V. Simple moving average formulaa. Assumes an average is a good estimator of future behavior.b. Moving average: add all numbers before together, divide by how many numbers are added.VI. Measures of forecast accuracya. Forecast error: A(t)- F(t)b. Forecast error tells us how far above or below the actual demand value wasc. Running Sum of Forecast Errors (RSFE) indicates bias in forecasts, which is the tendency of a forecast to be consistently higher or lowerThese notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.d. Error is the amount of what actually happened minus what you forecastede. Running sum = error amounts added togetherf. Bias= numbers are all too high, or numbers are all too low indicates a trend in your forecasting.g. Mean Absolute deviation (MAD): indicates average forecast error in absolute terms. MAD of 0 means perfect forecast. High MAD = bad.h. Absolute deviation is the positive form of the error (-125= 125 abs. deviation). Not added
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