MAN 3504 - Forecasting Independent Demand (6 slides per page)

MAN 3504 - Forecasting Independent Demand (6 slides per page)

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MAN 3504 - Forecasting Independent Demand 1 Forecasting Independent Demand Independent vs. Dependent Demand ± Independent Demand Item: • Item (product or service) whose demand does not directly depend on the demand for another item that you must manage AN 3504: Forecasting Independent Demand 2 for another item that you must manage (i.e., that you carry in your product line, or carry in stock, etc.) ± Dependent Demand Item: • Item (product or service) whose demand does directly depend on the demand for another item that you must manage (i.e., that you carry in your product line, or carry in stock, etc.) Why Forecast? ± All planning decisions are based on the forecast • Inventory levels • Production levels 3 • Employment levels • Financial needs • Purchasing needs • New equipment needs •E t c . Types of Forecasts ± Economic ± Technological 4 ± Demand (or sales) • Same thing?? Qualitative Forecasting Methods ± Consumer Survey ± Sales Force Composite 5 ± Jury of Executive (and/or Outside Expert) Opinion ± Delphi Method Quantitative Forecasting Methods ± Causal Models (Multiple Regression) ± Time Series Methods Moving Averages (Simple or Weighted 6 • Moving Averages (Simple or Weighted) • Exponential Smoothing Simple (or Single) Holt’s Model (or Double) • Time Series Decomposition Simple Regression (or Trend Projection) Seasonality Correction with Seasonal Indexes
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MAN 3504 - Forecasting Independent Demand 2 Time Series Models ± Identify patterns in past demand, and assume those patterns will repeat themselves in the future AN 3504: Forecasting Independent Demand 7 ± Does not attempt to identify the causes of the fluctuation in demand ± A time series have as many as six different components (or parts of the pattern) Components of a Time Series ± Base / Level / Horizontal ± Trend 8 ± Seasonality ± Cyclicality ± Randomness ± Irregular Shift Time Series Examples 9 and Seasonal Peaks Time Series Examples 10 12 3 4 Year Dem Moving Averages ± Simply using the average of some number of most recent demands as your forecast ± “Naïve” approach: Just using the most recent demand as the forecast (one period moving average 11 the forecast (one period moving average) ± The more periods you average together, the “smoother” your forecast (but least responsive) ± Trade-off between responsiveness and smoothing out randomness ± Good when only base and randomness present Naïve Approach Example Period (t) Demand (At) Forecast (Ft) 1 100 2 80 100 39 0 8 0 49 0
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MAN 3504 - Forecasting Independent Demand 3 Period (t) Demand (At) Forecast (Ft) 1 100 Two-Period Moving Average Example AN 3504: Forecasting Independent Demand 13 28 0 39 0 9 0 48 5 Exponential Smoothing ± A type of “weighted” moving average ± Weights most recent demands the most 14 ± Weights on older demands drop off exponentially, hence the name ± Very popular – very simple (really!) ± Only addresses base and randomness, and tries to catch shifts in the base Simple Exponential Smoothing Formula ) ( 1 1 1 + = t t t t F A a F F 15
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MAN 3504 - Forecasting Independent Demand (6 slides per page)

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