forecating - Forecasting Part I Forecasting • Trying to...

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Unformatted text preview: Forecasting Part I Forecasting • Trying to predict the future behavior of some process/variable based on past data • Fundamental to business planning – Most business decisions based to some extent on forecasts • Key forecasts in business: – Future demand for products – Future price of various commodities Why forecast demand? • We need to know how much to make ahead of time, i.e. our production schedule – How much raw material – How many workers – How much to ship to the warehouse in Denver • We need to know how much production capacity to build Monthly Demand for Furniture Monthly Demand 20 40 60 80 100 120 140 160 5 10 15 20 25 30 35 40 Month Demand 3-Month Lead time from Factory in China-150-100-50 50 100 150 200 250 300 1 35 69 103 137 171 205 239 273 307 341 375 409 443 477 511 545 579 613 647 681 715 749 783 817 851 885 919 Inventory Forecast Whenever you have: – Significant lead time in production – Variation in demand – Need for fast customer service (no back orders) • You will need to maintain inventory • The more accurate the forecast, the less the inventory required (why?) Why Forecast Raw Material Price? Raw Material Price 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Period Price ($/Unit) Price Other Examples of Time Series Data Monthly Australian Red Wine Sales 5 0 0 . 1 0 0 0 . 1 5 0 0 . 2 0 0 0 . 2 5 0 0 . 3 0 0 0 . 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 Series Yearly Level of Lake Huron 6 . 7 . 8 . 9 . 1 0 . 1 1 . 1 2 . 2 0 4 0 6 0 8 0 1 0 0 S eries Monthly new polio cases in the U.S.A., 1970-1983 0 . 2 . 4 . 6 . 8 . 1 0 . 1 2 . 1 4 . 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 S eries Monthly Traffic Injuries (G.B) beginning in January 1975 1 0 0 0 . 1 2 0 0 . 1 4 0 0 . 1 6 0 0 . 1 8 0 0 . 2 0 0 0 . 2 2 0 0 . 2 0 4 0 6 0 8 0 1 0 0 1 2 0 S eries U.S. Pop., 10-year Intervals, 1790--1980 0 . 0 E + 0 0 5 . 0 E + 0 7 1 . 0 E + 0 8 1 . 5 E + 0 8 2 . 0 E + 0 8 2 . 5 E + 0 8 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 S eries Annual Canadian Lynx Trappings 0 . 1 0 0 0 . 2 0 0 0 . 3 0 0 0 . 4 0 0 0 . 5 0 0 0 . 6 0 0 0 . 7 0 0 0 . 2 0 4 0 6 0 8 0 1 0 0 S eries Daily Dow Jones & All Ordinaries (Australia) Indices 1 0 . 0 0 1 0 . 5 0 1 1 . 0 0 1 1 . 5 0 1 2 . 0 0 1 2 . 5 0 1 3 . 0 0 1 3 . 5 0 1 4 . 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 S eries 1 9 . 5 0 1 0 . 0 0 1 0 . 5 0 1 1 . 0 0 1 1 . 5 0 1 2 . 0 0 1 2 . 5 0 1 3 . 0 0 1 3 . 5 0 1 4 . 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 S eries 2 1 st & 2 nd Law of Forecasting 1. In forecasting, we assume the future will behave like the past – If behavior changes, our forecasts can be terrible 2. Even given 1, t here is a limit to how accurate forecasts can be (or nothing can be predicted with complete accuracy) – The achievable accuracy depends on the magnitude of the noise component What if this happened? What if this happened?...
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forecating - Forecasting Part I Forecasting • Trying to...

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