2.1_Forecasting

2.1_Forecasting - Forecasting Sada Soorapanth Decision...

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1 Forecasting Sada Soorapanth Decision Sciences
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2 What is Forecasting? The process of predicting the future. Examples in business: Sales of existing products Customer demand patterns for new products New product/process cost estimates Availability of raw materials Actions by competitors
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3 Example: Customer Demand Patterns in the Auto Industry Henry Fords’ forecast of consumer demand in early 1900’s: Model T dominated the industry. General Motors’ forecast in early 1920’s: consumer wanted a broad range of choices 1927 Chevrolet sold > 1 millions cars and forced the end to the Model T.
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4 Time Horizons in Forecasting Time Horizon Measure Example Short Days or weeks Short term sales Contract staffing Intermediate Weeks or months Product family sales Labor needs Long Months or years Capacity needs Demand patterns
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5 Characteristics of Forecasts * They are usually inaccurate. A good forecast is more than single number. Aggregate forecasts are usually more accurate than single item forecasts. Why? The longer the forecast horizon, the less accurate (usually). * S. Nahmias, Production and Operations Analysis .
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6 Forecasting Methods Subjective: Examples: Customer surveys, sales/expert/executive opinion. Objective: Time series- uses past data. Associative- uses data from other sources. (Example: use of mortgage interest rates to predict home sales.)
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7 Time Series Forecasts Attempt to identify patterns. Time series patterns: Stable Trend Seasonality Cycles Randomness
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8 Time Series Forecasting Naïve Averaging Moving average Weighted moving average Exponential smoothing Trend Seasonality
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9 Notation A t = Actual value in period t. F t = Forecast for time period t. i.e., F t is the forecast made at the end of period t-1 after observing A t-1 , A t-2 , ….
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Notation 1 2 3 4 Forecast 100 90 95 90 Demand 80 95 75 81 F 3 = _______ A 3 = _______
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11 Naïve Forecasts Time series Forecast Example Stable F t = A t-1 Sold 100 cans of soup last week. Expect to sell 100 this week. Seasonal F t = A t-s s = # of periods before pattern repeats Sold 100 poinsettias last December. Expect to sell 100 poinsettias this Dec. Trend F t = A t-1 + (A t-1 - A t-2 ) Sold 400 cars two months ago, 500 cars last month. Expect to sell 600 cars this month.
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Example Problem Demand for the last four months was: a. Predict demand for July using a naïve forecast for stable demand. b. Predict demand for July using a naïve forecast for trend. Month Demand Mar 6 Apr 8 May 10 Jun 9
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Naïve Forecasts: Pros and Cons Pros: Simple and low cost. Cons: May not be accurate. Managerial Question:
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2.1_Forecasting - Forecasting Sada Soorapanth Decision...

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