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41 Pages

### 009-chap11Forecasting

Course: OM 302, Fall 2008
School: CSU San Marcos
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Word Count: 1042

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management Forecasting Forecasting operations operations management Predicting future events Qualitative methods Based on subjective methods Quantitative methods Based on mathematical formulas 2 Forecasting not often think of it as such. Examples: How long will it take me to complete the homework? How good a job will my teammates do on their portions of our group project? How long will it take me to...

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management Forecasting Forecasting operations operations management Predicting future events Qualitative methods Based on subjective methods Quantitative methods Based on mathematical formulas 2 Forecasting not often think of it as such. Examples: How long will it take me to complete the homework? How good a job will my teammates do on their portions of our group project? How long will it take me to drive to work? How long will it take me to drive to Mammoth? Will this class end early or on time? 3 operations management You "forecast" all the time, though you may Forecasting "cause" or effect the underlying pattern, monitor those, and then construct your forecast as best you can Example: What are the causal underlying effects that influence whether or not it will rain here Saturday? Casual effects: air movement patterns, air pressure changes, weather patterns in surrounding regions 4 operations management You need to find the underlying variables that Strategic Role of Forecasting Short term role of product demand Long term role of new products, processes, and technologies Focus on Total Quality Management Satisfy customer demand Uninterrupted product flow with no defective items 5 operations management Focus on supply chain management Components of Forecasting Demand operations management Time Frames Short-range medium-range long-range Demand Behavior Trends, cycles, seasonal patterns, random 6 Time Frame operations management Short-range to medium-range Daily, weekly, monthly, quarterly forecasts of sales data Up to 2 years into the future Long-range Strategic planning of goals, products, markets Planning beyond 2 years 7 Demand Behavior operations management Trend gradual, long-term up or down movement Cycle up & down movement repeating over long time frame Seasonal pattern periodic oscillation in demand which repeats Random movements follow no pattern 8 Forms of Forecast Movement: Trend Idealized Actual Random fluctuations Time Time 9 operations management Demand Demand Forms of Forecast Movement: Cyclic Ideal Actual Time Time 10 operations management Demand Demand Forms of Forecast Movement: Seasonal Idealized Actual Time Time 11 operations management Demand Demand Trend with Seasonal Pattern Time 12 operations management Demand Forecasting Methods operations management Qualitative methods Management judgment, expertise, opinion Use management, marketing, purchasing, engineering Quantitative methods Time series analysis, regression, or causal modeling Delphi method Solicit forecasts from experts 13 Forecasting Process 1. Identify the purpose of forecast 2. Collect historical data 3. Plot data and identify patterns 6. Check forecast accuracy with one or more measures 5. Develop/compute forecast for period of historical data 4. Select a forecast model that seems appropriate for data 7. Is accuracy of forecast acceptable? 8b. Select new forecast model or adjust parameters of existing model 8a. Forecast over planning horizon 9. Adjust forecast based on additional qualitative information and insight 10. Monitor results and measure forecast accuracy 14 operations management Time Series Methods operations management Assume patterns will repeat Naive forecasts Forecast = data from last period Statistical methods using historical data Moving average Exponential smoothing Linear trend line Demand? 15 Moving Average operations management Average several periods of data Dampen, smooth out changes Use when demand is stable with no trend or seasonal pattern 16 Moving Average operations management Average several periods of data Dampen, smooth out changes Use when demand is stable with no trend or seasonal pattern 17 i = 1 Di MAn = where n Di n n = number of periods in the moving average = demand in period i Simple Moving Average ORDERS PER MONTH 120 90 100 75 110 50 75 130 110 90 ??? MONTH Jan Feb Mar Apr May June July Aug Sept Oct Nov It's October 31st, how much Product should we order for Sale in November? 18 operations management Smoothing Effects 150 125 100 Orders 75 50 25 0 | Jan | Feb | Mar Actual | | | | Apr May June July Month | | Aug Sept | Oct | Nov 19 operations management Simple Moving Average ORDERS PER MONTH 120 90 100 75 110 50 75 130 110 90 ??? MONTH Feb Jan Mar Apr May June July Aug Sept Oct Nov i = 1 Di MA3 = 3 3 90 + 110 + 130 = 3 = 110 orders for Nov 20 operations management Previous 3 periods Simple Moving Average ORDERS THREE-MONTH PER MONTH MOVING AVERAGE 120 90 100 75 110 50 75 130 110 90 103.3 88.3 95.0 78.3 78.3 85.0 105.0 110.0 MONTH Jan Feb Mar Apr May June July Aug Sept Oct Nov 21 operations management Simple Moving Average ORDERS THREE-MONTH PER MONTH MOVING AVERAGE 120 90 100 75 110 50 75 130 110 90 ??? 103.3 88.3 95.0 78.3 78.3 85.0 105.0 110.0 MONTH Jan Feb Mar Apr May June July Aug Sept Oct Nov Previous 5 periods i = 1 Di MA5 = 5 5 90 + 110 + 130 + 75 + 50 = 5 = 91 orders for Nov 22 operations management Simple Moving Average ORDERS THREE-MONTH PER MONTH MOVING AVERAGE 120 90 100 75 110 50 75 130 110 90 103.3 88.3 95.0 78.3 78.3 85.0 105.0 110.0 FIVE-MONTH MOVING AVERAGE 99.0 85.0 82.0 88.0 95.0 91.0 MONTH Jan Feb Mar Apr May June July Aug Sept Oct Nov 23 operations management Smoothing Effects 150 125 100 Orders 75 50 25 0 | Jan | Feb | Mar Actual | | | | Apr May June July Month | | Aug Sept | Oct | Nov 24 operations management Smoothing Effects 150 125 100 Orders 75 50 25 0 | Jan | Feb | Mar Actual | | | | Apr May June July Month | | Aug Sept | Oct 3-month | Nov 25 operations management Smoothing Effects operations management 150 125 100 Orders 75 50 25 0 | Jan | Feb | Mar Actual | | | | Apr May June July Month | | Aug Sept | Oct 3-month 5-month | Nov 26 Weighted Moving Average operations management Adjusts moving average method to more closely reflect data fluctuations, often those most recent in time 27 Weighted Moving Average WMAn = Wi Di Adjusts i=1 moving where average Wi = the weight for period i, method to between 0 and 100 more closely percent reflect data fluctuations Wi = 1.00 28 operations management Weighted Moving Average Example MONTH August September October WEIGHT 17% 33% 50% DATA 130 110 90 29 operations management Weighted Moving Average Example MONTH August September October WEIGHT 17% 33% 50% DATA 130 110 90 3 November forecast WMA3 = i1 Wi Di = = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders 30 operations management Exponential Smoothing Averaging method Weights most recent data more strongly Reacts more to recent changes Widely used, accurate method 31 operations management Linear Trend Line y = a + bx where a b x y = = = = intercept (at period 0) slope of the line the time period forecast for demand for period x 32 operations management Least Squares Example: Raw Data x(PERIOD) 1 2 3 4 5 ...

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