=0.50(83000) + 0.30(67000) + 0.15(64000) + 0.05(48000) = 41‚500 + 20‚100 + 9‚600 + 2‚400 = $73‚600 $73‚600 is the forecast for 2013 Q2. Using exponential smoothing with a weight of 0.6 on actual values: a) If sales are $45‚000 and $50‚000 for 2010 and 2011‚ what would you forecast for 2012? (The first forecast is equal to the actual value of the preceding year.) Actual values are 2010: $45‚000 2011:
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Demand Forecasting Problems Simple Regression a) RCB manufacturers black & white television sets for overseas markets. Annual exports in thousands of units are tabulated below for the past 6 years. Given the long term decline in exports‚ forecast the expected number of units to be exported next year. |Year |Exports |Year |Exports | |1 |33 |4 |26
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real life. I would choose to use the exponential smoothing forecast method. Exponential smoothing method is an average method that reacts more strongly to recent changes in demand than to more distant past data. Using this data will show how the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place. I would find this information very useful when examining the increased production of a
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|Total | Problem 3: A firm uses simple exponential smoothing with [pic] to forecast demand. The forecast for the week of January 1 was 500 units whereas the actual demand turned out to be 450 units. Calculate the demand forecast for the week of January 8. Problem 4: Exponential smoothing is used to forecast automobile battery sales. Two value of [pic] are examined‚ [pic] and [pic] Evaluate the accuracy of each smoothing constant. Which is preferable? (Assume the forecast for
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accurate forecast possible so they can plan for the demands. There are forecasting tools that assist with making calculations to receive the best outcome by your company’s needs. The tools are moving average‚ weighted moving average and exponential smoothing. The moving average takes the total of actual demand for previous months then divides by the number of months added. The number of months that is used can be predefined such as using the previous three months. This is the simplest and easiest
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eightweek period have been 14‚ 9‚ 30‚ 22‚ 34‚ 12‚ 19‚ 23. a. Suppose that three-week moving averages are used to forecast sales. Determine the one-step-ahead forecasts for weeks 4 through 8. b. Suppose that exponential smoothing is used with a smoothing constant of α = 0.15. Find the exponential smoothing forecasts for weeks 4 through 8. c. Based on the MAD‚ which method did better? Solution: a. Week 4 5 6 7 8 b. and c. You may start ES forecast from week 1 or start ES forecast from week 4 using MA(3) forecast
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Name:________________________________ Part 1 Exponential Functions Project There are three parts to this project. You must complete Part 1 (60 points)‚ but you may choose to do either Part 2 or Part 3 (40 points each). You may also do all three parts for a total of 140 points; however‚ you must fully complete either Part 2 or Part 3 to get credit (NOT ½ of Part 2 and ½ of Part 3). This project is due on December 5th. Turning it in late forfeits your right to extra credit and there will be
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Exponential Smoothing Forecasting Method with Naïve start Formula: Ft = α (At-1) + (1 – α) (Ft – 1) where: Ft Forecast for time t Ft – 1 Past forecast; 1 time ahead or earlier than time t At-1 Past Actual data; 1 time ahead or earlier than time t α (read as alpha) as a smoothing constant takes the
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Introduction to Project Manager 2 Responsibilities of A Project Manager 3 Chapter-2: Project Manager & Arguments 4-6 As A Project Manager 4 Priorities Make Things Happen 5 Common ordered lists 6-8 Things Happen When One Say No 9 Keeping It Real 10 Know the Critical Path 11 Be Relentless 12 Be Savvy 13 Chapter-3: IS Manager & Answering
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SMOOTHING TECHNIQUES Several techniques are available to forecast time-series data that are stationary or that include no significant trend‚ cyclical‚ or seasonal effects. These techniques are often referred to as smoothing techniques because they produce forecasts based on “smoothing out” the irregular fluctuation effects in the time-series data. Three general categories of smoothing techniques are presented here: • Naive forecasting models are simple models in which it is assumed that the
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