=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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Choose one of the forecasting methods and explain the rationale behind using it in 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
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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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|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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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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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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Classificatory smoothing of Income with Extraordinary Items - Summary Within this paper they talk about whether extraordinary items are used to smooth ordinary or operating income over time. The role of extraordinary items was never really looked at become separately and that is what they wanted to look at. They talk about how previously the focus was on net income after extraordinary items but that it is important to look at net income before extraordinary items also
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Case 5-1 Income Smoothing a. Firstly‚ investors tend to invest in companies with stable earnings rather than one with volatile earnings. With stable earnings‚ there will be more likely an issuance of dividends and investors could easily predict the company’s future earnings compared to one with unstable earnings. With consistent earnings generated‚ it gives investors a secured feeling that it will again generate earnings as predicted. Confidence in the growth of rate of earnings is crucial because
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