Problem 1: Observations of the demand for a certain part stocked at a parts supply depot during the calendar year 1999 were Month January February March April May June Demand 89 57 144 221 177 280 Month July August September October November December Demand 223 286 212 275 188 312 a. Determine the one-step-ahead forecasts for the demand for January 2000 using 3-‚ 6-‚ and 12-month moving averages. b. Using a four-month moving average‚ determine the one-step-ahead forecasts for July through December
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000 is the forecast for 2013 b) What sales would you predict for 2013‚ using a weighted moving average with weights of0.50 for the immediate preceding year and 0.3‚ 0.15‚ and 0.05 for the three years before that? F2013 = 0.50A2012 + 0.3A2011 + 0.15A2010 + 0.05A2009 =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
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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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Carmen’s decides to forecast auto sales by weighting the three weeks as follows: |Weights Applied |Period | |3 |Last week | |2 |Twoweeks ago | |1 |Three weeks ago | |6 |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
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Forecasting In order for a business to be successful it must come up with the most 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
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shown in Figure 1 below. Figure 1 The total subscriber base as at June 2013 stood at 671.13 million. Figure 2 below shows the major GSM operator wise number of subscribers as at June 2013. Figure 2 In our project we have attempted to forecast the demand of mobile subscriptions in North India in the Month of December 2013 by use of the following models: Logistics Curve Gompertz Curve Bass Model Logistics Curve: A logistic function or logistic curve is a common sigmoid function‚ given
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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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