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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particular product (in thousands of dollars) for the years 2009 through 2012 havebeen $48‚000‚ $64‚000‚$67‚00 and $83‚000‚ respectively a) What sales would you predict for 2013‚ using a simple four-year moving average? F2013 = = $65‚500 $65‚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 +
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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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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 be predefined such as using the previous three months. This
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HTime series using Holt-Winters Forecasting Procedure Summary The Holt-Winters forecasting procedure is a simple widely used projection method which can cope with trend and seasonal variation. We can apply this method to lots of fields such as banking data analysis‚ investment forecasting‚ inventory controlling and so on. This paper shows us a practical banking credit card example using Holt-Winter method in Java programming for data forecasting. The reason we use Holt-Winter is that
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OF CONTENTS 1. EXECUTIVE SUMMARY 2. INTRODUCTION 3. LITERATURE SURVEY 4. METHODS/ANALYTICAL FRAMEWORK 5. HAZARDS 6. CONCLUSION 7. REFERENCES AND BIBLIOGRAPHY INTRODUCTION The Indian GSM Mobile Market in North India can be classified into 5 distinct phases (as shown as Figure 1) from the year 1998 till date. Interestingly the Indian Mobile market in North India has followed
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changed easily by using certain accounting methods or manipulating accruals. When discovered‚ this information will have a negative effect on a company ’s share price and its reputation in general. Methods of Income Smoothing In order to present a more positive result to shareholders and a more favorable view of company’s results‚ numerous methods exist that can be used by accountants. Most methods are achieved by using book entries. The Depreciation Method The Depreciation Method is one of the
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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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