Suppose actual quarterly sales (in thousands) for Year 5 are 193, 163, 207,and 105 respectively. Assuming there is no trend, forecast sales for the fourquarters of Year 5.Use seasonal indexes (rounded to 4 decimal places) from Part A.Report forecast answers as integers accurate to the nearest thousand.Quarter 1 forecast =thousandQuarter 2 forecast =thousandQuarter 3 forecast =thousandQuarter 4 forecast =thousandCalculate the mean absolute percent error (MAPE) for the four forecasts. Usethe integer forecast values you reported above. Report MAPE as percentaccurate to 2 decimal places. Do not round intermediate values.МАРЕ 3D% The following table shows quarterly sales (in thousand units) for a productover 4 years. The overall sales average for the entire 4 years is 166.125(thousand units).Fill in the missing values assuming there is no trend.Round seasonal indexes to 4 decimal places.Report Averages accurate to at most 2 decimal places.Report Actual sales ('000) accurate to the nearest integer.YearYearSeasonalYear 1Year 2Average4IndexQuarter2052111821.17681Quarter1521701581562Quarter1821901981933Quarter1281121130.71784

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Answered: Suppose actual quarterly sales (in…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 20P: The file P13_20.xlsx contains the monthly sales of iPod cases at an electronics store for a two-year...

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The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
The file P13_20.xlsx contains the monthly sales of iPod cases at an electronics store for a two-year period. Use the moving averages method, with spans of your choice, to forecast sales for the next six months. Does this method appear to track sales well? If not, what might be the reason?
The file P13_21.xlsx contains the weekly sales of rakes at a hardware store for a two-year period. Use the moving averages method, with spans of your choice, to forecast sales for the next 30 weeks. Does this method appear to track sales well? If not, what might be the reason?
The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
Sometimes curvature in a scatterplot can be fit adequately (especially to the naked eye) by several trend lines. We discussed the exponential trend line, and the power trend line is discussed in the previous problem. Still another fairly simple trend line is the parabola, a polynomial of order 2 (also called a quadratic). For the demand-price data in the file P13_10.xlsx, fit all three of these types of trend lines to the data, and calculate the MAPE for each. Which provides the best fit? (Hint: Note that a polynomial of order 2 is still another of Excels Trend line options.)
The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
The file P13_27.xlsx contains yearly data on the proportion of Americans under the age of 18 living below the poverty level. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Create a chart of the series with the forecasts superimposed from this optimal smoothing constant. Does it make much of an improvement over the model in part b? d. Write a short report to summarize your results. Considering the chart in part c, would you say the forecasts are good?
The following table shows quarterly sales (in thousand units) for a product over 4 years. The overall sales average for the entire 4 years is 126.0625 (thousand units).Fill in the missing values assuming there is no trend.Round seasonal indexes to 4 decimal places.Report Averages accurate to at most 2 decimal places.Report Actual sales ('000) accurate to the nearest integer. Year 1 Year 2 Year 3 Year 4 Average Seasonal Index Quarter 1 92 93 95 106 Quarter 2 105 106 105 101 Quarter 3 197 187 196 1.5211 Quarter 4 106 116 121 0.8865 PART BSuppose actual quarterly sales (in thousands) for Year 5 are 93, 123, 206, and 126 respectively. Assuming there is no trend, forecast sales for the four quarters of Year 5.Use seasonal indexes (rounded to 4 decimal places) from Part A.Report forecast answers as integers accurate to the nearest thousand.Quarter 1 forecast = thousandQuarter 2 forecast = thousandQuarter 3 forecast = thousandQuarter 4 forecast =…
To better plan for future growth of the restaurant, Karen needs to develop a system that will enable her to forecast food and beverage sales by month for up to one year in advance. Table shows the value of food and beverage sales ($1000s) for the first three years of operation. Food and Beverage Sales for the Vintage Restaurant ($1000s) Month First Year Second Year Third Year January 242 263 282 February 235 238 255 March 232 247 265 April 178 193 205 May 184 193 210 June 140 149 160 July 145 157 166 August 152 161 174 September 110 122 126 October 130 130 148 November 152 167 173 December 206 230 235 Managerial Report Perform an analysis of the sales data for the Vintage Restaurant. Prepare a report for Karen that summarizes your findings, forecasts, and recommendations. Include the following: A time series plot. Comment on the underlying pattern in the time series.…
If a firm wants to forecast its gross margin by product for the next year, how does the firm do so? Select an answer: product revenue projection times projected gross margin for the product total revenue projection times projected gross margin for the average product product revenue projection divided by projected gross margin for the product total revenue projection divided by projected gross margin for the average product
ABC is an online-to-offline platform that sells e-commerce products to offline customers through a network of agents. ABC gives a commission to agents for each sale made. ABC has 4 main product categories: electronics, fashion, supermarket, and others. Please refer to the exhibits for data sets pertaining to the questions below. Today is May 16th. Q : 1. Today is May 16th. We have got the interim result of the sales figures in the first half of May. Typically, the first half of the month constitutes of 40% of sales. Using this assumption, will we reach our May target? What % over the target will we over/under-deliver?answer : over/deliver by ... % of target Using that assumption, which category (or categories) will not reach the targeted sales figures in May?answer : ............
. Suppose that you are working as an analyst. Your task is to value company XYZ based on financial data available for this company. Discuss how you would go about doing this using themultiple growth rate Gordon growth model. Critically evaluate your approach by discussing any assumptions you make (about forecasts, discount rate etc.). What data is needed to value XYZ?

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