16. Company B have made a recent study on their historical data of the unit order price of a certainproduct. Based on this monthly time series they have discovered two behaviors regarding thefactor(i.e. price) they want to forecast. The first behavior was a positive trend in the values. Theywere able to fit a linear trend line (y=a+b*x) to the available data. However they have realizedthat it was not enough to account for all the fluctuations. The following investigation revealedthat the price values were also fluctuating based on seasonal effects. Eventually they were ableto build a forecasting system to predict the price of this certain product. All the trend linecomponents and seasonality indices are provided below. Seasonality factors are calculated to beused in a multiplicative manner. Please show the calculations for ONLY the cells that contain aquestion mark below.Period12345678910111213SlopeIntercept =Number of "seasons"234123412Season341234?Season Trend Index Forecast1??=??=7,5854MultiplicativeindicesIndex1,31,030,750,95?????

Here, given data, Intercept=85Slope=7.5The total number of seasons=4 Here, the linear regression…

Answered: Period Season Trend 1 1 2 2 3 3 4 4 5 1…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 29P: The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building...

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The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
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 Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
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_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
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?
Explain the trade-off between responsiveness and stability in a forecasting system that uses time-series data. Who needs to be involved in preparing forecasts? 3. How has technology had an impact on forecasting? What capability would an organization have to have to not need forecasts? Give three examples of unethical conduct involving forecasting and the ethical principle each violates
1. Omar has heard from some of his customers that they will probably cut back on order sizes in thenext quarter. The company he works for has been reducing its sales force due to falling demand andhe worries that he could be next if his sales begin to fall off. Believing that he may be able to convince his customers not to cut back on orders, he turns in an optimistic forecast of his next quartersales to his manager. What are the pros and cons of doing that?
q1(a)Imagine that you need to introduce a new gadget such as the Apple Watch. Determine which forecasting method is appropriate for projecting the future demand of such this gadget. b)ARIMA is mostly used to forecast future values using historical time series data, as we all know. Its primary use is in short-term forecasting with at least 38-40 historical data points and a small number of outliers. If we don't have at least 38 data points, we should consider using another strategy.
b. Explain the term “wrong” as it pertains to a good forecast
8.) Create an exponential smoothing model that minimizes the MSE for the data set. Use Solver to determine the optimal value of α. What is the optimal value of α? Prepare a line graph comparing the exponential smoothing predictions against the original data. What are the forecasts for the next 2 years using this technique? Year Sales 1 283 2 288 3 336 4 388 5 406 6 412 7 416 8 435 9 428 10 435 11 462 12 452 13 474 14 476 15 497 16 487 17 523 18 528 19 532 20 552

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Period Season Trend 1 1 2 2 3 3 4 4 5 1 6 2 7 3 8 4 9 1 10 11 12 2 3 4 ? ? ? ? ? Index Forecast ? ? ? ? ?