The total sales of a company doing business from Monday through Saturday. Number of Motor vehicles ('000) Mon Tue Wed Thurs Fri. Sat. Week 1 34 30 21 28 32 43 Week 2 35 28 23 27 31 40 Week 3 36 27 25 25 33 41 Week 4 32 29 23 23 38 38 (c) Illustrate the time series graph by including the data (y) and trend (t).

Number of Motor vehicles ('000)

Week 1

Week 2

Week 3

Week 4

Contemporary Marketing

18th Edition

ISBN:9780357033777

Author:Louis E. Boone, David L. Kurtz

Publisher:Louis E. Boone, David L. Kurtz

Chapter14: Pricing Strategies

Section14.2: Forecasting Demand

Problem 1LO

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Under what conditions might a firm use multiple forecasting methods?
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_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 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_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?
Use the sales data given below to determine: (a) the least squares trend line, and (b) the predicted value for 2003 sales. Year Sales (Units) 1996 100 1997 110 1998 122 1999 130 2000 139 2001 152 2002 164 To minimize computations, transform the value of x (time) to simpler numbers. In this case, designate year 1996 as year 1, 1997 as year 2, etc.
Consider the monthly sales data of a company for last year as well as first six month data for current year. Use a three- quarter weighted moving average, Forecast the sales of company for 3rd Quarter of current year. Use Weights of 4/7, 2/7 and 1/7, giving more weight to more recent data. Note, the 1ist quarter is Jan, Feb and March , 2nd quarter is Apr, May, June, 3rd quarter is July, Aug and Sept, and 4th quarter is Oct, Nov and Dec. Month Jan Feb Mar April May June July Aug Sept Oct Nov DecLast Year 100 125 135 175 185 200 150 140 130 200 225 250Current Year 125 135 135 190 200 190
Following data on the demand for sewing machines manufactured by Taylor and Son Co. have been compiled for the past 10 years. Year 1971 1972 1973 1974 1975 78 1976 1977 1978 1979 99 1980 106 Demand 58 65 73 76 87 88 93 in (1000 units) Please estimate the value of demand for next 3 years using trend analysis.
Consider the monthly sales data of a company for last year as well as first six-month data for current year. Use a three- quarter weighted moving average, Forecast the sales of company for 3rd Quarter of current year. Use Weights of 4/7, 2/7 and 1/7, giving more weight to more recent data. Note, the 1ist quarter is Jan, Feb and March, 2nd quarter is Apr, May, June, 3rd quarter is July, Aug and Sept, and 4th quarter is Oct, Nov and Dec. Month Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Last Yr 100 125 135 175 185 200 150 140 130 200 225 250 Current Yr 125 135 135 190 200 190
Consider the following quarterly demand level for electricity (in 1000 megawatts) in Mankweng from 2018 to 2022: 2018 2019 2020 2021 2022 Jan – March 21 35 49 60 10 - Apr – June 42 54 55 64 05 July – Sept Oct – Dec 60 91 95 99 12 14 74 80 1. Find the least squares trend line for electricity demand using Exx=0 method. 2. Find the seasonal index for each quarter. 3. Find adjusted seasonal index. 4. Find the De-seasonalized values for Quarter 1, 2,3 and 4 for the year 2023.
From the following annual data of sales (in 000 .$.) Find the trend values by using least square method. Also estimate the sales of 2014. Year 2004 2005 2006 2007 2008 2009 2010 Sales {In 000 $} 77 88 94 85 91 98 90
A newly operated company producing household items would want to forecast its sales volume for the next month. It has been in operation for ten (10) months now. For the past ten (10) months, forecast and sales data for its top selling Item A are as follow: Time Period Forecasted Value Actual Sales (Quantity) 10th month 405 345 9th month 400 380 8th month 410 400 7th month 370 375 6th month 330 360 5th month 320 325 4th month 320 315 3rd month 305 300 2nd month 290 295 1st month 300 280 The operations manager observes the fluctuations on the sales quantity over the 10-month period that the company is in operation. To forecast for the 11th month, the team decided to evaluate options on what forecasting method to use. Their options are: To use the Moving Average Method using the sales data for the past 10 months, To use the Exponential Smoothing Average assigning a smoothing constant of .6 and To use the Trend-adjusted Exponential Smoothing assigning smoothing constants α =…

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