1. United Dairies, Ic., supplies milk to several independent grocers throughout Dade County, Florida. Managers at United Dairieswant to develop a forecast of the number of half gallons of milk sold per week. Sales data for the past 12 weeks are:weekSalesweeksales1275033002310083100325029504280030001029001132003050123150a. Construct a time series plot. What type of pattern exists in the data?b. Compare a two-week moving average forecast with an exponential smoothing forecast for a = 0.4, which provides the betterforecasts using MSE as the measure of model accuracy?c. Create a linear trend projection model to make a sales forecast and evaluate the accuracy of the sales forecast.d. Based on the results, which is the best forecasting model to be used to forecast the 13th week sales?

Forecasting is predicting in advance the values through various methods like moving averages,…

Answered: a. Construct a time series plot. What…

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?
12-1. The Hartley-Davis motorcycle dealer in the Minneapolis- St. Paul area wants to be able to forecast accurately the de- mand for the Roadhog Super motorcycle during the next month. From sales records, the dealer has accumulated the data in the following table for the past year. Month January February March April Мay June Motorcycle Sales 7 10 8 7 12 July August September 10 11 12 October 10 November December 14 16 a Compute a three-month moving average forecast of demand for April through January (of the next year). b. Compore a five-month moving average forecast for June drough January. c. Compare the two forecasts computed in parts (a) and (b) using MAD. Which one should the dealer use for January of dhe next year?
2. Solve using Excel. The monthly sales for Telco Batteries, Inc., were as follows: Month Sales January February March April May 20 21 15 14 13 June 16 July August September 17 18 20 October 20 November 21 December 23 a. Plot the monthly sales data. Do you observe any trend, cycles or random variations? b. Forecast January sales using each of the following: a. Naïve method b. A 3-month moving average c. A 6-month weighted moving average using 0.1, 0.1, 0.1, 0.2, 0.2 and 0.3 with the heaviest weights applied to the most recent months d. Exponential smoothing using an x = 0.3 and a September forecast of 18. c. Compute the MAD and MAPE for the naïve method and 3-month moving average.
4.29 The number of disk drives (in millions) made at a plant in Taiwan during the past 5 years follows: YEAR DISK DRIVES 1 140 160 3 190 4 200 5 210 a) Forecast the number of disk drives to be made next year, using linear regression. b) Compute the mean squared error (MSE) when using linear regression. c) Compute the mean absolute percent error (MAPE). PX
4 Trinity General Hospital had the following number of patient admissions during the past 8 weeks Patient Week Admissions 1 120 145 3 95 4 112 5 130 6. 110 7 100 140 Develop a 3-weck weighted average forecast for Week 4 through 9 with weights a W1 = 0.2 W2 = 0.3 W3 = 0.5 Forecast patient admissions for week 9 using simple | exponential smoothing with a = 0.2. Assume that the forecast for Week 2 (F2) is the naïve forecast.
4. The following data gives the number of battery sales by one of the leading automobile company over the period of last six months. Month Actual Battery Sales January February 39 44 March 40 April May 45 38 June 43 a. Forecast the sales for the month of July using a 3-week moving average method. b. Forecast the sales for the month of July using a 3-week weighted moving average, with weights of 0.5, 0.3 and 0.2. Using 0.5 for the most recent month. c. Compute the forecast for the month of July using exponential smoothing with a = 0.5. (Assume the forecast for January was 39 batteries) d. Compute the errors of the of the forecast based on calculation in c. с.
1. Compute three-period moving average and forecasting errors for the following time series: Period (t): 1 2 3 4 5 6 7 8 9 10 Value (Xt): 15 27 20 14 25 11 15 20 25 22 compute mean absolute deviation (MAD) and mean square error (MSE) and interpret the obtained results. 2. What do you understand by the analysis of time series, explain in details? Give a brief introduction of forecasting.
Week Sales1 27502 31003 32504 28005 29006 30507 33008 31009 295010 300011 320012 3150Develop a three-week moving average. A.What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) B.What is the value of MSE (mean squared error) with regard to a three-week moving average? (Make sure no decimal place and no comma. EX: 1234) C.Develop the exponential smoothing with the alpha value of 0.20. What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) D.What is the value of MSE (mean squared error) with regard to the exponential smoothing? (Make sure no decimal place and no comma. EX: 1234) E.Which method is more accurate? (1 for Moving average, 2 for Exponential Smoothing)
4-Forecasting using Exponential Smoothing The first five periods of demand data are shown in the following table .Let the smoothing coefficient, alpha, equal 0.2.Compute the exponentially smoothed forecasts for periods one through four .Initialize the procedure with a forecast value for period one of 37. Period Aggregate Demand Forecast demand 0 - - 1 38 37 2 42 3 40 4 36 5 42 Determine the Running Sum of Forecast Errors (RSFE), the Mean Absolute Deviation, MADt-1,and the Tracking Signal(TS) at the end of each period. Let the initial MADt-1 for period 0 be equal to 2.
6) 4 Consider the following time series data. Month 1 2 3 4 5 6 7 Value 24 13 20 12 19 23 15 i) Construct a time series plot. What type of pattern exist in the data? ii)Develop a three – week moving average for the time series. Compute MSE and a forecast cast for week 8. iii)Use alpha = 0.2 to compute the exponential smoothing value for the time series. Compute MSE and a forecast for week 8. iv)Compare the three -week moving average forecast with exponential smoothing forecast using alpha = 0.2. Which appears to provide the better forecast based on MSE? Explain v)Use trial and error to find a value of the exponential smoothing. Coefficient Alpha that result in a smaller MSE than what you calculated for alpha = 0.2.
1. Solve manually. The following gives the number of pints of type A blood used at Woodlawn Hospital in the past 6 weeks. Pints Used 360 Week August 31 September 7 September 14 September 21 September 28 389 410 381 368 October 5 374 a. Forecast the demand for the week of October 12 using a 3-week moving average. b. Use a 3-week weighted moving average, with weights of 0.1, 0.3 and 0.6, using 0.6 for the most recent week. Forecast demand for the week of October 12. c. Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 360 and c=0.2. d. Compute the MAD for the 3-week moving average, 3-week weighted moving average and exponential smoothing models above.
The following gives the number of pints of type Bblood used a t Woodlawn Hospital in the past 6 weeks: a) Forecast the dema nd for the week of October 12 using a3-week moving average.b) Use a 3-week weighted moving average, with weights of .I , .3,and .6, using .6 for the most recent week. Forecast demand forthe week of October 12.c) Compute the forecast for the week of October 12using exponentialsmoothing with a forecast for August 31 of 360 and a = .2

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