The monthly sales for Yazici Batteries, Inc., were as follows:MonthJanFebMarApr MayJunJulAug SeptOctNovDecSales202116141116171922202024This exercise contains only parts b and c.b) The forecast for the next month (Jan) using the naive method =sales (round your response to a whole number).The forecast for the next period (Jan) using a 3-month moving average approach =sales (round your response to two decimal places).The forecast for the next period (Jan) using a 6-month weighted average with weights of 0.10, 0.10, 0.10, 0.20, 0.20, and 0.30, where the heaviestweights are applied to the most recent month = sales (round your response to one decimal place).sales (round your responseUsing exponential smoothing with a = 0.30 and a September forecast of 18.00, the forecast for the next period (Jan) =to two decimal places).Using a method of trend projection, the forecast for themonth (Jan) = sales (round your response to two decimal places).c) The method that can be used for making a forecast for the month of March is

Answered: b) The forecast for the next month…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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9) Gasoline sales Times Series week sales (1000s of gallons 1 17 2 21 3 19 4 23 5 18 6 16 7 20 8 18 9 22 10 20 11 15 12 22 With the above gasoline time series data, show the exponential smoothing forecast using alpha = 0.1 i)Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of Alpha= 0.1 or alpha=0.2 for the gasoline sales time series? ii)Are the results the same if you apply MAE as the measure of accuracy? iii)What are the results if MAPE is used?
Here are the actual tabulated demands for an item for a nine-month period (January through September). Your supervisor wants to test two forecasting methods to see which method was better over this period. MONTH ACTUAL January 120 February 145 March 146 April 171 May 154 June 182 July 138 August 135 September 146 a. Forecast April through September using a three-month moving average. b. Use simple exponential smoothing with an alpha of 0.20 to estimate April through September, using the average of January through March as the initial forecast for April. c-1. Calculate MAD for Three-month moving average and Exponential smoothing. c-2. Use MAD to decide which method produced the better forecast over the six-month period.
The following data are for calculator sales in units at an electronics store over the past nine weeks: Week Sales Week Sales 1 45 6. 53 2 50 7 59 3 44 59 4 51 9 64 5 57 Use trend projection with regression to forecast sales for weeks 10 - 13. What are the error measures (CFE, MSE, G. MAD, and MAPE) for this forecasting procedure? How about ? Obtain the trend projection with regression forecast for weeks 10 - 13. (Enter your responses rounded to two decimal places.) Period Forecast, F, 10 11 12 13
Centerville Bikes and Stuff (CBS) sells motorcycles and accessories. The number of helmets sold by CBS per week for the past six weeks follows. Week 1 2 3 Value 17 12 17 (a) mean absolute error MAE = (b) mean squared error MSE = Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. 4 % 5 6 (d) What is the forecast for week 7? 10 16 15 (c) mean absolute percentage error (Round your answer to two decimal places.) MAPE =
Develop two exponential smoothing forecasts for periods 2 through 11. For the first forecast, use a=0.1. For the second, use a=0.7. Assume that your forecast for period 1 was 205. Click the icon to view the time series data. Find the exponential smoothing forecast for each period when a=0:1 (enter your responses rounded to one decimal place). Period Forecast (0.1) 205 1 2 More Info Period 1 2 3 4 5 6 7 8 9 10 Print Demand 228 249 229 238 231 154 166 157 Demand 228 249 164 161 Done
Stationarity in a time Series refers to A.) The size of the RMSE of a Forecasting model B) the size of Variances of the model's estimates C) a method of forecast optimization D) lack of trend in a given time Series E) None of the options are correct.
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 .1, .3, and .6, using .6 for the most recent week. Forecast demand for the week of October 12. c) compute the forecast for the week of Oct 12 using exponential smoothing with a forecast for august 31 of 360 and alpha 0.2
January February March April May June a. Calculate forecasts for the remaining five months using simple exponential smoothing with a = 0.4. February March April May June ACTUAL FORECAST 100 85 96 103 82 66 90 MAD Forecasts 91 93 97 91 81 b. Calculate the MAD for all the forecasts, including January's. (Round your answer to 1 decimal place.)
Centerville Bikes and Stuff (CBS) sells motorcycles and accessories. The number of helmets sold by CBS per week for the past six weeks follows. Week 1 2 Value 17 14 15 3 (b) mean squared error MSE = % 4 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. (a) mean absolute error MAE = 5 6 (d) What is the forecast for week 7? 12 16 15 (c) mean absolute percentage error (Round your answer to two decimal places.) MAPE =
b. Compute 2s control limits for each forecast. (Do not round your intermediate calculations. Round your answers to 2 decimal places.) Forecast Method 1 Method 2 Control Limits
Let's say you are playing the stock market and below period 2020 data was provided. For "stock A" you use a 2 month moving average. For "stock B" you use exponential smoothing with (a = 0.3). What is the Forecast in Stock B for January 2021? Stock A Stock B ... Jan 0.11 Jan 14 Feb 0.20 Feb 11 Mar 0.03 Mar Apr May 1.20 Apr May 6. 0.50 9 Jun 0.03 Jun 12 Jul 0.10 Jul 16 Aug 0.11 Aug Sep 14 Sep 0.56 8 Oct 0.78 Oct Nov 0.44 Nov 4 Dec 0.10 Dec
After using your forecasting model for six months, you decide to test it using a tracking signal. Here are the forecast and actual demands for the six-month period: PERIOD FORECAST ACTUAL May 450 500 June 500 550 July 550 400 August 600 500 September 650 675 October 700 600 Find the tracking signal of each month

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