The monthly sales for Yazici Batteries, Inc., were as follows:MonthSalesJan19Feb Mar1721Apr MayJun Jul Aug Sept Oct12 15 18 15 19 20 20This exercise contains only parts b and c.b) The forecast for the next month (Jan) using the naive method = 22 sales (round your response to a whole number).Nov Dec20 22The forecast for the next period (Jan) using a 3-month moving average approach = 20.67 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 heaviest weights are applied to the mostrecent month = sales (round your response to one decimal place).

Forecasting is the process of predicting future events or trends based on past and present data.…

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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Assume that the data in columns A and B report the new constructions of residential homes, per month, in thousands, from 2015 to 2020 in the state of Oregon. a) Chart the data. Discuss any apparent trend and seasonal patterns. b) Use the "Tahoe Salt" method with linear trend to forecast new constructions for the 12 months of 2021. c) Add your model values and forecast to the data chart. d) Compute the MAD of your model. e) Compute and plot the tracking signal in a new chart. f) Discuss the quality of this model. What is the flagrant flaw? g) Suggest an approach that would yield better results in terms of forecast values, MAD, and tracking signal. An appropriate narrative argument is sufficient (i.e., one or two sentences). You do not need to do calculations for this part.
It wouldnt let me take a screenshot of the table so I had to copy and paste it in here. My question is: How do you Calculate a moving average forecast using the last 2 and 4 dates of stay. What is the prediction for 7/1/2021 for each technique? Discussion Question 1 - 200 room hotel Forecasts MA 2 Errors MA 4 Errors ES (0.8) Errors ES (0.5) Errors Date Day of Week Rooms Sold MA 2 MA 4 ES (0.8) ES (0.5) AE APE AE APE AE APE AE APE 6/1/2021 Tuesday 123 6/2/2021 Wednesday 109 6/3/2021 Thursday 140 6/4/2021 Friday 199 6/5/2021 Saturday 179 6/6/2021 Sunday 140 6/7/2021 Monday 117 6/8/2021 Tuesday 132 6/9/2021 Wednesday 108 6/10/2021 Thursday 151…
The following are monthly actual and forecast demand levels for May through December for units of a product manufactured by the Deborah Bishop Company in Des Moines: Month May June July August September October Actual Demand 105 82 110 112 108 106 130 120 Forecast Demand 100 108 97 98 108 102 105 107 November December For the given forecast, the tracking signal = MADS (round your response to two decimal places).
Tom Simpson, Director of the Chamber of Commerce for Exeter township is investigating the past ten years of tourist visits to the area. The following data has been gathered on number of tourists who signed into the local information center. Year Number of tourists 1 700 2 248 3 633 4 458 5 1410 6 1588 7 1629 8 1301 9 1455 10 1989 What is the forecasts for Year 11 using the simple moving method? (show in calculations in excel file)
Demand for oil changes at Garcia's Garage has been as follows: Month January February March April May June July August Number of Oil Changes 38 55 56 60 58 01 70 52 a. Use simple linear regression analysis to develop a forecasting model for monthly demand. In this application, the dependent variable, Y, is monthly demand and the independent variable, X, is the month. For January, let X=1; for February, let X 2; and so on. The forecasting model is given by the equation Y=X (Enter your responses rounded to two decimal places)
discuss the strategic importance of forecasting for Disney and demonstrate how Disney World systematically uses the first seven steps in the forecasting process to initiate, design and implement forecasting systems
Use regression or simple exponential smoothing with the following data. For exponential smoothing, assume the forecast for year 1 was the same as the actual and use an alpha of 0.7. (use excel to show formauls used) Year Sales 1 290 2 340 3 400 4 410 5 400 1. What is the tracking signal for the forecast?
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.
A check-processing center uses exponetial smoothing to forecast the number of incoming checks each month. the number of checks recewived in June was 40 million while the forecast was 42 million. A smoothibg constant of 0.15 is used. a) Using exponential smoothing and given a the forecast for the month of July={__} million checks received (round your response to one decimal place
If month one has 240,000 demands, month two 250,080 demands, month three 325,00 demands, month four 370,000 demands, month five 420,000 demands and month six 509,00 demands. Use a forecast for the first month of 240,000, an initial trend forecast of 50,000, and smoothing parameters of 0.35 for both demand smoothing and trend smoothing. Compute the forecasts and trends using double exponential smoothing
Which of the following are common metrics for measuring the relative quality of different forecasting methods? OAbsolutely Mean Mean Squared Accuracy O Mean Absolute Percent Error Mean Squared Response OMean Squared Error Mean Absolute Error (a.k.a. Mean Absolute Deviation)
d) Using exponential smoothing with α = 0.40 and the forecast for year 1 being 3,000, the forecast for year 6 = ? miles (round your response to the nearest whole number).

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