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:MonthMayJuneJulyAugustSeptemberOctoberFor the given forecast, the tracking signal ==MADS (round your response to two decimal places).NovemberDecemberActual Demand100801/10115105110125120Forecast Demand10010499101104104105109

A tracking signal is a tool utilized in statistics and management science to observe forecasts and…

Answered: The following are monthly actual and…

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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A large cement manufacturer uses exponential smoothing to forecast demand for a piece of pollution-control equipment. It appears that an increasing trend is present.Month At1 700 2 685 3 648 4 717 5 713 6 728 7 754 8 762 If the initial forecast for month 1 was 650 units and the trend over that period was 0 units, calculate FIT for the 9-month period. Use smoothing constants, Alpha = 0.1 andAlpha = 0.2.
Since the beginning of the year, the purchase manager at a department store has been recording sales data to be used to compute 4-period moving averages to forecast sales for an upcoming month. Sales data for the months of January through September are reported in the table below. Month January February March April May June July August September Sales 21 20 39 22 29 14 42 28 27 Computed forecast values are not rounded. Compute the four-period moving average forecast for October. value as a whole number by rounding. Specify the Compute the mean absolute deviation (MAD) for the four-period moving average forecasts. Specify the MAD as a whole number by rounding. Compute the mean squared error (MSE) for the four-period moving average forecasts. Specify the MSE as a whole number by rounding. Compute the mean absolute percentage error (MAPE) for the four-period moving average forecasts. Specify the MAPE as a whole number by rounding. CHECK ANSWER
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 =
In a retail store, the actual sales of a particular product (in thousands of units) over the past few months are as follows: Month Sales 1 16 2 22 3 18 4 20 5 23 Using exponential smoothing method with α (smoothing constant) of 0.75 and the given forecast for month 1 equal to 10, what is the forecast for month 6?
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
A company wants to generate a forecast for unit demand for year 2017 using exponential smoothing. The actual demand in year 2016 was 120. The forecast demand in year 2016 was 110. Using this data and a smoothing constant alpha of 0.1, which of the following is the resulting year 2017 forecast value?
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
Given the following data for demand at the XYZ Company, calculate the monthly forecast for 2003 using a 3-month moving average. Calculate the Forecast, Error, MAD (mean absolute percentage error), Bias and the TS (tracking signal) . Give the steps of the calculations in details
The monthly sales for Yazici Batteries, Inc., were as follows: Jan Feb Mar Apr May Jun Jul Aug Sept Oct 21 23 17 14 11 16 16 18 Nov Dec 20 20 20 24 Month Sales This 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 heaviest weights are applied to the most recent month= sales (round your response to one decimal place) Using exponential smoothing with a = 0.30 and a September forecast of 21.00, the forecast for the next period (Jan) = sales (round your response to two decimal places). Using a method of trend projection, the forecast for the next month (Jan) = sales (round your response to two decimal places). c) The method…
Bradley's Copiers sells and repairs photocopy machines. The manager needs weekly forecasts of service calls so that he can schedule service personnel. Use the actual demand in the first period for the forecast for the first week so error measurement begins in the second week. The manager uses exponential smoothing with a = 0.4. Forecast the number of calls for week 6, which is next week. Week Actual Service Calls 26 2 34 38 4 27 5 35 The forecast for week 6 is service calls. (Enter your response rounded to two decimal places.)
The monthly sales for Yazici Batteries, Inc., were as follows: Month Sales Jan 19 Feb Mar 17 21 Apr May Jun Jul Aug Sept Oct 12 15 18 15 19 20 20 This 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 Dec 20 22 The 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 most recent month = sales (round your response to one decimal place).

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