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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?


A) 100
B) 110
C) 111
D) 114
E) 120

F) B) and C)
G) A) and B)

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good forecaster is one who develops special skills and experience at one forecasting technique and is capable of applying it to widely diverse situations.

A) True
B) False

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The exponential smoothing method requires which of the following data to forecast the future?


A) The most recent forecast
B) Precise actual demand for the past several years
C) The value of the smoothing constant delta
D) Overall industry demand data
E) Tracking values

F) C) and D)
G) B) and E)

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Which of the following is the portion of observations you would expect to see lying within a plus or minus 2 MAD range?


A) 57.04
B) 89.04
C) 98.33
D) 99.86
E) 100.00

F) A) and D)
G) A) and C)

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company wants to forecast demand using the weighted moving average.If the company uses three prior yearly sales values ,and we want to weight year 2014 at 30%,year 2015 at 30% and year 2016 at 40%,which of the following is the weighted moving average forecast for year 2017?


A) 170
B) 168
C) 158
D) 152
E) 146

F) A) and C)
G) B) and D)

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Because the factors governing demand for products are very complex,all forecasts of demand contain error.

A) True
B) False

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In forecasting,RSFE stands for "running sum of forecast errors."

A) True
B) False

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company has a MAD of 10.Its wants to have a 99.7 percent control limits on its forecasting system.It's most recent tracking signal value is 3.1.What can the company conclude from this information?


A) The forecasting model is operating acceptably
B) The forecasting model is out of control and needs to be corrected
C) The MAD value is incorrect
D) The upper control value is less than 20
E) It is using an inappropriate forecasting methodology

F) B) and D)
G) C) and D)

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For every forecasting problem there is one best forecasting technique.

A) True
B) False

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Experience and trial and error are the simplest ways to choose weights for the weighted moving average forecasting model.

A) True
B) False

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Decomposition of a time series means identifying and separating the time series data into its components.

A) True
B) False

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Collaborative Planning,Forecasting,and Replenishment (CPFR) is a web-based tool used to coordinate demand forecasting,production and purchase planning,and inventory replenishment between supply chain trading partners.In practice CPFR often doesn't deliver on its' promise because; 1) Computer systems at supplier companies cannot be made to work with each other. 2) Forecast errors accumulate as data exchanges are made down the supply chain culminating in the "feast or famine" phenomena known as the "bullwhip effect." 3) Firms in a supply chain may not trust each other sufficiently to share information openly. 4) conflicting objectives between the profit-maximizing vendor and the cost-minimizing customer give rise to adversarial supply chain relationships


A) All of these
B) 2 and 4 above
C) 1 and 3 above
D) 1,3,and 4 above
E) 3 and 4 above

F) None of the above
G) C) and E)

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In the simple exponential smoothing forecasting model you need at least 30 observations to set the smoothing constant alpha.

A) True
B) False

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Continual review and updating in light of new data is a forecasting technique called second-guessing.

A) True
B) False

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The equation for exponential smoothing states that the new forecast is equal to the old forecast plus the error of the old forecast.

A) True
B) False

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company wants to forecast demand using the simple moving average.If the company uses three prior yearly sales values ,which of the following is the simple moving average forecast for year 2017?


A) 100.5
B) 122.5
C) 133.3
D) 135.6
E) 139.3

F) None of the above
G) A) and B)

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Bayesian analysis is the simplest way to choose weights for the weighted moving average forecasting model.

A) True
B) False

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The least squares method refers to:


A) A computation in linear regression
B) Selecting participants for the Delphi Technique
C) Time series decomposition into smaller and smaller units
D) Determining the smallest sources of error in a forecast
E) Calculating the running sum of forecast errors

F) A) and B)
G) A) and C)

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In a forecasting model using simple exponential smoothing the data pattern should remain stationary.

A) True
B) False

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MAD statistics can be used to generate tracking signals.

A) True
B) False

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