Exam 5: Demand Forecasting

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Some measures of forecasting accuracy include mean absolute deviation,mean absolute percentage error,and mean squared error.The formula for each is dependent on the forecast error,which is calculated by using the equation:

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Data Set E1 Periqd Sales Volume 1 10000 2 12400 3 14250 4 15750 5 20500 6 18500 7 15750 8 20500 9 21500 10 22550 -Using Data Set E1,what would be the forecast for period 7 using a four period moving average: (Choose the closest answer.)

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In the Delphi forecasting method,a group of internal and external experts are surveyed during several rounds in terms of future events and long-term forecasts of demand but the group members do not physically meet.

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Data Set E1 Periqd Sales Volume 1 10000 2 12400 3 14250 4 15750 5 20500 6 18500 7 15750 8 20500 9 21500 10 22550 -Using Data Set E1,what would be the forecast for period 6 using the exponential smoothing method? Assume the forecast for period 5 is 14000.Use a smoothing constant of a = 0.4 (Choose the closest answer.)

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Use the data set below (Data Set E3)to answer the questions that follow. Data Set E3 Peridd Sales Volume 1 1000 2 1200 3 1450 4 1750 5 2200 6 1750 a.Find the four-period simple moving average forecasts for Periods 5 and 6. b.Find the four-period weighted moving average forecasts for Periods 5 and 6 using weights of 0.05,0.15,0.30,and 0.50 from the earliest period to the latest period,respectively. c.Which set of forecasts is more accurate,the simple moving average forecasts or the weighted moving average forecasts? Why is that set of forecasts more accurate in this particular case (using Data Set E3)? d.Will that type of forecast always be more accurate? Why or why not?

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The objective of CPFR is to optimize the supply chain by improving demand forecast accuracy,delivering the right product at the right time to the right location,reducing inventories across the supply chain,avoiding stock-outs,and improving customer service.As a result of the many benefits attributed to CPFR,this business practice has been widely adopted.

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If a tracking signal is positive,which one of the following is true?

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One of the goals of an effective CPFR system is to minimize the negative impacts of the bullwhip effect on supply chains.

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Inaccurate forecasts can result in negative outcomes like:

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The modern day business environment must deal with a more homogenous consumer base,which has caused the evolution of a more "push" oriented environment where suppliers must focus on manufacturing high volumes of standardized goods.

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What does the acronym CPFR represent?

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According to textbook,which of the following companies is a leading forecasting software provider?

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Define Qualitative Forecasting.Under which circumstances would one utilize qualitative forecasting.

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Data Set E1 Periqd Sales Volume 1 10000 2 12400 3 14250 4 15750 5 20500 6 18500 7 15750 8 20500 9 21500 10 22550 -Using the actual demand shown in the table below,what is the forecast for May (accurate to 1 decimal)using a 4-month weighted moving average and the weights 0.1,0.2,0.3,0.4 (with the heaviest weight applied to the most recent period)? Nuv. Dec. Jan. Feb. Mar. Apr. 39 36 40 38 48 46

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As tighter control limits are instituted for the tracking signal,there is a greater probability of finding exceptions that require no action,but it also means catching changes in demand earlier.

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Data Set E2 Month Actual Farecast 1 10 11 2 8 10 3 9 8 4 6 6 5 7 8 -Based on the information in Data Set E2,what is the mean squared error (accurate to 2 decimals)?

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The following are all common qualitative forecasting models EXCEPT:

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Which of the following indices provided by the Institute for Supply Management (ISM)is considered the most important by economists because it is a composite of five weighted,seasonally adjusted indices?

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Some of the benefits of CPFR include strengthening partner relationships,providing an analysis of sales and order forecasts both upstream and downstream,and allowing collaboration on future requirements and planning.

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The following time-series approach to forecasting uses historical data to generate a forecast and works well when demand is fairly stable over time:

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