Using the data shown below in Table 1, compute the mean square error, mean absolute deviation, and mean absolute percentage error for the forecasts shown and then answer Questions Table 1 \(\begin{array}{|l|c|c|} \hline \text { Month } & \text { Forecast Demand } & \text { Actual Demand } \\ \hline \text { April } & 170 & 180 \\ \hline \text { May } & 225 & 200 \\ \hline \text { June } & 210 & 200 \\ \hline \text { July } & 260 & 240 \\ \hline \text { August } & 200 & 230 \\ \hline \end{array}\) -The mean absolute deviation (MAD) is:

A) less than or equal to 20. B) more than 20 but less than or equal to 30. C) more than 30 but less than or equal to 40. D) more than 40 but less than or equal to 50. A) less than or equal to 20. B) more than 20 but less than or equal to 30. C) more than 30 but less than or equal to 40. D) more than 40 but less than or equal to 50.

\(\begin{array}{|l|l|l|l|} \hline\text { Month } & \text { Sales } & \text { Absolute Error, Forecast 1 } & \text { Absolute Error, Forecast 2 } \\ \hline \text { Jan } & 35 & 5 & 2 \\ \hline \text { Feb } & 29 & 1 & 3 \\ \hline \text { Mar } & 39 & 4 & 4 \\ \hline \text { Apr } & 42 & 2 & 3 \\ \hline \text { May } & 51 & 3 & 1 \\ \hline \text { Jun } & 56 & 1 & 4 \\ \hline \end{array}\) -All of the following are important concepts in forecasting EXCEPT:

A) determining the planning horizon length. B) determining the time bucket size (i.e., year, quarter, month, week, day, etc.). C) determining a relationship between a single dependent variable and one or more independent variables. D) identifying cyclical patterns. A) determining the planning horizon length. B) determining the time bucket size (i.e., year, quarter, month, week, day, etc.). C) determining a relationship between a single dependent variable and one or more independent variables. D) identifying cyclical patterns.

Using the data shown below in Table 3 for the sales of a new CD at a store for the last 4 weeks, answer the questions Table 3 \[\begin{array} { | l | c | c | c | c | } \hline \text { Week } & 1 & 2 & 3 & 4 \\ \hline \text { Sales } & 112 & 105 & 125 & 118 \\ \hline \end{array}\] -The actual sales for week 5 were 105 units. The four-period moving average forecast for week 6 is:

A) less than or equal to 100. B) more than 100 but less than or equal to 110. C) more than 110 but less than or equal to 120. D) more than 120 but less than or equal to 130. A) less than or equal to 100. B) more than 100 but less than or equal to 110. C) more than 110 but less than or equal to 120. D) more than 120 but less than or equal to 130.

Using the data shown below in Table 1, compute the mean square error, mean absolute deviation, and mean absolute percentage error for the forecasts shown and then answer Questions Table 1 \(\begin{array}{|l|c|c|} \hline \text { Month } & \text { Forecast Demand } & \text { Actual Demand } \\ \hline \text { April } & 170 & 180 \\ \hline \text { May } & 225 & 200 \\ \hline \text { June } & 210 & 200 \\ \hline \text { July } & 260 & 240 \\ \hline \text { August } & 200 & 230 \\ \hline \end{array}\) -The mean absolute percentage error (MAPE) is:

A) less than or equal to 5%. B) more than 5% but less than or equal to 10%. C) more than 10% but less than or equal to 15%. D) more than 15% but less than or equal to 20%. A) less than or equal to 5%. B) more than 5% but less than or equal to 10%. C) more than 10% but less than or equal to 15%. D) more than 15% but less than or equal to 20%.

Exam 11: Forecasting and Demand Planning

The Espresso Cart had the following pattern of espresso sales over the last two weeks: Week 1 Week 2 Monday 873 Monday 912 Tuesday 904 Tuesday 859 Wednesday 911 Wednesday 906 Thursday 887 Thursday 900 Friday 899 Friday ? What is the forecast for Friday's sales using a three-day moving average?

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Question 1

Correct Answer:

Verified

The forecast for the next period (t + 1) is denoted as F_t+1 = (A_t+ A_t-1+ A_t-2 + … A_t-k+1)/k.F (Friday) = (859 + 906 + 900)/3 = 888.33

State Division of Motor Vehicles (DMV) statistics show the rate of new driver's license applications as shown below: Month Week Applications March 1 207 2 211 3 196 4 206 Apri1 1 238 2 199 3 215 4 212 a.Using a three-week moving average, what is the forecast for the first week in April? b.Using a four-week moving average, what is the forecast for the first week in April?

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Question 2

Correct Answer:

Verified

The forecast for the next period (t + 1) is denoted as F_t+1 = (A_t+ A_t-1+ A_t-2 + … A_t-k+1)/k.a.F(April, 1) = (211 + 196 + 206)/3 = 613/3 = 204.33
b.F(April, 1) = (207 + 211 + 196 + 206)/4 = 820/4 = 205

An R² of 0.70 mean 30% of the variability in the dependent variable was explained by the independent variable.

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(True/False)

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Question 3

Correct Answer:

Verified

False

A tracking signal provides a method for monitoring a forecast by quantifying _____.

(Multiple Choice)

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Question 4

Aggregate forecasts are generally much easier to develop whereas detailed forecasts require more time and resources.

(True/False)

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Question 5

In forecasting, irregular variation that is explainable can normally be discarded.

(True/False)

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Question 6

As the value of k is increased in a moving average forecasting model, the forecast reacts more slowly to recent changes in the time series.

(True/False)

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Question 7

Using the data shown below in Table 1, compute the mean square error, mean absolute deviation, and mean absolute percentage error for the forecasts shown and then answer Questions Table 1 Month Forecast Demand Actual Demand April 170 180 May 225 200 June 210 200 July 260 240 August 200 230 -The mean absolute deviation (MAD) is:

(Multiple Choice)

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Question 8

Based on the information shown below in Table 5, develop a forecast for June using both the two-period moving average model and the exponential smoothing model with ? = 0.10. For the exponential smoothing model, assume the forecast for February is 800. Answer questions Table 5 Month Actual Demand February 850 March 900 April 975 May 950 -The exponential smoothing model forecast for June is _____.

(Multiple Choice)

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Question 9

Trends are characterized by repeatable periods of ups and downs over short periods of time.

(True/False)

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Question 10

Month Sales Absolute Error, Forecast 1 Absolute Error, Forecast 2 Jan 35 5 2 Feb 29 1 3 Mar 39 4 4 Apr 42 2 3 May 51 3 1 Jun 56 1 4 -All of the following are important concepts in forecasting EXCEPT:

(Multiple Choice)

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Question 11

A single moving average is most appropriate for data with identifiable trends.

(True/False)

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Question 12

Based on the information shown below, develop forecasts for June using both a two-period moving average model and an exponential smoothing model with α = 0.10. For the exponential smoothing model, assume the forecast for February was 800. Month Actual Demand February 850 March 900 April 975 May 950

(Essay)

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Question 13

Mean absolute deviation (MAD), mean square error (MSE), and mean absolute percentage error (MAPE) forecast error metrics generally giving similar numerical results so it does not matter which one is used.

(True/False)

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Question 14

A seven-month simple moving average would approximately equate with alpha (α) factor _____ for simple exponential smoothing.

(Multiple Choice)

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Question 15

Ed Rogers owns an appliance store. Sales data on a particular model of a DVD player for the past six months are: Month Sales Jan 35 Feb 29 Mar 39 Apr 42 May 51 Jun 56 Forecast the sales for July using an exponential smoothing model with a smoothing constant of 0.40. Assume that the forecast for May was 36.25.

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Question 16

Define regression analysis and explain how it is an approach to forecasting.

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Question 17

Regression models are often used in forecasting to incorporate causal variables that may influence a time series.

(True/False)

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Question 18

Using the data shown below in Table 3 for the sales of a new CD at a store for the last 4 weeks, answer the questions Table 3 Week 1 2 3 4 Sales 112 105 125 118 -The actual sales for week 5 were 105 units. The four-period moving average forecast for week 6 is:

(Multiple Choice)

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Question 19

Using the data shown below in Table 1, compute the mean square error, mean absolute deviation, and mean absolute percentage error for the forecasts shown and then answer Questions Table 1 Month Forecast Demand Actual Demand April 170 180 May 225 200 June 210 200 July 260 240 August 200 230 -The mean absolute percentage error (MAPE) is:

(Multiple Choice)

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Question 20

The Espresso Cart had the following pattern of espresso sales over the last two weeks: Week 1 Week 2 Monday 873 Monday 912 Tuesday 904 Tuesday 859 Wednesday 911 Wednesday 906 Thursday 887 Thursday 900 Friday 899 Friday ? What is the forecast for Friday's sales using a three-day moving average?

An R² of 0.70 mean 30% of the variability in the dependent variable was explained by the independent variable.

A tracking signal provides a method for monitoring a forecast by quantifying _____.

Aggregate forecasts are generally much easier to develop whereas detailed forecasts require more time and resources.

In forecasting, irregular variation that is explainable can normally be discarded.

As the value of k is increased in a moving average forecasting model, the forecast reacts more slowly to recent changes in the time series.

Trends are characterized by repeatable periods of ups and downs over short periods of time.

Month Sales Absolute Error, Forecast 1 Absolute Error, Forecast 2 Jan 35 5 2 Feb 29 1 3 Mar 39 4 4 Apr 42 2 3 May 51 3 1 Jun 56 1 4 -All of the following are important concepts in forecasting EXCEPT:

A single moving average is most appropriate for data with identifiable trends.

Based on the information shown below, develop forecasts for June using both a two-period moving average model and an exponential smoothing model with α = 0.10. For the exponential smoothing model, assume the forecast for February was 800. Month Actual Demand February 850 March 900 April 975 May 950

Mean absolute deviation (MAD), mean square error (MSE), and mean absolute percentage error (MAPE) forecast error metrics generally giving similar numerical results so it does not matter which one is used.

A seven-month simple moving average would approximately equate with alpha (α) factor _____ for simple exponential smoothing.

Define regression analysis and explain how it is an approach to forecasting.

Regression models are often used in forecasting to incorporate causal variables that may influence a time series.

Using the data shown below in Table 3 for the sales of a new CD at a store for the last 4 weeks, answer the questions Table 3 Week 1 2 3 4 Sales 112 105 125 118 -The actual sales for week 5 were 105 units. The four-period moving average forecast for week 6 is:

Goods, Services, and Operations Management

Value Chains

Measuring Performance in Operations

Operations Strategy

Technology and Operations Management

Goods and Service Design

Process Selection, Design, and Analysis

Facility and Work Designs

Supply Chain Design

Capacity Management

Managing Inventories

Resource Management

Operations Scheduling and Sequencing

Quality Management

Appendix: Quality Management

Quality Control and Spc

Appendix: Queuing Analysis

Appendix: Modeling Using Linear Programming

Lean Operating Systems

Appendix: Simulation

Project Management

Appendix: Decision Analysis

Filters

Exam 11: Forecasting and Demand Planning

The Espresso Cart had the following pattern of espresso sales over the last two weeks: Week 1 Week 2 Monday 873 Monday 912 Tuesday 904 Tuesday 859 Wednesday 911 Wednesday 906 Thursday 887 Thursday 900 Friday 899 Friday ? What is the forecast for Friday's sales using a three-day moving average?

An R2 of 0.70 mean 30% of the variability in the dependent variable was explained by the independent variable.

A tracking signal provides a method for monitoring a forecast by quantifying _____.

Aggregate forecasts are generally much easier to develop whereas detailed forecasts require more time and resources.

In forecasting, irregular variation that is explainable can normally be discarded.

As the value of k is increased in a moving average forecasting model, the forecast reacts more slowly to recent changes in the time series.

Trends are characterized by repeatable periods of ups and downs over short periods of time.

Month Sales Absolute Error, Forecast 1 Absolute Error, Forecast 2 Jan 35 5 2 Feb 29 1 3 Mar 39 4 4 Apr 42 2 3 May 51 3 1 Jun 56 1 4 -All of the following are important concepts in forecasting EXCEPT:

A single moving average is most appropriate for data with identifiable trends.

Mean absolute deviation (MAD), mean square error (MSE), and mean absolute percentage error (MAPE) forecast error metrics generally giving similar numerical results so it does not matter which one is used.

A seven-month simple moving average would approximately equate with alpha (α) factor _____ for simple exponential smoothing.

Define regression analysis and explain how it is an approach to forecasting.

Regression models are often used in forecasting to incorporate causal variables that may influence a time series.

Using the data shown below in Table 3 for the sales of a new CD at a store for the last 4 weeks, answer the questions Table 3 Week 1 2 3 4 Sales 112 105 125 118 -The actual sales for week 5 were 105 units. The four-period moving average forecast for week 6 is:

Goods, Services, and Operations Management

Value Chains

Measuring Performance in Operations

Operations Strategy

Technology and Operations Management

Goods and Service Design

Process Selection, Design, and Analysis

Facility and Work Designs

Supply Chain Design

Capacity Management

Managing Inventories

Resource Management

Operations Scheduling and Sequencing

Quality Management

Appendix: Quality Management

Quality Control and Spc

Appendix: Queuing Analysis

Appendix: Modeling Using Linear Programming

Lean Operating Systems

Appendix: Simulation

Project Management

Appendix: Decision Analysis

Filters

An R² of 0.70 mean 30% of the variability in the dependent variable was explained by the independent variable.