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Exam 3: Forecasting

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Question 21

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Data on the last three years of demand are available as follows: $\begin{array} { l l l l l } &&& { \text { Season } } \\\text { Year } & \text { Spring } & \text { Summer } & \text { Fall } & \text { Winter } \\\hline \text { Three years ago } & 18 & 10 & 26 & 42 \\\text { Two years ago } & 26 & 18 & 34 & 50 \\\text { Last year } & 34 & 26 & 42 & 58\end{array}$ What is the linear regression trend line for these data (t = 0 for spring, three years ago)?

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Question 22

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Given the following data, develop a linear regression model for y as a function of x. $\begin{array} { l l } \mathrm { x } & \mathrm { y } \\\hline 2 & 20 \\4 & 25 \\6 & 30 \\6 & 32 \\8 & 40\end{array}$

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Question 23

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Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast.

For the data given below, what would the naive forecast be for period 5? $\begin{array} { l l } \text { Period } & \text { Demand } \\\hline 1 & 58 \\2 & 59 \\3 & 60 \\4 & 61\end{array}$

Given the following historical data and weights of .5, .3, and .2, what is the three-period moving average forecast for period 5? $\begin{array} { l l l l } \text { Period } & \text { Value } & \text { Period } & \text { Value } \\\hline 1 & 138 & 3 & 148 \\2 & 142 & 4 & 144\end{array}$

Correlation measures the strength and direction of a relationship between variables.

The president of State University wants to forecast student enrollments for this academic year based on the following historical data: $\begin{array} { l l } \text { Year } & \text { Enrollments } \\\hline \text { 5 years ago } & 15,000 \\4 \text { years ago } & 16,000 \\\text { 3 years ago } & 18,000 \\2 \text { years ago } & 20,000 \\\text { Last year } & 21,000\end{array}$ What is the forecast for this year using a four-year simple moving average?

Which of the following is a potential shortcoming of using sales force opinions in demand forecasting?

Forecasts based on time-series (historical) data are referred to as associative forecasts.

A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD.

The shorter the forecast period, the more accurately the forecasts tend to track what actually happens.

The primary method for associative forecasting is:

Which of the following might be used to indicate the cyclical component of a forecast?

A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data: $\begin{array} { l l } \text { Year } & \text { Attendance } \\\hline \text { Four years ago } & 10,000 \\\text { Three years ago } & 12,000 \\\text { Two years ago } & 18,000 \\\text { Last Year } & 20,000\end{array}$ What is this year's forecast using the naive approach?

The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data: $\begin{array} { l l } \text { Month } & \text { Number of Visits } \\\hline \text { April } & 100 \\\text { May } & 140 \\\text { June } & 110 \\\text { July } & 150 \\\text { August } & 120 \\\text { September } & 160\end{array}$ What is this month's forecast using a four-month weighted moving average with weights of .4, .3, .2, and .1?

Exponential smoothing adds a percentage (called alpha) of the last period's forecast to estimate the next period's demand.

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: $\begin{array} { l l } \text { Year } & \text { Demand } \\\hline \text { 5 years ago } & 900 \\\text { 4 years ago } & 700 \\\text { 3 years ago } & 600 \\\text { 2 years ago } & 500 \\\text { Last year } & 300\end{array}$ What is the forecast for this year using a three-year weighted moving average with weights of .5, .3, and .2?

Given forecast errors of -5, -10, and +15, the MAD is:

A manager is using exponential smoothing to predict merchandise returns at a suburban branch of a department store chain. Given a previous forecast of 140 items, an actual number of returns of 148 items, and a smoothing constant equal to .15, what is the forecast for the next period?

Consider the data below: $\begin{array} { l l } \text { Period } & \text { Demand } \\\hline 11 & 81 \\12 & 75 \\13 & 82\end{array}$ Using exponential smoothing with alpha = .2, and assuming the forecast for period 11 was 80, what would the forecast for period 14 be?