Jun 25 2020

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Chapter 03: Understanding the Forecasting Process

Forecasting techniques generally assume an existing causal system that will continue to exist in the future.
TRUE

Forecasts depend on the rules of the game remaining reasonably constant.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Medium
Learning Objective: 03-03 Evaluate at least three qualitative forecasting techniques and the advantages and disadvantages of each.
Topic Area: Features Common to All Forecasts

For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques.
FALSE

If growth is strong, alpha should be large so that the model will catch up more quickly.

AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Medium
Learning Objective: 03-05 Describe averaging techniques; trend and seasonal techniques; and regression analysis; and solve typical problems.
Topic Area: Forecasts Based on Time-Series Data

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

Flexibility to accommodate major changes is important to good forecasting.

AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Easy
Learning Objective: 03-02 Outline the steps in the forecasting process.
Topic Area: Steps in the Forecasting Process

Forecasts for groups of items tend to be less accurate than forecasts for individual items because forecasts for individual items don’t include as many influencing factors.
FALSE

Forecasting for an individual item is more difficult than forecasting for a number of items.

AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Medium
Learning Objective: 03-02 Outline the steps in the forecasting process.
Topic Area: Features Common to All Forecasts

Forecasts help managers plan both the system itself and provide valuable information for using the system.
TRUE

Both planning and use are shaped by forecasts.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Easy
Learning Objective: 03-01 List the elements of a good forecast.
Topic Area: Introduction

Organizations that are capable of responding quickly to changing requirements can use a shorter forecast horizon and therefore benefit from more accurate forecasts.
TRUE

If an organization can react quicker, its forecasts need not be so long term.

AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Medium
Learning Objective: 03-02 Outline the steps in the forecasting process.
Topic Area: Elements of a Good Forecast

When new products or services are introduced, focus forecasting models are an attractive option.
FALSE

Because focus forecasting models depend on historical data, they’re not so attractive for newly introduced products or services.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Easy
Learning Objective: 03-05 Describe averaging techniques; trend and seasonal techniques; and regression analysis; and solve typical problems.
Topic Area: Forecasts Based on Time-Series Data

The purpose of the forecast should be established first so that the level of detail, amount of resources, and accuracy level can be understood.
TRUE

All of these considerations are shaped by what the forecast will be used for.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Medium
Learning Objective: 03-02 Outline the steps in the forecasting process.
Topic Area: Steps in the Forecasting Process

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

Forecasts based on time series data are referred to as time-series forecasts.

Time series techniques involve identification of explanatory variables that can be used to predict future demand.
FALSE

Associate forecasts involve identifying explanatory variables.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Hard
Learning Objective: 03-04 Compare and contrast qualitative and quantitative approaches to forecasting.
Topic Area: Forecasts Based on Time-Series Data

A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys.
FALSE

Most people do not enjoy participating in surveys.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Easy
Learning Objective: 03-03 Evaluate at least three qualitative forecasting techniques and the advantages and disadvantages of each.
Topic Area: Qualitative Forecasts

The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast.
TRUE

A consensus among divergent perspectives is developed using questionnaires.

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

Exponential smoothing adds a percentage to the last period’s forecast error.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Medium
Learning Objective: 03-05 Describe averaging techniques; trend and seasonal techniques; and regression analysis; and solve typical problems.
Topic Area: Forecasts Based on Time-Series Data

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

Long-term forecasting is much more difficult to do accurately.

AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Easy
Learning Objective: 03-07 Compare two ways of evaluating and controlling forecasts.
Topic Area: Monitoring the Forecast

Forecasting techniques that are based on time series data assume that future values of the series will duplicate past values.
FALSE

Time-series forecast assume that future patterns in the series will mimic past patterns in the series.

Trend adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period’s actual demand.
FALSE

Trend adjusted smoothing smoothes both random and trend-related variation.

Forecasts based on an average tend to exhibit less variability than the original data.
TRUE

Averaging is a way of smoothing out random variability.

The naive approach to forecasting requires a linear trend line.
FALSE

The naïve approach is useful in a wider variety of settings.

The naive forecast is limited in its application to series that reflect no trend or seasonality.
FALSE

When a trend or seasonality is present, the naïve forecast is more limited in its application.

AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Easy
Learning Objective: 03-05 Describe averaging techniques; trend and seasonal techniques; and regression analysis; and solve typical problems.
Topic Area: Forecasts Based on Time-Series Data

The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques.
TRUE

Often the naïve forecast performs reasonably well when compared to more complex techniques.

A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average.
FALSE

More data points reduce a moving average forecast’s responsiveness.

In order to update a moving average forecast, the values of each data point in the average must be known.
TRUE

The moving average cannot be updated until the most recent value is known.

AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Hard
Learning Objective: 03-05 Describe averaging techniques; trend and seasonal techniques; and regression analysis; and solve typical problems.
Topic Area: Forecasts Based on Time-Series Data

Forecasts of future demand are used by operations people to plan capacity.
TRUE

Capacity decisions are made for the future and therefore depend on forecasts.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Easy
Learning Objective: 03-01 List the elements of a good forecast.
Topic Area: Introduction

An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago.
TRUE

Weighted moving averages can be adjusted to make more recent data more important in setting the forecast.

Exponential smoothing is a form of weighted averaging.
TRUE

The most recent period is given the most weight, but prior periods also factor in.

A smoothing constant of .1 will cause an exponential smoothing forecast to react more quickly to a sudden change than a smoothing constant value of .3.
FALSE

Smaller smoothing constants result in less reactive forecast models.

The T in the model TAF = S+T represents the time dimension (which is usually expressed in weeks or months).
FALSE

The T represents the trend dimension.

Trend adjusted exponential smoothing requires selection of two smoothing constants.
TRUE

One is for the trend and one is for the random error.

An advantage of “trend adjusted exponential smoothing” over the “linear trend equation” is its ability to adjust over time to changes in the trend.
TRUE

A linear trend equation assumes a constant trend; trend adjusted smoothing allows for changes in the underlying trend.

A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend.
TRUE

Seasonal relatives are used when the seasonal effect is multiplicative rather than additive.

In order to compute seasonal relatives, the trend of past data must be computed or known which means that for brand new products this approach can’t be used.
TRUE

Computing seasonal relatives depends on past data being available.

Removing the seasonal component from a data series (de-seasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative.
TRUE

Deseasonalized data points have been adjusted for seasonal influences.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Hard
Learning Objective: 03-05 Describe averaging techniques; trend and seasonal techniques; and regression analysis; and solve typical problems.
Topic Area: Forecasts Based on Time-Series Data

If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of regression analysis.
TRUE

Patterns reflect influences such as trends or seasonality that go against regression analysis assumptions.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Hard
Learning Objective: 03-08 Assess the major factors and trade-offs to consider when choosing a forecasting technique.
Topic Area: Associative Forecasting Techniques

Curvilinear and multiple regression procedures permit us to extend associative models to relationships that are non-linear or involve more than one predictor variable.
TRUE

Regression analysis can be used in a variety of settings.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Medium
Learning Objective: 03-08 Assess the major factors and trade-offs to consider when choosing a forecasting technique.
Topic Area: Associative Forecasting Techniques

The sample standard deviation of forecast error is equal to the square root of MSE.
TRUE

The MSE is equal to the sample variance of the forecast error.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Easy
Learning Objective: 03-07 Compare two ways of evaluating and controlling forecasts.
Topic Area: Forecast Accuracy

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

The association between two variations is summarized in the correlation coefficient.

MAD is equal to the square root of MSE which is why we calculate the easier MSE and then calculate the more difficult MAD.
FALSE

MAD is the mean absolute deviation.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Medium
Learning Objective: 03-06 Explain three measures of forecast accuracy.
Topic Area: Forecast Accuracy

In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naïve forecast would yield.
TRUE

With alpha equal to 1 we are using a naïve forecasting method.

A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern.
FALSE

Forecast methods are generally considered to be performing adequately when the errors appear to be randomly distributed.

AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Medium
Learning Objective: 03-07 Compare two ways of evaluating and controlling forecasts.
Topic Area: Forecast Accuracy

A control chart involves setting action limits for cumulative forecast error.
FALSE

Control charts set action limits for the tracking signal.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Medium
Learning Objective: 03-07 Compare two ways of evaluating and controlling forecasts.
Topic Area: Monitoring the Forecast

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

Large absolute values of the tracking signal suggest a fundamental change in the forecast model’s performance.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Medium
Learning Objective: 03-07 Compare two ways of evaluating and controlling forecasts.
Topic Area: Monitoring the Forecast

The use of a control chart assumes that errors are normally distributed about a mean of zero.
TRUE

Over time, a forecast model’s tracking signal should fluctuate randomly about a mean of zero.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Hard
Learning Objective: 03-07 Compare two ways of evaluating and controlling forecasts.
Topic Area: Monitoring the Forecast

Bias exists when forecasts tend to be greater or less than the actual values of time series.
TRUE

A tendency in one direction is defined as bias.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Easy
Learning Objective: 03-07 Compare two ways of evaluating and controlling forecasts.
Topic Area: Monitoring the Forecast

Bias is measured by the cumulative sum of forecast errors.
TRUE

Bias would result in the cumulative sum of forecast errors being large in absolute value.

AACSB: Reflective Thinking
Bloom’s: Remember
Difficulty: Medium
Learning Objective: 03-07 Compare two ways of evaluating and controlling forecasts.
Topic Area: Monitoring the Forecast