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.
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: Associative Forecasting Techniques
- 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.
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
- 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.
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
- 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.
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
- Forecasts based on an average tend to exhibit less variability than the original data.
TRUE
Averaging is a way of smoothing out random variability.
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
- The naive approach to forecasting requires a linear trend line.
FALSE
The naïve approach is useful in a wider variety of settings.
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 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.
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
- 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.
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
- 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.
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
- Exponential smoothing is a form of weighted averaging.
TRUE
The most recent period is given the most weight, but prior periods also factor in.
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
- 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.
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
- 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.
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
- Trend adjusted exponential smoothing requires selection of two smoothing constants.
TRUE
One is for the trend and one is for the random error.
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
- 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.
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
- 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.
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
- 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.
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
- 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.
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: Associative Forecasting Techniques
- 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.
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
- 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
Chapter 03: Understanding the Forecasting Process
- Seasonal relatives can be used to de-seasonalize data or incorporate seasonality in a forecast.
TRUE
Seasonal relatives are used to de-seasonalize data to forecast future values of the underlying trend, and they are also used to re-seasonalize de-seasonalized forecasts.
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
Chapter 03: Understanding the Forecasting Process
- The best forecast is not necessarily the most accurate.
TRUE
More accuracy often comes at too high a cost to be worthwhile.
AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Medium
Learning Objective: 03-08 Assess the major factors and trade-offs to consider when choosing a forecasting technique.
Topic Area: Elements of a Good Forecast
Chapter 03: Understanding the Forecasting Process
- A proactive approach to forecasting views forecasts as probable descriptions of future demand, and requires action to be taken to meet that demand.
FALSE
Proactive approaches involve taking action to influence demand.
AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Hard
Learning Objective: 03-02 Outline the steps in the forecasting process.
Topic Area: Using Forecast Information
Chapter 03: Understanding the Forecasting Process
- Simple linear regression applies to linear relationships with no more than three independent variables.
FALSE
Simple linear regression involves only one independent variable.
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: Associative Forecasting Techniques
Chapter 03: Understanding the Forecasting Process
- An important goal of forecasting is to minimize the average forecast error.
FALSE
Regardless of the model chosen, so long as there is no fundamental bias average forecast error will be zero.
AACSB: Reflective Thinking
Bloom’s: Understand
Difficulty: Medium
Learning Objective: 03-07 Compare two ways of evaluating and controlling forecasts.
Topic Area: Forecast Accuracy
Chapter 03: Understanding the Forecasting Process
- Forecasting techniques such as moving averages, exponential smoothing, and the naive approach all represent smoothed (averaged) values of time series data.
FALSE
The naïve approach involves no smoothing.
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
Chapter 03: Understanding the Forecasting Process