Chapter 4: Forecasting

Identify and briefly describe the two general forecasting approaches.

Qualitative and quantitative. Qualitative is relatively subjective, quantitative uses numeric models.

What is qualitative forecasting model, and when is its use appropriate?

Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When quantitative data are difficult to obtain, qualitative models may be appropriate.

Explain the value of seasonal indices in forecasting. How are seasonal patterns different from cyclical patterns?

Seasonal patterns are of fixed duration and repeat regularly. Cycles vary in length and regularity. Seasonal indices allow "generic" forecasts to be made specific to the month, week, etc., of the application.

What is the Mean absolute deviation (MAD)>

The average forecast error using absolute values of the error of each past forecast.

Explain the meaning of the correlation coefficient. Discuss the meaning of a negative value of the correlation coefficient.

The correlation coefficient measures the degree to which the independent and dependent variables more together. A negative value would mean that as X increases, Y tends to fall. The variables both move, but move in opposite.

What is forecast error?

The difference between actual demand and what was forecast.

What is the Mean absolute percent error (MAPE)?

The mean absolute deviation divided by the average demand; the average error expressed as a percentage of demand.

What is the smoothing constant alpha?

The parameter in the exponential smoothing equation that controls the speed of reaction to differences between forecasts and actual demand.

What is decomposition?

The process of identifying and separating time series data into fundamental components such as trend and seasonality.

Give examples of industries in which demand forecasting is dependent on the demand for other products.

There are many examples. Demand for raw materials and component parts such as steel or tires is a function of demand for goods such as automobiles.

Explain why such forecasting devices as moving averages, weighted moving averages, and exponential smoothing are not well suited for data series that have trends.

There is no mechanism for growth in these models; they are built exclusively from historical demand values. Such methods will always lag trends.

What is the purpose of the tracking signal?

Tracking signals alert the user of a forecasting tool to periods in which the forecast was in significant error.

What effect does the value of the smoothing constant have on the weight given to the recent values?

When the smoothing constant, sigma, is large (close to 1.0), more weight is given to recent data; when sigma is low (close to 0.0), more weight is given to past data.

What is weighted moving average?

A forecast made with past data where more recent data are given more significance than older data.

What is linear regression analysis?

A forecasting technique that assumes that past data and future projections fall around a straight line.

What is the Tracking signal?

A measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand.

What is exponential smoothing?

A time series forecasting technique in which each increment of past demand data is decreased by (1 - Alpha).

Define time series.

A time series is a sequence of evenly spaced data points with the four components of trend, seasonality, cyclical, and random variation.

What is the primary difference between a time-series model and an associative model?

A time-series model predicts on the basis of the assumption that the future is a function of the past, whereas an associative model incorporates into the model the variables of factors that might influence the quantity being forecast.

Explain adaptive forecasting.

Adaptive forecasting refers to computer monitoring of tracking signals and self-adjustment if a signal passes its present limit.

What is smoothing constant delta?

An additional parameter used in an exponential smoothing equation that includes an adjustment for trend.

A skeptical manager asks what medium-range forecasts can be used for. Give the manager three possible uses/purposes.

Any three of: sales planning, production planning and budgeting in, cash budgeting, analyzing various operating plans.

How do you choose weights?

Experience and trial and error are the simplest ways to choose weights. As a general rule, the most recent past is the most important indicator of what to expect in the future, and therefore, it should get higher weighting. However, if the data are seasonal, weights should be established accordingly.

What is the basic difference between a weighted moving average and exponential smoothing?

Exponential smoothing is a weighted moving average where all previous values are weighted with a set of weights that decline exponentially.

Which forecasting technique can place the most emphasis on recent values? How does it do this?

Exponential smoothing weighs all previous values with a set of weights that decline exponentially. It can place a full weight on the most recent period (with an alpha of 1.0). This, in effect, is the naive approach, Which places all its emphasis on last period's actual demand.

What is Casual relationship forecasting?

Forecasting using independent variables other than time to predict future demand.

What is the difference between a dependent and an independent variable?

Independent variable (x) is said to explain variations in the dependent variable (y).

What three methods are used to determine the accuracy of any given forecasting method? How would you determine whether time-series regression or exponential smoothing is better in a specific application?

MAD, MSE, and MAPE are common measures of forecast accuracy. To find the more accurate forecasting model, forecast with each tool for several periods where the demand outcome is known, and calculate MSE, MAPE, or MAD for each. The smaller error indicates the better forecast.

Give examples of industries that are affected by seasonality. Why would these businesses want to filter out seasonality?

Nearly every industry has seasonality. The seasonality must be filtered out for good medium-range planning (of production and inventory) and performance evaluation.

Research and briefly describe the Delphi technique. How would it be used by an employer you have worked for?

1. Assembling a group of experts in such a manner as to preclude direct communication between identifiable members of the groups. 2. Assembling the responses of each expert to the questions or problems of interest. 3. Summarizing these responses. 4. Providing each expert with the summary of all responses. 5. Asking each expert to study the summary of the responses and respond again to the questions or problems of interest. 6. Repeating steps (b) through (e) several times as necessary to obtain convergence in responses.

Briefly describe the steps that are used to develop a forecasting system.

1. Determine the purpose and use of the forecast. 2. Select the item or quantities that are to be forecast. 3. Determine the time horizon of the forecast. 4. Select the type of forecasting model to be used. 5. Gather the necessary data. 6. Validate the forecasting model. 7. Make the forecast. 8. Implement and evaluate results.

Identify three forecasting time horizons.

1. Short-range (under 3 months); medium-range (3 months to 3 years); long-range over 3 years.

What are the three pieces of data needed to forecast the future in the exponential smoothing method?

1. The most recent forecast. 2. The actual demand that occurred for that forecast period. 3. Smoothing constant alpha.

What is a moving average?

A forecast based on average past demand. It can be useful in removing the random fluctuations for forecasting. The idea here is to simply calculate the average demand over the most recent periods.