Chapter 4 – Forecasting – Flashcards
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Forecasting
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the art and science of predicting future events.
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Time horizons fall into three categores
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1. Short-range forecast 2. medium-range forecast 3. long-range forecast
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three types of forecasts
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1. economic forecast 2. technological 3. demand
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economic forecast
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address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators.
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technological forecast
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are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment.
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demand forecast
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are projections of demand for a company's products or services.
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Forecasts of demand drive decisions in
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1. supply-chain management 2. human resources 3. capacity
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Forecasting follows seven basic steps
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1. determine the use of the forecast 2. select the items to be forecasted 3. determine the time horizon of the forecast 4. select the forecasting model 5. gather the date needed to make the forecast 6. make the forecast 7. validate and implement the results
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quantitative forecasts
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forecasts that employ mathematical modeling to forecast demand
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qualitative forecasts
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forecasts that incorporate such factors as the decision maker's intuition, emotions, personal experiences, and value system.
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jury of executive opinion
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a forecasting technique that uses the opinion of a small group of high-level managers to form a group estimate of demand.
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delphi method
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a forecasting technique using a group process that allows experts to make forecasts
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sales force composite
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a forecasting techinque based on salespersons' estimates of expected sales.
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market survey
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a forecasting method that solicits input from customers or potential customers regarding future purchasing plans
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time series
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a forecasting technique that uses a series of past data points to make a forecast
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naive approach
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a forecasting techique which assumes that demand in the next period is equal to demand in the most recent period
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moving averages
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a forecasting method that uses an average on the n most recent periods of data to forecast the next period.
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exponential smoothing
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a weighted-moving average forecasting technique in which data points are weighted by an exponential function
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smoothing constant
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the weighting factor used in an exponential smoothing forecast, a number greater than or equal to 0 and less than or equal to 1
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mean absolute deviation (MAD)
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a measure of the overall forecast error for a model
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mean squared error (MSE)
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the average of the squared differences between the forecasted and observed values
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mean absolute percent error (MAPE)
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the average of the absolute differences between the forecast and actual values, expressed as a percent of actual values.
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trend projection
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a time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts.
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seasonal variations
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regular upward or downward movements in a time series that tie to recurring events
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cycles
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patterns in the data that occur every several years
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linear-regression analysis
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a straight-line mathematical model to describe the functional relationships between independent and dependent variables.
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standard error of the estimate
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a measure of variability around the regression line -its standard deviation
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coefficient of correlation
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a measure of the strength of the relationship between two variables
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coefficient of determination
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a measure of the amount of variation in the dependent variable about its means that is explained by the regression equation
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multiple regression
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an associative forecasting method with more than one independent variable
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tracking signal
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a measurement of how well a forecast is predicting actual values
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bias
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a forecast that is consistently higher or consistently lower than actual values of a time series
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adaptive smoothing
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an approach to exponential smoothing forecasting in which the smoothing constant is automatically changed to keep errors to a minimum
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focus forecasting
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forecasting that tries a variety of computer models and selects the best one for a particular application
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The forecasting time horizon that would typically be easiest to predict for would be the
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short-range.
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A forecast that projects a company's sales is a(n):
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demand forecast
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Quantitative methods of forecasting include
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exponential smoothing.
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The method that considers several variables that are related to the variable being predicted is
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multiple regression.
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The forecasting model that is based upon salesperson's estimates of expected sales is
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sales force composite.
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Decomposing a time series refers to breaking down past data into the components of
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trends, cycles, seasonal and random variations.
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With regard to a regression-based forecast, the standard error of the estimate gives a measure of
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the variability around the regression line.
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When using exponential smoothing, the smoothing constant
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can be determined using MAD.
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A tracking signal
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must be either 1, 0, or -1 for the first predicted value
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If demand is 106 during January, 120 in February, 134 in March, and 142 in April, what is the 3-month simple moving average for May?
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132
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Given last period's forecast of 65, and last period's demand of 62, what is the simple exponential smoothing forecast with an alpha of 0.4 for the next period?
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63.8
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A forecasting technique consistently produces a negative tracking signal. This means that
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the forecast technique consistently over predicts.
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A regression model is used to forecast sales based on advertising dollars spent. The regression line is y=500+35x and the coefficient of determination is .90. Which is the best statement about this forecasting model?
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The correlation between sales and advertising is positive.
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Linear regression is most similar to
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the trend projection method of forecasting.
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Time series patterns that repeat themselves after a period of days or weeks are called
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seasonality.
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Which of the following is NOT a time-series model? naïve approach moving averages linear regression exponential smoothing
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linear regression
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Which of the following statements is NOT true regarding? forecasting? A. Forecasting is exclusively an objective prediction. B. Forecasting is the art and science of predicting future events. C. Forecasting may involve taking historical data and projecting them into the future with a mathematical model. D. A forecast is usually classified by the future time horizon that it covers.
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Forecasting is exclusively an objective prediction.
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A forecast that addresses the business cycle by predicting planning indicators is A. a technological forecast. B.a demand forecast. C. an economic forecast. D. an environmental forecast.
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an economic forecast
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A forecast that projects a? company's sales is A. an environmental forecast. B. a demand forecast. C. an economic forecast. D. a technological forecast.
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demand forecasst
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CPFR is
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?collaborative, planning,? forecasting, and replenishment.
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The goal of CPFR is to
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create significantly more accurate information that can power the supply chain.
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Which of the following statements is NOT? true? A. When excess capacity? exists, cost can decrease. B. When capacity is? inadequate, customers can be lost. C When capacity is? inadequate, market share can shrink. D. When excess capacity? exists, cost can increase.
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When excess capacity? exists, cost can decrease.
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Which of the following is the FIRST step in a forecasting? system? A. Determine the use of the forecast. B. Select the items to be forecasted. C. Select the forecast? model(s). D. Determine the time horizon of the forecast.
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Determine the use of the forecast
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Which of the following is the FINAL step in a forecasting? system? A.Select the forecast? model(s). B.Validate and implement the results. C Gather the data needed to make the forecast. D. Make the forecast.
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Validate and implement the results.
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Which of the following is a reality each company faces regarding its forecasting? system? A. Outside factors that we cannot predict or control often impact the forecast. B. Product family forecast are less accurate than individual product forecasts. C. After automating their predictions using computerized forecasting? software, firms closely monitor only the product items whose demand is stable. D. Most forecasting techniques assume there is no underlying stability in the system.
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Outside factors that we cannot predict or control often impact the forecast.
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Which of the following forecasting steps comes directly after determining the time horizon of the? forecast? A. Select the forecasting? model(s). B. Make the forecast. C. Select the items to be forecasted. D. Gather the data.
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Select the forecasting? model(s)
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Which of the following is a quantitative forecasting? method? A. market survey B. sales force composite C. jury of executive opinion D. exponential smoothing
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exponential smoothing
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Which of the following is a qualitative forecasting? method? A. linear regression B. naive approach C. trend projection D. Delphi method
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Delphi method
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Which forecasting model is based upon? salespersons' estimates of expected? sales? A. market survey B. jury of executive opinion C. Delphi method D. sales force composite
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sales force composite
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Which of the following is NOT a? time-series model? A. naive approach B. moving averages C. linear regression D. exponential smoothing
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linear regression
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What is a data pattern that repeats itself after a period of? days, weeks,? months, or? quarters?
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seasonality
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A tracking signal
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is a measurement of how well a forecast is predicting actual values.
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Forecasting that tries a variety of computer models and selects the best one for a particular application is referred as
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focus forecasting.
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Which one of the following statements is NOT true about the forecasting in the service? sector? A. Detailed forecasts of demand are not needed. B. Demand patterns are often different from those in? non-service sectors. C. Hourly demand forecasts may be necessary. D. Forecasting in the service sector presents some unusual challenges.
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Detailed forecasts of demand are not needed.
