Chapter 8
5 Basic Patterns
1. Horizontal 2. Trend 3. Seasonal 4. Cyclical 5. Random
3 Categories of Forecasting Methods
1. Judgment 2. Casual 3. Time-Series Methods
Two influences cyclical patterns arise from
1. The business cycle 2. The service or product life cycle
Key Decisions on Making Forecasts
1. What to forecast 2. What type of forecasting technique to select for different items
Backorder vs. Stockout
A backorder adds to the next period's demand requirement, whereas a stockout does not
Backorder
A customer order that cannot be filled when promised or demanded but is filled later
Executive Opinion
A forecasting method in which the opinions, experience, and technical knowledge of one or more managers are summarized to arrive at a single forecast; all of the factors going into judgmental forecasts would fall into the category executive opinion
Judgment Methods
A forecasting method that translates the opinions of managers, expert opinions, consumer surveys, and salesforce estimates into quantitative estimates Some cases, the only practical way to forecast because not enough adequate historical data
Trend Projection with Regression
A forecasting model that is a hybrid between a time-series technique and the causal method
Tracking Signal
A measure that indicates whether a method of forecasting is accurately predicting actual changes in demand CFE/MAD or CFE/MADt
Mean Squared Error (MSE)
A measurement of the dispersion of forecast errors (∑Et^2 )/n
Mean Absolute Deviation (MAD)
A measurement of the dispersion of forecast errors (∑|Et| )/n
Standard Deviation of the Errors (σ)
A measurement of the dispersion of forecast errors √((∑(Et-Ebar)^2 )/(n-1))
Cumulative Sum of Forecast Errors (CFE)
A measurement of the total forecast error that assesses the bias in a forecast; Bias error, results from consistent mistakes - the forecast is always too high or too low CFE=Ʃ Et
Mean Absolute Percent Error (MAPE)
A measurement that relates the forecast error to the level of demand and is useful for putting forecast performance in the proper perspective ((∑|Et|/Dt)(100))/n (expressed as a percentage) MAPE is larger with more demand units
Multiplicative Seasonal Method
A method ereby seasonal factors are multiplied by an estimate of average demand to arrive at a seasonal forecast
Additive Seasonal Method
A method in which seasonal forecasts are generated by adding a constant to the estimate of average demand per season; Assumption that the seasonal pattern is constant, regardless of average demand
Forecast
A prediction of future events used for planning purposes Methods may be based on mathematical models that use available historical data, or on qualitative methods that draw on managerial experience and judgments, or a combination of both
Delphi Method
A process of gaining consensus from a group of experts while maintaining their anonymity; useful when no historical data are available from which to develop statistical models
Casual Methods
A quantitative forecasting method that uses historical data on independent variables, such as promotional campaigns, economic conditions, and competitors' actions, to predict demand
Seasonal
A repeatable pattern of increases or decreases in demand, depending on the time of day, week, month, or season
Time-Series Analysis
A statistical approach that relies heavily on historical demand data to project the future size of demand and recognizes trends and seasonal patterns
Market Research
A systematic approach to determine external consumer interest in a service or product by creating and testing hypotheses through data-gathering surveys
Weighted Moving Average Method
A time-series method in which each historical demand in the average can have its own weight; the sum of the weights equals 1.0; Allows you to emphasize recent demand over earlier demand
Simple Moving Average Method
A time-series method used to estimate the average of a demand time series by averaging the demand for the n most recent time periods F(t+1)=(Sum of last n demands)/n=(Dt+D(t-1)+D(t-2)+⋯+D(t-n+1))/n May involve the use of as many periods of past demand as desired Large values of n should be used for demand series that are stable; small values of n should be used for those that are susceptible to changes; naive method at n=1
Naive Forecast
A time-series method whereby the forecast for the next period equals the demand for the current period, or Forecast = Dt; Works best when the horizontal, trend, or seasonal patterns are stable and random variation is small
Exponential Smoothing Method
A weighted moving average method that calculates the average of a time series by implicitly giving recent demands more weight than earlier demands Most freqently used Requires only: the last period's forecast, the actual demand for this period, a smoothing parameter, alpha (a), which has a value between 0 and 1.0 F(t+1)=αDt+(1-α) Ft
Holdout Sample
Actual demands from the more recent time periods in the time series that are set aside to test different models developed from the earlier time periods
Backlog
An accumulation of customer orders that a manufacturer has promised for delivery at some future date; Firms that are most likely to use backlogs make customized products and tend to have a make-to-order strategy
Technological Forecasting
An application of executive opinion to keep abreast of the latest advances in technology
Stockout
An order that cannot be satisfied, resulting in a loss of the sale
Mean Bias
Average forecast error CFE/n
5 Basic Measures of Forecast Error
CFE MSE (σ) MAD MAPE
Quiz: A forecast with a large cumulative sum of forecast errors (CFE) indicates
Consistent forecasting mistakes - the forecast is always too high or too low
Quiz: The _________ of forecasting is a process of gaining consensus from a group of experts.
Delphi method
Quiz: Cumulative sum of forecast errors are always positive
False
Quiz: Judgment methods of forecasting should never be used with quantitative forecasting methods
False
Quiz: Mean Absolute Deviations can be negative
False
Quantitative Methods
Include casual methods, time-series analysis, and trend projection with regression
Stock-Keeping Units (SKUs)
Individual item or product that has an identifying code and is held in inventory somewhere along the supply chain, such as in a distribution center
2 General Types of Forecasting Techniques
Judgment and Qualitative Methods
Contextual Knowledge
Knowledge hat practitioners gain through experience, such as cause-and-effect relationships, environmental cues, and organizational information that may have an effect on the variable being forecast 1. Salesforce estimates 2. Executive opinion 3. Market research 4. Delphi method
Smoothing Parameter (a)
Larger a emphasize recent levels of demand and results in forecasts more responsive to changes in the underlying average Smaller a treat past demand more uniformly and result in more stable forecasts Requires an initial forecast
Criteria for Selecting Time-Series Methods
Least detectable bias; Lowest MAPE, MAD, or MSE; Using a holdout sample analysis; Meeting managerial expectations of changes in the components of demand 1. Minimizing bias (CFE) 2. Minimizing MAPE, MAD, or MSE 3. Maximizing r2 for trend projections using regression 4. Using a holdout sample analysis 5. Using a tracking signal 6. Meeting managerial expectations of changes in the components of demand 7. Minimizing the forecast errors in recent periods
MSE vs MAD
MSE penalizes single outlier errors more than MAD
Quiz: Managers that use data in period t as the forecast in period t+1 are implementing which of the following forecast method.
Naive Forecasting
Base data vs. Nonbase data
Nonbase data reflects irregular demands
Error
Random error results from unpredictable factors that cause the forecast to deviate from the actual demand
Complementary Products
Services or products that have similar resource requirements but different demand cycles
MSE, σ, MAD
Small -> forecast is typically close to actual demand Large -> possibility of large forecast errors
Aggregation
The act of clustering several similar services or products so that forecasts and plans can be made for whole families
Forecast Error
The difference found by subtracting the forecast from actual demand for a given period; Et = Dt - Ft If Et>0; underestimated (+) If Et<0; overestimated (-) If Et=0; no forecast error
Horizontal
The fluctuation of data around a constant mean
Quiz: Assume that a timeminusseries forecast is generated for future demand and subsequently it is observed that the forecast method did not accurately predict the actual demand. Specifically, the forecast errors were found to be: Mean absolute percent error = 10% Cumulative sum of forecast errors = 0 Which one of the statements concerning this forecast is TRUE?
The forecast has no bias but has a positive standard deviation of errors
Salesforce Estimates
The forecasts that are compiled from estimates of future demands made periodically by members of a company's salesforce; group most likely to know which services or products customers will be buying in the near future and in what quantities
Cyclical
The less predictable gradual increases or decreases in demand over longer periods of time (years or decades)
Demand Management
The process of changing demand patterns using one or more demand options Complementary products, promotional pricing, prescheduled appointments, reservations, revenue management, backlogs, backorders, and stockouts
Planning
The process of making management decisions on how to deploy resources to best respond to the demand forecasts
Time Series
The repeated observations of demand for a service or product in their order of occurrence
Trend
The systematic increase or decrease in the mean of the series over time
Random
The unforecastable variation in demand
Quiz: Forecast error is found by subtracting the forecast from the actual demand for a given period.
True
Time-Series Methods
Use historical information regarding only the dependent variable; Based on the assumption that the dependent variable's past pattern will continue in the future
Revenue Management (Yield Management)
Varying price at the right time for different customer segments to maximize revenues yielded by existing supply capacity; Works best if customers can be segmented, prices can be varied by segment, fixed costs are high, variable costs are low, service duration is predictable, and capacity is lost if not used (perishable capacity)
Quiz: A manager uses data on demand at time t-1 but not at time t to predict the demand at time t+1. Which of the following best describes this type of forecasting method.
Weighted Moving Average
Quiz: Judgment methods may be the only practical way to make a forecast when
there is no historical data due to a new product introduction
