Operations Management Chapter 4 Flashcards
Forecasting
The art and science of predicting future events
How is a forecast classified
by the future time horizon it covers
Short-range forecast
Up to a year, but usually less than 3 months. Used for planning purchasing, job scheduling, workforce levels, job assignments and production levels
Medium-range forecast
3 months to 3 years. Used for sales planning, production planning and budgeting, cash budgeting and analysis of various operating plans
Long-range forecast
3 years +, used for planning new products, capital expenditures, facility locations or expansions, and research and development
How does short-range forecasts differ from medium and long-range forecasts
1) Medium and long-range deal with more comprehensive issues
2) Short-range employs different methodologies
3) Short-range tends to be more accurate
Product Life Cycle
1) Introduction
2) Growth
3) Maturity
4) Decline
Three major types of forecasts
1) Economic
2) Technological
3) Demand
Economic Forecast
Addresses the business cycle by predicting inflation rates, money supplies, housing starts and other planning indicators
Technological Forecast
Concerned with the rates of technological progress
Demand Forecast
Projections of demand for a company’s products or services. Also called sales forecasts, drive a company’s production, capacity, and scheduling systems and serve as inputs to financial, marketing and personnel planning
7 Steps of Forecasting
1) Determine the use of the forecast
2) Select the items to be forecast
3) Determine the time horizon of the forecast
4) Select the forecasting models
5) Gather the data needed to make the forecast
6) Make the forecast
7) Validate and implement the results
Two general approaches to forecasting
1) Quantitative
2) Qualitative
Quantitative Forecasts
Use a variety of mathematical models that rely on historical data and/or associative variables to forecast demand
Qualitative Forecasts
Incorporate such factors as the decision makers intuition, emotions, personal experiences, and value system in reaching a forecast
4 Major Qualitative Techniques
1) Jury of Executive Opinion - uses the opinion of a small group of high-level managers to form a group estimate of demand
2) Delphi Method - Using a group process that allows experts to make forecasts
3) Sales Force Composite - Based on salespersons’ estimates of expected sales
4) Consumer Market Survey - Solicits input from customers or potential customers regarding future purchasing plans
5 Quantitative Forecasting Methods
I. Time-Series Models A. Naive approach B. Moving averages C. Exponential Smoothing D. Trend projection II. Associative Model A. Linear regression
Time-series Models
Uses past data points to make a forecast
Naive Approach
Assumes demand in the next period is equal to demand in the most recent period
Moving Averages
Average of the most recent “N” periods to forecast the next
Exponential Smoothing
Weighted moving average technique where data points are weighted by an exponential function
Smoothing Constant
The weighting factor used, number between 0 and 1
Trend Projection
Fits a trend line to a series of historical data points and then projects the line into the future
Time-series models has 4 components
1) Trend
2) Seasonality
3) Cycles
4) Random Variation
Trend
Gradual upward or downward movement of data over time
Seasonality
A data pattern that repeats itself after a period
Cycles
Patterns that appear every several years
Random Variation
“blips” caused by chance and unusual situations
Forecast Error
Actual Demand - Forecast Value
MAD
Mean absolute deviation - Measure of the overall forecast error for a model
MSE
Mean Squared Error - Average of the squared differences between forecast and observed values
MAPE
Mean absolute percent error - this is the easiest measure to interpret
Seasonal variations
Regular upward or downward movements in a time series that tie to recurring events
Associative Models
Incorporate several variables or factors that might influence the quantity being forecast
Linear-Regression Analysis
A strait-line mathematical model to describe the functional relationships between independent and dependent variables
Standard Error of the Estimate
Also called Standard Deviation of the Regression - Measures the error from the dependent variable to the regression line rather than to the mean
Coefficient of the Correlation
A measure of the strength of the relationship between two variables
Coefficient of the Determination
A measure of the amount of variation in the dependent variable about it’s mean that is explained by the regression equation
Multiple regression analysis
An associative forecasting method with more than one independent variables
Tracking signal
Measurement of how well a forecast is predicting actual values
Cumulative Error / MAD
Positive Tracking Signals
Demand is greater than forecast
Negative Tracking Signals
Demand is less than forecast
Bias
A forecast that is consistently higher or consistently lower than actual values of a time series
Adaptive Smoothing
Approach to exponential smoothing forecasting in which the smoothing constant is automatically changed to keep errors to a minimum
Focus Forecasting
Tries a variety of computer models and selects the best one for a particular application
2 Principles of Focus Forecasting
1) Sophisticated forecasting models are not always better
2) There is no single technique that should be used for all products or services
