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1、Chapter 3,Demand Forecasting,Overview,Introduction Qualitative Forecasting Methods Quantitative Forecasting Models How to Have a Successful Forecasting System Computer Software for Forecasting Forecasting in Small Businesses and Start-Up Ventures Wrap-Up: What World-Class Producers Do,Introduction,D
2、emand estimates for products and services are the starting point for all the other planning in operations management. Management teams develop sales forecasts based in part on demand estimates. The sales forecasts become inputs to both business strategy and production resource forecasts.,Forecasting
3、 is an Integral Part of Business Planning,Forecast Method(s),Demand Estimates,Sales Forecast,Management Team,Inputs: Market, Economic, Other,Business Strategy,Production Resource Forecasts,Some Reasons Why Forecasting is Essential in OM,New Facility Planning It can take 5 years to design and build a
4、 new factory or design and implement a new production process. Production Planning Demand for products vary from month to month and it can take several months to change the capacities of production processes. Workforce Scheduling Demand for services (and the necessary staffing) can vary from hour to
5、 hour and employees weekly work schedules must be developed in advance.,Examples of Production Resource Forecasts,Long Range,Medium Range,Short Range,Years,Months,Days, Weeks,Product Lines, Factory Capacities,Forecast Horizon,Time Span,Item Being Forecasted,Unit of Measure,Product Groups, Depart. Ca
6、pacities,Specific Products, Machine Capacities,Dollars, Tons,Units, Pounds,Units, Hours,Forecasting Methods,Qualitative Approaches Quantitative Approaches,Qualitative Approaches,Usually based on judgments about causal factors that underlie the demand of particular products or services Do not require
7、 a demand history for the product or service, therefore are useful for new products/services Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events The approach/method that is appropriate depends on a products life cycle stage,Qualitative Met
8、hods,Educated guess intuitive hunches Executive committee consensus Delphi method Survey of sales force Survey of customers Historical analogy Market research scientifically conducted surveys,Quantitative Forecasting Approaches,Based on the assumption that the “forces” that generated the past demand
9、 will generate the future demand, i.e., history will tend to repeat itself Analysis of the past demand pattern provides a good basis for forecasting future demand Majority of quantitative approaches fall in the category of time series analysis,A time series is a set of numbers where the order or seq
10、uence of the numbers is important, e.g., historical demand Analysis of the time series identifies patterns Once the patterns are identified, they can be used to develop a forecast,Time Series Analysis,Components of a Time Series,Trends are noted by an upward or downward sloping line. Cycle is a data
11、 pattern that may cover several years before it repeats itself. Seasonality is a data pattern that repeats itself over the period of one year or less. Random fluctuation (noise) results from random variation or unexplained causes.,Seasonal Patterns,Length of Time Number ofBefore Pattern Length of Se
12、asonsIs Repeated Season in PatternYear Quarter 4Year Month 12Year Week 52Month Day 28-31Week Day 7,Quantitative Forecasting Approaches,Linear Regression Simple Moving Average Weighted Moving Average Exponential Smoothing (exponentially weighted moving average) Exponential Smoothing with Trend (doubl
13、e exponential smoothing),Long-Range Forecasts,Time spans usually greater than one year Necessary to support strategic decisions about planning products, processes, and facilities,Simple Linear Regression,Linear regression analysis establishes a relationship between a dependent variable and one or mo
14、re independent variables. In simple linear regression analysis there is only one independent variable. If the data is a time series, the independent variable is the time period. The dependent variable is whatever we wish to forecast.,Simple Linear Regression,Regression EquationThis model is of the f
15、orm:Y = a + bXY = dependent variableX = independent variablea = y-axis interceptb = slope of regression line,Simple Linear Regression,Constants a and bThe constants a and b are computed using the following equations:,Simple Linear Regression,Once the a and b values are computed, a future value of X
16、can be entered into the regression equation and a corresponding value of Y (the forecast) can be calculated.,Example: College Enrollment,Simple Linear RegressionAt a small regional college enrollments have grown steadily over the past six years, as evidenced below. Use time series regression to forecast the student enrollments for the next three years. Students StudentsYear Enrolled (1000s) Year Enrolled (1000s)1 2.5 4 3.2 2 2.8 5 3.33 2.9 6 3.4,
