Session 6 - Forecasting

Session 6 - Forecasting - BusinessForecasting Session6Chap.4

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Business Forecasting Session 6 – Chap. 4 OM 301-002, Operations Management, Spring 2010
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Agenda Major Subjects Forecasting Concepts Data vs. Techniques Data Patterns Managing Forecasting  Process Averaging Techniques Smoothing Techniques Regression Techniques Error/Accuracy Measures Learning Objectives Know the general forecasting  process and identify main  managerial issues Learn time series averaging  and smoothing techniques See how regression  techniques can be used in  causal relationships  forecasting Understand the differences  among various error  measures
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General Concepts Motivation, Purpose, User Motivation: uncertainty within leadtime Purpose: decision support User: managers (decision makers) making a decision  with no certain future information Approaches A crystal ball? Expert opinions! Historical data!! Also … managerial knowledge!!!
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Data vs. Techniques Types of Data Time series A single measure observed over  successive increments of time  last 6 months) Cross-sectional Multiple measures observed at a  single point of time (e.g., major  stock market indices at the end  of a day) Hybrid Multiple measures observed  over successive increments of  time (e.g., major daily stock  market indices over last 6  months) Types of Techniques Qualitative techniques Jury of executive opinion Delphi method Sales force composite Consumer market survey Quantitative techniques Time series models Averaging techniques,  smoothing techniques,  decomposition techniques,  ARIMA (Box-Jenkins)  techniques, … Associative/Causal models Regression techniques,  discrete choice techniques,  Bayesian techniques
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This note was uploaded on 09/21/2011 for the course OM 301 taught by Professor Naor during the Fall '09 term at George Mason.

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Session 6 - Forecasting - BusinessForecasting Session6Chap.4

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