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Detailed Course Information

 

Fall 2017
Nov 23, 2017
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OPIM 274 - Business Forecasting
This course builds upon the regression models studied in OPIM 173 and related introductory courses in statistics. The aim is to further develop statistical modeling skills for business applications, with particular emphasis upon time series, which form the inputs for sales forecasting and production planning as well as much financial and economic activity. The reason for examining time series is to enable us to make forecasts and to ascribe to those forecasts an adequate measure of the uncertainty surrounding the forecasts. 
•	Why do we forecast? The reason that businesses forecast is to aid the process of planning.
•	  Why plan?  Planning enables you to modify your actions in response to potential challenges and opportunities.  If a forecast does not have the potential to change your actions, it is useless. If you do not respond to change your business probably will not survive very long.  
If we accept the premise that forecasting is vital to a business, how should we carry out the task?  We must first recognize that there is no such thing as a universal best method, but rather we need to develop an arsenal of different approaches and to recognize when each approach is appropriate. 

The choice of forecasting method depends upon:
•	The objectives of the exercise [e.g. short-term production planning, investment planning, medium-term budgetary planning, long-term strategic planning]
•	The importance of the task in hand [financial impact per item forecast, # of items]
•	The extent to which other variables can affect the outcome, and whether or not those factors are under our control [e.g. YES: own production; NO: GDP, weather]
•	The quantity and quality of data available, and when those observations become available [e.g. macroeconomic variables, new movies]
•	The number of forecasts required [e.g. 10,000 product lines; US unemployment]

The course will focus primarily upon quantitative methods for forecasting, ranging from purely extrapolative approaches to causal modeling, but with some coverage of judgmental methods. 

Model building and forecasting are not passive activities.  Students will be expected to bring their laptops to class and to work on different forecasting assignments both in class and in team-based projects.

3.000 Credit hours
3.000 Lecture hours

Levels: Undergraduate
Schedule Types: Lecture

Operations & Information Mgmt Department

Course Attributes:
Mean Grade is Calculated

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