MANGT 515 Prospective Students
Sample Syllabus
Sample Lesson
Introduction
2.1 Reasons for Forecasting in Project Management
2.2 Forecasting Methods for Projects
2.2.1 Qualitative Forecasting Methods
2.2.2 Quantitative Forecasting Methods
Time Series Analysis
Short Term Forecasting Methods
Naïve Approach
Simple Averages
Moving Averages
Exponential Smoothing
Example of Exponential Smoothing
Trend-Adjusted Exponential Smoothing
Example of Trend-Adjusted Exponential Smoothing
2.3 Intermediate Term Forecasting
Example of Linear Regression
Evaluating the "Fit" of the Regression Line
2.3.2 The Correlation Coefficient (r)
2.3.3. The Standard Error of the Estimate (syx)
2.3.4 Limitations in Forecasting Using Linear Regression
2.3.4 Limitations in Forecasting Using Linear Regression (continued)
2.4 Forecasting the Project End Conditions
2.5 S-curve Forecasting
2.6 Technological Forecasting*

Short Term Forecasting Methods

In many situations, a forecast is often required of what will happen in the immediate future without much regard for what will happen in the longer term. This is a common situation with many production processes where a forecast has to be made at the end of one period of the orders that are going to be received in the next so that production schedules can be set for the next period. For the most part, short term forecasts do not require sophisticated analysis techniques. If historical data in the form of a time series exists, then the forecaster can use any of the following techniques for short term forecasting: the naïve approach, simple averages, moving averages, and exponential smoothing. Let's discuss each in detail.