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*

2.4 Forecasting the Project End Conditions

One particular issue that is always of concern to the project manager is the point at which the project will end. This can be of supreme importance if the project has to be completed by some opening date that has been fixed well in advance, like the Olympic Games, for example. One possible approach is the use of a Slip Diagram, which is a simple graphical technique based on linear trend estimation. This method was covered in Module 1/MANGT 510 and hence the coverage here will be brief. The slip diagram as a method of predicting the project end date is based on two sets of time series; a regular series of estimates of when the project will end and a record of when those estimates are made. Plotting one data series against the other will reveal whatever pattern that exists and if the relationship is linear, it can be extrapolated to produce a forecast of the completion date. Slip Diagrams can also be used to forecast significant milestones in a project. A sample slip diagram is presented below in Figure 2.6.

slip diagram graphic
Figure 2.6 Construction of a "Slip Diagram"