MANGT 515: Cost and Value Management

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*

Naïve Approach

In this approach, the forecast for the current period is the value of the previous observation of the time series. This approach to forecasting has found wide use due to its simplicity. It can be used with a time series that may be stable, has seasonal variations or has a trend component. In a project situation, this approach, in the absence of any other information, could be used for predicting the number of staff available to perform activities in the next reporting period. Also, in the case of a resource scheduling routine in use with a reporting period as short as one week, the naive forecast may be the most appropriate forecasting method for planning next week's work and allocating staff to tasks. Using this approach, the forecast for period t+1 is,

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