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*

Moving Averages

In this method the next period's forecast is the average of the previous n actual values.

With this method the assumption is that the most recent events are the best indicators of the future with significant random fluctuations in the time series. This approach produces a moving average that is relatively more sensitive to recent movements in data and forecast responsiveness can be increased by reducing the value n. As this method uses only the most recent periods that are relevant, it greatly reduces the problem of forecast lag inherent in the simple averaging technique. The choice of the number of data values to be included in the moving average is arbitrary and is left to the judgment of the forecaster.

It should be noted, however, that while the moving average method uses the data from most recent periods, it still assigns equal importance to all periods of data included in the base of the moving average. Consequently, even with this method there is bound to be some forecast lag. This problem can be resolved to a certain extent by using an extension of the moving average called the weighted moving average. In this method, the forecaster assigns more weight to most recent values in the time series. For example, the most immediate observation might be assigned a value of 0.5, the next most recent value a weight of 0.3, and so on. The sum of the weights, however, should be equal to 1. For example, the forecast using a weighted moving average with four recent periods (n = 4) using weights of w₁ = 0.5, w₂ = 0.3, w₃ = 0.2, w₄ = 0.1, is given by:

page 10 of 24