عنوان

Utilizing Predictive Analytics to Aid Project Continuity Decision Making

Number

TL50235

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Utilizing Predictive Analytics to Aid Project Continuity Decision Making

General Material Designation

[Thesis]

First Statement of Responsibility

Hossain, Mohammed Altaf

Subsequent Statement of Responsibility

Fossaceca, Johan M.

Name of Publisher, Distributor, etc.

The George Washington University

Date of Publication, Distribution, etc.

2019

Text of Note

141 p.

Dissertation or thesis details and type of degree

D.Engr.

Body granting the degree

The George Washington University

Text preceding or following the note

2019

Text of Note

Although corporations collect project information such as performance indicators, they tend to use this information only to estimate the cost of future projects rather than as a way of predicting potential project failures. By not using performance indicators from prior projects to predict future project success or failure, organizations are not taking advantage of information that can be used to avoid spending time, money, and resources on projects that are likely to fail. If companies were able to accurately predict project failures, they could avoid incurring the opportunity cost of carrying out projects that should be cancelled early in their life cycle. This praxis utilizes predictive analytics based on project performance indicators as inputs to identify potential project failure candidates allowing managers to stop project work and redirect project resources to other potentially successful projects. In this study we demonstrate that predicting project failure based on past performance of similar projects could enable organizations to make scientific, data-driven, and evidence-based decisions on whether a project should continue. The proposed model recommended in this praxis yields an average 98.56% prediction accuracy with 0% False Positive Rate (FPR). Data used in this praxis is from a large size organization with employees numbering in the tens of thousands. By predicting projects likely to failure after phase two based on past project performance data, this study found that the organization would save about

Subject Term

Engineering

Subject Term

Management

Hossain, Mohammed Altaf

Fossaceca, Johan M.

The George Washington University

Electronic name

p

[Thesis]

276903

a

Y