Application of Data Mining Tools to Predicate Completion Time of a Project

Estimation time and cost of work completion in a project and follow up them during execution are contributors to success or fail of a project, and is very important for project management team. Delivering on time and within budgeted cost needs to well managing and controlling the projects. To dealing with complex task of controlling and modifying the baseline project schedule during execution, earned value management systems have been set up and widely used to measure and communicate the real physical progress of a project. But it often fails to predict the total duration of the project. In this paper data mining techniques is used predicting the total project duration in term of Time Estimate At Completion-EAC (t). For this purpose, we have used a project with 90 activities, it has updated day by day. Then, it is used regular indexes in literature and applied Earned Duration Method to calculate time estimate at completion and set these as input data for prediction and specifying the major parameters among them using Clem software. By using data mining, the effective parameters on EAC and the relationship between them could be extracted and it is very useful to manage a project with minimum delay risks. As we state, this could be a simple, safe and applicable method in prediction the completion time of a project during execution.

Authors:

• Seyed Hossein Iranmanesh
• Zahra Mokhtari

Keywords:

• Data Mining Techniques
• Earned Duration Method
• Earned Value
• Estimate At Completion.

References:

[1] D.F. Cioffi, 2006, "Designing project management: A scientific notation
and an improved formalism for earned value calculations." International
journal of project management. Vol.24, no.2, 136-144.
[2] D.F. Cioffi, 2005, "A tool for managing project: an analytical
parameterization of the S-curve", international journal of project
management, vol.23, no.3, 215-222.
[3] F. Anbari, 2003, "Earned value project management method and
extensions", Project management journal, vol.34, no.4, 12-23.
[4] G.Vinter, S. Rozenes, S. Spraggett, 2006, "Using data envelope analysis
to compare project efficiency in a multi-project environment"
International Journal of project management. Vol.24, no.4, 323-329.
[5] H.Iranmanesh, N.Mojir, S. Kimiagari.2007 "A new formula to estimate
at completion of a project-s time to improve earned value management
system". IEEM, Singapore.
[6] Q.W. Fleming, J.M. Koppelman. 2000, "Earned value project
management". 2nd ed. Newtown Square, NJ: project Management
Institute, INC.
[7] Vanhoucke, Vandevoorde, 2005."A simulation and evaluation of earned
value metrics to forecast the project duration". Faculteit Economic En
Bedrijfskunde.
[8] D.C. Bower. 2007. "New Directions in Project Performance and
Progress Evaluation". A thesis submitted to fulfil the requirements for
the Degree of Doctor of Project Management. School of Construction,
Property and Project Management RMIT University Melbourne,
Australia
[9] D.T. Larose, "Discovery Knowledge In Data: an introduction to data
mining", Published by John Wiley & Sons, Inc., Hoboken, New Jersey,
2005.