Predicting the critical path changes using sensitivity analysis: a delay analysis approach. Issue 5 (30th April 2020)

• Record Type: Journal Article
• Authors: Hammad, Mohammed; Abbasi, Alireza; Chakrabortty, Ripon K.; Ryan, Michael J.
• Is Part Of: International journal of managing projects in business. Volume 13:Issue 5(2020)
• Page Start: 1097
• Page End: 1119
• Publication Date: 2020-04-30
• Subjects: Project management -- Project schedules -- Critical path method -- Critical path prediction -- Monte Carlo simulation; Project management -- Periodicals; Electronic journals; 658.404
• DOI: 10.1108/IJMPB-07-2019-0184
• Languages: English
• ISSNs: 1753-8378

Abstract

Abstract : Purpose: This research presents a framework that allows project managers to predict the next critical paths (CP(s)) and to take extra care when planning and executing those activities that have the potential to cause changes in a project's current CP(s). Design/methodology/approach: The method presented here is based on an assessment of each activity's contribution to the overall schedule variance, which involves assigning a probability distribution function to each activity duration in the project. A sensitivity analysis is also carried out, which forms the basis of identifying which activity most affects the project completion date and therefore will have the greatest effect in changing the CP. Findings: The authors' analysis reveals that the most appropriate probability density function (PDF) for the targeted project is the normal distribution. However, the aim of this work is not to determine the most suitable distribution for each activity but rather to study the effect of the activity distribution type on the CP prediction. The results show that the selection of the appropriate probability distribution is very important, since it can impact the CP prediction and estimated project completion date. Originality/value: This research work proposes a delay analysis scheme which can help the project manager to predict the next CP and to improve performance by identifying which activity is the bottleneck. On the other hand, the simplicity arises from the fact that this method does not require any expensive machines or software to generate results.