Abstract :

The aim of this research was to develop a prediction model for delays in pipeline construction in the oil and gas industry in Bahrain. A mixed methods research design guided by a pragmatic philosophical stance was implemented. Focus group discussions with experts (N = 8) in pipeline construction were used to develop a list of 47 delay factors that were most applicable to the research problem. A web-based questionnaire (N = 93) was administered to collect quantitative data on the importance and occurrence frequency of a delay factor. The importance of delay factors was performed using the Relative Importance Index (RII) while ranking of frequency of occurrence was performed using the Frequency Adjusted Importance Index (FAII). Multiple linear regression was performed in SPSS to model the prediction of both duration of delay and cost of delay in USD. Multiple linear regression using real Key Performance Index (KPI) data from an existing company was also employed to validate the prediction models.

The findings of this study established that inadequate project planning, budgeting, and scheduling, scope variation, and late materials delivery were the main delay causing factors in the pipeline construction in the oil and gas industry in Bahrain. Of significant importance is that most of the main delay factors were related to owners or their representatives and contractors.

The respective regression model showed that scope variations and delay in drawings preparation of the projects were the significant predictors of duration of project delays in days. However, the two variables could predict for almost 34% of duration of delay. On the other hand, the prediction model for cost of delay revealed a higher prediction power with more delay variables. The regression model showed that poor site management and supervision; inefficient and poor performance of contractors/subcontractors; contractor’s lack of planning at preconstruction; weather effect; rework due to errors, mistakes and defective works during construction; and delay in drawings preparation of the projects were the main predictors of project cost increase due to delays. However, these variables could almost predict 69% of the cost increase resulted from delays.