The MIIS Eprints Archive

Workflow Modelling of Construction Projects

Murphy, E. and Champneys, A. and Batarfi, Hanan and Barter, Edmund and Budd, Chris and Dong, Ran and Foniok, Jan and Kulesza, Kamil and Lacey, Andrew and Li, Xiaodong and Rodrigues, Francisco and Wendland, Alex and Yim, Ambrose (2018) Workflow Modelling of Construction Projects. [Study Group Report]

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This report details the work carried out by the Study Group on workflow modelling of con- struction projects.
Data on the progress of about a hundred projects over a single five-year planning period were provided by Heathrow Airport (the client) and their four Tier 1 construction contrac- tors. These data are mapped and analysed. Several unusual features are discovered. For example, most projects undergo several tens of adjustments in their scope and price such that while most projects are technically completed under budget, the price and duration is significantly higher than originally planned.
The main question addressed was whether an optimised scheduling of the project would lead to decreased costs and more rapid completion.
First, a machine learning approach is used to gain insight onto which factors are most significant in predicting the final cost and duration of each project. If more data were available, these methods could be further exploited to allow for predictions to be made on which projects are likely to over-run or go over budget and to examine connections between projects at the subcontractor level.
In addition to the data-centric approach, a complementary mathematical model was de- veloped to gain a better understanding of the effect of resource constraints on cost and price extension due to resource competition of concurrent projects, ignoring the confound- ing effect of scope creep seen in the data. The model takes the form of a discrete time stochastic simulation, whose parameters are fit to the existing data. Tentative conclusions from the model indicate that better outcomes can be achieved by spreading out project start dates, and by prioritising completion of smaller projects.
While more data is needed to validate the model, the results suggested that gains can be made if more thoughtful scheduling of projects is implemented, and also if the prioritisation of projects is monitored and adjusted intelligently.
Our major recommendation to Heathrow Airport is to collect or retrieve more data, as outlined in the report, so that both models can be made more realistic and useful. This would allow Heathrow Airport and their contractors to develop and test strategies to make the system more efficient, ultimately saving time and money.

Item Type:Study Group Report
Problem Sectors:Machines / Production
Study Groups:European Study Group with Industry > ESGI 138 (Bath, UK, July 16-20, 2018)
UK Study Groups > ESGI 138 (Bath, UK, July 16-20, 2018)
Company Name:Heathrow
ID Code:753
Deposited By: Bogdan Toader
Deposited On:11 May 2019 14:02
Last Modified:11 May 2019 14:02

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