Construction and engineering projects rarely progress exactly as planned. When delays hit – bad weather, scope changes, supply chain problems – the project team has to work out how much additional time is warranted and what it will cost.
The conventional approach treats that calculation as though the answer could be precisely known and relies on single-point estimates that take no account of the uncertainty inherent in complex programmes.
Our research, originally published in Construction Management and Economics, puts forward an alternative that is grounded in how uncertainty actually manifests in project schedules.
Current methods and their failings
The Critical Path Method (CPM), despite its widespread use, depends on assumptions that live projects frequently violate.
Activity durations are denominated as single-point estimates and presented as certain and accurate, when they are not.
Sequencing is treated as fixed, when in practice even a modest disruption can shift the critical path and near-critical paths entirely.
Interdependencies between activities – often the real source of cascading delays – tend to be underrepresented or ignored.
The result is a planning framework whose reliability degrades as project complexity increases.
The literature is blunt on this point: treating forecasts as certainties has been challenged for decades, yet the sector's record of cost and time overruns suggests this remains poorly reflected in practice.
The Programme Evaluation and Review Technique (PERT) introduced three-point estimates to project management in the 1960s.
This brought probability into the picture but was hindered by merge bias and the impracticality of applying the technique across a full-length programme.
However, its foundational concepts continued to inspire academic inquiry.
Time Impact Analysis (TIA) follows a conventional sequence: identify the delay event, build a fragment network (fragnet) representing the new or changed scope, insert it into the programme and recalculate completion dates.
In principle the process is uncomplicated, but the outputs rest on assumptions that rarely hold – durations are fixed, the critical path is assumed to stay put and mitigation measures are modelled as though they will succeed in full, which may not hold true in reality.
TIA is also poorly sensitive to causation. It can show you the delay, but it will not tell you why the programme was vulnerable to it or which activities were always likely to overrun.
Moreover, it lacks the analytical capability to deal with partial mitigation success or risks that only crystallise after the original analysis has been completed.
Identifying and testing alternative methodologies
To address these shortcomings, our research project proposed combining TIA with Quantitative Schedule Risk Analysis (QSRA) to produce a risk-adjusted approach to the estimation of delays.
Traditional TIA delivers a single deterministic delay figure but cannot capture real-world variability.
Combining TIA with QSRA expresses delays as probability ranges with confidence levels, merging causal logic with statistical rigour – flagging which activities are most sensitive to variation and exposing probabilistic critical paths that the deterministic model cannot see.
This hybrid approach should be the new standard for defensible, realistic prospective delay measurement.
It lets you test different fragnet configurations and mitigation strategies, so that you can compare the likely consequences.
Conditional branching allows activities to be switched on or off depending on the circumstances – prolonged permit approvals, weather-dependent resources, repeat inspections – accommodating the dynamic nature of project delivery.
Where the same activities appear across several probabilistic paths at once, bottlenecks become visible and these can be addressed through targeted resource allocation. In forensic delay analysis, this increases the credibility and accuracy of delay measurements.
Testing the new approach
To ascertain the benefits and potential shortcomings of the approach, we convened a focus group of ten senior practitioners with experience in the nuclear, transport, rail, energy, defence and utilities sectors.
Participants ranged from risk managers and programme managers to heads of schedule and other project controls professionals – experts with ten to 20-plus years of hands-on experience in change management, delay analysis and risk quantification.
They were selected through purposeful sampling, a deliberate strategy given the niche character of the subject. Random sampling would have been counterproductive because this kind of evaluation requires people who have actually built QSRA models and run TIA analyses, not just read about them.
The focus group was presented with a simulated nuclear submarine programme – a cost- and resource-loaded Primavera P6 schedule prepared directly using Oracle software – and was presented with two parallel analyses: one deterministic TIA and one using the proposed hybrid.
The probabilistic completion date was taken at the P50 confidence level from a Monte Carlo simulation in Primavera Risk Analysis and set against the deterministic result.
The focus group's reception was broadly supportive, though with reservations. Participants noted that the risk-adjusted approach can refine claims, making entitlement calculations more precise.
The group agreed that being able to present a methodologically backed position, with a clear audit trail, could be the difference between a claim being accepted or being escalated.
The predictive power of the probabilistic outputs was valued – particularly the ability to run scenario analyses, verify schedule sensitivity to specific variables and allocate resources on the basis of quantified risk drivers rather than estimates unsupported by methodology.
In addition, the method's potential to combine TIA and QSRA to conduct forensic postmortems and uncover root causes of delay using as-built data retrospectively was also valued.
Prospectively, the probabilistic view of future scenarios and their likely implications were seen as further positives.
Finally, the rigorous data collection the process demands was recognised as improving project control awareness during delivery, while also highlighting useful information documenting lessons learned that might otherwise be undetected.
Potential risks in new approach
Several participants raised concerns over subjectivity in estimating risk likelihoods and impacts and the danger that this could be exploited to support a predetermined output, for example in contentious environments where evidence is essential to resolving commercial disputes.
Participants also underscored the inconvenience associated with the process.
Modern CPM schedules are already dense and difficult to communicate via Gantt charts; layering probabilistic analysis on top may cause further problems in understanding and add significant effort to processes.
Project data is notoriously contentious, fragmented and incomplete. Even just agreeing on input values and their acceptable quality characteristics can take more time than a full simpler assessment.
Several observations were made about contractual complications. Forensic delay analysis still leans heavily on retrospective, deterministic methods, and contract managers or lawyers may resist the new approach due to unfamiliarity with the underlying methods.
Some pointed out that where a scope change does not materially alter the risk profile of the works, the extra analytical effort would be hard to justify.
The group recognised that standardisation of the proposed approach may be desirable, particularly in relation to the QSRA component.
Forensic delay analysis has its protocols, but QSRA in construction remains largely unregulated.
Financial services and insurance have well-defined frameworks for risk modelling; construction does not.
Without accepted technical guidelines, practitioners are forced to navigate the modelling process without detailed directives on reliability and reproducibility, and analytical outcomes across projects become difficult to compare.
We argue for standardised but flexible frameworks – detailed enough to support repeatability and trust, loose enough to accommodate the diversity of real projects.
A few participants suggested benchmarking as a practical starting point for evaluating model quality.
Conclusions
For large, complex, long-duration programmes – where time extensions can cause cost overruns of tens or hundreds of millions of pounds – the cost–benefit case for the new approach looks strong.
These projects tend to have the resource base, experienced teams and delivery timelines to absorb the additional effort.
Even on smaller undertakings, the effort could be worthwhile if the costs of the analysis are outweighed by potential savings or by the value of better-informed decisions.
The proposal does not ask practitioners to adopt anything unfamiliar in isolation: TIA is standard practice, QSRA is almost routine.
The argument is that combining them into a synergistic approach will maximise the collective value of both techniques.
It is important to acknowledge the limitations of this study. The methodology was tested on a simulated schedule, not a live project and focus group dynamics may have been shaped by the varying technical knowledge of participants.
In addition, the method may face adoption challenges due to its unfamiliarity within commercial, project and legal frameworks.
But treating forecasts as certainties on projects defined by uncertainty is difficult to defend when probabilistic tools are readily available.
The tools exist. We believe that this integration will stimulate further adoption and research into hybrid methodologies.
Dr Grzegorz Grzeszczyk is primary author and a researcher at the School of Civil Engineering, University of Leeds
Dr Mohammed Abdelmegid is a lecturer in engineering management at the School of Civil Engineering, University of Leeds
Prof. Christine Unterhitzenberger is a professor of project management at the School of Civil Engineering, University of Leeds
Dr Tristano Sainati is an associate professor of project management at the School of Management, Politecnico di Milano and the Department of Leadership and Organizational Behaviour, BI Norwegian Business School
Contact Grzegorz: Email
Related competencies include: Project feasibility analysis, Research methodologies and techniques, Risk management
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