Schedule risk analysis (SRA) is a quantitative risk analysis technique to forecast project schedule outcomes, assess what drives them and to estimate schedule contingency. All schedules are plans for future events and thus inherently involve uncertainty. Using the Monte Carlo Method, Schedule Risk Analysis replaces single values with probability distributions for task durations to quantify plan uncertainty to assess whether the current allocations of time to complete a program of work are sufficient. In addition, risks describing events that may or may not happen are inserted in the Schedule Risk Analysis model to determine the probabilistic effect of significant risks on the project model.

Schedule contingency is then measured in terms of percentile (or “P”) values that indicate the percentage confidence that works will be completed on or before a given date. Correlation is measured between activities in the schedule model and the project durations of interest to identify the main drivers of schedule risk, expressed as “sensitivities”. The schedule risk analysis process also has secondary benefits including “stress testing” of the plan, and exposure of those areas of the programme that may be sensitive to change.


Planning is all about predicting the future, so nothing is absolutely certain, yet ordinary (“deterministic”) scheduling software makes no allowance for this, allowing only a single “best guess” duration for any activity. Such planning tools are described as deterministic because they determine single values for schedule dates.

Further, such scheduling tools like Primavera P6 have no facility for modelling risk events - events that may or may not occur but that would affect the timing of the project schedule if they did. Thus the planner has to choose whether to include a risk event or exclude it from the schedule.

Normal or “deterministic” planning software is useful for calculating a single end date for a project, but is unable to be used to find out how likely that date is to be achieved. Such planning software is all too often misused to make a schedule fit a preconceived date. Unless the plan is assessed by people knowledgeable about the time required to carry out similar projects (i.e., is able to benchmark the schedule), or who are familiar with the detailed execution strategy and durations of key tasks in the project, the flawed basis of the schedule is often not apparent until the investment decision has been made and execution is well under way.

This is the case where the project plan has been constructed as a “backwards-pass” schedule, made to try to justify a desired end date by squeezing activity durations and logic. Such an approach ignoring the need for realistic contingency allowances and excluding the possibility of risk events occurring is not only wishful thinking, but almost certainly doomed to fail.

In the past and even presently, project proponents have relied on the addition of “rule-of-thumb” contingency (eg +/- 15%) to set more realistic targets, allowing for assumed levels of uncertainties of completing the project “averaging out” to the selected contingency percentage. However, this method is crude at best, and provides no auditable justification for the calculated completion date incorporating such contingency. The allowance may be overly generous or completely inadequate.

Schedule Risk Analysis is capable of addressing these issues. By ranging each individual element within a schedule according to its inherent uncertainty, we are able to provide much more detailed and justifiable ranging inputs. Additionally, by incorporating risk events, we are able to model those potential events that may or may not occur, but would change the project timing if they did.

The Schedule Risk Analysis model, incorporating realistic duration uncertainties and significant risk events, is simulated many hundreds or thousands of times, randomly choosing duration values from within each defined activity duration range for each iteration and including each risk event according to the percentage probability of occurrence, ensuring that over the many iterations, the frequencies of selection of the random duration values match the duration probability distributions defined for each task and risk event task. The entire critical path calculation outcomes are recorded for each “iteration” (forward pass latest early dates and backward path earliest late dates, together with Free and Total Float, for each task). Statistical analysis of these results then provides an auditable and mathematically justifiable basis for allocating schedule contingency. All inputs to the schedule model are available for examination and may be adjusted if examination of the results indicates that changes should be made.

As indicated in the introduction, there are other benefits of conducting Schedule Risk Analysis, such as the ability to expose the major determinants of uncertainty within the project. Often it’s not the original critical path that is found to be the main driving path through the project, due to higher uncertainty existing in a deterministically non-critical pathway. Identifying this early can help the project management team introduce cost-effective measures that may save days, weeks or even months of project duration!


Schedule Risk Analysis has been proven to be useful at all phases of a project’s lifecycle. However, there is a direct relationship between the timing of the identification of project uncertainties and the ability to act on these uncertainties to maximise the positive effect on risk outcomes. As the diagram below demonstrates, the earlier project uncertainties are identified, the more time will be remaining to plan and manage ways of reducing threats and increasing opportunities, effectively reducing our risk exposure. Conversely, the later the implementation of such measures, the more expensive they become, as shown in the following indicative graph.

Ability to Change Risk Exposure vs. Cost of Changing Risk Exposure over project time

Risk Assignment Serial or Parallel

Schedule Risk Analysis Pre-Execution

Schedule Risk Analysis during tendering or prior to Financial Investment Decision potentially gives the greatest benefit, as it enables the best understanding and therefore opportunities for control of schedule uncertainties before commitment to project execution. Maximising control of the project schedule usually minimises the potential for cost and schedule over-runs in the project. Schedule Risk Analysis can therefore be a major determinant in assessing and managing the potential for profit or loss on a project effectively.

Performing Schedule Risk Analysis at these early stages also maximises ability to plan for things that may or may not occur (risk events) as well as the potential for alternate execution strategies (probabilistic branching). This is a clear advantage over normal “deterministic” plans that are limited to only known scope and one execution pathway. Modelling these uncertainties enables assessment of the relative benefits of different strategies, including any additional uncertainties that one strategy may introduce compared to others.

Schedule Risk Analysis “stress tests” the project schedule, helping ensure that its construction is robust before using it as a major decision making or control tool on the project. Additionally, the analysis identifies and ranks the probabilistic critical paths through the project, highlighting those areas in which effort could best be expended to ensure timely project delivery.

Outputs of the analysis enable setting of realistic targets for completion of the project and its intermediate milestones, allocating contingency levels as appropriate at the project and organisational levels.

Schedule Risk Analysis in Execution

Schedule Risk Analysis in execution serves a different, but still important role. Unlike pre-execution, the schedule targets are already set, but performance must still be tracked against these objectives.

Conducting Schedule Risk Analysis during execution helps identify and highlight emergent trends and risks on a project. Through actual occurrence of risk events or even because of risk treatments implemented as a result of early analyses, activities identified as critical in the early phases of the project may have shifted. Some critical pathways may have disappeared and others may have emerged. The project team may be too focused on managing previously identified critical paths to notice the emergence of other project logic that may overtake the original pathways as primary threats to project schedule objectives. Early identification is crucial to maximising ability to deal with project uncertainties to capitalise on opportunities and respond to threats.

Continued schedule risk analysis through execution serves to provide realistic assessments of schedule performance against objectives. All too often in projects, the “magic dates” syndrome plays out, where the early activities slip, but the end milestone dates remain fixed. Schedule Risk Analysis shines a spotlight on this because of the “merge bias” effect whereby increasingly overlapped converging parallel paths of activities have decreasingly small chances of being achieved on time due to the probability of the milestone (logic node for the parallel paths) being completed on time is the product of the probability of each of the paths being completed on time.

Another useful function of Schedule Risk Analysis during execution is to re-assess where in the logic network schedule contingency is required, when more may be required and when contingency may be released due to expiration of exposure to uncertainty and applied risk events.


There are many factors that contribute to schedule uncertainty in a project environment. What these are will depend on a myriad of influences including the type of project, the project setting, and those who are involved in its execution. However, these factors can ultimately be narrowed down to a few types or classes of schedule uncertainty, as described below:

Duration Uncertainty: This is a broad classification that refers to any one or more of a multitude of factors which can influence the working time taken to complete a package of work. Examples of duration uncertainty factors include:

Quantity uncertainty: Uncertainty over how many units of work are required to be completed.

Rate / productivity uncertainty: Uncertainty over how many hours are required per unit of quantity.

Staffing uncertainty: Uncertainty over how many workers will be available to complete the specified works.

Risk Events: These are events that may or may not occur, but if they do, will impact on one or more aspects of project timing. Examples of different types of risk events are:

Engineering / Design Risks: eg. “There is a risk that re-design may be required due to changes in client specifications, resulting in delays to issuance of purchase orders.”

Procurement / Fabrication / Supply Risks: eg. “There is a risk that a transport barge may be lost at sea due to extreme conditions, resulting in loss of modules.”

Construction Risks: eg. “There is a risk of industrial relations disputes at site due to pay conditions, resulting in downtime while negotiations are completed.”

Commissioning Risks: eg. “There is a risk of commissioning delay due to damaged and/or faulty equipment, resulting in downtime while the problem is resolved”

Business Risks: eg. “There is a risk that permit conditions may be changed due to a change of government, resulting in a reduced requirement for environmental rectification and associated schedule savings.”

Logic Uncertainty: In basic planning, we’re limited to one type of logical link between activities. They’re either always there, or we don’t put them in at all. However, in probabilistic scheduling, we can model links that may or may not exist between tasks, or even choose between alternate pathways for the schedule to follow…

Probabilistic Links: These are links that may or may not exist between two tasks. The link is assigned a probability of existence and switches on an off accordingly during simulation, similar to a risk event. Probabilistic links could be used to model, for example, the situation in which there’s only a 70% chance that a permit may be required to commence a particular construction activity.

Probabilistic Branching: This can be used to model situations in which there are two or more potential and mutually exclusive solutions or pathways for completing an objective. An example of this might be building a bridge versus building a tunnel to achieve grade separation from a busy road. Each pathway is assigned a probability (the sum of which must total 100%), then either is selected in each iteration when the project is simulated, according to their percentage probability of selection.

Calendar Uncertainty: Both duration uncertainties and calendar uncertainties affect the overall duration of a task. However, unlike duration uncertainties which influence the working time taken to perform a package of work, calendar uncertainties determine the times in which this work can be performed. There are two main types of calendar uncertainties:

Weather Uncertainty: Weather uncertainty refers to both the variations in normal working downtime associated with inclement weather, as well as downtime associated with one-off weather events such as cyclones (elsewhere typhoons or hurricanes).

Working Roster Uncertainty: At the early stages of a project, there may still be uncertainty surrounding the roster arrangements for workers. Will the office staff work a 7½ hour day or an 8 hour day? What shift rotation or time on / time off patterns will the construction staff work? These types of uncertainties can have significant impact on the project completion date.

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