Tips for creating a schedule model suitable for Monte Carlo simulation
The first step in performing a risk analysis of your project schedule is to create a basic schedule model in either Oracle Primavera or Microsoft Project. There are several things to consider in building such a model:
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Use an appropriate schedule logic with start and end milestones
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Include tasks that may or may not occur
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Assign most likely estimates of task durations
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Assign tasks to work breakdown structures (WBSs) if possible
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Create parent and child task structures at various levels for large schedules
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Add milestones anywhere you would like to get estimates of finish dates
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Ensure the schedule is sufficiently detailed to be able to pinpoint exactly where a risk event may affect the schedule
Schedule logic
Monte Carlo simulation on project schedules is most easily performed when one only uses Finish-Start relationships, which have a causal and temporal logic that is easy to understand. However, project schedules can be built with a number of dependency relationships for defining when a task starts and finishes. Tamara can interpret any network logic you use, but because Tamara runs Monte Carlo simulations – randomly changing the durations of tasks and adding/removing risk events – logic other than Finish-Start can produce unexpected logical conflicts not present in the baseline model created in Primavera or MS Project.
Relationships linking tasks
A good quality schedule model will have the various tasks linked together with some logic. In particular, there should be no tasks for which the finish date is not somehow linked to the estimated end of the project. The types of linkage are:
FS: Finish(A)-Start(B)
This is the most common dependency logic, the method recommended in project planning best practice, and the one we strongly encourage you to use. It says that Task A must be complete before Task B can start. All projects can be structured into a Finish-Start network with a bit of care.
SS: Start(A)-Start(B)
This means that neither task can start without the other. In general, it really means that both tasks are waiting for some other trigger (like getting approval to proceed). It is better to model the relationship explicitly with the trigger event using Finish-Start logic, even if this trigger event has a duration of zero.
FF: Finish(A)-Finish(B)
This mean that neither can finish without the other. It is an approximation to the situation where both tasks need to be integrated together (another task that depends on both A and B being complete).
Lags
This means that one is waiting for something to happen, e.g. approval after submission, which is not being explicitly incorporated into the schedule model. We recommend that lags are avoided in schedule models built for Tamara, and the event that is being waited for is explicitly included.
Negative lags
Finish-Start logic is sometimes defined with a negative lag, meaning that task B will start when Task A finishes, minus some time (i.e. sometime before Task A is finished). Negative lags should be very much avoided. In reality, we cannot know when Task A will finish with certainty until it has actually done so. This becomes important when uncertainty is incorporated into the schedule as it can create some logical inconsistencies – e.g. when the negative lag is shorter than the simulated duration of Task A.
The usual reason for including negative lags is that one wants to plan a task to start in parallel with its predecessor after a certain amount of progress has been achieved on the predecessor. It is better to split Task A into two, and model Task B as starting when the first part of Task A is complete.
Positive lags
Finish-Start logic is sometimes defined with a positive lag, meaning that task B will start when Task A finishes plus some time. The usual reason for including positive lags in Finish-Start logic is that the lag is effectively representing another task, or set of tasks, that are not explicitly being modelled. We recommend that one explicitly models these extra tasks if possible.
Fixed start or finish dates
Fixed-value schedule models (i.e. models with no uncertainty) often include ‘hard’ start or finish dates. This implies that there is no uncertainty about these dates. Reality is often different, so you should be very careful about including these ‘hard’ values.
Tamara will respect any fixed dates that you include in the model, so be aware that this could lead to an unrealistic result, particularly if you see that Tamara is simulating a task to be finished very often on the hard finish date (and starting on the hard finish date). This implies that Tamara is being forced to finish a task on a specific date, even if reality says that there is a high probability it would exceed that date.
Grouping tasks
A key failure of schedule risk analysis modelling in the past has been to acknowledge systemic uncertainty drivers that can affect large parts of your project at the same time. For example, you may be using a contractor to install all the IT for your project. There may be many different tasks for which some IT involvement is necessary. If the contractor turned out to be very slow, disorganized, or unskilled, many activities in your project could be adversely affected.
Tamara provides a unique way to deal with this that is easy to implement, simple to understand, and quick to execute even for extremely large schedules. In order to take advantage of this capability, group tasks into categories in the baseline schedule model built with primavera or MS Project. You can use WBS, CBS and other labels for this. Examples would be: ‘welding’, ‘detailed engineering’, ‘procurement of raw materials’, etc.
Incorporating potential activities
Some activities may or may not need to be undertaken, for example redesigning some part of the system after initial testing. These activities should be included within the baseline model. If they are unlikely to occur, set the duration to zero in the baseline model. Tamara will allow you to assign a probability of their occurrence and an uncertain duration later.
Save the baseline schedule model
Remember to save the model in a location that the Tamara user will have access to. Tamara can access project plan models from Primavera and Project servers, as well as single model files stored on a drive.
ModelRisk
Monte Carlo simulation in Excel. Learn more

Tamara
Adding risk and uncertainty to your project schedule. Learn more

Navigation
- Risk management
- Risk management introduction
- What are risks and opportunities?
- Planning a risk analysis
- Clearly stating risk management questions
- Evaluating risk management options
- Introduction to risk analysis
- The quality of a risk analysis
- Using risk analysis to make better decisions
- Explaining a models assumptions
- Statistical descriptions of model outputs
- Simulation Statistical Results
- Preparing a risk analysis report
- Graphical descriptions of model outputs
- Presenting and using results introduction
- Statistical descriptions of model results
- Mean deviation (MD)
- Range
- Semi-variance and semi-standard deviation
- Kurtosis (K)
- Mean
- Skewness (S)
- Conditional mean
- Custom simulation statistics table
- Mode
- Cumulative percentiles
- Median
- Relative positioning of mode median and mean
- Variance
- Standard deviation
- Inter-percentile range
- Normalized measures of spread - the CofV
- Graphical descriptionss of model results
- Showing probability ranges
- Overlaying histogram plots
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- Effect of varying number of bars
- Sturges rule
- Relationship between cdf and density (histogram) plots
- Difficulty of interpreting the vertical scale
- Stochastic dominance tests
- Risk-return plots
- Second order cumulative probability plot
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- Risk analysis modeling techniques
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- Modeling expert opinion
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- The Chi-Squared Goodness-of-Fit Statistic
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- Matching the properties of the variable and distribution
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- Does a parametric distribution exist that is well known to fit this type of variable?
- Censored data
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- Does the random variable follow a stochastic process with a well-known model?
- Fitting a distribution for a discrete variable
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- Fitting distributions to data
- Technical subjects
- Comparison of Classical and Bayesian methods
- Comparison of classic and Bayesian estimate of Normal distribution parameters
- Comparison of classic and Bayesian estimate of intensity lambda in a Poisson process
- Comparison of classic and Bayesian estimate of probability p in a binomial process
- Which technique should you use?
- Comparison of classic and Bayesian estimate of mean "time" beta in a Poisson process
- Classical statistics
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- Bootstrap
- The Bootstrap
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- The Jackknife
- Multiple variables Bootstrap Example 2: Difference between two population means
- Linear regression non-parametric Bootstrap
- The parametric Bootstrap
- Bootstrap estimate of prevalence
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- Example: Parametric Bootstrap estimate of the mean of a Normal distribution with known standard deviation
- The non-parametric Bootstrap
- Example: Parametric Bootstrap estimate of mean number of calls per hour at a telephone exchange
- The Bootstrap likelihood function for Bayesian inference
- Multiple variables Bootstrap Example 1: Estimate of regression parameters
- Bayesian inference
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- Hyperparameters
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- Constructing a Bayesian inference posterior distribution in Excel
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- Markov chain Monte Carlo (MCMC) simulation
- Introduction to Bayesian inference concepts
- Bayesian estimate of the mean of a Normal distribution with known standard deviation
- Bayesian estimate of the mean of a Normal distribution with unknown standard deviation
- Determining prior distributions for correlated parameters
- Improper priors
- The Jacobian transformation
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- Bayesian analysis example: The Monty Hall problem
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- Likelihood functions
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- Analyzing and using data introduction
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- Monte Carlo simulation
- RISK ANALYSIS SOFTWARE
- Risk analysis software from Vose Software
- ModelRisk - risk modeling in Excel
- ModelRisk functions explained
- VoseCopulaOptimalFit and related functions
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- Tamara - project risk analysis
- Introduction to Tamara project risk analysis software
- Launching Tamara
- Importing a schedule
- Assigning uncertainty to the amount of work in the project
- Assigning uncertainty to productivity levels in the project
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- Adding cost uncertainty to the project schedule
- Saving the Tamara model
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- Correlation with project schedule risk analysis
- Pelican - enterprise risk management
- ModelRisk Cloud system
- ModelRisk Cloud introduction
- Getting your software ready
- Starting ModelRisk Cloud
- Uploading a risk analysis model
- Creating a new scenario for the risk analysis model
- Running a Monte Carlo simulation of the model
- Uploading a SID (Simulation Imported Data file)
- Building a risk analysis model that uses SIDs
- Viewing the Monte Carlo results from a simulation run
- Administrator's use of ModelRisk Cloud
- Preparing a risk analysis model for upload to ModelRisk Cloud
- ModelRisk Result Viewer
Enterprise Risk Management software (ERM)
Learn more about our enterprise risk analysis management software tool, Pelican
