Multiple WBS vs Grouping

Multiple WBS vs Grouping

What is the difference between having multiple Work Breakdown Structures (WBS) and having multiple ways to group activities?
This is one of the challenging topics we discuss in our Planning & Scheduling masterclasses because, on the surface, the two approaches can look very similar.
In both cases, the underlying activities and logic remain unchanged, and both methods provide alternative perspectives analyse project delivery models but there is an important distinction:
A WBS is a grouping structure, but not every grouping structure is a WBS.

Multiple Work Breakdown Structure

A WBS is not simply a summarised view of project activities. It is part of the project delivery model. Each WBS element can exist as its own project object, with metadata that can be stored, controlled, calculated and analysed.

Depending on the system, this may include:

• Unique ID
• Responsible manager
• Planned or anticipated start and finish dates
• Category
• Priority
• Weighting
• Access rights
• Budgets and costs
• Progress
• Probabilities
• Level of risk
• Assigned resources
• Activity counts and other calculated metrics

Some of this information is calculated from activities and lower-level WBS elements. Other data points are entered directly against the WBS element.

For example:
• Responsibility may be assigned directly to a WBS element.
• Progress and forecast information can be compared between reporting periods or what-if scenarios.
• A project may need to calculate the probability of completing a particular WBS element by a target date or within a target cost.
• A WBS element may effectively represent a sub-project with its own resources, controls and management approach.

Grouping works differently.

Grouping

When grouping is applied, the software reorganises the same activities according to selected classification criteria and may calculate summary information for each resulting group but the group itself is generally not a persistent project object with its own stored metadata.

It exists because a set of activities currently satisfies particular grouping rules. Change the grouping criteria and the previous structure, together with its summarised information, disappears.

The key distinction is:
WBS metadata is stored and controlled. Grouping is a temporary analytical view of the current model.

Scheduling

Another important difference is that WBS-level information influence project calculations. Depending on the system, WBS metadata can affect activity dates, durations, earned value calculations, progress calculations, access rules and other model outputs.
Grouping normally does not.

Interface

A WBS also gives us a uniquely identifiable object that can be tracked between different versions of a model or even between different systems. 

Each WBS can serve as a basis for trend analysis. If a project needs to understand how a particular part of the work has changed across multiple updates or scenarios, the same WBS element can be compared directly between relevant versions.

With grouping, the required structure usually needs to be recreated consistently in each version before any comparison can be performed. Even then, the analysis may be more difficult because many scheduling systems support comparison and trend analysis at the WBS level, but not for alternative groupings.

For example, in Primavera P6 a project may create a sophisticated grouping structure using activity codes and use it as the primary way to organise and analyse the schedule. However, P6’s in-system comparison functions are fundamentally WBS-based. A grouping created from activity codes is a view of the activities, not an equivalent analytical structure.

Project management systems provide direct comparison of WBS data between project versions, while equivalent comparison of temporary groups created from activity codes is not available.

WBS can also provide a structural interface between different project management systems. Scheduling, Financial, BIM, Risk, and Benefits management systems can map information to the established WBS structure. Information can therefore be exchanged and analysed through the same structural framework.

A temporary grouping is much less suitable for this purpose.

A practical example

One of my programs contained multiple projects, while delivery was organised through releases. Each release included deliverables from several different projects.

• Project Managers needed to manage and analyse activities belonging to their projects.
• Release Managers needed to control activities associated with their releases.
• Test Manager needed to analyse testing workload, progress and cost, even though testing activities were spread across different projects, phases and releases.
• The same applied to Change Management and other functional leaders.

Each manager needed to see and manage the same program data through a different structural perspective. The original program structure was project-focused. That worked well for Project Managers, but it made control from other management perspectives very inconvenient. It became so limiting that the Program Manager considered restructuring the entire program. That would have solved some problems but created others. Instead, we created alternative WB structures.

The activities, logic and underlying delivery model remained the same, but different managers could now analyse and control the program using the structure most relevant to their responsibilities.

Multiple WBS in Spider Project

Multiple WBS is one of key methods in SDPM. Spider Project provided with demo project that include multiple WBSs.

The primary WBS is based on investment & delivery structure.

Alternative WBS is based on project processes.

As we can see, we may plan and control project cost from different perspectives and, for example, run cost risk analysis for any element in any structure.

Summary

Grouping answers:

“How do I want to view these activities right now?”

A WBS answers a much broader question:
“How do I want this part of the project be managed, controlled, measured and integrated within the project model?”

Both are valuable but they are not interchangeable. Grouping is primarily a reporting view. WBS is part of the project management model.

Alex Lyaschenko

PMO | Portfolio Planning & Delivery | PMP | P3O Practitioner | AgilePM Practitioner | Six Sigma

Risk Dumping

Risk Dumping

Project management guidelines list many risk response strategies: avoid, mitigate, transfer, accept and escalate but they overlook perhaps the most popular strategy of all: risk dumping.

Risk dumping: the practice of adding a risk to the risk register, assigning it to someone else, and then behaving as though responsibility for the risk has disappeared.

Throughout my career, across many projects, programs and portfolios, I have observed the same behaviour repeatedly. A project manager identifies a risk, adds it to the risk register and informs senior management. Then something strange happens.

The project manager begins treating the risk as a collective responsibility. Because management has been informed, they believe their job is done.

The risk is documented.
The risk is visible.
The risk may even be discussed every risk review meeting, but nobody is actively managing it.

Documenting and communicating a risk are important parts of risk management, but awareness does not replace ownership. Escalating a risk does not automatically transfer responsibility for mitigating it.

A risk register is a management tool, not a responsibility-dumping ground.

A risk is not managed because it appears in a report. It is managed only when someone takes ownership, implements agreed actions and monitors whether those actions are actually reducing the exposure.

If the risk remains unchanged while the register is regularly updated, you are not managing risk. You are managing risk information.

How common is risk dumping in your organisation?

Alex Lyaschenko

PMO | Portfolio Planning & Delivery | PMP | P3O Practitioner | AgilePM Practitioner | Six Sigma

Monte Carlo Analysis Challenges: Predicting Project Cancellation

Monte Carlo Analysis Challenges: Predicting Project Cancellation

Statistically, we know that some projects will never be delivered. Projects are cancelled for a variety of reasons.

Other projects go through multiple reincarnations and deliver after several attempts. Decisions to stop a project, redefine its scope, or change the delivery strategy are common realities of project delivery.

Project analysis methods should be able to predict such possibilities. However, none of the commonly applied methods today can reliably estimate the probability of project cancellation.

There are two primary reasons for this: mathematical and psychological.

The Mathematical Challenge

A strong candidate for predicting project cancellation is Monte Carlo Simulation (MCS).

However, this would require a fundamental shift in how MCS is typically applied.
MCS generates thousands of possible project outcomes. In theory, a properly designed simulation model considers all possible outcomes, not just the outcomes that lead to project completion. In practice, this is rarely the case.

If project cancellation is a possible outcome, the simulation model should explicitly include a “project cancelled” scenario. This would allow analysts to estimate the probability of cancellation alongside the probability distribution of delivery dates, costs, and benefits for projects that are successfully completed.

Without modelling cancellation as a valid outcome, the simulation can’t predict it, regardless of how sophisticated the analysis may appear.

The Psychological Challenge

The second challenge is the quality of the simulation model itself.

Project teams, sponsors, and organisations are often reluctant to acknowledge the possibility of project cancellation. As a result, forecasting models are designed to answer the question:

“When will the project finish?”

rather than the more fundamental question:

“Will the project finish at all?”

Many projects are cancelled because of risks that are known but never formally documented or analysed. This is not necessarily because project teams fail to recognise them, but because they are too complex, politically sensitive, or difficult to address.

I refer to these as “elephants in the room”— systemic issues that create a significant probability of project failure. If such risks are excluded from the simulation model, the analysis will systematically ignore some of the most important project outcomes.

This creates an optimism bias that removes critical failure scenarios from the analysis before the simulation even begins.

Conclusion

To use Monte Carlo Simulation effectively for project forecasting, we must move beyond estimating delivery dates and costs. Simulation models should explicitly represent cancellation and major scope changes as legitimate outcomes.

At the same time, organisations must be willing to acknowledge systemic risks and understand how they impact project outcomes.

Only then can Monte Carlo Simulation can provide a realistic view of project uncertainty, including the possibility that the project may never be delivered at all.

Alex Lyaschenko

PMO | Portfolio Planning & Delivery | PMP | P3O Practitioner | AgilePM Practitioner | Six Sigma

Monte Carlo Simulation is easy!

Monte Carlo Simulation is easy!

We previously discussed the challenges of applying Monte Carlo Simulation (MCS) correctly, but the way it is commonly applied in project analysis is not just bad—it is ugly.

How (do not) to apply MCS in project delivery:

1. Take whatever project model is available. An Excel table or a CPM schedule created for progress tracking will do.

2. Assume all required resources will magically be available exactly when needed. Ignore material shortages, cash-flow constraints, physical-space limitations, access restrictions, and other real-world constraints. It is not your problem to solve.

3. Quietly assume that activity durations represent carefully developed “most likely” estimates based on expert judgement and extensive historical data. No need to verify this assumption.

4. Take whatever Monte Carlo tool is available.

5. Add a few obvious risks to a deterministic risk register (where each risk effectively has a single impact). Assess probability and impact by categories. No need to define them – everyone knows what each category means. Focus mainly on threats. Opportunities can wait.

6. Configure a risk matrix in the MCS tool! Not sure about the settings? Defaults must exist for a reason.

7. Import the schedule and risk register. Map risks to activities. Spend plenty of time selecting the “correct” probability distribution for every activity.

8. Manufacture duration uncertainty by applying ± % or ± days around deterministic durations. If uncertainty data was collected when the estimates were originally developed, ignore it. There is a good chance your MCS tool can’t import it properly anyway.

9. Select the number of iterations and run the simulation. Fewer iterations mean more visible variability and a faster analysis. Win-win.

10. Review the results. Adjust assumptions, distributions, model parameters, or risk attributes until the desired outcome appears.

11. Auto-generate an impressive QRA report. Make it look as though producing the report required weeks of sophisticated analysis.

12. Ignore contradictions with previous reports – the previous analysis was completed months ago, so there is a reasonable chance nobody will notice. If someone does, explain that the latest analysis is “more comprehensive” and includes “new risks”.

13. Do not even try to calculate the current probability of achieving previous targets. It might expose uncomfortable truths about the credibility of your analysis. Instead, establish new P80 or P90 duration/cost and present them as the latest risk-adjusted targets.

14. Proudly present the results as a quantitative assessment of project uncertainty.

15. (Bonus step) Present the findings and explain how advanced quantitative risk analysis improves decision-making at the next project management conference.

Alex Lyaschenko

PMO | Portfolio Planning & Delivery | PMP | P3O Practitioner | AgilePM Practitioner | Six Sigma

Artificial Project Periods

Artificial Project Periods

Project delivery is a continuous process.
Like an assembly line, work flows seamlessly: an 8:00 AM start follows a 5:00 PM finish the previous day, and Monday naturally continues where Friday ended.
However, not every activity can be interrupted.
You can’t start boiling water at 4:55 PM, stop at 5:00 PM, and expect to make tea at 8:00 AM the next morning. Some processes must start and finish within the same uninterrupted working period.
This leads to two important rules.

Rule #1: Model uninterrupted processes correctly

If a process cannot be interrupted, this constraint must be explicitly programmed in the project delivery model:

  • Some tasks require “immediately” after logic allowing a successor start no later than defined period (or without any delay).
  • Other activities should not be scheduled to start unless there is sufficient time for it to finish within the available working period.

Traditional CPM dependency types (FS, SS, FF and SF) cannot model this behaviour. They require additional project data modelling capabilities.
If these technological constraints exist but are not represented correctly, then both what-if analysis and Monte Carlo Simulation will produce unreliable results because the underlying delivery model does not reflect reality.

Rule #2: Avoid creating artificial project periods

Artificial working periods make projects slower.
Although organising work into predefined blocks usually simplify planning and control, it often introduces hidden delays between predecessor and successor activities. These delays may not immediately affect the delivery date, but they accumulate and consume valuable project float. When uncertainty inevitably occurs, that lost float is no longer available to absorb disruptions.
Common examples include:

  • Critical features planned to start at the next Agile sprint instead of commencing them immediately after enabling features are ready;
  • a four-hour task being allocated an entire working day simply because it is easier to plan;
  • features developed during an Agile sprint waiting for the next production release;
  • fixed review and approval periods that delay work for administrative rather than technical reasons.
    These are not technological constraints. They are programmed delays.

The RCPM approach

Effective project delivery requires project processes to be planned around real-world constraints. This is the foundation of the Resource Critical Path Method (RCPM), which focuses on accurate project data modelling. Rather than simplifying reality through unrealistic assumptions, RCPM enables project teams to model and manage complexity, resulting in more reliable planning, forecasting, and decision-making.

The Resource Critical Path Method (RCPM) addresses both challenges.

An RCPM delivery model represents project processes as it is can be executed. It introduces uninterrupted (“no-break”) periods only where they are genuinely required by technology or the nature of the work, while eliminating artificial time blocks that add no value.

The result is an optimised delivery model that more accurately reflects reality, preserves valuable project float, improves the reliability of scenario analysis and Monte Carlo Simulation, and ultimately enables faster and more predictable project delivery.

Alex Lyaschenko

PMO | Portfolio Planning & Delivery | PMP | P3O Practitioner | AgilePM Practitioner | Six Sigma