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

Project Risk Management: Success–Failure Spectrum

Project Risk Management: Success–Failure Spectrum

The maturity of project risk management spans a broad spectrum of practices, from value-creating decision support to actively harmful pseudo-analysis. To describe this spectrum meaningfully, we must define the best, natural, and worst states, along with the intermediate stages between them.

Best

The project develops a quantitative risk simulation model that reflects real delivery conditions, considering all existing constraints, possibilities and resource volatility. This model needs to be:

  • Fully integrated with schedule, cost, resources and benefits
  • Built on realistic assumptions and credible data
  • Actively used to evaluate options, trade-offs, and decisions

In this state, risk management function is used as a decision-support system, not a reporting obligation.

 

Better

The project applies qualitative risk analysis, where risks are:

  • Assessed individually
  • Prioritised using structured professional judgement
  • Managed through defined mitigation and monitoring actions

This approach improves risk awareness but offers limited predictive capability.

 

Baseline (Natural State)

Risk management is primarily intuition-driven:

  • Risks are not formally documented
  • No meaningful qualitative or quantitative analysis is performed
  • Decisions rely largely on experience and gut feeling

This is the most common and “natural” state across projects.

 

Worse

Misapplied qualitative, scoring, or pseudo-quantitative methods are introduced:

  • To demonstrate compliance rather than generate insight
  • Using arbitrary scales, weak assumptions, or invalid models
  • Producing outputs that are largely ignored by decision-makers

At this stage, risk management becomes ceremonial. It is worse than intuition-driven, as projects spend energy performing no value-added risk management rituals.

 

Worst

Management actively uses the outputs of misapplied risk analysis to make decisions:

  • False precision creates unwarranted confidence
  • Poor models override professional judgement
  • Decisions become systematically biased by misleading data

At this point, risk management causes more harm than having no risk management at all.

 

Reference to Monte Carlo Simulation

Misapplied Monte Carlo Simulation belongs to the Worse and Worst categories.

  • If QRA is performed periodically but its results are ignored, the outcome is wasted effort and cost, with limited direct harm.
  • If flawed results are used to drive decisions, the impact is often actively damaging project outcomes.

Unfortunately, misapplied Monte Carlo Simulation is common. Two typical pathways lead projects into the Worst state:

  1. Forced QRA
    The project team is required, contractually or externally, to perform QRA, while management doesn’t realise that the reports may be formally compliant but practically misleading.
  2. Premature escalation from “Better”
    Projects that reach the Better level attempt to progress by introducing quantitative simulations. While they recognise the value of integrated analysis, they underestimate the effort, skill set, and technological capability required. As a result, maturity collapses from Better directly to Worst.  In this case, projects regress from meaningful qualitative analysis to misleading pseudo-rigour, creating an illusion of sophistication without insight. The outcome is a dramatic drop in risk-management maturity.

 

Inspired by Douglas W. Hubbard

Alex Lyaschenko

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

Next-Gen Project Risk Management Technologies

Next-Gen Project Risk Management Technologies

Mature project management is critical for innovations. At the same time, innovative technologies could also be applied for project delivery. This would speed up technology development and implementation even further. Next-Gen technology platform for Project Risk Management could completely change our view of Project Management in the near future. Are you ready for this change?

In episode N S3E55 of Project Chatter podcast two experts discussed “How Technology is driving Next-Gen Project Risk Management”.  

Both presenters have perfectly described various challenges in taking existing systems for project risk management to the next level. I don’t think they have mentioned though that such “intellectual project management systems” already exist and even widely used in some parts of the world, especially for major infrastructure projects. I am also very passionate about this topic and would like to discuss this a bit further.

There are the key challenges and ideas that I’ve captured from the podcast episode:

  • Standardisation of project management data is the most critical constraint now. We have a lot of historical data, but it is hard to reuse.
  • There is no feasible progress in risk management functionality in Primavera and, even, there are no expectations of having any valuable improvement soon.
  • Construction, comparing to the Oil & Gas industry, is not mature in risk management.
  • New methods to manage project risks expected from AI systems.
  • Risk management has to be integrated with a scheduling platform to embed results of the schedule risk analysis back into the schedule.
  •  It is hard to educate students and PMs to apply risk methods correctly as Risk platforms are too expensive.
  • Next-Gen systems are going to support us with Risk identification process (based on historical data).
  • Role of schedulers could be significantly different after such systems implemented.

Let’s review how the above challenges addressed in the “Next Gen” project management tools and where this could take us in the future. I am going to use Spider Project, project management tool, as an example. This tool has the most mature schedule risk management and very sophisticated authorisms, but there are other tools on the market, which could offer similar solutions.

Standardisation

  • Majority of project schedules are duration or effort driven. It makes data standardisation process very difficult.
  • Duration in many project activities depends on assigned resources, their current availability, practical quantity and productivity.
  • Activities in different projects may have the same type of work but have different volume of work which also has an impact on activity durations.

Data standardisation has to be volume-based

Corporate norms (or standards) usually include all aspects of project management:

• Standard WBS(es);
• Types of work;
• Type of resources (people & equipment);
• Skills;
• Resource productivity;
• Material norms;
• Cost per unit;

Activity durations in different projects are calculated based on a volume of work and available resources. 

It is a dynamic characteristic, not a fixed type of data.

If the volume of work or resources demand/supply is changed, the system could automatically re-adjust durations, offer the best sequence of work and recalculate resource critical path.

Risk Database

Additionally, corporate norms should include “historical risks” based on the lesson learned from previous projects. Enterprise portfolio corporate standards include not only estimates of the typical process, activity, resource, and assignment parameters but also project templates.  Risks are converted into “scheduling fragments” with a list of mitigation and/or action plan activities. 

Probability of each risk estimated initially for each project with historical data taking into the account.   

This library of schedule templates is not just used for risk identification but also allows schedulers to develop schedules for new projects much quicker.

Risk analysis methods

Existing challenges in project risk simulation addressed by high-technology systems.

Project risks and uncertainties usually managed to determine:

  • Project risks that require maximum attention
  • Achievable project targets with sufficient probabilities
  • Contingency reserves to meet the project targets
  • Current probabilities of meeting these targets
  • Resources required for the reliable achievement of project targets
  • Project activities that require maximum attention

Project risk optimisation systems need to support: 

  • Activity ranging (to manage uncertainties);
  • Advanced resource assignments;
  • Schedule and risks integration;
  • Risk evaluation methods;
  • Schedule optimisation methods;

The common risk simulation method is the Monte Carlo technique that simulates project execution many times, each time with new, randomly selected initial data, based on the expected probability distributions. In my view, Monte Carlo analysis, as a good mathematical method. It gives a reliable result but has two disadvantages:

 

  • It is very sensitive to the quality of risk data;
  • Monte Carlo simulation has high accuracy but low precision if the number of iterations is not sufficiently large.

Alternative schedule risk analysis methods which are less sensitive to the quality of risk data and size of schedule are more practical and are easier to use. I believe in the future we still could use MC method for some type of projects but mostly as an exception, not the rule.

 If you interesting more in Risk Analysis Challenges, there is very good presentation to read:

Training

Spider project has fully functional demo version with some limitation on a number of tasks. This version is free and is used by many education providers and not just to show the tool’s functionality but, what is more important, how to apply advanced risk methods and techniques correctly.

Processes

The Next-Gen risk management systems are also going to change portfolio and project management processes.

 

  • A project prioritisation process is going to be build based on organisational bottlenecks (resources, skills, processes), not just based on strategic themes and available funds.
  • Integration of different type of projects (Waterfall, Agile & Hybrid) in a single dynamic model allows includes portfolio level risks into the analyse.
  • Ability to present information from different points of views will change project analysis process.

Scheduling Role

Presenters in the podcast have also discussed whether a scheduling role will be redundant soon and how new technology could change schedulers role and responsibility.

We actually could look into the future!!! The data standardisation helps to simplify schedule development & maintenance processes. Organisations that have already implemented advanced systems in project risk management have reduced a number of schedulers but require highly analytical skills. Schedule optimisation algorithms offer the most optimal solutions to schedule analysts. Armed with such powerful systems, the analysts now play the role of “analytical centre” in the project portfolio. Their recommendations support portfolio managers with critical portfolio decisions.

Industry Standards

While many of us are expecting dramatic changes in Project Delivery in the near future, such changes are impossible without mentality shift. We are able to learn much more from the previously delivered projects but only if we start using structural project delivery approach. The good news is that we do not need to be pioneers anymore. We could learn from industries and countries which already adopted their project delivery to the new standards.

Spider Project is the most popular tool for Project Management Delivery in Construction and Oil & Gas industries in some European, Asian and South American countries. Corporate norms, volume-driven and risk-driven scheduling are absolute standards for companies in these industries now. Thousands of projects, from small renovation projects to Olympic Games portfolio applied corporate norms and risk-driven methods. This generates a lot of structural project management data, which is used for project analytics and helps to improve schedule optimisation algorithms even further.

Alex Lyaschenko

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