The paradox of project planning – four strategies for planning successful projects

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Developed by Viktor Lukas Müller Thomsen

Contents

Abstract

The paradox of project planning refers to the inherent tension between the desire to thoroughly plan a project and the limitations of our ability to predict the future. In his book How Big Things Get Done, Bent Flyvbjerg presents strategies to that can mitigate what he calls “The Iron Law of Megaprojects: over budget, over time, under benefits, over and over again”. According to Flyvbjergs research on more than 16.000 projects in 136 countries, only 8.5 percent delivers on budget and in time, while a mere 0.5 percent deliver on cost, time and benefits IronLaw [1].

This article presents four heuristic strategies for project, program, and portfolio managers to mitigate the Iron Law in projects of all sizes, not just megaprojects. The article suggests practical methods and tools that project managers can use and provides an overview of the topic, with references to detailed literature for further reading.

Think slow, act fast

Projects, by their very nature, have a finite timeline during which they must be completed. Projects that extend beyond their projected timeline are more likely to fail, not only because they exceed their planned duration, but also because they become vulnerable. As time passes, external factors introduce additional risks to the project's success. Recent events such as the COVID-19 pandemic, global supply chain disruptions caused by the grounding of a single container ship in the Suez Canal, and the war in Ukraine, which has caused inflation in the global market, illustrate external factors that can derail even the most carefully planned projects. Therefore, there is a significant incentive to expedite project completion to reduce risk. However, attempting to execute projects within a compressed timeframe is certain recipe for disaster.

According to Professor Bent Flyvbjerg, the right way to deal with this paradox, is to start thinking about projects in two phases: first, planning; and then delivery BigThings: [2] . Planning the project include sufficiently researching, analyzing and testing, until there is a reliable road map of the way forward. Delivery is the executing the plan, bringing the project to life. The most important distinction is that planning is almost always much cheaper than delivering a project. Planning happens using computers, paper, physical models and prototypes which is cheap, but it builds the foundation for executing the project, which is expensive. According to Flyvbjerg, projects do not "go wrong", they start wrong, when project managers neglect to plan projects thoroughly.

Therefore, the first heuristic for project managers is to take your time during the planning phase to come up with a detailed and well thought out plan, thereby reducing time spend delivering, and increasing chances of project success [3].

The Fuzzy Front End

A good way to start planning projects is through what is called "the fuzzy front end". It refers to the initial period of a project, from idea conception to development readiness. This phase is critical as it involves customer interactions, problem identification, process improvement opportunities, and potential sales leads. While these may not be formally documented, they can provide valuable insights into customer needs and preferences. Building strong customer relationships during this phase is crucial for project successFuzzy: [4] . Even though the fuzzy front end is often associated with innovation projects, most projects can benefit from going through this phase in the hands of a capable project manager.

Some good practices for managing the fuzzy front end include:

  1. Create a strong vision to anchor the project team toward a common goal.
  2. Enrol key stakeholders who are committed towards the vision.
  3. Involve customers and end user as early as possible.

For more information see: Project Management in the Fuzzy Front End Addressing the Problem of Fuzzy Front End

Plan iteration into your project

Only a select few can excel at performing a task perfectly the very first time. Most people need time to practice and learn, and the same is true for projects. Therefore, it is wise to include mechanisms for learning in project planning to ensure that knowledge can be acquired and utilized. Through his extensive work and research on large projects, Professor Bent Flyvbjerg, has identified two crucial factors for planning successful projects: replicable modularity in project design and speed iteration. The following is a guide for project managers on how to implement iteration and learning in projects.

Minimum Viable Product

One approach to iteration is the concept of the minimum viable product (MVP), which involves creating a basic version of a product or service that can be tested and refined through feedback from early users. This allows for quick validation of ideas and early identification of potential issues, which can save time and resources in the long run. It is insufficient to build a prototype evaluated only for internal quality by engineers and designers. The MVP must be put in front of potential customers, in order to gather real feedback. In projects, creating the MVP can be a part of project planning before the final product is put into delivery, or the MVP can be the final product that is produced and send to market. This is often seen within tech, where many products sold are newer and better versions of the same concept.

For more information see Wikipedia: Minimum Viable Product


Maximum Virtual Product

It is, however, not always possible to make a MVP. Projects such as building a dam, sending a rocket into space or building a nuclear reactor cannot be 90 percent done, and are notoriously difficult to make physical iterations of. When physical models are not viable option, digital models - also called Digital Twins - can be used instead. The digital twin is an approach that utilizes virtual simulations and modeling to test and refine a product or process before physical implementation. This can be particularly useful in complex projects, as it allows for experimentation and iteration in a controlled environment, reducing the risk of costly mistakes during implementation.

Both MVP and Digital approaches involve rapid iteration and testing, which can help teams identify and address issues early on in the project lifecycle. By implementing these approaches, project managers can create an environment that encourages learning and adaptation, ultimately leading to more successful project outcomes.

Build with LEGO

Building upon the second factor identified by Flyvbjerg; modularity in project design Modular: [5] . Replicability in projects allows people to learn, and thereby become better at doing what they do. It is therefore important to consider and what type of modularity can be applied in a project. Within project, program, and portfolio management, modularity can be described on three levels according to Baldwin & Clark [6]:


  1. Product modularity refers to the ability to break a product down into smaller, independent components that can be combined in different ways to create different versions of the product. For example, a computer manufacturer may use a modular design for their laptops, allowing customers to choose different configurations of components such as the processor, memory, and storage.
  2. Process modularity refers to the ability to break down the process of creating a product or delivering a service into smaller, independent components. This can allow for greater flexibility and customization in the production or delivery process. For example a windfarm, that consist of individual windmill modules that are then repeated one after another. The use of the same module, repeated over and over allows for learning experiences through repeated processes of installation and maintenance. It also highly affects scalability, in the sense that modules can be added or removed in order to achieve the desired size.
  3. Organizational modularity refers to the ability to break down an organization into smaller, independent components that can be combined in different ways to achieve different goals. This can allow for greater flexibility and adaptability in the face of changing circumstances, as well as easier management of different parts of the organization. For example, a large corporation may have different divisions or subsidiaries that operate independently but can be combined in different ways to achieve different strategic goals.


The third heuristic is find ones "LEGO", or module that can be implemented in projects. Leveraging modularity can be a powerful tool for improving the scalability, flexibility, adaptability, and learnability in projects. By breaking down complexity into smaller, more manageable components, and by designing products, processes, and organisations in a modular way, it becomes easier to test, learn from, and improve upon what we do.

For more information about product and process modularity see Modularisation: A modern process for project management

Curb your optimism!

When it comes to large-scale projects, one of the biggest challenges is completing the project within the allocated budget and timeframe. Often, these projects exceed both the budget and timeline, which can be attributed to poor planning. While problems during development can cause a project to go over budget, it is often the case that the allocated budget was too optimistic to begin with. This can, according to Flyvbjerg be attributed to two phenomena: optimism bias and strategic misrepresentationCurb: [7].

Optimism bias refers to the tendency for people to be overly optimistic when making predictions about the future. Daniel Kahneman's work has shown that people are often systemically overconfident and optimistic when it comes to planning. This is because people tend to base their estimates past experiences or similar situations that turned out well, thereby skewing the picture favourably [8].

On the other hand, strategic misrepresentation is when those presenting projects for approval knowingly understate costs and overstate benefits. For example, a contractor may present an overly optimistic budget to win a contract, or politicians give false promises in order appear favorable, without the necessary funding.

To address these issues, Flyvbjerg has developed reference class forecasting. This method involves identifying a "reference class" of similar projects or situations based on objective criteria such as size, complexity, scope, and context. Data on past projects or situations within the reference class is then gathered and used to make a forecast.

To use reference class forecasting, three steps are required. Firstly, identify a relevant reference class of past, similar projects. The class must be broad enough to be statistically meaningful but narrow enough to be truly comparable with the specific project. Secondly, establish a probability distribution for the selected reference class using credible, empirical data. Finally, compare the specific project with the reference class distribution to establish the most likely outcome for the specific project.

Overall, reference class forecasting can be a useful tool for anyone making predictions about the future by eliminating optimism bias and strategic misrepresentation entirely. It can, however, also serve as a heuristic way of thinking by enabling project teams to take the "outside view". When planning projects, estimations should be based on past projects, and not on own perceived ability.

For more detail see Optimism bias, Strategic Misinterpretation and Reference Class Forecasting (RCF).

References

  1. ↑ Flyvbjerg, Bent, Introduction: The Iron Law of Megaproject Management,” in The Oxford Handbook of Megaproject Management, ed. Bent Flyvbjerg (Oxford, UK: Oxford University Press, 2017), 1–18.
  2. ↑ Flyvbjerg, B., & Gardner, D. (2023). How big things get done: The surprising factors behind every successful project, from home renovations to space exploration. Macmillan.
  3. ↑ Think Slow and Act Fast, A New Way of Thinking about Risk Management in Times of Turbulence and Uncertainty | PMO Advisory</i>. (n.d.). Retrieved May 9, 2023, from https://www.pmoadvisory.com/blog/think-slow-and-act-fast
  4. ↑ Addressing the Problem of Fuzzy Front End | PMO Advisory. (n.d.). Retrieved May 9, 2023, from https://www.pmoadvisory.com/blog/addressing-the-problem-of-fuzzy-front-end/.
  5. ↑ Flyvbjerg, Bent, 2021, "Make Megaprojects More Modular," Harvard Business Review, November-December issue, pp. 58-63.2
  6. ↑ Baldwin, Carliss & Clark, Kim. (2007). Modularity in the Design of Complex Engineering Systems. 10.1007/3-540-32834-3_9.
  7. ↑ Flyvbjerg, Bent. (2008). Curbing Optimism Bias and Strategic Misrepresentation in Planning: Reference Class Forecasting in Practice. European Planning Studies. 16. 3-21. 10.1080/09654310701747936
  8. ↑ Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
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