4 Lessons Learned from Project Failures in Construction

Project delays and failures lead to billions of dollars in losses as companies lose to their competitors. The 2024 Construction Association report highlights that 53% of companies face delays or project abandonments due to cost-related challenges. Additionally, over one-third of firms have had to postpone or cancel projects as a result of increasing interest rates. Post-K, the Berlin Airport, and Kemper are notorious examples of delayed projects, each resulting in huge time and cost overruns. We can blame politics, volatile environments, macroeconomics, and many more factors, but problems that lie at the heart of one project are often not problems in another. Looking back, Epicflow has recognized certain patterns of behavior that can put the fates of projects at stake. Here are the lessons we can learn.

Lesson #1: Poor resource planning in mega projects costs more than money.

The Register has reported on potential delays to new fiscal 2020 and cost escalations for the Post-K Computer, Fujitsu’s flagship that’s expected to perform calculations much faster than Japan’s K Computer. Engineers working on Post-K informed management that they were behind schedule and would not be able to meet their due dates. This delay suggests that Fujitsu hasn’t yet found a way to create a balance between workloads and employee capacity.

Because Fujitsu suffered shortcomings in planning and estimation, the scientific, industrial, and economic sectors will have to forego the benefits of the Post-K computer for a couple more years. Let’s think about the ripple effects of the Post-K Computer’s delay. As a result of planning flaws, Japanese scientists may lose several years of opportunities to conduct even more accurate scientific calculations than are possible with the current K Computer. According to IDC analysts, “the Post-K computer is required for the simulation in the time scale of biological reactions in the millisecond level, a capability already achieved by the US.” If the Post-K Computer were to be released as planned, Japanese researchers could reach their milestones faster.

Lesson #2: Monitoring progress against every specific project target is vital.

Who could have predicted that a famous German construction project would appear on a crash course after 15 years of thorough planning?

“The real project status is unknown” sounds like a dead end for any project, but this time it was referring to the Berlin Brandenburg Airport. By failing to monitor project progress and KPIs, managers have stymied the construction.

In 1993, the members of Berlin Brandenburg Flughafen Holding GmbH (BBF) decided on a location for a new airfield – the Berlin Brandenburg Airport, expected to replace two increasingly congested airports. The new airport promised to offer the busiest runways in Germany and to support 45 million passengers annually. The planning stage took nearly 15 years and the construction finally started in 2006. Despite intentions to open the airport in 2011, its executive officers encountered a series of delays and cost overruns resulting from poor construction planning, execution, and management. The airport still hasn’t been opened and has already suffered a seven-year delay.

Let’s figure out what the main bottlenecks were to running this project on time and on budget. As suggested in CIO, management couldn’t efficiently report the real project status. The chairman of the supervisory board, Klaus Wowereit, failed to communicate project vitals to continue running the project. The board of executives assessed this lack of acknowledgment as total neglect from Wowereit’s side. If he had monitored progress against every target, senior managers would have been able to optimize work, adjust goals, and make appropriate changes to the initial project plan.

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Lesson #3: Don’t choose a utopian idea over a feasible solution.

The key goal of the Kemper project in Mississippi was to build a coal power plant that could prove the concept of clean coal, which was considered an imperative in the United States. The main intentions behind clean coal efforts were to lessen emissions of carbon dioxide, sulfur dioxide, nitrogen oxides, and mercury, all of which lead to climate change. Unfortunately, the project didn’t live up to its initial expectations and once again proved Murphy’s law that anything that can go wrong will go wrong. Kemper went excessively over budget alongside a three-year delay. Expected to cost $2.4 billion, the price tag grew to $7 billion. The true reason for failure was that gasification systems were incapable of working as planned, and so the plant had to be converted to burning natural gas instead. The government’s hopes of providing a cost-effective solution were blown sky-high. MIT Technology reviewer Michael Reilly considers Kemper “just the latest sign that clean coal, after decades of research and billions of dollars of development, is an oxymoron—at least in any economically realistic sense.”

What can managers learn from this case? Tom Fanning, the chief executive of Southern Company, which is responsible for the project’s construction and operations, blames the “unknown unknowns” – or in simple terms, project uncertainties – comparing them to bad weather. In this case, bottlenecks could have been predicted, the flow regulated, and the budget determined if the idea had been more realistic.

Lesson #4: Re-evaluate goals based on real-time progress.

Planning is the stage at which we want to predict the feasibility of ideas and approve them, but managing a construction site in real-time is a totally different ball game. There are cases when things simply don’t go according to plan because uncertainties pop up along the way and re-planning takes a long time. The reality is that our plans are never set in stone.

One more example of poor planning accompanied by uncertainty is the establishment of the Hinkley Point C nuclear power plant. The financial scope of this megaproject has ballooned significantly. EDF Energy initially estimated costs at £18 billion ($23 billion) in 2016, but recent projections suggest it could soar to £32 billion ($39 billion). Delays have compounded these challenges. The first reactor’s base was completed in June 2019, with the second following on schedule in summer 2020. However, pandemic-related disruptions and subsequent labor shortages and inflation pushed EDF to extend the project’s completion date to 2027 by May 2022.

The above-mentioned cases of project delays and failures show us that it’s impossible to streamline project management without having real-time updates. Each case described above suffered from a downside of planning – the planning stopped at the initial stage and was dropped once the objects were under construction. Only with accurate information at the right time can we create harmony among our plans, goals, and implementation.

Managing projects in line with these lessons may sound tough, but these goals are entirely reachable with Epicflow. Practically speaking, they can be achieved with Epicflow’s algorithms without the need to replace your existing project management software. Epicflow uses your existing data from MS Project or Jira and visualizes this project data in the following graphs:

Pipeline for achieving complete visibility over team performance, including inactive and completed projects
Historical load graph for presenting group performance, capacity, load, and output over time, which provides insights for improving performance in the future
Future load graph for helping you take control of resource planning and for predicting the impact of additional projects on existing projects in your pipeline
Task list for getting a real-time overview of all projects in your portfolio, sorted by priority
Remaining weeks, showing the amount of work remaining for each resource group
User mapping, showing the progress of the resource pool and balancing resources across groups
What-if analysis for testing project and resource management decisions and implementing the best one into the project environment
Competence management for assigning people to tasks based on the required competence level and other attributes