Ever since the introduction of Primavera in 1983, the project scheduling community has matured leaps and bounds. However, despite technological advancements, major delays continue to plague constructors & operators.
University of Oxford research shows that globally fewer than 1 in 10 projects deliver on time & on budget. So what’s behind all these delays and how can constructors and operators overcome the failures of their scheduling software?
Failure #1: Lack of Insights To Overcome Complexity
Data is NOT the issue. In fact, most project teams are overwhelmed by the sheer amount of data and schedule versions circulating in any given project. Psychological research by luminaries such as Herbert Simon and Alan Baddeley has shown that human working memory is limited.
In Nobel Laureate Herbest Simon’s words, “the capacity of short-term memory is five chunks”. In fast-moving project settings, human beings have a limited capacity to analyze and interpret the data in real time, leading to major missteps in project execution.
GANTT charts, excel spreadsheets & P6/MS Project have helped deliver improved outcomes, but the pace of change and complexity of modern capital projects has surpassed the limits of these technology tools. Project teams don’t have the working memory bandwidth to track all the moving parts of a project.
Machine and Artificial Intelligence offers ways to increase project team working memory to overcome project complexity.
Complexities are on the rise, driven by supply chain bottlenecks, staffing shortages, government oversight/regulation and an increasing number of subcontract partners.
- Whatever teams pay attention to gets done, but in complex projects, teams do not know what to pay attention to. Project teams need insights to prioritize critical and urgent tasks and track their completion.
- Project teams need proactive alerts of things prone to getting stuck and how to de-bottleneck them.
- Project teams don’t just need to track their schedules, they need to optimize them with support from AI (as there are too many variables to accurately predict and mitigate risk without AI).
- Existing tools do not provide robust risk assessment capabilities
- Legacy solutions’ risk analysis have been superseded with machine learning approaches that identify risks much earlier. Nip the evil in the bud.
Failure #2: Cognitive Biases
When tools like Primavera P6 & MS-Project were originally delivered, they changed the way projects were delivered by creating structure and efficiency. Yet the wealth of data stored in these systems of record is not systematically analyzed to find valuable insight.
Nobel Laureate Daniel Kahneman’s research has shown: Human beings are prone to optimism bias. We take longer to do things than we estimate. Even simple routine tasks like going to the grocery takes longer than we expect. The time slippage magnifies in more complex tasks.
Projects are composed of many repetitive tasks that do not achieve repetitive outcomes. The point of failure is a lack of historical analysis to inform future projects; leads to bias or blind spots.
Here again, the data is NOT the issue. The data of past completed projects are available in the form of baseline, weekly or monthly update, and as-built schedule files in MS-Project (.mpp format) or Primavera P6 (.xer format).
Project teams don’t have the time to analyze that historical data and draw insights on which activities tend to run super late (and hence require an optimism bias uplift). Which activities tend to have too much cushioning and can be compressed in duration.
Another mistake project teams make is to think of their project as being unique. Such a uniqueness bias, well-documented in psychological research, leads people to discard valuable information from past comparable projects.
- Project teams need machine intelligence to systematically analyze historical data to find source of bias and blind spots
- Project teams need scalable ways to transfer learning from their whole portfolio to each new project. Even really groundbreaking projects break down into many shared repetitive tasks.
Failure #3: Lack of Follow-through
Insights on their own are great, but how does that translate to action?
In legacy tools, project controls and work management tend to be decoupled, particularly in poor-performing projects. Today’s teams need to be able to combine project controls with intelligent workflows that assign resources to the most critical activities. Even minor delays at the start or finish of an activity can accumulate into death by a thousand cuts for the project.
- Legacy tools require human interpretation and then manual task assignment and tracking
- These tools don’t have a feedback mechanism to track progress at user and task level
- There is minimal follow through, given the lack of a centralized hub for project schedulers and executives to understand project status and progress against critical tasks
What project teams can do about it
Introducing Foresight by Foresight Works. Review project schedules instantaneously w/ AI powered risk analysis & automated assignment & tracking of critical tasks. You don’t need to reinvent the wheel; Foresight works within your existing capital project management software investments, to provide robust insights and task management. Contact us to learn more.
