Budgeting and scheduling are constant challenges in the construction industry. According to a recent survey conducted by IDC, approximately 78% of commercial construction projects are delayed and/or over budget.
The cause? In many cases, these cost overruns and delays are the result of a mismanaged project schedule. The good news, however, is that these schedules also hold the potential solution.
Megaproject teams amass significant amounts of valuable data within scheduling tools such as Primavera P6 or MS-Project, including schedule baselines, outcomes, and more. All they need is a simple way to make sense of it all and understand where projects went wrong so they can learn how to avoid similar errors in the future.
In this blog post, we'll explore the limitations of traditional scheduling software and explain how Machine Learning and Artificial Intelligence (AI) can help Project Teams overcome the growing complexity of capital projects and achieve on-time delivery.
Since the 1980s, Oracle Primavera and Microsoft Project have revolutionized megaproject management by providing systems of record at scale. These software excel at storing expansive project data regarding budgets, expenses, resources, and timelines from multiple sources. This enhanced visibility into the work has allowed teams to work more efficiently and with greater ease than in past decades.
But despite these great benefits to Project Management (PM) teams, traditional scheduling software has struggled to keep up with the growing complexity of today's capital megaprojects. PMs need a better way to make sense of the mountains of data they collect.
Scheduling technology hasn't evolved to keep pace with the added complexity of modern megaprojects. This disconnect contributes heavily to two key problems facing construction project stakeholders: cost overruns and delays.
The truth is that today's megaprojects are so large and complex that no human team could possibly analyze the massive amounts of data they are collecting in a meaningful way. This means that project teams are not getting all the value from the schedule data trapped in their Primavera P6 or MS-Project records.
Some of the key challenges that today's project teams face due to traditional scheduling software include:
Cost overruns and delays have remained high and constant for megaprojects over the last 70 years despite the widespread adoption of tools like P6 and MS-Project but it doesn't have to be this way.
Teams can work around these shortcomings by utilizing a system of intelligence, powered by AI, to enhance the capabilities of Primavera P6 and MS-Project.
The key to better project management is in the data itself. By studying the intricate details, teams can better understand the job and what is happening. This kind of analysis makes it possible to predict future scenarios and critical paths, repair schedule deficiencies, and update their plans with confidence
Yet that means reviewing historical durations and baselines, studying logical flows, and identifying critical path delays and missing logic. Given the time-consuming and expensive nature of this process, it is essentially physically impossible to perform.
Artificial Intelligence (AI) and Machine Learning solutions make it possible to analyze and interpret vast swaths of data that no human possibly could. This leads to a more proactive approach to project management vs. the current reactive approach to delays and setbacks.
Oracle Primavera & MS-Project are widely used in the project management space. And while these tools have helped to deliver improved outcomes, the pace of change and growing complexity of modern capital projects have surpassed their limitations.
Foresight seamlessly integrates with fragmented sources of raw information to bring personalized insights to project executives and teams anywhere anytime.