Data Analytics: Squeezing the Most Out of Your Scheduling Program

It’s essential to remind ourselves of exactly what construction scheduling is. An article in Software Advice lays it out as clearly as we have ever seen:

“Construction planning and scheduling refers to the process through which a construction business maps out exactly what will take place during a project, what resources are needed, when key activities will take place, which personnel will be involved, and anything else pertinent to the project.”

So, the construction schedule represents the strategy, the blueprint, and the timeline behind a project, all rolled together. Hundreds of companies capitalize on this reality by developing scheduling programs based on the Critical Path Method (CPM). They are all effective to varying degrees, but the better ones simplify the process and produce incredibly accurate schedules.

However, there is a major disconnect. Most of the industry believes “scheduling” involves creating a baseline schedule and merely updating it on a monthly basis. To be sure, scheduling is a far more complex endeavor. Project schedulers rarely have the time to study performance data, challenge durations, question the sequencing, and review performance history. Much less, do they have time meet with subs and then adjust the schedule to be realistic and achievable–regardless of how aggressive the project manager wishes the schedule to be.

The Complexity of Project Scheduling: Beyond Baseline Updates

A McKinsey study of projects across the globe found that the average delay for construction projects was 2.10 years.

Naturally, this leads to some gargantuan headaches.  A recent article in Construction Executive states, “An alarming 75% of all commercial construction projects are delayed or over budget due to mismanagement of construction schedules.” The next part is especially enlightening and encouraging:

“Those schedules also hold the potential solution. Construction companies unwittingly collect significant amounts of valuable data through scheduling tools such as Primavera P6 or MS Project. Now all that’s needed is a simple way to make sense of it all–to understand where projects went wrong and how to avoid those mistakes in the future.”

To top it all off, most construction scheduling programs simply aren’t strong enough to manage a job effectively. According to data collected by SmartPM, over 70% of project schedules receive lower than a “B” grade. Meaning, the schedules are unreliable for managing a job effectively and, worse, are not at a quality level where the data can be effectively analyzed.

That doesn’t mean the scheduling program itself is below par. It just means that best practices were not followed due to inexperience, time constraints, or other stressors affecting the person building and updating the schedules.

With all that as a backdrop, let us pose a question that is at the heart of this blog. Since you’ve invested so much money in your construction scheduling program, why wouldn’t you want to get the most out of it? In other words, why would you not invest in a companion tool to maximize your scheduling program’s execution?

The Forrest Through The Trees: Data Analytics

Construction data analysis comes into play here. Despite the appearance and achievability of schedules, they often contain mistakes like missing logic ties and incorrect data (crew logic, durations, etc.). It doesn’t matter how it happens. But there’s a chance you’ll run into scheduling roadblocks on a project that could be identified and remediated earlier.

That’s not to say that scheduling programs have a built-in deficiency. Schedules simply aren’t designed to self-diagnose. As a matter of fact, they are SO user-friendly. Because often, schedule changes occur to keep an end date that is physically and mathematically impossible. If any real speed bumps are present in the schedule, a data analytics program will highlight them.

For instance, a quality data analysis program will catch missing logic ties. It will tell you whether your project milestones are realistic and, if they aren’t, when you can expect them to be reached at the current pace. Supplying predictive analytics ensures schedule adherence and budget control. It also accounts for schedule changes. Whether these changes are related to subcontractor schedules, design revisions, or any of a thousand different things, the program spits out a reliable picture of how the schedule is impacted.

Getting Started with Schedule Analytics

Data-analysis programs are most effective when used throughout the life of a project.  Most of our clients have specifically added SmartPM to their projects for the entire team, from the site to the C-suite, to see the most current status of each job. It’s a running account–in real-time–of where everything is, where it’s going, and where attention must be focused to address current or future risk(s).

Here’s an added bonus: a good data analytics program helps companies produce better schedules up front. We’ve seen clients whose SmartPM usage repeatedly showed the same issues and areas of concern. By reviewing these recurring patterns, schedulers pay extra attention to those trouble spots, training them to produce a schedule that is more accurate from the start.

There are countless reasons as to why a data analytics program are so…well, constructive.

Working in concert with a high-quality scheduling program helps eliminate project delays and minimize budget overruns. Most importantly, it reduces the chances of a legal dispute. And if a legal dispute arises, data analytics help to definitively pinpoint who is responsible for each setback.

But in the end, it comes down to producing better, more effective schedules. Effective schedules evolve based on the in-depth insights a data analytics product can generate. Your scheduling program is capable of turning out good schedules. Why not turn them into great ones?