Customer Stories

Transforming Safety Management at Suffolk with Predictive Analytics

05/28/2025

Company

Suffolk is one of the 25 largest general contractors in the United States. They manage some of the most complex, sophisticated projects, serving clients in every major industry sector, including healthcare, life sciences, education, gaming, transportation/aviation, federal government and public work, mission critical and commercial.

Size:

3,000 employees

Industry:

Construction

Technology Used:

Posit Workbench, Posit Connect, Databricks

Solutions Delivered:

ML Models (for Safety, Insurance, Scheduling), Interactive Applications, APIs

72%

Total Recordable Incident Rate (TRIR) Reduction

56%

Lost Time Incident Rate (LTIR) Decrease

“Historically, contractors manage jobsite safety based on lagging indicators and adjust their process in response. We knew we could do better. By leveraging data, artificial intelligence and predictive analytics, we decided to proactively identify where risk exists on our projects and how we can eliminate that risk.”

Matt Swaim, J.D.

EVP National Operations/Environmental Health & Safety at Suffolk

According to the Bureau of Labor Statistics, over 150,000 construction site injuries occur every year. To mitigate incidents before they occur, Suffolk’s safety and data teams joined forces to develop a safety model that integrates data on staffing, trade partners, observations, incident history, project schedule, and core project details to proactively assess project risk.

“Over the years, we have made significant progress in equipping every employee with real-time predictive insights,” said Aleksey Chuprov, VP of Data Analytics and AI at Suffolk. “Those insights power optimal decision-making and best-in-class project outcomes across our seamlessly connected, analytics-driven platform.”

Using Posit Team, the data science team at Suffolk standardized their model workflow and communicate safety scores to stakeholders in two separate ways:

Safety Dashboard to provide key insights (percentage of high risk projects, any projects with incidents, how predictions compare to historical predictions over time, etc.) at both the project and portfolio level to help project teams see risk in a few different ways
Weekly Portfolio Risk Email with a concise list of projects flagged as high risk, plus the top features driving each score

Avoid the immediate rush to build many dashboards.

When people want to get involved in data, the most tangible way they think about data is a dashboard. Start with a bespoke analysis and then when you get to the point of understanding what is really important to the business, codify that and put it into the dashboard. You’ll have fewer dashboards, but they will be more meaningful.

Considering alternative methods of delivering information if a dashboard isn't necessary.

For data that isn't very dynamic and doesn't require filtering, exception reports sent via email might be more appropriate. Suffolk is moving such reporting to Posit Connect for automation and notifications when certain thresholds are crossed.

Lesson Learned

Meet stakeholders where they are to make data insights actionable

Blake Abbenante, Director of Data Science and Analytics at Suffolk, highlighted that while technical aspects constitute 80% of data work, the remaining 20% centers on effectively communicating results to ensure end users comprehend how data can enhance their job efficiency and impact the business.

When Blake came onboard, much of the data science work had grown organically and there was no cohesive enterprise-grade infrastructure to develop and deploy solutions for the team. Their prior workflow was dependent on local Alteryx scripts, and they ingested a fair amount of one-off data sources (e.g., data that had been extracted and manicured in Excel).

As a result there were bottlenecks on the execution of the model, as well as questions on the lineage and reproducibility of the data. Often with the data being ingested from Excel, it was not able to be replicated from its original ‘sources’.

Suffolk needed a solution for seamlessly communicating their analyses and models created in Python and R with business teams. While existing dashboards could support static data, they needed a reproducible solution that would allow site managers and project executives to interact with deployed models to make decisions backed by data.

Blake Abbenante estimates that by moving to Posit, the team has “reduced deliverability time by ~20% simply by not having to search through SharePoint for Excel files.”

Stakeholders may place more trust in their subjective experience than what data is telling them.

This isn’t necessarily pushback, but they know better as there’s nuance in the actual management of a project that is not always captured in the available data.

Generally, before introducing a new project, identify the key stakeholders (site manager, superintendents, Project Executives) who have more intimate knowledge of projects themselves to get their insight and feedback. They can then be a part of the initial rollout.

In partnering with stakeholders, be upfront that there’s probably data that is currently not represented. To continuously enhance our models, we need to gather more nuanced data. This requires collaboration with stakeholders (those on the job sites) to determine how to collect the necessary level of detail. Then by incorporating this additional data into future iterations, we can consistently improve the model's fidelity.

Lesson Learned

Partner with strategic business stakeholders

Suffolk's journey with Posit

2018

Suffolk aims to proactively identify project risks. Safety & Data teams collaborate on a project for field teams to use a safety app to record potential jobsite hazards, which are photographed and documented.

2019

Suffolk utilizes a 3rd party model to evaluate risk across projects to answer questions such as “Which types of hazards are most prevalent across our job sites?”

2022

To minimize external dependencies & improve reliability, Suffolk brings the safety model in-house. The primary motivations were doubts regarding the previous provider's stability and the unacceptable risk associated with external reliance for Suffolk's safety operations.

2023

To enhance risk assessment, Suffolk continues to iterate on the original version of the safety model. This strategic move allowed them to utilize evolving data streams (staffing, trade partners, observations, incident history, etc.) and establish a more transparent and reproducible system, ultimately leading them to adopt Posit Workbench. They still had to demonstrate value of Posit Connect as they already had a couple of other visualization tools (Tableau, Alteryx)

2024

Suffolk onboards Posit Connect so that they can productionize their machine learning workflows and seamlessly present prediction records and model results to business stakeholders in a user-friendly way.

2025

Suffolk is leveraging machine learning models for innovation in safety, insurance, and scheduling. They have developed accompanying Shiny applications and deployed APIs with Git-backed deployment to Posit Connect to support these efforts.

Enhanced Data Governance with Posit Team and Databricks

With the partnership of Databricks and Posit, Suffolk leverages row-level permissions in their interactive applications. This ensures business stakeholders can use their own credentials to access a personalized view with only the data they should see through a single, shared app.

For a more technical detailed walkthrough of how this is made possible with Posit Team + Databricks, you can view Max Patterson’s Workflow Demo below:

For the safety model, the way the data was presented was very important. The initial framing of the data as “having a higher likelihood of incident” made it something project teams wanted to avoid. This can have an impact on how people collect the data or tune out the results, just by how the solution is framed. It’s crucial to clearly communicate to stakeholders what you’re doing and how it can help make them more efficient.

The future

Suffolk aims to be the future leader of AI in the built world and it’s a company-wide initiative to figure out how they can best leverage AI. Some of the initial use cases so far have been chat agents to retrieve specific information about particular domains or extracting data out of PDFs, but future use cases of AI are to augment and speed along design of buildings themselves and in creating entire schedules for project planners. If it may take a planner 2 weeks now to do the scheduling planning, is there a way to get them 80% of the way in a matter of hours or minutes.

For data scientists interested in taking the first step into AI, Blake recommends checking out the ellmer and chatlas packages from Posit. He adds, “It’s really easy then to integrate it to any other kind of work that you’ve done or if you happen to be using any of the Posit suite of tools, so building it into a Shiny app or whatever else. I think that is probably the easiest way to get started.