FORO NEEDED A CUSTOM APPLICATION FOR INDOT

FORO is a growing start-up that promises “Better Insights. Better Decisions.” Its core offerings are robust applications and consulting services for large companies and organizations that need complex data processing, verbatim text analysis, and improved team collaboration. Alliance has worked with FORO for years to develop its standalone SaaS product as well as custom applications for FORO’s clients.

One of the most successful applications built was for the Indiana Department of Transportation (INDOT). INDOT manages hundreds of state construction projects every year, and is a leading state in implementing a cost-efficiency method of project bundling. By taking several small projects, and attaching them to a large project, the cost savings are significant for the State of Indiana.

The Problem

INDOT for years has done this process manually which usually takes several staff members weeks to figure out.
Unknown whether manual process was leading to optimized results.
FORO and its machine-learning suite was brought in to do predictive analytics and bundling on the projects. Alliance Systems was contract by FORO to complete the application deliverables. The goal was to design and develop an application that would allow INDOT staff to run its project set through a Machine Learning Bundling Application using different variables to return bundles with potential cost savings amounts and scores.
Brett Boston, CEO of FORO

The process to bundle projects together is vital to INDOT. However, the pain-points were very real. It involved several staff members meeting for weeks trying to find the right Program to implement. INDOT knew they were saving money but had to no idea if they were maximizing those savings. They knew they needed our help.

Alliance Brings in Machine-Learning and UX/UI to Deliver a Robust Solution

The FORO project for INDOT was split into two distinctive cycles. The first was for FORO and INDOT to work with Alliance’s AI/Machine Learning team to develop the rules and criteria needed to create project bundles. Alliance developers took the criteria given by INDOT and worked on creating the machine learning algorithm which was facilitated through a custom-built API. The algorithm was adjusted several times to increase the cost-efficiency of the produced bundles. The end-product was a proof-of-concept application that demonstrated the results of the various runs types that were created and refined that produced the most efficient bundles.

Once the bundling methods were established, an online application was designed and built for the INDOT staff to run the projects through the Machine Learning Bundling Application. The Alliance UX/UI team held several discovery sessions to discuss the workflow process used to create Programs (the set of bundled projects). Prototypes were designed and reviewed by FORO and INDOT and then were developed into a working application. The final interface allows INDOT staff members to review, edit, and select the best produced bundles to complete its Program for the next fiscal year.

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The Results of the Application Have a Large Impact

The Project Bundling Application has proven to be a huge success for FORO and INDOT. The Machine Learning Bundling Application produced a more accurate Program while saving INDOT weeks of time making them more efficient. FORO is now in the proces of on-boarding more states to use its system.
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Extra $107,939,462 in Cost Savings

The Machine Learning algorithm projected to save INDOT an additional 40% in project cost. These extra cost savings allow INDOT to increase the number of projects in a fiscal year and take on more large-scale specialty projects.

From Weeks to Days

The application maximizes efficiency with customized decision-making. INDOT will now be able to develop a Program in just a few days, instead of the weeks the manual process took.
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Better Bundles

The FORO results were also more accurate. With the manual process, assumptions had to be made on bundling to get the program completed at all. FORO challenged those assumptions and produced a more accurate bundle set with new scenarios to produce better results.