Over the last few decades, transportation management systems (TMS) have become one of the few linchpin solutions that witnesses en masse adoption by trucking businesses, making TMS solutions integral to the seamless working of the road freight industry. However, with digitalization, there has been a need for TMS providers to refashion their solutions and develop data analytics services – to help users with predictive maintenance and improved visibility into operations.
Teknowlogi, a California-based transport automation company, is pushing boundaries on traditional TMS solutions through its Tai platform that analyzes millions of data variables in real-time, helping companies leverage insights to improve operational efficiency. FreightWaves spoke with the Teknowlogi team to understand the platform and the necessity the company found in disrupting, what they call as a “technologically stagnant” TMS market.
“Automation of processes and having a good workflow is just one aspect within the evolution of TMS solutions. The more important one is the artificial intelligence (AI) aspect and the machine learning (ML) algorithms that can help drive greater efficiency in business practices,” said Walter Mitchell, the CTO of Teknowlogi. “The point of Tai is to build and communicate efficient business practices throughout the organization and to automate some of those processes.”
Tai acts as a virtual assistant to company executives; it can materialize the organizational wants throughout the TMS and communicate it to ground-level staff in context-appropriate ways. For instance, Tai can look into payment and shipment booking patterns to glean insights into the rate of shipment bookings versus the rate of payments, helping employees connect with customers that need to be alerted of negatively spiralling patterns.
To train Tai’s ML algorithms, Teknowlogi used applied intelligence, which Mitchell defined as a “combination of artificial intelligence and consulting intelligence.” Consulting intelligence is an umbrella term that encompasses the tribal knowledge and decade-long industry experience that the company has in the TMS landscape. This strategy helped Teknowlogi develop models that were more reality-tuned, as opposed to having models that relied entirely on historical data and statistical algorithms.
“Over the last decade, we have been able to aggregate data through our customer base from the TMS we’ve deployed. But all that data is not enough sometimes, and when that happens, we apply consulting intelligence,” said Mitchell. “Introducing a bit of data bias using consulting intelligence makes these ML algorithms highly accurate and effective for making changes across the organization.”
Though Teknowlogi considers shippers and non-asset carriers to be its target customer base, the Tai platform does draw attention from the asset owners as well, primarily because the solution was designed to integrate with any pre-existing supply chain software – including TMS products that are market rivals to Teknowlogi’s TMS solution.
“We realized that as a technology provider, for us to shift the method of thinking and the trajectory of the industry, we should not just look to automate processes and make operations easier, but should also identify and eliminate business constraints up and down supply chains through the Tai platform,” said Spencer Askew, the CEO of Teknowlogi.
Several reports have pointed out the glaring disconnect that exists between users and software applications within the freight industry, where users quickly outpace the applications that their companies have licenses for or built on their own. Teknowlogi realized that this was a macro-level problem and built Tai to continually learn from and harness the tribal knowledge of the workforce – helping companies retain capability even when their employees retire or leave the organization.
“We recognized that our quest to enable the industry was not going to come from a rip-and-replace methodology. We had to build a software that could interact quite nicely and work with pre-existing TMS, ERP or home-grown systems.” said Askew. “Tai helps us accomplish our goal of building a platform that makes the industry smarter and helps legacy software systems to upgrade and become more intelligent versions of themselves.”
