The COVID-19 pandemic has upended supply chains, changing logistics stakeholders’ perspectives on risk within their operations.
Businesses now more than ever are scrambling to gain more visibility into their processes. This visibility is essential as it remains one of the hurdles for businesses to understand their association with stakeholders from both upstream and downstream operations.
Interos, a risk management company, is looking to ease this struggle by using artificial intelligence (AI) to make sense of operations-related data that typically has remained siloed and unused.
“We realized early on that the industry had a big data problem,” said Jennifer Bisceglie, the founder and CEO of Interos. “What Interos does is to help companies visualize their global sub-tier supply chains and help them understand the opportunities and risks there.”
Once companies gain a better understanding of their sub-tier relationships, Interos proceeds to show them their alternatives in the supply chain, improving operational resiliency — a strength sorely missed within the industry as COVID-19 laid siege to global supply chain operations.
“We allow companies to look across a myriad of risk factors, including financial risk, operational risk, geographic risk and cyber risk,” said Bisceglie. “Before Interos, there was no proper way for our customers and most companies around the world to truly understand who they’re doing business with and what countries they’re doing business in.”
Most companies in the market have some understanding of their first-tier relationships, namely the ones they have an active contract with. From then on, companies lose track of their second- and third-tier partners and have negligible control over how those relationships evolve.
“Herein likes the risk. For instance, the U.S. has the ‘Buy American’ act, which says that a certain percentage of the goods that you buy for specific customers and programs have to be manufactured in the U.S.,” said Bisceglie. “While this is a great economic plan, it is not necessarily a risk management or security plan because the rest of that product can be manufactured in China. And this can cause problems when there happens to be a trade war or strained relations, like today.”
In such a situation, Interos can quickly help businesses understand their concerns, highlight the blind spots in their existing sub-tier relationships and lead them to fix the issues.
Bisceglie explained that the way supply chain risk was perceived has drastically changed over the last few years, courtesy of the trade wars, oil price volatility and COVID-19. Interos rode the wave, consolidating its presence in the market and acquiring one of its data aggregation partners in 2018. This helped the company scale up its platform to offer AI-based insights to its customers.
In early 2019, Interos saw a Series A investment of $8.35 million from Silicon Valley-based venture capital (VC) firm Kleiner Perkins. Bisceglie contended that Kleiner Perkins invested in the company as it saw Interos tackling a problem that wasn’t solved before.
This was followed by a Series B investment of $17.5 million led by Venrock Ventures earlier this year that Bisceglie said happened because the VC firm saw Interos’ ability to do pattern analysis.
“This is about visualizing a problem and getting ahead of it before disruptions occur. Since this investment in February 2020, we’ve seen a 500% increase in simple inbound requests for help from our customer base globally,” she said.
Within the industry, there has been a massive change in both the questions raised within businesses and those who pay attention to risk-related issues.
“For the first time in my career, I see risk reports going to the CEO and the board’s desk. Every company now realized that they are a supply chain, whether it’s a university with students or a meat manufacturing plant — everything’s a supply chain and we are all hyper-connected,” said Bisceglie. “Right now, Interos is the only company using technology to sort out risks. We do it better and in a way that has not been done before.”
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