Artificial Intelligence in Supply Chain: Solve the Data Problem First

Artificial Intelligence (AI) was certainly one of the most talked about technologies in the industry last year and there’s no doubt that it will continue to have an impact on supply chain and logistics processes in the months and years ahead. Where are we today with AI technology and its use in the industry? What have been the biggest lessons learned to date? What can we look forward to in the year ahead in terms of AI innovation?

Those were some of the questions I explored with Adam Compain, CEO at ClearMetal (a Talking Logistics sponsor) in a recent episode of Talking Logistics.

“From a technology standpoint, the capabilities are already here,” said Compain, “but from an industry education standpoint and where companies are today on their transformation journey, we’re still in the early stages but getting more progressive.”

“We speak to a lot of companies,” added Compain, “and it’s clear which ones are early in that transformation process and are just trying to educate themselves and which ones are further along and are starting to take action. If I were a large retailer, manufacturer, or third-party logistics provider, I know where on that spectrum I’d like to be because we’ve seen this movie play out before and understand what happens to companies that are too slow to adapt compared to those that are on the cutting edge to differentiate and compete.”

Looking back over the course of 2017, what has surprised Adam the most? What have been his biggest learnings or takeaways?

“First, it’s surprising how many companies out there are looking to solve world hunger all at once,” said Compain. “They believe, despite talking about a transformation journey, that they can solve everything at once. As we know and history has shown, transformation is a journey, it’s not a snap of the fingers. What’s most important is the [approach and methods you use] to adapt and solve the underlying data issues that will enable you set up your supply chain for the long term — true transformation rather than slapping a bandaid on it.”

“The other surprising thing,” he added, “is that people are looking for off-the-shelf technology that looks sexy without taking a fundamentally different approach — that is, without solving the data problem first.”

To illustrate his point, Compain compared Google Maps with the negative experience Apple had when it first introduced Apple Maps:

What Google Maps did very well is that it made sense of all the data around points of interest around the world before ever providing search, navigation, and real-time traffic capabilities. In contrast, Apple Maps came out and [experienced issues] because it hadn’t solved the underlying data problem.

We view supply chain challenges in a similar way. You first have to solve the data problem before providing applications.

I encourage you to watch the rest of my conversation with Adam for additional insights and advice on this topic, including his thoughts on what’s holding companies back from embarking on this digital transformation journey and the key industry trends and themes that are setting the stage for continued AI innovation. Then post a question or comment and keep the conversation going!