How AI will influence Supply Chain and Logistics ?

Artificial intelligence (AI) now touches almost every industry and walk of life.

Driverless cars are chauffeuring themselves through Pittsburgh’s roads, robots dominate the activity within Amazon’s warehouses, and increasingly sophisticated algorithms for demand planning and design have gone digital. Personalization and machine learning continue to improve customers’ experiences and open countless doors for the tech industry—and the supply chain space lies right in the middle of many of these changes.

According to a study by Gartner, supply chain organizations expect the level of machine automation in their processes to double in the next five years. Today’s computing capacity and big data sets empower supply chain leaders to make smarter decisions. So, what could the future hold for supply chain managers?

As machine learning grows increasingly complex, tools may eventually make better decisions than even the best supply chain managers. Predictive analytic capabilities allow machines to automatically place orders based on a customer’s order pattern, minimizing delivery time—which means next-day delivery will become too slow for some customers. Key players in tech and supply chain, such as Amazon, Oracle, Salesforce, and GE, are already positioning themselves to take advantage of these benefits.

Amazon is a clear leader in supply chain sophistication, and is rumored to have 1,000 employees working in AI. On top of that, the e-commerce giant is also bringing AI to the masses through Amazon Machine Learning and Amazon Echo, changing the lines of communication between consumers and suppliers. The data it collects helps sense demand on a precise, individual shopper level.

Similarly, Oracle’s Adaptive Intelligent Applications leverage insights generated from its Data Cloud to learn from more than 5 billion business and consumer profiles. Its enterprise applications can automatically find the best options to distribute goods across specific geographic locations, bringing down costs for shippers.

At Dreamforce, Salesforce revealed details about its newest AI service, Einstein, which could push Salesforce into the demand tracking arena. The new service collects and processes data from customers’ email, social media accounts, calendar, and devices—providing Salesforce with a wealth of information to enhance their machine learning algorithms. Einstein has the potential to shake up supply chain management by boosting Salesforce’s power-player status into the predictive demand market.

But predicting deliveries is much more than just being able to pull up the manufacturing and shipping schedules. With smart supply chain management technology, companies can look at historical shipping times and manufacturing details, and combine that with external data feeds like weather reports.

“You can start promising against the predicted inventory levels, not the planned inventory levels,” Zweben said. “And promising customers based on what’s likely to happen, as opposed to what’s supposed to happen. Now you’re seeing around corners.”

As a result, logistics isn’t the first area companies think of when they look at deploying AI technologies.

According to a recent Forrester survey of global decision makers, use of AI in SCM lags far behind marketing, product management, and customer support. Only 13 percent of companies report that logistics is the area of their organization that is leading or evaluating the investment and adoption of AI systems.

Supply chains typically involve large numbers of outside partners, some of whom may be farther behind technologically than others. In addition, there are data quality and interoperability issues, experts say.

“Getting data from all these sources, that’s the big challenge,” he says. And once the data is collected, it’s not always in immediately usable form. “A supplier might have data at one level of detail, and a distributor might have it at a different level of detail. A supplier might have data on an individual product, but the distributor might only have data based on the container.”

But that’s not to say that companies aren’t trying to solve this problem.

“Every client we talk to, in the Fortune 400 segment, are all interested in understanding, exploring and proof of concept,” says Frank Meerkamp, managing director for applied intelligence at Accenture. “There are a lot of opportunities for AI in supply chain management.”

In addition to analyzing supply chain data and making logistics-related predictions, AI technologies are used elsewhere in supply chain management, as well.

For consumers, one of the most obvious uses of artificial intelligence is with personal assistants like Siri, Alexa, and Google. These chatbots bring together search, voice recognition and NLP, all powered by AI.

The same approach can be used to create virtual agents that can help companies more easily pull information from ERP says Meerkamp. That’s going to be common within the next decade or so, he says.

Another common use of AI is for image recognition. That can play a role in inventory management, says Jason Goldberg, senior vice president of the commerce and content practice at SapientRazorfish.

One example of this in practice is the Amazon Go store, he says. Target has also been testing using a robot with a stereoscopic camera to roam store aisles and take inventory. Walmart recently expanded a similar pilot project to 50 stores. “It’s more critical than ever that retailers have accurate in-store inventory, and computer vision is emerging as a primary technology to do so,” he says.

The technology will also help improve human productivity, she adds. “The combination of human intelligence and AI and automation can translate into time saving, reduced operating expenses and the elimination of manual errors. Employees will be able to shift their focus to non-routine, analytical and creative tasks — while still being assisted, augmented, by AI for these activities.”


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One comment on “How AI will influence Supply Chain and Logistics ?”

  1. You raise a very interesting and important point about the need for properly formatted and standardized data to be able to train the technology in the first place. I had focused more on the bottleneck being the technology, algorithms, and infrastructure themselves, but not so much in the data. I do agree with you overall that there is so much potential for AI to positively impact supply chain and operations management given that the fundamentals of that field is based upon mathematical models, which is the perfect use case for tech, computers, and AI.


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