AI’s Predictive Insights Will Drive Greater Cost Savings

Companies are looking to artificial intelligence as the pressure to create a more agile supply chain while driving down costs heats up.

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A lack of visibility across the supply chain, coupled with unprecedented levels of data, is increasingly leading companies to artificial intelligence (AI)-backed solutions. 

AI has the ability to streamline supply chain and logistics functions, while at the same time drive down costs. In fact, the technology is already delivering a competitive advantage for early adopters by cutting shipping times and costs.

In the report, Artificial Intelligence in Logistics: A collaborative report by DHL and IBM on implications and use cases for the logistics industry, DHL and IBM cite various real-world examples of the ROI companies can achieve with these technologies and how they can positively impact the bottom line. They include:

  • Transitioning away from legacy ERP systems to advanced analytics, increased automation, and hardware and software robotics and mobile computing
  • Taking operations from reactive to proactive and planning from forecast to prediction
  • Shifting processes from manual to autonomous and services from standardized to personalized.

The report also notes that AI technologies can use things like advanced image recognition to track the condition of shipments and assets, bring end-to-end autonomy to transportation and predict fluctuations in global shipment volumes before they occur.

For example, CEVA Logistics’ AI-powered supply chain is now 90 percent faster since adopting IBM’s cloud and AI solution dubbed Watson. CEVA solved the lack of visibility in its supply chain by putting its business documents in an electronic data interchange (EDI) format. This update has had a significant impact on the performance of its supply chain, including zero downtime and no interruption to data flows, even at peak shopping times.

While IBM is now helping clients like CEVA achieve greater cost savings through an AI-empowered solution, Watson was initially created to improve IBM’s own supply chain.

“A shortage of hard drives created an unexpected distribution in our ability to serve clients,” explains Jeanette Barlow, vice president strategy and offering management, supply chain solutions, IBM Watson Customer Engagement. “Having these shortages was not an acceptable thing. It [Watson] started out as what we call the transparent supply chain initiative here at IBM. They began with just, ‘let’s have transparency.’ And part of that was creating the technology and the ability to bring that sort of operation center or dashboard up. Then they began to look at, ‘we now can start to see; how can we start to augment what we’re looking at with external data, with patterns that Watson recognizes as a guide for action when we do have disruptions?”

The key to Watson, Barlow adds, is its performance as a learning platform.

“How do we democratize that data that might typically sit with a few key, battle-hardened supply chain professionals?” she asks.

Ultimately, IBM’s AI solution is focused on alerting the user of possible supply disruptions, followed by what the downstream and upstream impact would be. Barlow says Watson has allowed IBM to drive an 18 percent reduction in inventory levels and reduce late orders, while expediting shipment orders by 75 percent, saving millions on inventory and reduced freight. But more importantly, she adds, IBM was able to give a better quality of service, with disruptions management and incident response plummeting from 18 days to hours to now minutes.

Making Better Decisions

According to Geoff Webb, vice president of product marketing at PROS, a cloud software company, he is seeing increased pressure for companies and their supply chains to be more agile—whether it’s quicker responses to the market, supplier demands or customer behavior.

“There is more of an acceleration and a drive toward agility that is driven by a need to be more responsive,” he adds. “All of that is based on an ability to process a huge amount of information and then apply a deep analytic skill set to it, where you can present advice, guidance and a capacity to offer real insight to the business itself.”

Thus, organizations are now realizing they need to start driving investment in their supply chain in ways they had not previously thought of. That’s where the benefits of AI come into play.

According to IBM’s Barlow, AI is a “visibility recommendation platform…not an executive engine.” Meaning, the emerging technology is meant to provide users with answers, options and solutions, which the user can then use to make the best possible decision and take action. Companies must use the insights gleaned from their AI-enabled solution to push execution and invoke a change. Barlow says you will go back to your core execution engines, such as ERP, to do that.

She adds that AI’s greatest benefit may be in its “ability to adjust the execution across the supply chain in a far timelier manner, so that you are able to meet or exceed your client’s expectations.

“You can have all the agility in the world if you carried enough extra inventory, but no one can afford to do that, right? So, [AI’s greatest benefit is] that ability to adjust to either changes in demand or changes in supply more quickly so that your commitment to the client is consistently met,” she says. [AI helps you] spot those disruptions and the possible implications, make recommendations and help you make better decisions more quickly.”

Webb agrees with Barlow calling AI a “tool” that beyond helping users make better decisions, ultimately can help them understand what questions they should be asking to drive business more effectively.

“The direction we see the technology heading is to augment the decision-making process of people,” Webb says. “The objective is not necessarily to remove the human element, but to enhance the capacity that human beings have…to augment their capacity to run their business by taking [things] off their plate, again, the challenges of managing very large volumes of data and making decisions faster and more effecively.”

For technology company Aera Technology, the augmentation of the decision-making process is at the core of its AI-based solution.

“Our vision is to enable the self-driving supply chain,” says Fred Laluyaux, president and CEO of Aera.

That vision has already become a reality for its Germany-based customer Merck KGaA, which transformed its supply chain with Aera’s AI-enabled technology by consistently predicting supply shortages, spikes in demand and bottlenecks.

Aera’s AI-based solution connects with a business to understand how it works, empowering it to make real-time recommendations, predict outcomes and automate operations. The company’s technology, Laluyaux says, was built on four principles: understand, recommend, predict and act.

It’s All About the Data

Not all data is created equal—but that’s where AI excels.

“The capacity to draw meaningful insight from data is where artificial intelligence really shines,” says Webb. “It allows me to look at things I would have missed or across a broader set of data I could not previously process. And do it faster. That ability to deliver real insight, even insight I wasn’t necessarily looking for at first is where AI is going to make the big difference.”

Webb continues that the old adage of “garbage in, garbage out” doesn’t necessarily apply to AI.

“If you can’t work on good information, you’ll struggle to get good answers from it. However, one of the other nice things that AI is able to do is…kind of grind through lots of data and look for things where you can actually clean and groom data and make it more usable,” he adds. “You can also more quickly spot data that’s not really usable or is bad, as well as bring broader sets of information for analysis, even on a mundane level.”

Indeed, a global survey of more than 2,300 IT and business leaders by MIT Technology Review Insights and Pure Storage found that 86 percent of respondents believe data is key to making better business decisions. However, almost 80 percent had concerns about how to analyze data, specifically naming volume, collection and analysis as challenges.

Respondents also voiced concern over job security; however, those who work more closely with data were consistently more enthusiastic about adopting AI in the business.

But despite concerns, more than 80 percent of IT and business leaders surveyed expect that AI will have a positive impact on their industry, and almost two-thirds expect to invest in AI solutions in the near future.

Creating the Right Culture

Implementing AI solutions (like many new technologies) ultimately depends on a successful cultural shift.

“In my history with new technologies, some of the early challenges are as much organizational and cultural as they are technological,” notes IBM’s Barlow.

Success with any new technology, she says, is driven by how well it aligns to a business focus.

“What are the business challenges and opportunities for your company that better insights could address or leverage?” Fundamentally, you need that mental space at an organizational level to bring clarity in solving challenges and opportunities,” Barlow says.

Once you’ve established the gaps you are trying to fill, the clarity around how AI can be used for your particular business becomes clear.

“Organizations need to be very purposeful of, and focused on the business outcome that they’re trying to address. When you have that clarity and alignment between the business lead and the IT lead, it becomes far easier for organizations to embark on this journey,” Barlow says.

Andrew Lynch, president of Zipline Logistics, a digitally-enabled, managed transportation partner specializing exclusively in the consumer goods sector, couldn’t agree more.

“Operational/organizational alignment is the only way in these really challenging transportation environments for shippers, BCOs and buyers of transportation to realize any impactful savings or to avoid significant service disruptions that can occur with trying to meet budgets through tactical transactional behavior,” he says.

PROS’ Webb adds to that message. He says in order for AI to be widely accepted, people have to know and believe that it can solve their problems for them. In other words, users must learn to trust AI.

“That is a lot to ask organizations with technology that has yet to have widespread adoption,” Webb notes, adding, “Overcoming that, not just the understanding, but the belief and then the trust, is the requirement that we as a company like PROS, where we have a strong AI background, our job is to help them make those steps. The good news is that there is proof out there that it works. We have customers that do it, and that have been doing it for a long time.”

That trust may come easier than we think, however. A recent survey by Harvard Business Review, which polled 1,770 managers from 14 countries and interviewed 37 executives in charge of digital transformation at their organizations, found that 78 percent of the surveyed managers believe they will trust the advice of intelligent systems in making business decisions in the future. 

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