Artificial intelligence has made noticeable changes to technologies around the world. Perhaps AI’s most notable potential, however, is its role in the supply chain industry.
AI has changed the supply chain process from reactive to proactive, which creates a larger change in how data-driven processes will operate in the future. The true role of AI in the supply chain is to enhance and augment human intelligence and decision making. That is much different than what some people view as making human intelligence obsolete, according to experts at Supplyframe.
AI has a twofold role in supply chains. The first is automating repetitive tasks and processes across supply chain functions. The second is realizing new forms of strategic decision making and collaboration.
As technologies like AI and ML (machine learning) become more commonly used in supply chains, Kinaxis, a supply chain management software provider, believes that these tools can help, but only when companies identify the root of the business issues. Otherwise, investments in AI will not pay off.
The pandemic has forced companies in almost every industry to rethink their supply chains. That push has moved industries away from reliance on other nations to a new goal of improving their own capabilities to produce materials.
Because of this, the value of shrinking and localizing the supply chain process through the use of AI is more apparent than ever. That positions AI to be a vital tool.
AI has tremendous potential to impact the global supply chain. It can do this by taking over time-consuming and error-prone manual work. This can involve AI more efficiently predicting demand, improving delivery times, reducing costs, and taking over customer support roles, according to Ryan Abbott, professor of law and health sciences at the University of Surrey School of Law and adjunct assistant professor of medicine at the David Geffen School of Medicine at UCLA.
“The complexity of global logistics networks involving hundreds of sourcing, production, and distribution systems makes the use of AI critical to ensuring smart and agile decisions,” he told TechNewsWorld.
Intelligent Automation
AI seems to be a mixed bag of solutions in dealing with supply chain issues. AI is sometimes used to predict logistics patterns, and even customer behavior — but is hardly ever used to bring the true value of achieving better yields, having faster product iterations, and a sense of safety and security. It is very possible though, according to Matthew Putman, cofounder and CEO of Nanotronics.
“[The supply chain] is an old system where bottlenecks exist in so many places,” he told TechNewsWorld.
In the context of supply chain, “IA” may be a better nomenclature for artificial intelligence, noted Suresh Acharaya, professor of practice at the University of Maryland Robert H. Smith School of Business. He has started to refer to AI as IA or “intelligent automation” instead.
“There is some value in streamlining predictably repetitive actions — if this happens execute plan A, else execute plan B,” he told TechNewsWorld.
For instance, if there is not enough inventory, make sure it gets shipped to the highest priority order. These kinds of actions have been automated for some time and continue to be automated even more, he explained.
“However, the power of AI is in predicting (or sensing) a possible outcome, long before it even happens, and recommending a proactive action,” Archarava noted.
In the inventory example, it is about sensing the likelihood of a shortage and finding ways of mitigating it by finding viable supply alternatives; and yes, in that sense the power of AI is in being proactive rather than reactive, he quipped, adding that all aspects of the supply chain lend themselves to intelligent automation.
Looking at the planning space, for instance, machine learning can vastly improve the forecast of consumer demand. But forecasting is not an end in itself. Intelligent automation can then execute the optimal production or replenishment strategies.
That same technology can be applied to transportation, warehouse, and store supply management. For example, in transportation planning, AI can understand the uncertainties associated with the movement of goods from delivery time variabilities to product perishability.
In the warehouse and in the store, AI can help improve labor efficiencies. Likewise, in the space of reverse logistics, AI can significantly improve the prediction and management of returned items, a growing area fueled by the growth of ecommerce, said Acharaya.
“So, one does not have to view AI strictly with the lens of gadgets such as drones or robots or driverless vehicles. There are algorithmic advances brought about by machine learning that can drive tremendous efficiencies in the supply chain,” he said.
Some Hidden Roles
AI plays a role in supply chain management in ways that may not be obvious to the casual observer. For example, effective supply chains require cash optimization for both customers and their suppliers, added Shan Haq, vice president of corporate strategy and development at Transcepta.
“Many customers deploy discount management strategies that balance a supplier’s need for flexible, predictable, timely payments. The most advanced strategies incorporate accounts payable solutions that leverage AI in their platforms,” he told TechNewsWorld.
The technology is also helping the smallest suppliers behind the scenes. The process of sending invoices and receiving payments has leveraged AI to extract data automatically from invoices, verify and match approved orders, and resolve issues. The result is dramatically reduced manual effort in accounts payable and suppliers who are paid timely, he observed.
“AI is a learning technology. Ultimately, if AI can mature to the point that the learning translates to predictive technology in a meaningful way, we will see massive positive change to supply chain operations,” said Haq.
Supply Chain Woes
A tendency exists for consumers and businesses to place the blame for product shortages on somebody else’s poor planning. The causes of supply chain interruptions are more deeply rooted and are only made worse by the pandemic.
The reason for the current supply chain operations struggling to meet the needs of vendors and consumers is very simple, Haq believes. Behavior has changed.
Take the frequently mentioned example of paper products. Consumers have increased the need for these products as much as they have changed where they need them.
The pandemic has kept people at home. So distribution to restaurants and offices needs to shift to the supermarket and consumer delivery services. That dynamic was not predicted, and supply chains needed time to adjust, explained Haq.
Another reason is the pressure to please pre-pandemic consumer expectations, according to Harish Iyer, vice president of industry and solutions at Kinaxis.
“Today’s consumers have become accustomed to the Amazon effect — placing their orders and expecting deliveries in one or two days. In turn, this expectation transmits itself up the supply chain and places more pressure on companies to deliver items almost instantaneously,” he told TechNewsWorld. “However, many companies are still operating with siloed, sequential processes that are too slow to keep pace with the speed of today’s businesses and consumer expectations.”
AI can break down these siloes to make end-to-end visibility into the whole supply chain operation. That gives companies a better positioning to meet both vendor and consumer expectations. Their supply chains can operate more efficiently and are resilient enough to meet consumer and vendor expectations, even amidst day to day variability or unforeseen volatility, Iyer explained.
Confronting Supply Chain Issues
AI is a buzzword popular among many companies. But business leaders simply cannot invest in an AI solution without first consulting supply chain planners to understand the root issue and what needs to be solved, cautioned Kinaxis’ Iyer. Otherwise, they will find that the solution may not solve the issues that are most important to their company.
“By starting with the problem and focusing on the business value, business leaders can apply the right technique to the right problem and gain ROI from their AI investment more quickly,” he said. “When business leaders select the right AI solution for their company’s specific needs, the supply chain planner is empowered to make fast, more confident decisions.”
AI is still relatively nascent. In the supply chain world, the use cases currently that benefit the most from AI are transaction related, offered Transcepta’s Haq.
“Managing supplier data, receiving a digital invoice, and paying suppliers are all areas where AI has already taken hold. Look for advances in the not-too-distant future that focus not only on transactions but collaboration,” he said.
The biggest hurdle in overcoming supply chain issues is the hype. Non-AI professionals tout this as magic, and it isn’t, argued University of Maryland’s Archaraya. These are intelligent algorithms that sense and detect patterns in a faster and better way.
These are sensors and devices that communicate with each other transmitting and receiving information at incredible speed. They are mechanisms, mostly cloud based, that process and crunch through tremendous amounts of data.
“So, it is important to understand what the underlying components are that form the AI ecosystem and not to be carried away by the hype,” he concluded.
AI to the Rescue
Political moves or other world events that change conditions so quickly can prevent rapid enough responses. Add to such unanticipated events the “just in time” manufacturing strategy used for decades. It has enormous value in reducing inventory waste, according to Nanotronics’ Putman.
The key to what AI might address is that the supply chain itself is optimizing for the same goal, not just every member of the chain. The point would be not to blame a supplier, or a node in a production line, but have an AI agent work towards corrective action that fixes any errors, he explained.
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