10/19/2022 by Russ Felker
The following is an opinion article on AI in supply chains, written by Russ Felker, Chief of Technology (CTO) of Trinity Logistics.
Artificial intelligence (AI) continues to grow its presence in our everyday lives, businesses, and now, supply chains. In a recent MHI Annual Industry Report, 17 percent of respondents said they use AI, with another 45 percent stating they will begin using it in the next five years. And of more than 1,000 supply chain professionals surveyed, 25 percent stated they plan to invest in AI within the next three years. While AI in supply chains has its benefits, it continues to be overhyped as a replacement for human cognitive abilities.
The technologies leveraged by today’s AI offerings fall flat when applied to the complex day-to-day of supply chain interactions. We need to stop chasing the inflated promises of artificial intelligence and start focusing on the very powerful pattern recognition and pattern-application technologies marketed today as AI to support our teams more effectively. Instead of focusing on AI, we need to reorient on CAI (computer-aided intelligence).
Now, this might seem like a semantic argument, and to a certain extent it is, but the difference between artificial intelligence (AI) and computer-aided intelligence (CAI) is distinct. You might ask, “What does it matter if the technologies are being put in place and create efficiencies?” “So what if it’s called AI?” I would say it makes all the difference in the world.
First off, let’s talk about the technologies backing the products that include AI. As with many technology implementations, they are, by and large, applying rulesets to data. Being able to quickly process a defined pattern against a large data set is both no mean feat and hugely beneficial in a supply-chain setting. In the end, however, these implementations are no different than a rules engine – albeit one with a high degree of complication. For example, take an area of the supply chain that has had this form of technology applied to it, quite successfully, for many years – route optimization.
Optimizing a single route is relatively simple but optimizing the routes of multiple vehicles in conjunction with related schedules of item delivery commitments and layering in things like round-trip requirements and least amount of non-productive miles (miles driven without a load) and the level of complexity moves well beyond what an individual could do in a reasonable period of time. What can take on this type of task is a processing engine designed to apply complex patterns within a given boundary set – and that’s what current implementations of AI can do. And they do it well.
The first problem comes in when we examine the stated goal of AI – the ability for a machine to work intelligently. The difference between hype and reality is in how we interpret a keyword – intelligence. Even the most recent and hyped AI systems continue to fail at the same core intelligence functions such as understanding nuanced context and broader application of existing patterns.
Take Gato from DeepMind, a division of Alphabet, as an example. While it can examine an image and draw basic conclusions, the context and understanding are both entirely missing from its analysis. Tesla provides another example where a driver had to intervene as autopilot couldn’t recognize a worker holding a stop sign as something it should avoid. These limitations minimize the tasks for which AI technologies can, and should, be leveraged.
The second problem is related to the first. The acceptance of “AI” from teams has been wrought with, at a minimum, intense change management and, in the worst case, rebellion. If you are bringing in AI to a team, why wouldn’t they draw the conclusion that your goal is to replace them? To start down the path of both realistic expectations from senior management and more widespread adoption of technology, we must change the approach we take with stakeholders impacted by implementations of AI. We need to talk about CAI.
Just the acronym alone talks to a much more practical and achievable marriage between a person and a computer. It’s not the computer that’s intelligent; it’s the person using the computer. What a computer can be taught to do, is to effectively deliver relevant information to a person at the time they need it based on their job function and recognized point in the process. So instead of using a technology such as a recommendation engine to pick a product you might like or a movie you’re likely to want to watch, let’s turn our focus to delivering salient business information to our people. We can effectively use analytics and machine learning to create data recommendations and deliver those recommendations directly to users in their primary applications at the right time in their process, so they don’t have to go find data in multiple reports or sites. Once a pattern is recognized, by people, and the data is organized correctly, again, by people, we can use things like machine learning and analytics to deliver that result set effectively and consistently.
What this approach achieves is reduced interaction by a person and the machine reclaiming time for people to connect with customers outside of transactional conversations. By providing relevant data in-process, you make your team more efficient in their use of the system and create more opportunities for person-to-person interactions and relationships. The goal of any system implementation should be to reduce the time needed for a person to interact with it to achieve the desired result. This is different from having the perspective of the machine doing what a person does – which can be a misguided goal of AI. Instead, the system needs to be built to strategically leverage AI in areas that support the reduction of repetitive, rote work, enabling teams to focus on higher-value work.
As a 3PL, a large part of our work tends to gravitate toward the identification and management of exceptions, but many times that is reactionary. We can leverage the technologies present today to enhance exception identification and management. Via AI-enabled supply-chain systems, information can be more present for teams to apply their intelligence, experience, and skill to solving issues optimally. The ability to recognize early in the life of a load the potential of a delayed delivery enables teams to make proactive adjustments with the receiving facility and the recipient. We can gather documents automatically and provide the information in a consumable fashion reducing the amount of manual effort to extract relevance from the documents.
As a 3PL we rely on two primary skills – intelligent use of data and building and maintaining relationships. Neither a computer nor an algorithm can do either of those alone, but a person backed by a Computer-Aided Intelligence system can. Creating systems that focus on CAI is what allows Trinity’s true source of intelligence, our team, to shine and deliver consistently phenomenal results for our customer partners. Now, you might be the exception and prefer to converse with a chatbot, but I’m guessing if you read this far, you’d rather talk to a person – which is what you get when you call Trinity – a person, backed by computer-aided intelligence systems, who is ready to do the work to create a relationship with you and deliver phenomenal results.
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