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We have been talking a lot, lately, about the Internet of Things (IoT) and Artificial Intelligence (AI). So much so that it’s now difficult to differentiate the real from the not-so-real or purely ‘marketing’ IoT and AI. Data mining isn’t AI. Marketers have been doing it for a good three decades, and others likewise. It’s using intelligent correlations and cohorts to find patterns and latent needs. That’s not much that is artificial about the issue nor situation.
There should be a new marketing codebook with these lines: “Thou shalt not cite IoT and AI in vain.” I don’t know how, but the salesperson calls my latest watch “AI enabled,” whether they have AI or not. The clock is not even smart; at best, it’s just digital. When you wipe off the not-so-real jargon and look at the actual applications of AI and IoT, they are aplenty. But how do we find what is actually true — in a world so taken with these terms? It’s simple.
In a more enterprise setting, does it offer better/faster delivery routes for your logistics movement each time you use it? Does it incrementally better itself with a singular goal of improving the results, learning and adjusting? If yes (to any), then it’s AI.
A recent use-case comes to mind. The company I am associated with, LogiNext, used Kalman filters (algorithm). NASA made the Kalman filter famous when they used the algorithm in their effort to better direct satellites in near and outer space. According to a paper, right back from 1985,
“The Kalman filter in its various forms has become a fundamental tool for analyzing solving a broad class of estimation problems.”
The company in question used an updated iteration of the Kalman filter to fix vital tracking information of hundreds of trucks moving across the country. Hence, each tracking point was, then, accurate up to 3×3 yards. What’s the impact?
The updated algorithm, with the layer of Kalman filter, learns from the tracking errors. It is essential as the tracking is hardware and network coverage dependent. It identifies patterns in the tracking data to understand what is ‘credible’ monitoring and what’s an error. The system would itself know which tracking data to use and which to ignore, growing the accuracy with continued functioning.
In turn, this would ensure that the information going into the system for processing and route planning is accurate. More importantly, avoiding another case of ‘garbage in, garbage out.’ It would be more consistent with incrementally better plans each time it’s used.
Logistics is primarily a game of Service Level Agreements, SLAs. A company/carrier needs to adhere to these basic unit agreements, SLAs, or minimum viable service levels. It may be when a shipment leaves, the quality of the truck or environment for the cargo, the time when it needs to reach, etc. These SLAs are the code of conduct for carriers, drivers, and companies. They are specific to each shipment. SLA breaches are a serious affair and may result in delays and eventual penalties.
So, with SLAs at the center stage, when you must track a package from perhaps LA to NY, you would expect a continuous flow of information regarding the location and state of your package, along with tracking the adherence to the all-important SLA, the ‘promised delivery time.’ How is your estimated time of arrival (ETA) looking as the package is exchanged between carriers, hubs, delivery centers, and the final mile couriers?
It’s a dynamic logistical world where even local traffic and weather may become disruptors. If you simplify the entire end-to-end movement of your package – there’s the pickup, the hub-to-hub movement, and the delivery. It’s possible that all this would be dealt with different drivers, trucks, etc., changing multiple hands. How would you know if any of these drivers are more prone to speeding or delays? How would you know if the truck loaded with your package is well-equipped to handle it? All of the maneuverability allows logistic leaders to use AI right now.
It’s the system, an intricate-interwoven-intelligent ecosystem of software and devices where right from the moment the package leaves your hand; it’s tracking capture the unique id and driver details, aligning-in all possibilities, down to the climate in New Jersey a day from the end-delivery time.
This system picks the best-suited driver and trucks for the package as per the promised timelines, nature of the package (perishable, fragile, sensitive, burdensome, etc.), route requirements and delays expected/predicted, hours of service for each driver (ELD/DoT compliances), etc.
All the information is beamed-up into a single screen where a manager can view all his/her trucks across state lines, and the possibilities of any delays whatsoever. This monitoring empowers the manager (and the brand involved) to take on corrective measures and avoid final delays for the end-customer.
Furthermore, this kind of detailed analysis and pin-point accuracy of multiple systems seamlessly talking to each other adds on a layer of predictability. Here the manager can efficiently predict, how many, trucks would continue to accommodate the possible load coming in, correctly. This is without having the need to dip into the spot markets.
All this brings us to the summation of the main ‘gains’ of IoT and AI with real-world applications in logistics.
Perhaps it’s time we speak of AI and IoT as “tools,” which they are. They aren’t ‘magic’ solutions to each of our problems. Just last week my investment advisors told me that they could double my savings. When I asked them how they planned to do it, they quickly came back with ‘We’ll use AI.’ The funny part was that I wasn’t supposed to ask anything else. Well, I did, and now I am looking for better investment advisors.
Moral: Don’t let the terms bog you down. Look beyond them to the real-world applications, and they may amaze you.
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