#11: Optimizing System Monitoring with Fewer Sensors, Advancing in Spare Part Predictions, and Revolutionizing Physiotherapy Treatments
๐ Greetings, esteemed readers and AI enthusiasts, as we unfurl the eleventh edition of Good AI Vibes! ๐
Our journey through the AI cosmos continues, and with every issue, we strive to bring forth the most transformative AI applications that are carving new pathways in various sectors. If you've just discovered us, welcome aboard ๐ค โ you're in for a thrilling ride into the future of innovation. Don't forget to subscribe below to stay updated! ๐
In this edition, we unfold three pioneering applications of AI, each one serving as a beacon of innovation and practicality:
๐ก Maximum Impact, Minimum Sensors: How AI Reimagined System Monitoring (Industry: Technology, Media & Telecommunications / Business Function: Technology Advancement & Digital Transformation)
๐ Cruising Ahead: AI's Precision in Predicting Spare Part Needs (Industry: Industrial Manufacturing, Transportation & Logistics / Business Function: Supply Chain, Logistics & Sustainable Operations)
๐ฅ Healing Beyond Borders: AI's Quantum Leap in Precision Physiotherapy (Industry: Public Sector & Essential Services / Business Function: Technology Advancement & Digital Transformation)
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Without further ado, let's journey together through the transformative tales that AI has penned in various industries.
To infinity, and beyond AI! ๐๐ค
Maximum Impact, Minimum Sensors: How AI Reimagined System Monitoring
What's the critical hurdle?
Meet Arjun, the Operations Director of a bustling International Airport. Every day, Arjun's challenges are much like navigating a labyrinth. From ensuring flights take off on time, to luggage arriving at the right belt, to managing unpredictable weather patterns, his role is a critical balance of precision and rapid response. A single hiccup in any part of his sprawling airport system can ripple into hours of delay, frustrated passengers, and an ensuing PR nightmare.
Imagine a day when a luggage belt at Gate 15 malfunctions during peak hour, causing hundreds of bags to be rerouted. The domino effect? Flights delayed, passengers missing connections, and a significant financial blow for airlines and retailers. It's not just an operational mess; it's a credibility issue that can damage the airport's reputation.
How was this initially tackled?
Historically, Arjun and his team relied on a network of sensors spread across the airport. These sensors, akin to watchdogs, were strategically positioned at runways, luggage belts, and fuel stations, relaying data in real-time about any irregularities or potential breakdowns. These sensors were their eyes and ears, alerting them of a brewing storm or an equipment failure.
Why did the initial approach fall short?
While these sensors were invaluable, they weren't flawless. The main challenge? Knowing exactly where to place them for optimal results. It's like setting up security cameras in a vast mansion and still finding blind spots where mischief can occur unnoticed. And by the time one of these sensors detected an issueโlike our luggage mishap at Gate 15โit was often too late. The damage was done, and Arjun's team was left playing catch-up.
How did AI revolutionize the solution?
Enter the genius of AI. Instead of relying on intuition or trial and error for sensor placements, AI combed through mountains of data from the airport's operations. From past flight delays and luggage mishaps to daily passenger flows and weather patterns, AI analyzed everything. Armed with this data, it pinpointed the most vulnerable spots and recommended optimal sensor placements.
Take our Gate 15 example. AI, after sifting through the data, would likely have indicated the need for an additional sensor near that luggage belt, flagging potential issues even before they became major headaches. This was more than just placing sensors; it was about crafting a smarter, more responsive monitoring system.
For Arjun, this wasn't just another tech upgrade. It was like having a crystal ball that could predict and prevent potential chaos. And the results spoke for themselves. The system was not only more accurate but significantly faster, catching issues in the bud and saving the airport countless hours and dollars. For the airport, AI wasn't just a toolโit was a trusted co-pilot.
Main reference: Farid, M., Solav, D., Worden, K., et al. (2023). Data-driven sensor placement optimization for accurate and early prediction of stochastic complex systems. Journal of Sound and Vibration, 543.
See other references at the end.
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Cruising Ahead: AI's Precision in Predicting Spare Part Needs
What's the critical hurdle?
Sergio, the fleet manager of a bus services company, finds himself in a conundrum every month. With hundreds of buses zipping across the city, ensuring that each vehicle operates efficiently is his topmost priority. Imagine, during the rush hour, one of his buses breaks down due to a faulty spare part, causing inconvenience to passengers and loss of revenue to the company. Such situations can tarnish the reputation of the company and upset its loyal customer base. The true complexity of Sergioโs role lies in accurately predicting which spare parts will be needed and when. After all, a bus fleet doesn't just have a handful of parts; it has thousands. If Sergio stocks too many parts, it's a waste of resources. Too few, and buses might be stuck in the garage, causing potential service disruptions.
How was this initially tackled?
Before the age of sophisticated technology, Sergio and his team had a straightforward approach. They would look at past data, considering factors like the number of vehicles in the fleet, previous breakdowns, and periodic maintenance records. Using traditional regression-based methods, they'd make educated guesses about future spare parts needs. For instance, if a specific type of bus frequently broke down in the past, they'd stock more parts for that model in the coming month.
Why did the initial approach fall short?
While Sergio's initial strategy might sound reasonable, it had its fair share of pitfalls. Traditional forecasting methods only gave him a limited view, akin to looking through a keyhole. Such methods often failed to capture the ever-changing dynamics of fleet operations and the numerous variables involved. As a result, even with the best intentions and hard work, their predictions were often off the mark. Consequently, Sergio would sometimes find his inventory stocked with parts that werenโt needed immediately, while urgently required ones were missing. It was a never-ending game of catch-up, with resource wastage on one end and unmet demands on the other.
How did AI revolutionize the solution?
Enter the transformative power of AI. Instead of relying solely on past data and linear patterns, AI can analyze intricate relationships among variables and make more informed predictions. In this specific use case, various methods were used to forecast the demand for spare parts. While traditional methods did provide some accurate results, it was the AI model that stood out.
Here's how it worked: The AI system ingested various data points, from the number of buses to the mean time between failures. The AI then sifted through this data, recognizing patterns and nuances that might escape the human eye.
To paint a picture, let's say the company introduced electric buses in 2023. While Sergio's old methods might overlook the distinct maintenance needs of these buses, the model picks up on it. The AI could predict that a particular spare part, exclusive to electric buses, might see a surge in demand in the coming months.
The proof was in the results. When the AI's predictions were tested against real data, they were spot on, showcasing the highest accuracy and least deviation among all methods tested. For Sergio, this meant more efficient operations, reduced costs, happier customers, and a significant edge over competitors.
Main reference: ฤฐfraz, M., Aktepe, A., Ersรถz, S., รetinyokuล, T., et al. (2023). Demand forecasting of spare parts with regression and machine learning methods: Application in a bus fleet. Journal of Engineering Research, 11(2), 100057.
See other references at the end.
Healing Beyond Borders: AI's Quantum Leap in Precision Physiotherapy
What's the critical hurdle?
Max runs an elite healthcare clinic nestled in the pristine Swiss Alps, dedicated to the rehabilitation of world-class athletes. When these titans of sport, from marathon runners to Olympic skiers, suffer injuries, they trust Max. Time isnโt just money; it's a potential gold medal or a world record. Every physiotherapy session must be picture-perfect. However, given the current global scenario, many of Maxโs clients are scattered worldwide. The immense challenge? Ensuring every athlete receives precise feedback on their rehabilitation exercises without direct supervision. For instance, consider Elsa, a professional skier aiming for the Winter Olympics. A single wrong move in her rehab could delay her return by weeks, jeopardizing her entire career. The stakes are high, both for the athletes and for Max's reputation.
How was this initially tackled?
In his relentless pursuit of excellence, Max adopted the most feasible solution available: video-call sessions. These calls allowed real-time monitoring, enabling therapists at the clinic to provide feedback to athletes. For those elite clients who demanded utmost precision, Max even arranged for advanced depth cameras or wearable sensors. These tools captured every movement in detail. For instance, with these tools, a marathon runner in New Zealand could mimic a run, and the equipment would provide a detailed analysis of his posture and stride.
Why did the initial approach fall short?
However, not all that glitters is gold. Video quality varied significantly due to internet connectivity issues, and even a minor lag could lead to misjudgments. Depth cameras, while revolutionary, required intricate setups and werenโt very portable, proving to be a logistical nightmare for athletes always on the move. The wearable sensors, despite their state-of-the-art tech, had limitations in capturing certain nuanced movements, particularly in dynamic sports. The integrity of Maxโs services was at stake. He needed something groundbreaking, and he needed it now.
How did AI revolutionize the solution?
Max's clinic, in partnership with leading tech innovators, implemented a novel system leveraging regular cameras athletes already had. This system utilized deep learning to interpret videos of the athletes performing their exercises. Here's the brilliance: it could map out three-dimensional skeletal positions, offering insights nearly as detailed as an in-person session. Every limb movement, every muscle stretch, got scrutinized.
For example, if a tennis player was working on his shoulder rotation after an injury, the AI system would analyze the video feed, observe the angles and speed of rotation, and provide feedback. It would highlight if the shoulder's movement deviated from the optimal path or if the speed was too fast, risking further injury.
The benefits? Immense. Athletes received feedback instantly, enhancing their recovery process and ensuring exercises were spot-on. For Max, it meant reduced overheads, no need for specialized equipment dispatches, and most importantly, upholding the gold standard of care that the clinic promised.
By tapping into the magic of AI, Max not only overcame the barriers of distance and technology but also transformed his business model to be more efficient, resilient, and athlete-centric, ensuring that champions remained champions, regardless of where they were.
Main reference: Aytutuldu, ฤฐ., & Aydin, T. (2022). Performance Assessment of Physiotherapy and Rehabilitation Exercises with Deep Learning. In 2022 30th Signal Processing and Communications Applications Conference.
See other references at the end.
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