#14: Elevating Industrial Mobility, Responding to Communication Consumerism, and Mapping Market Moves through Media Mentions
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Issue no.14 of Good AI Vibes is here. This is your go-to spot for the freshest AI scenarios in business. Stay tuned and discover more ways to power up your business with AI.
In this edition, we're featuring: 👇
🏭 Efficiency in Every Move: AI’s Touch in Modern Factory Forklifts
📱 Syncing with Subscribers: AI's Pulse on Telecom Bundles
📈 Forecasting Stocks: Where AI and Social Media Converge
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Efficiency in Every Move: AI’s Touch in Modern Factory Forklifts
What's the critical hurdle?
Meet Devika, a smart factory operations manager. Every day, she grapples with ensuring smooth operations in her expansive warehouse. Central to her logistics are Autonomous Forklifts (AFs) which transport bulky items from one point to another. But here's the twist - these AFs run on batteries. If, during crucial production hours, an AF's battery depletes too quickly, it can bring operations to an unexpected halt. Such interruptions not only mean delivery delays but can lead to hefty operational costs. A significant part of this challenge stems from energy consumption during the forklift's movement. The longer an AF can run without frequent recharges, the more seamless the operations. But striking a balance between efficient movement and energy conservation has always been a tricky feat.
How was this initially tackled?
Before the world of AI came into play, the solution revolved around the vehicle's kinematic model. Essentially, it's a system that guides how the forklift should move based on basic physics. It would chart out how fast or how sharply a forklift should turn to efficiently reach its destination.
Why did the initial approach fall short?
The kinematic model, though systematic, had its limitations. While it could provide a general movement plan, it didn't fully account for the many intricacies that come with real-world operations. It missed out on how the forklift's weight distribution affected its movement or how different accelerations could either save or waste energy. This meant that, in practice, the AF could end up consuming more energy than intended or not move as efficiently as possible. The frequent need for recharges would then interrupt factory operations, creating a chain of inefficiencies.
How did AI revolutionize the solution?
This is where AI's brilliance shines. Instead of just sticking to a predetermined movement plan, the new system digs deeper. It begins by understanding the vehicle's behavior, especially concerning its driving wheel, which affects its energy use. With this foundational knowledge, the system then trains itself on a multitude of data points. This allows it to predict the best possible movement strategy for any situation. The result is a dynamic, intelligent motion planning method that ensures the AFs operate at peak energy efficiency. Factory operations now see AFs that are not only more efficient in their tasks but also considerably more energy-conserving, minimizing disruptions and maximizing productivity.
Main reference: Mohammadpour, M., Kelouwani, S., Gaudreau, M-A., Zeghmi, L., Amamou, A., Bahmanabadi, H., Allani, B., Graba, M., Deng, X., et al. (2024). Energy-efficient motion planning of an autonomous forklift using deep neural networks and kinetic model. Expert Systems with Applications, 237(Part C), 121623.
See other references at the end.
Syncing with Subscribers: AI's Pulse on Telecom Bundles
What's the critical hurdle?
Amara, the VP of Sales for a bustling telecommunications company, finds herself at a crossroads. Picture this: the younger demographic, let’s call them the "Digital Natives," are obsessed with Instagram and YouTube. Meanwhile, the working professionals, the "On-the-Go Pros," can't seem to live without Spotify for their daily commutes. Here lies the quandary: How can Amara bundle these preferences into the company's complementary services like mobile data or home broadband to keep both demographics satisfied and loyal? Fail to deliver, and the subscriber churn looms large. Case in point, after launching a generic bundled package last quarter, Amara saw a concerning dip in renewals from the "Digital Natives" group, costing the company a notable chunk of their expected revenue.
How was this initially tackled?
Traditionally, Amara's strategy was a blend of market insights and a sprinkle of intuition. She'd have her team run polls on social media, asking subscribers about their favorite online platforms. Let's say they found 60% of "On-the-Go Pros" were addicted to Spotify. The response? Offer free Spotify streaming for three months with a 6-month broadband package. Sounds pretty straightforward, right?
Why did the initial approach fall short?
The hiccup here was multifaceted. First off, these promotions often played catch-up. By the time Amara's team rolled out the Spotify offer, TikTok had become the new rage among "Digital Natives," making the initial offer less enticing. Moreover, manually identifying and bundling services based on generic user groups missed the nuances. For instance, what about the "Digital Native" who is also a budding musician and prefers Spotify over TikTok? These nuances, when overlooked, contributed to the menacing shadow of subscriber churn.
How did AI revolutionize the solution?
AI took the stage with flair. Instead of broad strokes, the AI system painted detailed portraits of subscriber preferences. It gauged real-time usage metrics, like spikes in YouTube streaming after 5 pm (hinting at the "Digital Natives" binge-watching) or Instagram's peak activity during lunch hours.
Consider this: The AI identified that a subset of "On-the-Go Pros," perhaps those into fitness, were coupling Spotify with YouTube (maybe for workout videos?). In response, the AI suggested a bundle - unlimited Spotify streaming with an additional 5GB exclusively for YouTube, all paired with their regular mobile data.
Another masterstroke? The AI caught that some "Digital Natives," particularly college-goers, were heavy users of Instagram but also leaned on Spotify for study playlists. The system's recommendation: a student package bundling Instagram benefits with free Spotify access during study hours.
The aftermath of these AI-driven initiatives was nothing short of spectacular. Within a quarter, Amara's company saw a 10% surge in new bundle subscriptions and, more importantly, a 4% dip in subscriber churn.
Main reference: Soltani, R., Ashrafi, M., Seyyed Esfahani, M. M., Farvaresh, H., Al-Mashraie, M., et al. (2024). Competitive pricing of complementary telecommunication services with subscriber churn in a duopoly. Expert Systems with Applications, 237(Part C), 121447.
See other references at the end.
Forecasting Stocks: Where AI and Social Media Converge
What's the critical hurdle?
Meet Eun-ji, an experienced portfolio manager at a leading investment firm. Eun-ji is always on the hunt for opportunities to outperform the market, especially with small-cap stocks. But she faces a unique challenge. These stocks, often lesser-known companies with smaller market capitalizations, are notoriously tricky to predict. Unlike large-cap stocks with ample data from institutional investors and detailed analyst reports, small-cap stocks can be sensitive and volatile, reacting quickly to market events and retail investor sentiment.
For Eun-ji, a sudden price surge or dip in these stocks can make or break her investment strategy. And while the traditional financial metrics and news reports are helpful, they often don't paint the full picture. For instance, a tweet from a prominent influencer or a viral Reddit thread can drive significant momentum for a small-cap stock, leaving Eun-ji scrambling to understand and adapt.
How was this initially tackled?
To address this challenge, Eun-ji relied on conventional market analysis. This involved a close study of balance sheets, earnings reports, and market news. She would also use technical indicators, a series of data points derived from stock price movements and trading volume, to help forecast where these stocks might head next. For example, if a particular stock consistently reached a certain price and then went downward, she could consider this a resistance level and make her decisions accordingly.
Why did the initial approach fall short?
While the traditional methods gave Eun-ji a baseline understanding of the market, they had their limitations. Conventional market analysis often lagged behind real-time events. In a world where information travels faster than ever, thanks to social media platforms, relying solely on traditional methods meant Eun-ji was always a step behind. These methods didn't account for the rapid and unpredictable nature of social media sentiments. Without incorporating this 'alternative data', she was missing out on a rich vein of insights, leading to missed opportunities or unexpected downturns.
How did AI revolutionize the solution?
Enter the new age solution: harnessing the power of social media data. Imagine a system that scours social media platforms, picking up investor sentiments, emotions, and reactions in real-time. This AI solution doesn't just collect vast amounts of data; it smartly identifies and processes valuable insights from the noise.
Here's how it works: The AI sifts through social media posts, looking for keywords linked to specific small-cap stocks. But it's not just any keyword. The AI identifies "social keywords", indicators that have shown a strong correlation with stock price movements. So, if a certain phrase or topic begins trending among retail investors on Twitter or Reddit, the AI takes note.
Let's illustrate with a scenario: Say there's a buzz on social media around a new patent secured by a small tech company. This chatter becomes a social keyword. If the AI identifies this trending sentiment and its past correlations, it can forecast, with impressive accuracy, that the company's stock price might see an upward surge of over 10% in the next week.
The beauty of this approach? The results are staggering. Investment strategies infused with these AI-derived social indicators didn't just match the market benchmarks – they considerably outperformed them. For business leaders, especially those in investment and finance sectors, this isn't just an evolution; it's a revolution.
Main reference: Choi, M., Lee, H. J., Park, S. H., Jeon, S. W., Cho, S., et al. (2024). Stock price momentum modeling using social media data. Expert Systems with Applications, 237(Part C), 121589.
See other references at the end.
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