#21: Influencer Strategies, Efficient Steel Manufacturing, and Home Service Uber-Model
Happy New Year, AI Enthusiasts! ๐
Welcome to 2024 and the very first issue of this year's Good AI Vibes! As we step into a new year brimming with possibilities, our newsletter continues to be your essential guide to the most innovative AI applications reshaping businesses.
In Issue #21, we're diving into these exciting AI stories: ๐
๐ท The AI Influence: Mastering Content Choices on Video Platforms
โ๏ธ Steel Production Rebooted: Optimizing the Transition Times Between Casting and Rolling
๐ง On-Demand and On-Point: AI's Take on Technician Deployment
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Let's kickstart 2024 with these fascinating AI developments! ๐
The AI Influence: Mastering Content Choices on Video Platforms
What's the critical hurdle?
Meet Isla, the Head of Growth at a dynamic social media agency in London. Her challenge is to guide social media influencers in making the right content decisions on streaming video platforms. These influencers, each with unique skills and performance levels, face a crucial dilemma: should they stick to their tried-and-tested content style, playing it safe, or venture out of their comfort zone to try something new? This decision is pivotal as it affects their ability to attract and retain a large audience amidst fierce competition. The problem becomes even more complex considering the timing of their strategic actions. Isla's predicament is not just about advising on content strategy; it's about understanding the intricate balance between consistency and innovation. Failure to strike this balance could mean a loss of audience engagement and revenue, a serious blow for both the influencers and her agency.
How was this initially tackled?
Initially, the approach was more intuitive and less data-driven. Isla and her team would analyze trends, review past successful content, and rely on their experience to advise influencers. They'd suggest either sticking to a familiar content style that had historically worked well (reinforcement-seeking) or experimenting with new content types to capture new audiences (variation-seeking). This method was based on observational insights and gut feeling, where decisions were made by looking at what seemed to work in the general market.
Why did the initial approach fall short?
Despite its practicality, this initial approach had its shortcomings. It lacked personalized insights and couldn't adapt to the fast-changing dynamics of social media. Each influencer is unique, and what works for one might not work for another. Also, the rapid evolution of audience preferences made it challenging to keep up just by observing trends. This method didn't provide the nuanced understanding needed to make informed decisions about when to stick to a familiar content style or when to innovate. Consequently, influencers often missed opportunities to maximize their audience engagement and revenue.
How did AI revolutionize the solution?
The game-changer came with the introduction of an AI-driven strategy. This AI solution analyzes vast amounts of data from various streaming video platforms to provide tailored advice for each influencer. It takes into account the influencerโs historical performance, audience engagement metrics, and broader market trends. By processing this data, the AI can predict the potential success of either reinforcing a current content style or trying something new. Additionally, it advises on the optimal timing for implementing these strategies. For example, for an influencer known for cooking videos, the AI might suggest the perfect time to introduce a new cuisine based on audience behavior and current trends, without alienating the existing viewer base. The benefits are substantial: more targeted content strategies, increased audience engagement, and, importantly, higher revenue potential. This AI application not only helps influencers make more informed decisions but also enhances Isla's agency's ability to provide cutting-edge, data-driven advice, keeping them ahead in a highly competitive market.
Main reference: Jiang, L., Chen, X., Miao, S., & Shi, C. (2024). Play it safe or leave the comfort zone? Optimal content strategies for social media influencers on streaming video platforms. Decision Support Systems.
See other references at the end.
Steel Production Rebooted: Optimizing the Transition Times Between Casting and Rolling
What's the critical hurdle?
Imagine Rajesh, the Head of Production at a large steel manufacturer, facing a daunting challenge. In the complex world of steel production, efficiency is king. Rajesh's plant specializes in transforming molten steel into high-quality coils, a process that involves alloying, continuous casting, cutting into slabs, and finally, hot rolling. The hurdle? Scheduling this intricate ballet of processes efficiently. Each step must align perfectly to avoid delays and wastage, a task akin to solving a vast, dynamic puzzle daily. Mistimed operations not only lead to increased costs but also result in lower output quality, directly impacting the company's bottom line and market reputation. The problem is amplified by the plant's commitment to safety and logical production constraints, which are non-negotiable yet further complicate scheduling.
How was this initially tackled?
Before the advent of advanced solutions, the plant relied on traditional scheduling methods. These methods, often manual or semi-automated, were based on experience and standard operational procedures. A team would draft a daily casting schedule, taking into account various factors like production capacity, availability of resources, and safety protocols. This process, while familiar and somewhat effective, was rooted in a more static approach to production management, lacking the dynamism to adapt to real-time changes and complexities of modern steel manufacturing.
Why did the initial approach fall short?
The traditional approach, despite its best intentions, was not sufficiently equipped to handle the scale and intricacy of modern steel production demands. Its major drawback was the inability to dynamically adjust to changing production conditions and requirements. This rigidity often led to inefficiencies, such as delays in production, suboptimal utilization of resources, and increased operational costs. Furthermore, adherence to safety and logical production constraints was more challenging to maintain, increasing the risk of manufacturing incidents. As a result, the plant struggled to optimize its operations fully and capitalize on potential cost savings and productivity enhancements.
How did AI revolutionize the solution?
Enter the era of AI. The solution for Rajesh's plant was a state-of-the-art AI. Instead of relying on static, pre-determined schedules, this AI system dynamically generates daily casting schedules, considering all the complex variables involved in the steel production process. It's a super-efficient planner that can process hundreds of variables and thousands of constraints in real-time, ensuring optimal use of resources while strictly adhering to safety and production guidelines. The AI doesn't just simplify scheduling; it redefines it by calculating the most efficient production pathways. This includes optimizing the transition times between casting and rolling, ensuring maximum throughput while adhering to strict safety and production norms. The result? A significant reduction in operational costs, by up to 40%, and a marked improvement in overall efficiency and safety.
Main reference: Torres, N., Greivel, G., Betz, J., Moreno, E., Newman, A., & Thomas, B. (2024). Optimizing steel coil production schedules under continuous casting and hot rolling. European Journal of Operational Research.
See other references at the end.
On-Demand and On-Point: AI's Take on Technician Deployment
What's the critical hurdle?
Meet Alexa, the COO of a thriving internet-based company that connects customers with independent technicians for home-based technical and maintenance services. This business model is akin to Uber, where technicians operate autonomously, offering their skills to customers through Alexa's platform. They aren't employees of the company but have agreements to provide services when requested. The challenge Alexa faces is multi-faceted. The company must efficiently route and schedule these independent technicians, who work from their own locations and have varied skill sets and availability. Each day brings a new set of customer demands, making this jigsaw puzzle of logistics even more complex. The difficulty lies in not only ensuring the quickest, most cost-effective dispatch of technicians but also in matching the right technician with the right job, considering their unique skills. For Alexa, this isn't just a logistical issue; it's pivotal for maintaining high service standards and competitive pricing, crucial for the company's reputation and profitability.
How was this initially tackled?
Initially, the company relied on a rudimentary scheduling system, which functioned more on manual coordination supplemented by basic digital tools. This approach was somewhat straightforward: when a customer requested a service, the system would alert available technicians in the vicinity, and the job would go to the first responder. This method was somewhat effective in quickly connecting customers with technicians but lacked sophistication and foresight.
Why did the initial approach fall short?
The simplicity of the initial approach soon became its downfall as the company scaled. Alexa noticed several inefficiencies: increased travel times and costs for technicians who might not be the closest or most suited for the job, misalignment between technician skills and customer needs, and an inability to adapt swiftly to changes, like sudden service requests or cancellations. This method resulted in missed opportunities for more efficient routing, cost savings, and a better alignment of services to customer needs, crucial for a business operating in the sharing economy.
How did AI revolutionize the solution?
The introduction of an AI-powered routing and scheduling system marked a significant shift in how Alexa's company operated. Unlike the initial system, this AI solution intelligently processes inputs such as technician skills, customer requirements, geographical data, and real-time availability. For example, it might assign a complex electrical repair job to a nearby technician with proven expertise in that area, rather than just to any available technician. This smart allocation meant reduced travel times and costs, better matching of jobs with technician skills, and enhanced flexibility in managing dynamic schedules. The implementation of this AI system translated into tangible benefits: operational efficiency, heightened customer satisfaction, and an improved bottom line, revolutionizing the way the company managed its network of independent technicians.
Main reference: Nowak, M., Szufel, P. (2024). Technician routing and scheduling for the sharing economy. European Journal of Operational Research.
See other references at the end.
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