Good AI Vibes #3
Addressing Supplier Instability, Rethinking Car Sharing Dynamics, and Improving Offshore Wind Operations
Hi there and this is the third edition of Good AI Vibes! We welcome the new readers with open arms and hope you enjoy reading our newsletter as much as we enjoy curating it.
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In each issue, we present 3 groundbreaking and inventive applications of AI that contribute positively to a range of sectors. Every instance will feature a concise summary of the challenge, the AI solution employed to address it, and the outcomes attained. In this edition of Good AI Vibes, we showcase the following:
Balancing Unreliable and Reliable Suppliers: AI's Role in Supply Chain Success During Uncertain Times (Cross Industry / Supply Chain Management)
AI's Role in Streamlining Free-Floating Car Sharing: Optimizing Supply and Demand for Urban Mobility (Shared Mobility / Operations Management)
Weathering the Storms: How AI is Enhancing Offshore Wind Farm Maintenance (Energy / Operations and Maintenance)
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Let's not wait any longer and dive into this issue's AI use cases that are making a real positive difference in various industries.
Balancing Unreliable and Reliable Suppliers: AI's Role in Supply Chain Success During Uncertain Times
What's the business problem?
The business challenge here is a pressing one: companies must make strategic sourcing decisions in an uncertain environment, where suppliers may experience unexpected disruptions. For instance, imagine a company that produces protective masks. During the COVID-19 pandemic, the demand for masks surged, causing a critical shortage of essential raw materials such as non-woven polypropylene fabric. In this case, the mask manufacturer must decide whether to rely on a low-cost, but potentially unreliable supplier with limited access to the fabric or choose a more expensive, but reliable supplier with guaranteed fabric availability. Such a dilemma forces companies to weigh the benefits of using low-cost, unreliable suppliers against the stability of more expensive, reliable suppliers while ensuring continuity in their supply chains.
What's the contribution of AI and how the solution methodology works?
AI plays a crucial role by simulating various scenarios involving unreliable and reliable suppliers, providing decision-makers with valuable insights. By considering factors such as price differences, demand fluctuations, and disruption probabilities, AI models identify the optimal balance between risk and cost. Using historical data, supplier performance metrics, demand forecasts, and disruption likelihoods, AI can recommend the best sourcing strategy, taking the guesswork out of supply chain management.
What are the results?
The results reveal that AI can significantly improve companies' sourcing decisions by suggesting strategies that reduce reliance on a single supplier type. This dual-sourcing approach enables businesses to minimize risks and costs simultaneously. As the AI model continuously analyzes and adapts to changing information, businesses can maintain a resilient supply chain, ready to weather disruptions without compromising efficiency.
AI's Role in Streamlining Free-Floating Car Sharing: Optimizing Supply and Demand for Urban Mobility
What's the business problem?
Imagine navigating the busy streets of London, relying on free-floating car-sharing services to get around town. As a user, you expect to find a vehicle nearby when needed, but when the system isn't optimized, you might end up stranded without a car in sight. This is the business problem car-sharing providers face - balancing supply and demand to create a seamless experience for users. Currently, users individually make short-term reservations, causing an uneven distribution of vehicles and leading to unmet demand, longer wait times, and frustrated customers.
What's the contribution of AI and how the solution methodology works?
Enter AI - the game-changer for car-sharing services. With cutting-edge optimization algorithms working behind the scenes, AI can match supply and demand like a virtuoso conductor, directing cars to where they're needed most. By processing real-time data on customer requests, vehicle locations, and usage patterns, AI can enhance vehicle allocation across various booking processes, from instant confirmations to service promises and batch-processing customer requests. It's like having a smart traffic controller for shared cars that adapts to the ever-changing urban environment.
What are the results?
So, what's the payoff? By embracing AI-driven optimization-based matching mechanisms, car-sharing providers can significantly improve their services. Of course, this requires customers to be open to an altered booking process. The benefits are abundant: better vehicle distribution, increased availability, and happier customers. The cherry on top? Optimizing shared vehicle utilization means less congestion and cleaner air, paving the way for greener and more sustainable urban living.
Weathering the Storms: How AI is Enhancing Offshore Wind Farm Maintenance
What's the business problem?
Managing a vast offshore wind farm with dozens of turbines is no easy feat, especially when regular maintenance is required. Consider two turbines at opposite ends of the wind farm needing urgent repairs, specialized technicians, and equipment, all within a limited timeframe to avoid production losses. Add to this the vast distance between turbines and the unpredictability of sea conditions. Navigating these complexities and efficiently managing maintenance activities while considering travel time, equipment needs, and weather uncertainties is the business challenge to be addressed, ensuring a reliable and cost-effective source of renewable energy.
What's the contribution of AI and how the solution methodology works?
By utilizing advanced algorithms, AI assesses a variety of factors, including weather conditions, turbine locations, and travel time, to create the most effective routes and schedules for executing maintenance tasks. For instance, AI can prioritize critical maintenance work and ensure technicians are dispatched efficiently to tackle multiple turbines in the most logical order. It ingests real-time data like wind speeds and wave heights to make adjustments to the plan when necessary, helping avoid costly delays.
What are the results?
Applying this AI-driven optimization method at an offshore wind farm in the UK demonstrated significant results. Compared to traditional methods, maintenance costs were reduced, and the wind farm's overall efficiency improved. This smart routing and scheduling system streamlines the entire maintenance process, leading to more reliable clean energy production, lower operational expenses, and a sustainable future for our planet.
We hope that this issue of Good AI Vibes will inspire you to explore the possibilities and potential of AI in your own business, and perhaps even spark new ideas on how you can leverage AI to enhance your products or services. Stay tuned for more exciting AI use cases in our next issue of Good AI Vibes.
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