#23: Query Handling, Container Selection, and Carbon Prices
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Dive into Issue #23 of Good AI Vibes, where we continue our journey through the innovative landscape of artificial intelligence. With each issue, we aim to shed light on AI's transformative power across different sectors, inspiring businesses to harness its potential for groundbreaking solutions.
In this edition, we delve into use cases that not only showcase AI's versatility but also its critical role in driving efficiency and sustainability in business operations:
π Cutting the Wait: AI's Impact on FMCG Sales Efficiency
π Packing Smarter, Not Harder: AI's Role in Auto Parts Logistics
π± Eco-Smart Predictions: AI's Answer to Carbon Pricing Challenges
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Cutting the Wait: AI's Impact on FMCG Sales Efficiency
What's the critical hurdle?
Meet Harlow, Chief Revenue Officer at a bustling large-scale FMCG retailer. Harlow's daily challenge revolves around ensuring smooth communication between the business and its B2B buyers, including market owners, who frequently inquire about pricing, payment terms, discounts, and campaigns. This communication is crucial, as it directly influences customer satisfaction, loyalty, and, ultimately, the company's revenue. The critical hurdle here is the overwhelming volume of inquiries that need personalized, accurate responses quickly. Traditional support channels, like phone support, struggle to keep up, leading to long wait times and frustrated buyers, which in turn could tarnish the retailer's reputation and impact sales negatively.
How was this initially tackled?
Initially, this retailer, like many others, relied on human support agents to handle these inquiries over the phone. This approach allowed for personalized interaction, with agents using their training and knowledge base to provide responses. They managed inquiries by consulting internal systems, databases, and product teams to deliver the most current information on pricing, discounts, and more. For example, when a buyer called to ask about a discount for a large order, an agent would check the latest campaign details and negotiate terms directly.
Why did the initial approach fall short?
The reliance on human agents meant that during peak times or out-of-hours, buyers faced delays in getting responses, affecting their purchase decisions and satisfaction. The cost of scaling this model to match demand was also prohibitively high, and the risk of human error could lead to inconsistencies in information provided to buyers. Despite the efforts to train agents and streamline processes, the system couldn't efficiently handle the volume and complexity of inquiries, leading to a bottleneck in communication.
How did AI revolutionize the solution?
Enter the AI revolution with the utilization of LLM-powered Q&A bots. This AI solution transformed the retailer's approach to handling B2B buyer inquiries. Instead of relying solely on human agents, the retailer now uses AI-driven bots that are capable of understanding and responding to complex queries about pricing, payment terms, discounts, and campaigns instantly. These bots are designed to interpret the intent behind the questions and provide accurate, up-to-date responses by accessing the same databases and information sources used by human agents. For instance, when a buyer asks about the payment terms for a particular order, the bot can instantly retrieve and communicate the relevant information, personalized to the buyer's history and order details. This not only reduces wait times but also ensures consistency in the information provided, enhancing buyer satisfaction.
The benefits of this AI-driven approach are manifold. The retailer witnessed a significant reduction in response times, from hours or even days to mere seconds. Operational costs decreased as the bots could handle an unlimited number of inquiries simultaneously, 24/7, without the need for breaks or shifts. This scalability meant that the retailer could maintain high levels of service during peak times without additional costs. Moreover, the accuracy and consistency of responses improved, reducing the risk of human error and ensuring that all buyers received information that was up-to-date and tailored to their needs.
Main reference: Good AI Vibes research.
Packing Smarter, Not Harder: AI's Role in Auto Parts Logistics
What's the critical hurdle?
Imagine Kevin, the Head of Supply Chain at a company that supplies parts to one of the globe's leading car manufacturers. Kevin faces a daily challenge that seems as complex as a Rubik's Cube: selecting the optimal container for transporting thousands of different auto parts from his facility to the car assembly plant. This isn't just about getting parts from point A to B; it's about doing it in the most cost-effective, efficient way possible. Why is this so crucial? Because the transportation strategy directly impacts the bottom line, from how many parts you can fit into a single container to managing the costs associated with moving these parts across continents, not to mention the handling and return of empty containers. It's a logistical puzzle that keeps Kevin up at night, as the slightest inefficiency could mean increased costs, delays in production, and ultimately, a dent in the company's reputation.
How was this initially tackled?
Traditionally, the solution was more of an educated guess than a science. Supply chain managers like Kevin would rely on their experience, basic software, or even physical trials to estimate how many parts could fit into a container. They would then choose containers based on these estimates, prioritizing the bulk loading of parts to maximize space. This method was straightforward but lacked precision and failed to consider the unique shapes, sizes, and handling requirements of different parts.
Why did the initial approach fall short?
The conventional method was akin to fitting square pegs in round holes. It didn't account for the diversity of auto parts, leading to inefficient space utilization and higher transportation and handling costs. Moreover, the absence of a strategic approach meant that Kevin often had to deal with the logistical nightmare of managing surplus containers or, worse, facing shortages, disrupting the supply chain and affecting production timelines.
How did AI revolutionize the solution?
Enter the game-changer: an AI-powered software that transforms the container selection process from guesswork to precision. This software utilizes databases, geometry, and optimization algorithms to not only estimate how many parts can fit into a container but also strategically places them to maximize space and minimize costs. It considers the parts' shapes, sizes, and handling requirements, ensuring that each container is used to its fullest potential. This AI solution doesn't just stop at loading; it also integrates economic analysis to calculate the total cost of each container choice, including transportation, handling, and the return of empty containers. For Kevin, this means he can now make informed decisions, selecting the most cost-effective container for each part type, which leads to significant savings in transportation and handling costs.
Main reference: Cildoz, M., Mateo, P.M., Alonso, M.T., ParreΓ±o, F., Alvarez-Valdes, R., & Mallor, F. (2024). The optimal container selection problem for parts transportation in the automotive sector. Expert Systems with Applications.
See other references at the end.
Eco-Smart Predictions: AI's Answer to Carbon Pricing Challenges
What's the critical hurdle?
Imagine Sharon, a sustainability officer at a leading manufacturing company. Her challenge? To align the company's production plans with its sustainability goals while navigating the volatile world of carbon pricing. This is crucial because misaligned carbon strategies can lead to financial penalties, operational inefficiencies, and a tarnished brand reputation. The unique aspect of this challenge lies in predicting carbon prices, which are as unpredictable as the weather, influenced by policies, market demand, and global events. For Sharon, and the business she represents, getting carbon price forecasts wrong means overspending on carbon credits or facing operational disruptions β a significant hurdle in their path to sustainability and profitability.
How was this initially tackled?
Before AI stepped into the picture, companies like Sharon's relied on traditional methods for forecasting carbon prices. This often involved analysts sifting through historical data, policy announcements, and market trends to manually predict future prices. They would use these forecasts to adjust their production plans and emission strategies, attempting to balance operational costs with carbon footprint reduction. An example of this approach could be an analyst preparing quarterly reports to guide the procurement of carbon credits and inform production scheduling.
Why did the initial approach fall short?
The traditional methods struggled for several reasons. Firstly, the sheer complexity and unpredictability of global carbon markets made accurate forecasting nearly impossible. Analysts could not account for sudden policy shifts or market reactions in real-time. Furthermore, manual analyses were time-consuming and prone to human error, leading to decisions based on outdated or inaccurate forecasts. This inefficiency resulted in either excess spending on carbon credits or failure to meet emission targets, both of which could harm the company's finances and reputation.
How did AI revolutionize the solution?
Enter AI, the game-changer for Sharon and her team. The AI solution for carbon price forecasting processes vast amounts of data from diverse sources, including market trends, policy changes, and environmental factors, in real-time. By leveraging machine learning, it identifies patterns and predicts future carbon prices with remarkable accuracy. This AI-powered insight allows companies to optimize their production plans and emission strategies dynamically, ensuring they buy carbon credits at the best price and adjust production to minimize costs and emissions. Imagine a tool that tells Sharon exactly when to buy carbon credits and how to adjust production schedules, reducing emissions and costs simultaneously. The benefits are tangible: reduced operational costs, compliance with regulatory standards, and enhanced corporate sustainability.
Main reference: Hong, J.-T., Bai, Y.-L., Huang, Y.-T., & Chen, Z.-R. (2024). Hybrid carbon price forecasting using a deep augmented FEDformer model and multimodel optimization piecewise error correction. Expert Systems with Applications.
See other references at the end.
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In case you missed our last edition, catch up on all the insights from Good AI Vibes #22 right here!