Designing AI-driven Meal Demand Prediction Systems
Alicia Cabrejas Leonhardt, Maximilian Kalff, Emil Kobel, and Max Bauch
This study outlines the design of an Artificial Intelligence (AI) system for predicting meal demand, with a focus on the airline catering industry. Through interviews with various stakeholders, the researchers identified key system requirements and developed nine fundamental design principles. These principles were then consolidated into a feasible system architecture to guide the development of effective forecasting tools.
Problem
Inaccurate demand forecasting creates significant challenges for industries like airline catering, leading to a difficult balance between waste and customer satisfaction. Overproduction results in high costs and food waste, while underproduction causes lost sales and unhappy customers. This paper addresses the need for a more precise, data-driven approach to forecasting to improve sustainability, reduce costs, and enhance operational efficiency.
Outcome
- The research identified key requirements for AI-driven demand forecasting systems based on interviews with industry experts. - Nine core design principles were established to guide the development of these systems, focusing on aspects like data integration, sustainability, modularity, transparency, and user-centric design. - A feasible system architecture was proposed that consolidates all nine principles, demonstrating a practical path for implementation. - The findings provide a framework for creating advanced AI tools that can improve prediction accuracy, reduce food waste, and support better decision-making in complex operational environments.
Host: Welcome to A.I.S. Insights, powered by Living Knowledge. I’m your host, Anna Ivy Summers. Host: Today, we're diving into a challenge that many businesses face but rarely master: predicting what customers will want. We’re looking at a fascinating new study titled "Designing AI-driven Meal Demand Prediction Systems." Host: It outlines how to design an Artificial Intelligence system for predicting meal demand, focusing on the airline catering industry, by identifying key system requirements and developing nine fundamental design principles. Here to break it all down for us is our analyst, Alex Ian Sutherland. Welcome, Alex. Expert: Thanks for having me, Anna. Host: Alex, let's start with the big picture. Why is predicting meal demand so difficult, and what happens when companies get it wrong? Expert: It’s a classic balancing act, Anna. The study really highlights the core problem. If you overproduce, you face massive food waste and high costs. In aviation, for example, uneaten meals on international flights often have to be disposed of, which is a total loss. Expert: But if you underproduce, you get lost sales and, more importantly, unhappy customers who can't get the meal they wanted. It's a constant tension between financial waste and customer satisfaction. Host: A very expensive tightrope to walk. So how did the researchers approach this complex problem? Expert: What's really effective is that they didn’t just jump into building an algorithm in a lab. They took a very practical approach by conducting in-depth interviews with people on the front lines—catering managers, data scientists, and innovation experts from the airline industry. Expert: From those real-world conversations, they figured out what a system *actually* needs to do to be useful. That human-centric foundation shaped the entire design. Host: That makes a lot of sense. So, after talking to the experts, what were the key findings? What does a good AI forecasting system truly need? Expert: The study boiled it down to a few core outcomes. First, they identified specific requirements that go beyond just a number. For instance, a system needs to provide long-term forecasts for planning months in advance, but also allow for quick, real-time adjustments for last-minute changes. Host: So it has to be both strategic and tactical. What else stood out? Expert: From those requirements, they developed nine core design principles. Think of these as the golden rules for building these systems. A few are particularly insightful for business leaders. One is 'Sustainable and Waste-Minimising Design.' The goal isn't just accuracy; it’s accuracy that directly leads to less waste. Host: That’s a huge focus for businesses today, tying operations directly to sustainability goals. Expert: Absolutely. Another key principle is 'Explainability and Transparency.' This tackles the "black box" problem of AI. Managers need to trust the system, and that means understanding *why* it's predicting a certain number of chicken dishes versus fish. The system has to show its work, which builds confidence and drives adoption. Host: So it’s about making AI a trusted partner rather than a mysterious tool. How does this translate into practical advice for our listeners? Why does this matter for their business? Expert: This is the most crucial part. The first big takeaway is that a successful AI tool is more than just a smart algorithm. This study provides a blueprint for a complete business solution. You have to think about integration with existing tools, user-friendly dashboards for your staff, and alignment with your company's financial and sustainability goals. Host: It's about the whole ecosystem, not just a single piece of tech. Expert: Exactly. The second takeaway is that these principles are not just for airlines. While the study focused there, the findings apply to any business dealing with perishable goods. Think about grocery stores trying to stock the right amount of produce, a fast-food chain, or a bakery deciding how many croissants to bake. This framework is incredibly versatile. Host: That really broadens the scope. And the final takeaway for business leaders? Expert: The final point is that this study gives leaders a practical roadmap. The nine design principles are essentially a checklist you can use when you're looking to buy or build an AI forecasting tool. You can ask vendors: "How does your system ensure transparency? How will it integrate with our current workflow? How does it help us track and meet sustainability targets?" It helps you ask the right questions to find a solution that will actually deliver value. Host: That's incredibly powerful. So to recap, Alex: predicting meal demand is a major operational challenge, a tightrope walk between waste and customer satisfaction. Host: AI can provide a powerful solution, but only if it’s designed holistically. This means focusing on core principles like sustainability, transparency, and user-centric design to create a practical roadmap for businesses far beyond just the airline industry. Host: Alex Ian Sutherland, thank you so much for these fantastic insights. Expert: My pleasure, Anna. Host: And thank you to our audience for tuning in to A.I.S. Insights, powered by Living Knowledge. Join us next time.
