Nanyang Technological University | Singapore
Si Han Ding | Chia Ler Chan | Zhi Wei Chin
We all like buffets. Free flow of food, amazing spread of dishes, food that is prepared beforehand and at your service. It’s like a bed of roses, pleasing to the eyes and smelling great as the aroma wafts through from afar. Yet, beneath the façade, a key underlying issue that many are unaware about is the devastating amount of food waste that occurs at buffets. To put the issue in proportion, a recent Forbes article reported that nearly half of food cooked at hotel buffets are wasted, amounting to an estimated $100 billion food wastage in the hospitality sector. Most of this food waste is generated due to overproduction because buffets currently rely on chefs to predict and decide when and how much to cook. However, they are unable to accurately do so because of the imperfect information about the amount of food left in buffet trays outside the kitchen. This exact problem of inaccurate prediction leading to food wastage has yet to be addressed by current solutions in the market. At SmartBuffets, we seek to tackle this problem with Machine Learning. So, what exactly is SmartBuffets? We’ve prepared a video (https://youtu.be/436rSZ6XPIo) for you that highlights what it is and its capacity to positively impact environmental, social and economic sustainability. Through real-time monitoring of customer flow and rate of food consumption, our machine learning algorithms send timely notifications to the chefs about what dish and when to cook. SmartBuffets integrates seamlessly into existing buffet operations, optimizing kitchen processes and enhancing the dining experience of the customer. Essentially, SmartBuffets harnesses machine learning to help companies reduce food waste, resulting in cost savings. Concurrently, it significantly reduces greenhouse gas emissions such as methane, a byproduct of food waste. By reducing food wastage, we hope to rechannel food resources to those experiencing poverty and hunger, contributing to a sustainable future together.