AI suggested orders

目前，在食品外带行业，三明治、卷饼、沙拉等保质期短的食品的浪费可能超过其收入的5%。这对零售商和制造商来说都是代价高昂的，因为他们无法准确预测每天的销量。仅英国的光纤行业就价值约220亿英镑，因此浪费问题每年约为11亿英镑。通过我们的公司Mezze Software Ltd，在过去的3年里，我们通过现有的B2B电子商务平台处理了超过1.2亿英镑的Food to Go产品订单。我们的客户（如Samworth Brothers、simple Lunch、Real Wrap Co.等三明治制造商）一直向我们表达了预测订单的挑战，以及这种浪费给他们和他们的客户带来的成本，以及对这种浪费对环境的影响的担忧。我们的一些客户已经使用非行业专用软件来解决这个问题，但收效甚微。我们的项目旨在证明，在提供正确数据的情况下，机器学习模型可以比人类更准确地预测三明治的订单，而且成本对大型和小型制造商来说都是可以承受的。我们的模型的目标是在项目结束时正确预测三明治订单的准确率至少达到96%，从而改善当前的行业浪费水平。准确率提高1%，我们的一位客户就可以节省超过30万个三明治，节省超过63万英镑。为了达到这种精度，我们的目标是测试各种机器学习算法的基本精度水平。在此之后，我们将迭代基本机器学习模型，尝试使用历史浪费、天气、季节性、地理位置、当地事件和成分等变量来提高准确性。我们还将为订购三明治的零售商构建一个交互界面，以提供订单准确性的反馈，以便我们进一步微调模型。我们将在这个项目上与我们的客户密切合作。有了他们的意见，我们可以逐步在现实世界中与现有客户一起试用新产品，并监控准确性，同时也要记住最终产品的成本。然后，该试验可以过渡到为这些真正的客户取代人工订购。

Currently in the food to go industry, wastage of short shelf-life food, like sandwiches, wraps, salads, etc., can be in excess of 5% of its revenue. This is costly to both retailers and manufacturers who fail to accurately predict how much they will sell each day. The FtG industry in the UK alone is worth ~£22B and therefore the wastage problem is approximately £1.1B per year. Through our company Mezze Software Ltd, over the past 3 years we have processed over £120 million of orders of Food to Go products through our existing B2B eCommerce platform. Consistently our clients (sandwich manufacturers like Samworth Brothers, Simply Lunch, Real Wrap Co. Tiffin Sandwiches) have voiced to us the challenge of predicting orders and the cost of this waste to them and their customers as well as concern over the environmental impact of this waste. Some of our customers have already looked at non-industry specific software to solve this issue with very little success. Our project aims to prove that a machine learning model, when provided the right data, could predict orders of sandwiches more accurately than any human could, and at a cost that is affordable for both large and small manufacturers. Our model would aim to be at least 96% accurate at correctly predicting orders of sandwiches by the end of this project, improving on the current industry waste levels. A 1% increase in accuracy would save over 300,000 sandwiches being wasted for just one of our clients, saving over £630,000\. To achieve this accuracy we aim to test various machine learning algorithms for a base level of accuracy. After this we would iterate over the base machine learning model experimenting with variables such as historic waste, weather, seasonality, geographical location, local events and ingredients to improve accuracy. We will also build an interface for the retailers ordering the sandwiches to interact with, to provide feedback of accuracy of the orders so that we can further fine tune the model. We will collaborate closely with our clients on this project. With their input we can progressively trial the new product in the real world with existing customers and monitor accuracy but also keeping in mind the cost of the end product. The trial can then transition to replacing human ordering for those real customers.