O kompaniji

Thanks to modern lifestyle and the global drive for convenience, fast food and fast-casual restaurant chains are growing all over the world at an unprecedented pace. Potential in the EU alone, with clear appetite for quick and easy dining options, is staggering. Consumers across the world cite feeling too time constrained to prepare food at home or to sit down at a full-service restaurant. This is a global trend that benefits fast-food, fast-casual and delivery food models.

Although the fast-gastro business has several IT challenges in the food supply and preparation, none of them is so important as food preparation digitalization. McDonalds and other big chains already work on fully autonomous robots which are preparing food and catering around clients. Big data and IoT is used to speed processing and food delivery, where delivery platforms use the networking models to connect food production units with clients faster and more reliably than ever before. Delicious food needs to be in the hands of customers as soon as possible and piping-hot as possible. This is the big challenge of modern fast food units.

Data Kitchen aims to use IoT, Big Data, ML and AI to digitize modern fast food kitchens solving several challenges which are present currently.

First, the challenge of product production - each product has several important components during its production.

  • First is ingredients, which ingredients a cook needs to put in a product to get a customer ready meal? Say we want to create a "burrito de pollo", for that product we need tortilla, chicken grilled meat, salsa verde, rice, black beans, tomatoes, cheese, etc. If a fast food chain has 50 products, this challenge is anything but easy to solve and typically it is done in an "old-fashion" way - each product has ingredients' lists which are printed and put somewhere near the cook's sight. It can be a lot of printing around the walls of a 1.000 units chain and a lot of changes when the list of products change or update, not to mention a slow process of a cook finding an appropriate list in the first place.
  • Second is an order of placing these ingredients in the product, in which order should a cook put ingredients that the product is professionally made. An example would be a burger preparation where puting cheddar cheese under a meat patty, instead of putting it on top to melt, is a big no-no in the burger industry.
  • Third come normatives of each product, do a cook put 150 or 200 or 300 grams of pulled pork in a particular burrito? On a scale of one product this is not as important, but on a scale of 1.000.000 products per year, normatives play a big role in profitability of the unit (more meet, less profitable the business is - less meet, more unhappy customers the business has). Normatives and how to precisely measure them plays a huge role in the economics of the gastro business.
  • As a fourth important component we have time of preparation - it is not the same if a cook bakes meet patty of a burger 20% over-time or 20% under-time. In the first scenario the burger is going to be dry and over-burned, in the second too meaty and potentially a health hazard. Exact times of preparation play a vital role in the quality of products, thus making business successful or bankrupt.
  • And last, but not least, time to produce, where all the above components, together with multi-cook production lines play a crucial role to produce and deliver a product in the shortest period of time possible. Products' orders are coming in fast in peak times of lunch or dinner, all cooks need to play in orchestration to deliver as many products as possible in as short periods of time as possible.

The challenge of education and training comes next in the line. In the food industry personnel changes fast, new cooks come on board, others leave - the challenge is how to educate and train the newcomers as fast as possible. Also, new products and processes come often to stores, distributed from a headquarter, being seasonal products (e.g. spring light burrito), event-driven products (e.g. football world cup special burger), or something else, things change all the time for all stores. All cooks need to be trained into new stuff coming, all the time. It is currently a very cumbersome, error-prone, and slow process in deploying training sessions across multiple-store businesses.

And then comes the challenge of productivity and tracking - which cooks work optimal, which outperform or underperform? What is the average time to produce a burger, burrito, or something else? What are the bottlenecks of the production, what can go faster, where the number of steps can be optimized... Time is money, thus work processes and productivity always plays a big part in the profitability game in big businesses.

Our project "The Machine" is building a modern, cloud-based, distributed IT solution to help in all of the above challenges using cloud-native SaaS products heavily dependent on IoT, Big Data, ML, AI, and state-of-the-art UX responsive, heavy-duty industry solutions.



Theodolindenstraße 16A, München, Nemačka

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