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Grab-a-table!

Story: Waiting is frustrating while dining out. We all have been in a situation while dining out when we’re waiting for someone to take our order and then once it is punched, we don’t know if the order is ready or not. We’ve also certainly experienced that sometimes the order is missed out then we need to remind the waiter to look into our order and get the food faster. It’s not a good experience for the customers neither the restaurants, and it occurs because of the inefficiency in the catering system. Solving that problem is exactly the intention of this app. Our objective was to develop a catering application which can be used at every table (in form of kiosks/booths/or phone app) in restaurants, food joints, clubs, bars, etc. The application accomplishes two goals of a restaurant operation.

  • Enhances a Diners experience
  • Provides business insights to the restaurants

The app workflow challenges the traditional ordering process in a restaurant and introduces a new system design where customers could order through a digital menu and track their orders through a web interface. Further, as a consequence of the new design, restaurants become capable of capturing the data about the operational aspects of the business. The insights generated on those data points, ultimately help the restaurant/restaurant managers to have a greater visibility of the operations and improve the business accordingly. This app would have two kinds of user personas.

  • This would be used by customers to look at the menu, place orders, and pay bills. The customers would be able to add items in their cart and place orders and then pay bills being at their table. This way the application would enable no-contact ordering that will reduce order-queue wait times and make the ordering process hassle-free. This would also be preferable during the unprecedented times of social distancing which brought a major shift in the ways of communication and physical interaction in places of gathering.

  • The application would provide a status of available items on the menu basis the raw material inventory available in the restaurant. This would assist restaurants to replace the pen-paper process with a systematic order management process. The order placed by customers would enter a queue consisting of all orders and be assigned basis priority/preparation time. Once the order is ready, it’ll be available at checkout for customers to pick up. At the end of their meal, customers can drop reviews under their order for future improvements. That way will have greater visibility into the catering operations.

The data captured by the system can be broadly divided into 4 segments:

  1. Orders
  2. Customers
  3. Inventory
  4. Payments

It can be further used for generating insights that would help restaurants make better operational decisions regarding promotions and procurement. A few analytics applications could potentially be:

  1. MoM raw material forecasting based on orders
  2. Daily customer trends
  3. Capacity/resource planning in the restaurants based on hourly demand
  4. Sentiment analysis based on rating and food reviews

The software system is scalable and with the improved accessibility of web and cloud technology, it could be affordable and used by small and medium restaurants and help them to streamline their catering operations. Using the business tools of the app as described, the restaurant managers would be able to run the business more efficiently as they could make informed decisions based on the data. Beyond the usual efficiency improvements, such a system would also be effective in a modern (post-covid era) setting where people want to reduce the spread of infections by eliminating the contacts. Due to very same reasons, most of the bars and restaurants have already adopted the QR code-based menu which we think is a good enough market validation for the success of such a system, we intend to more capabilities to the archaic “pdf menu” to be a radical improvement in the customer experience.

Demo

Linked

Technical Document

Includes features, schema-design, data sorucing and detailed documentation. Link

Special credits

Food Delivery Application on Django - Legion Script