Every shift in a restaurant kitchen is a flurry of activity. Orders come in, courses are fired, meals are prepared, lines are restocked, servers call back changes; the list goes on and on. If you’re not using a kitchen display system, none of this activity is tracked digitally and any insights you might glean from it are lost. If you are using a KDS, then this activity generates a tremendous amount of data. The trick is figuring out how to turn this data into action.

Giving your data meaning

Raw data is just that – raw. To use a heavy-handed kitchen analogy, the data that comes out of your KDS is like receiving a truck of raw ingredients. You would never turn around and serve raw ingredients to your guests because the act of preparing the food (turning the raw ingredients into meals) is what creates the experience for the customer. It’s the same with data. Any system that collects raw data and gives you a dashboard or an Excel export is asking you to prepare your own food. The best systems prepare the data in a way that adds meaning. 

With a kitchen system, the raw data might tell you how long every order was open at each station. Meaningful data would tell you how many orders were ready on time, how many customers waited too long for their food, and which station or item created the bottleneck that sank the shift. 

Bring me solutions, not problems

You’ve probably heard this about employees. The bad ones don’t even recognize problems. The average ones recognize the problems and bring them to their manager. The good ones bring the manager a solution to the problem. A technology that shows you raw data in a dashboard is at best an average employee. The dashboard might surface a problem, but it’s up to you to diagnose and then solve the problem. 

At Fresh KDS, we believe that technology should at minimum be a good employee. It should identify problems and offer you a solution to the problem. That’s why our kitchen intelligence agent makes recommendations instead of just answering questions or presenting recaps. Each recommendation is a proposed solution to an identified problem. 

Your next great employee

Great employees not only recognize problems and develop solutions, but they also implement those solutions for you. That’s the next step of the intelligence agent. With each recommendation, it learns from your feedback. With each implemented change, it measures the effectiveness of the recommendation. From there, you can choose to give the agent more autonomy, letting it proactively implement recommendations for you. 

Imagine having a Wall Street-level analyst who understands all the nuances of your brand, can crunch numbers incredibly quickly, and looks at your kitchen data every morning. This is the future of restaurant technology, and the future is here.