Use Real-Time Analytics For Real-Time Operations


Inside Kitopi’s cloud kitchens, data is as vital as salt. Waseem Akram Syed, Director of Data at Kitopi uses data to drive innovative products, pitch promotions and bring operational efficiency.

He talks about training staff, maintaining the quality of food offerings from their partner brands, and running analytics on getting the best from vendors and the most from ingredients. A big part of the job is also keeping an eye on the competition.

Kitopi’s cloud kitchen platform boasts of 3,000 employees across UAE, KSA, Bahrain, Qatar and Kuwait, partners with more than 200 brands and was the first Middle Eastern company to secure an investment by SoftBank. It announced its unicorn status in 2021, one of the fastest on the block.

Excerpts from an interview;

Do you think edge analytics and cloud computing enhance productivity?

Edge analytics is not a new concept for us. Even before we had data warehouses, data lakes and a central data repository, we were already doing this kind of edge analytics where we were collecting the data and computing at the same spot. Now, with the introduction of the cloud and advanced infrastructures that we have, we move to the central database and leverage all the power of integrating multiple data sources. By doing that, we have started losing the advantage of conducting analytics at the source. It’s really important that we strike a balance between central analytics and edge analytics to be more productive.

At Kitopi, we run cloud kitchens. It’s really important for us to maintain the time and the quality of the food. We have to make a lot of decisions in real-time. We get the order in the kitchen, commie or the chefs start cooking the food. Then it’s packed and handed over to the driver to deliver. It takes nine to 10 minutes for the order to be fulfilled and leave the kitchen. We can’t expect the data to go to a central repository where it would be processed and sent back. For us, edge analytics is something which plays a major role, so we can use real-time analytics for real-time operations. So, we try to have our analytics run in the six minutes where the order lives inside the kitchen.

The chef or the commis know within a fraction of second what they have to do or if the food that is cooked is living up to the expectations or not. In this way, edge analytics boosts not only productivity but also the customer experience.

What are the data challenges that cloud kitchens face?

Cloud kitchens are a tricky business. We cook the food and deliver it to the customer but the customer doesn’t directly order with us. For example, the customer orders with one of the food aggregators we have partnered with. But for the customer, it’s a mobile app where they order food from their favourite restaurant, which forwards the request to the cloud kitchen.

So, the kitchen doesn’t know when the customer has placed the order. All we know is when the order was received without the user’s history, preferences, etc.

So, how do we impress the customer when we don’t know them enough to do so? Lack of data is one challenge from a customer’s point-of-view.

From a business perspective, the main question for any cloud kitchen is – where do I set it up? Geography-wise and demography-wise, what makes sense while considering the spending appetite of the population. The next question is – what should be the brands inside my kitchen? After this, we have to consider which brands go well together to optimise resources and costs.

We don’t build one kitchen per brand. We have one kitchen where we host 30 to 120 brands depending on the size of the kitchens.

Another factor that we have to keep tracking is the competition. Every minute, every 15 minutes, every 30 minutes, you should be studying your competitive landscape and how it is impacting your orders. Even though you don’t own the brand completely, or own the customers completely, you should be aware of these factors that drive sales, affect quality and impact customer experience.

How can operational data analytics help make better decisions?

At Kitopi, we are a cloud kitchen and a food business so it’s operationally complex. We have 200 brands which we have partnered with which includes close to 45 cuisines. In addition, we have to source ingredients. Supply chain is one of the biggest parts to make up the full operations. The other big part is the actual kitchen.

When it comes to supply chain management, knowing the right vendors and the history of the vendors is important. We do a lot of analytics in this space. If we can source the raw materials at the right price and the best quality, it makes a tremendous difference.

Within the kitchen, we use analytics to train the staff. It could include deciding which cuisines can be handled by which commis. This is important to determine the right number of people for the right number of brands. and the positioning of the kitchen. It’s complex because each kitchen is designed differently to optimise the workflow.

We refer to it as R&D because an operational study for one kitchen cannot be applied to another kitchen.

Operational analytics is something which keeps us busy 85 per cent of the time. The remaining time we spend building amazing products in the backend to help the business operation.

What big data trends do you see this year, and beyond?

I can talk about the big data focus for Kitopi this year. One of our big priorities is understanding the competition landscape. You need to keep an eye on how the competition is doing, what kind of promotions other aggregators are running, or how they have positioned themselves.

When a consumer searches for a brand which is one of the franchises we handle, we need to know how it’s being positioned by each aggregator. For example, if the aggregator de-prioritises that brand, the number of orders falls.

There is a lot of streaming data to help the business grow faster. We strive to give the best quality and delivery time based on customer expectations so it’s important to know what the customers and the competition is saying.

Apart from this, we take on tasks like scraping the menu or studying the menu of the competition to come up with innovative products and combinations. We are looking at big data trends that will help us solve business challenges.

Give us a teaser of your session.

I will be speaking about bringing data from diversified data sources. This session is about sharing all the challenges and the experiences that I have faced. This data is not just from the diverse sources, but diversified in nature and diversified in its data format as well.

The second edition of Velocity, the Middle East’s largest summit focused on data, analytics and strategy will be held on May 17-18, 2022, in Dubai, UAE.

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