The Analytics behind Biryani
Biryani is more than a meal. It’s a comfort food. There are stories of weddings being called off because of biryani. Most of us even find solace in biryani, You can’t find an occasion re-union or a get together where biryani isn’t served. Personally, I love biryani and all variants of it.
Being an analytics person, however, it has become an involuntary brain activity for me to bring in analytics into everything. So, when a new joint that serves only biryani opened up next to my office, I noticed that it was always full. As far as fast food joints are concerned, there is at least one or two days when the business goes dull. But that wasn’t the case with this eatery.
It was buzzling with people gathering and waiting to find a table. The amount of takeaways going out is equally overwhelming here. Just to point it out, the restaurant isn’t posh. It’s simple and well maintained.
Usually, biryani joints are usually busiest during the Fridays or Wednesdays, with Friday being the best day for team lunches and Wednesday because of the mid-week blues. But this place was crowded every single day.
With biryani being the first love for most of us, I decided to do some analytics on why this particular biryani joint was always crowded and what it takes for a serving of biryani to reach you.
Analytics and the Food Joint Set up
The most obvious fascination that I learned was that the entire venture was the result of analytics data analytics at the minutest and the most incredible levels. What most of us tend to overlook the restaurant worked on them and brought out significant changes that brought in crucial changes in terms of customer experience and satisfaction and operational expenses.
So, during my research phase, I learnt that the restaurant incorporated analytics at every stage of its operations. Firstly, the shop was the result of analytics.
The place is part of a popular chain of restaurants that came out with a speciality biryani eatery just to meet the needs of customers.
When deciding on the niche of the restaurant, the chain made use of analytics to come up with results. With data from KOT(Kitchen Order Tokens) and online orders to reviews on websites, it figured out that the most commonly ordered dish in the restaurant was biryani especially, chicken boneless biryani. Through specific data touch points, it also found out that Tuesday saw the least takers for biryani. With the restaurant also serving north Indian, Chinese and south Indian dishes, data showed that biryani was the most popular order to be served.
There was just one decision biryani. This was one of the crucial pieces of insights that it uncovered with analytics.
Next, it also set itself apart in its service by making it quick and making sure more people ate at a given time. The supply-chain management involved analytics, which made sure there were enough variants of biryani ready for takers, regardless of the volume. With analytics, again, it was found that an average table finished their meal within 25 minutes, including a beverage. For that, an order had to be taken by the stewards within 2 minutes of guest’s arrival and be served in the next 5 minutes. Being the comfort food, guests usually shifted their focus to the food after it arrived and after the next 20 minutes, the table would be free for next service.
Analytics and Portion Control
Besides, what was also interesting was the amount of wastage the restaurant managed to reduce. Though most of us never leave a single grain of biryani on our plates, we often leave garnishes, accompaniments and flavouring agents. The restaurant found a way to incorporate them into the food by blending them together and adding them with the ginger garlic paste so that along with the flavour, coriander leaves and other garnishes never went to the garbage bin.
The most obvious fascination was that the entire set up was the result of analytics - data analytics at the minutest and the most incredible levels
I was also surprised the first time when I noticed that no plate had cloves, mace, bay leaves, star anise and other dry spices. Besides, they even classified their serving portions to cater to the different takers of biryani and avoid further wastage. They introduced portions of quarter plate biryani to cater to food enthusiasts (not foodie) like me. Usually, it’s after your plates over by 80 percent that our stomach begin to feel content. This restaurant allows you to eat just the right amount.
Analytics and Production Volume
Analytics is involved in every step of making biryani. More so, in its service too! Analytics is involved in deciding how much biryani has to be made each day and correspondingly decide on the volume of raw materials to be purchased. For a bulk production of 20 kilograms of biryani, analytics can tell you how much meat is required, the vegetables, the spices, flavouring agents and the volume of water and fuel.
Raw materials, again, tie back to the crucial concept of supply-chain management, where stock is replenished as per the demand. Being a busy restaurant, the joint cannot afford to run out of ingredients and analytics will take care of the supply chain management of all raw materials.
Apart from the main ingredients, the ones for biryani’s accompaniments have to be taken into consideration as well. Onion, for instance, has to be considered as a raw material for biryani and for an accompaniment as well.
If you think these are the factors, the game is just half over. For it to get served onto your plate, a new set of analytics comes into the picture. A simple example is plates! The number of plates in the restaurant has to be thrice the number of restaurant’s cover, considering the turnaround time for pot washing. Imagine making a customer wait for food because there are no plates to serve! Also, factors like breakage have to be considered to maintain the supply-chain.
For a layman, it’s that twenty minutes of me time with biryani. For a data scientist, it’s how the plate got there. That’s data science for you!