Case Study II

Company Name – Myntra 
Round – 2 (Case Study)

Topic:- Suppose you have a restaurant and there is a crunch of sitting space, apart from that you have one kitchen door which is generally crowded with the waiters and food delivery guys which leads to delay in serving the food. Would you like to create room for one more small window to expand the kitchen and make the service quicker?
What will be the impact on the already small sitting space?
How to optimize the above scenario?

P.S. – The approach shared below is not the best which you can think of. You may add your points and think through the problem

Approach:-Take a stand and always talk about your solution with the mindset of a data scientist. Throw numbers wherever possible 
If I were the owner of the restaurant, I would have added a small room for some selected food items. Looking at the data, we can conclude on a few food items which are served most often to the dining customers as well as to the delivery customers.

Suppose it’s a Punjabi restaurant and the best sellers are Naan, Butter Chicken, and Punjabi Biryani. Once we have the data, we can have these items prepared in the main kitchen, but the delivery/serving should be done on the small room/window which is newly created. What about the space crunch? The waiters will have a hard time going back and forth?
The space crunch needs to be compromised and in return, we will be providing quicker service which should compensate for space. The serving time and sitting time of the customer will decrease which might counter the space crunch as more number of customers can be entertained. 

There is one more problem with the restaurant and that is the wastage of raw materials as the shelf life of a few ingredients are very low. How to counter it?

Looking at the data we can come up with a few nodes of this problem. We can ask these questions to the data:-
1. Which all items are sold the most?
2. What is the key ingredient of these items?
3. Day-Food item pair, with this we can get which all items are sold on which day

We can predict the amount of demand in the coming week and can act accordingly. But prediction might not work the best in every case, right?
I agree, to counter this we can have a variable price menu, so if the shelf life of a particular item is low, we can give a discount on these items or can give a combo offer to clear the stock at the very end of the day. There were questions on the formula which you will use to determine the new price in the above situation. Basically, you need to come up with parameters and you have to decide the importance of the parameters by either giving them multiplicative or additive importance in the formula or you can club your answer with any other offer. Following were the discussion points:-

1. We can take those items which will get wasted by the end of the day(looking at the stock near the closing time) and can set a variable price. Suppose, you realize at 9 pm that at least 100 kgs of Biryani will be wasted tonight(by 12 am), then you can set a variable price on Biryani
Cost Price of 1 kg of Biryani – 100
Selling Price of 1 kg of Biryani – 200
Stock left – 100 Kgs
Time left – 3 hours
Aim – To minimize the loss Revised Price = ((180 – x)/180)*Selling Price
where x is the number of minutes after 9
So, at 9:30 pm, the price of the Biryani will be = ((180-30)/180)*200 = Rs. 166

This is one way where we can help the restaurant in clearing the stock.

We can also give the customers a Mega Offer, where if they buy a Biryani today, then they will get some y% discount tomorrow. This will also help in retaining the customers.
Apart from that, we can also give them more Biryani at a lesser price. Suppose Rs. 166 is the price at 9:30 pm, then we can offer them 1.5 kgs of Biryani at Rs.210. This will clear the stock easily.

Keep thinking about more points.


3 thoughts on “Case Study II”

  1. Hello xtramous1,


    I am following your blog for a week now and I found that there is no article posted in “Day 4 – Case Study II” It seems to be blank.

    Do we have some case study for Day 4? Kindly let me know.

    I am also enjoying your books on Amazon. I have already Purchased, R programming in 6 hours, 100 Interview Questions on Business Analysis.

    Looking forward to take your 100 days challenge and become a data scientist.


    1. Hey Harish,
      We will be putting on content on the Day 4, 5 and 6.
      Till then keep practicing on the other days.
      We believe that you will find other articles interesting too 🙂


  2. Hey xtramous1,

    The part where you gave an example of calculating the revised price. How did you create the logic for that? I am unable to get that logic.

Leave a Reply

Your email address will not be published. Required fields are marked *