Project 2 - Supply and Demand Gap Analysis

AIM:

TO FIND THE SUPPLY AND DEMAND GAP OF THE GIVEN DATA.

Introduction:

• To view the supply and demand summary data and their variance
• To view the difference between supply and demand by seasonality
• Root Cause analysis of gap between Supply and Demand

Methodology

WE HAVE TO LOAD THE DATA FROM THE GIVEN FILE INTO THE POWER BI APP , after loading the data into the power bi app, we should start performing the actions for various factors to get the supply and demand gap analysis. the process of doing is not yet easy because some of the values will show error and some of the values will be a different data type, so loading our data into the power bi app is a lightly brain using way technique but if we understand it is easy.

The calculations should be done with the given data and should be submitted with a resultant opinion of the working user with the pbix file with a dashboard.

SUPPLY AND DEMAND GAP ANALYSIS:

• Uber : Number of trip requests and their status. There is a huge supply demand gap: Only 42 percent of the total trip requests are being completed. It means a supply demand gap of 58%. Clearly more problematic status is car unavailability.
• Uber : Analysing trip status from Airport to city and City to Airport Pickup point Airport : 53%- non availability of cars and 6%- cancellations of the total trip requests - Supply demand gap of 53% exits Pickup point City: 27%- non availability of cars and 30%- cancellations.
• Uber: Analysing trip status in hourly time slots. Most problematic timeslots with highest gap: 0500-0900hrs and 1700-2100hrs with more than 400 requests and approx. 150 getting completed. A supply demand gap of 70-80% exists in these timeslots.
• Uber: Analysing trip status in hourly time slots at Airport and City More problematic Pickup point is Airport - 59% (53%- non availability of cars and 6%- cancellations) supply demand gap. Pickup point City has 57 % (27%- non availability of cars and 30%- cancellations) supply demand gap.
• Uber: Supply Demand gap Analysis at Airport and City More problematic Pickup point is Airport - 59% (53%- non availability of cars and 6%- cancellations) supply demand gap. Pickup point City has 57 % (27%- non availability of cars and 30%- cancellations) supply demand gap. City to Airport Supply Demand Gap is highest in the morning slot with major cause being cancellations -City to Airport Supply Demand Gap is highest in the morning slot with major cause being cancellations. Airport to City Supply Demand Gap is highest in the evening with major cause being unavailability of cars.
• City to Airport Supply-Demand gap of 57% (43% of total trips requests are completed across all the timeslots). 27% is due to Unavailability of cars and 30% due to cancellation. Most Problematic timeslots: Highest demand during 0500hrs-0900hrs with a supply demand gap of 70- 76%, mainly due to cancellations. Airport to City Supply-Demand gap of 59% (41% of total trips requests are completed across all the timeslots). 53% is due to Unavailability of cars and 6% due to cancellation. Most Problematic timeslots: Highest demand during 1700Hrs to 2100hrs with the highest supply demand gap of 76-83%, mainly due to non-available cars Insights from the Uber Trips Data

Conclusions and Recommendations:

Considering timeslots with highest supply demand gaps from airport to city i.e. 1700-2100hrs, only 70-80 requests each side are completed from City-Airport and Airport-City and there are a total of 300 cars which creates a huge supply demand gap of 70-80%.

Recommendation

1: The reason of cars unavailable could be due to the cars completing other trips hence its recommended to provide more cars in order to fill the gap. Recommendation

2: The reason of unavailability of cars could be due to cars not being at service to customers i.e. they are off duty, then it is recommended that the those cars which are not present in the trip data at those timeslots must be investigated for the reason of being off duty. This count of number of request id is 418 at 1700hrs and number of driver ids is 186 which shows that all 300 cars are not at service in that hour and if they are at service then they are not present in these two pickup points Problematic Timeslots with huge supply demand gap.

3: Cancelling the requests is not due to cars being in transit for serving other requests but there is some other unknown reason which is evident from the fact explained in next point. It is recommended to check why cancelling requests is in such high numbers. Feedback from drivers could be the possible solution. Even when the cars are present at the pickup point, and possible if they are cancelling a request to serve other requests then that is not happening in reality.