Project 2 - Supply and Demand Gap Analysis

Project 2 - Supply and Demand Gap Analysis

Aim:

The aim of this analysis is to detect the supply and demand of Uber cabs around the country for the month of July, 2016. The analysis will aid in identifying the problems and working on it to develop the measures required to overcome the issues.

Introduction:

Uber Technologies is a company which provides mobility service, allowing users to book a car and a driver to transport them or deliver food or packages across the country. It connects with the user through its app wherein the customer willing to avail Uber services can demand for a ride and depending on the availability choose the type of ride. The fare for the ride is already mentioned by Uber according to the destination shared by user and hence the payment is made to the driver after the completion of ride. It has its operations in approximately 72 countries and 10500 cities as of December 2021.

Problem statement:

Uber has around 122 million users across the world. In India the Uber users are approximately 85 million per month. Providing cabs for such a multitude at a given hour is the issue that Uber faces as it has a limited number of cars available at a particular time. The data provides an insight to the availability of cars to the users for the pick points Airport and City. In this study the data collected shows the quantity of cars available, cancelled and trips made by the users per request made. The number of trips cancelled has to be analyzed to reduce its occurrence. In addition to that, the problem of cars not available has to be attended to so that the user can depend on Uber cars for their transport.

Methodology:

The dataset has been collected from the website: www.kaggle.com. The raw data was then cleaned by removing duplicates. The datatype for all the columns in the table was correctly assigned. The data was then uploaded in Power BI application for further analysis. In the Power BI tool, the data was further transformed as per the requirements. And charts were made to analyze the data and problems were looked into through its graphical representation.

Analysis (Data sheets pertaining to it):

According to the data available of July 2016, total of 6745 trips were requested. The total rides requested are analyzed in this study where the pickup points are Airport and City. It was found that around 3500 rides were requested from the city in comparison to Airport, where around 3200 rides were requested in Uber app. From the total 6745 trips around 41.97% of rides were successful trips where the users had requested for a ride and reached their destination and paid the amount. It was found that 1260 rides, pertaining to approximately 19% of the total rides were cancelled by the users. And from the total rides requested, around 2650 rides did not have cars available for the user to select and the confirm the ride. The study also shows that maximum rides were requested on Wednesday (3) than any other days of the week with around 2644 rides and from that only 1139 rides were completed and 510 were cancelled. Moreover 995 rides requested did not have cars available at that particular day. More rush for Uber rides weas also seen in Friday followed by Monday. It can be seen that the at a given requested time 1638 requests were not successful as cars were not available and further 759 cars were cancelled by the users. Around 2.6k rides were successful rides with their drops made to their respective destinations. Here the analysis shows thar driver with ID 22 has 16 successful rides made in a month as compared to his counterparts.

Insights:

The study emphasizes on the fact the Uber rides are in a huge demand among its users and its very important to gratify to their needs. Uber should look into its availability of cars issue when a ride is being requested. The number of cars Uber has is comparatively less therefore there is a shortage of it. The cars are cancelled maybe because the driver may have been at a great distance from the user pertaining to which the user might have cancelled the ride. In addition to this, if the driver selected for the ride chooses not to go to the destination or has issues with his vehicle may have requested for the cancellation of ride thereby bringing a loss to the user as well as to Uber. Business people mostly ride to and fro from Airport or City and therefore there is a huge demand for rides during weekdays rather than weekends. In a particular time, the number of drops made show the trips were successful as compared to rides requested but no trips made at all because of cancellation or unavailability of cars. The study also reflects that some drivers are very much efficient in their jobs and therefore made more rides effective as compared to other drivers in Uber.

Recommendations:

Uber should increase its rented cars to a much greater amount in order to minimize the issue of non-availability of cabs for booking. If more cars are available the user can quickly and easily get a ride for its destination. Since maximum need is in City as compared to Airport Uber should consider providing most of its cab in such areas so that people can easily find a ride to go to their destination without any hassle thereby minimizing the number of cancellations by users. Drivers should be encouraged more by giving incentives for their extra rides or by giving them coupons for achieving their goal that month so that the drivers become more eloquent in their job and reduce the number of cancellations. The requested time to their drop time is also to be curtailed further to increase the user usage of Uber app. Since business people are the targeted clientele Uber should look into the availability of cars on weekdays more in comparison to weekends in order to collect more revenue from such successful rides.

Conclusions:

Uber is already a successful company providing rides to its users for a long while now. The company needs to work on its supply based on the demand for its cab. Uber will fare more than its peers and grow more towards success if the analysis done and recommendations made are taken into consideration. This issue of supply and demand can thus be minimized.