Project 2 - Supply and Demand Gap Analysis
Supply and Demand Gap Analysis
AIM
The main aim of this project is to collect the data and analyse it according to the dataset and visualize its insights using the Power BI software. And present the key points that describe the Gap in the Supply and Demand.
INTRODUCTION
Here we have the cab trip dataset which contains the details of pickup point , driver id , status , request timestamp and drop timestamp. Using this dataset we are going to analyse the supply and demand gap which is considered one of the important parameter in the strategic management decision tool. Main objective of this case study is to find the actual reason of gap between demand and supply of the cars
PROBLEM STATEMENT
Uber, being one of the reputed cab service providers , the possibility of having an dis-satisfied customers can affect the business in future. So it is necessary to bridge the gap and fill the deficit.
METHODOLOGY
Import and organize the data: The data is imported and it is cleaned and organised in the way that is suitable for analysis.
Explore and visualize the data: Use the various visualization tools in Power BI to explore the data and identify trends and patterns. This may involve creating charts, graphs, and other visualizations to represent the data in a way that is easy to understand.
Analyse the data: Use the analytical tools in Power BI to perform more in-depth analysis of the data.
ANALYSIS (PREPARING SHEETS)
There are the attributes associated with each request made by a customer:
Request id: A unique identification number that is generated before each trip.
Time of request: The date and time at which the customer booked the trip request
Drop-off time: The drop-off date and time, when the trip was completed
Pick-up point: The point from which the request was made
Driver id: The unique identification number of the driver
Status of the request: The final status of the trip, that can be either completed, cancelled by the driver or no cars available
Travel time : Using the Time of request and Drop-off time the time taken for travelling from pickup point to the destination is calculated.
INSIGHT
From the analysis we found that about 42% of Trips got completed, 38 % of Trips has no cars to further proceed and 20% of Trips got cancelled.
City pickups has the most completed Trips and most cancelled Trips.
Airport pickup has the shortage of cars.
City has slightly high number of pickup point compared to the airport.
On the basis of travel timing City pickup customers has the highest average time travelled.
4095 driver id has taken trip which is good and total no. of request for pickup is 6745.
Only 301 drivers has been recorded. Based on ID.
RECOMMENDATION
Uber should provide higher monetary incentives for the driver taking a trip at peak time from City .
Set realistic targets for drivers and reward them on weekly basis.
In order to reduce the unavailability of cars at Airport we can encourage drivers to take trips from City to Airport with extra incentive so that the car availability at the Airport increases.
CONCLUSION
The most number of no availability cars in Airport trips is in the evening peak hour , which is maybe due to high number of flights arrival but at that time the city rides are also at peak so if the driver reaches the Airport at the morning he has to wait a long time to get a ride from airport, so in order to face the situation around 3.30 pm drivers who are around the Airport region is encouraged to take rides to Airport so that there will be more available cars at the Airport at evening.