Introduction
According to Mišić & Perakis., Operational analytics is the concept where most of the organization implement into its business organizations so as improve the existing operations in the organization(Mišić & Perakis, 2019). The operational analytics technique include the usage of data mining tools and aggregation techniques for better decision making and business planning too. According to Borthakur., Organizations with operational analytics will have particular set of instructions on how to perform the target analysis in a quick wayand the way of using the information provided by the operational analytics(Borthakur, 2019). Since this concept is having a greater influence on the business decision making, this report aimed at selecting one popular organization which faced significant organizational issues and provide solution to that issue using the concept of operational analytics.
The report is all about defining the organizational issues that were been faced by Uber, which is the most popular ride sharing organization. The report also includes the analytical data that supports the identified issues. Merely based on the issues, three alternative courses of actions will be specified with the analytical data to support the courses of action and then recommend one best course of action.
Facts surrounding the case
Transportation industry has had transformed the infrastructure, the consumers, and their preferences too. According to O’Toole & Matherne., In that scenario, Uber, an American mobility service provider, evolved as a market leader in the transportation industry that connectspeople and places in the domains of food, transportation and then shipping too(O’Toole & Matherne, 2017). Uber has developed to become a technology and transportation sector leader. This great organization had been introduced in the year 2009 and also become leader in the field of riding. The headquarters of the organization are located at San Francisco, California, United States and was founded by Travis Kalanick and Garrett Camp. The present CEO of the great organization is Dara Khosrowshahi. Now the organization contains 29,300 number of employees according to the 2021 report with revenue of 5.78 billion dollars(Uber , 2020).
Uber Cab Services
Uber features apps for Google Android, Apple iOS, and the web.Customers are connected to local screened drivers who will provide trips in their personal vehicles using the application. Customers pay reasonable rates for such service, which are often less expensive than taxis.67 percent of the market share in US was occupied by the Uber cab services in the year of 2019(Uber , 2020). In the year of 2018, the Uber eats had also become most popular where 24 percent of market share was occupied in the food delivery business.
Uber cabs are currently available in over 72 countries and offer a diverse range of transportation alternatives, ranging from low-cost trips to two-wheelers, cars to SUVs.Apart from taxi business, it also handles Uber Eats which operates as a food business and0 Uber Freights as logistics and is also making improvements regarding self-driving cars too. The acquisitions of uber include Autocab, Drizly, Cornershop, Postmates, Mighty AI, Careem, JUMP bikes, Otto, Swipe Labs, complex polygon etc. (Pathak, 2017).
Whereas the Covid-19 pandemic had also an adverse impact on the ridesharing market. Majorly because of the unethical poaching practices ad operations the organization had also become controversial too. Though it has been increasing its market presence, this organization had also been banned in certain countries.
Significant Organizational Issues
Uber had encountered several hurdles over the last few years and the most current organizational issues were stated below.
Cancellation issues
One major issue that Uber is facing with is the cancellation issue. According to Mahapatra & Telukoti.,Unfortunately, due to the huge cancellations made by Uber drivers, which would be a key barrier for Uber passengers, that’s not viable(Mahapatra & Telukoti, 2018).Neither Ola nor Uber, two of India’s biggest taxi services, are known regarding its recurrent cancellations even after discovering the online payment. The issue is that the clients after booking the ride, before the acceptance of the ride by the driver, the customers are cancelling the ride.
Human resources problem
Uber, rather than using great business procedures to expand its company, it preferred to engage in unethical business activities. According to Irwin.,A former Uber employee leaked documents in 2014 revealing a covert scheme known as “Operation SLOG”(Irwin, 2014). Suppling long term operations with a growth program is what a SLOG represents. According to Vara., Where the documents contain the list of independent contract profiles that switches Uber towards low profit rides. It also revealed that Uber had also contacted and cancelled over 5,000 Lyft rides(Vara, 2014).
Safety and lobbing issues
Uber was accused of inability to pay its ride-sharing system user-friendly for both providers and passengers.Despite the fact that Uber undertakes criminal record checks and screenings as part of its hiring process, these have recently been plagued by allegations of rape and domestic violence against Uber drivers.According to Dhaddha & Lalwani., A lady filed a complaint with Uber in July 2014 after the driver appeared to sexually attack her while she was knocked out from the back(Dhaddha & Lalwani, 2021).
Analytical data to support the identified issues
Analytical data to support the cancellation issues
The above is the analytical data visualization that supports the cancellation issues faced by Uber organization. From almost 100 percent of the pie chart, only 28.1% people had gotten their rides i.e., the trips completed are just 28.1 percent and the cancelled trips are of 48.8%, whereas the rest 22.9% trips are not completed because of no availability of cars. Whereas car availability also determines the as cancelled trips.
Figure: Showing Cancellation issues with the representation of pie chart(Chakravarthi, 2021)
According to Chakravarthi., Many reasons could cause the cancellation of a ride, such as peak morning or peak evening hours (Chakravarthi, 2021)).During peak hours in the morning and evening, the highest demand for rides occurs, which may result in ride cancellations due to delays in pickup or a shortage of available cars.
Safety and lobbing issues
Figure: Safety issues reported on Uber drives (Drum, 2019)
During 1.3 billion trips, 2018 made 3,045 sexual assaults.(Drum, 2019))
Or, more directly:
1.3 billion trips have seen 3,045 sexual assaults recorded. (Drum, 2019)) This equates to little more than 2,300 complaints per billion rides, and roughly 2.3 per million. Where most assaults were related to touching the sexual body parts.
In the US alone, 2019) of McCarthy’s study reported sexual assault cases in the year 2018 and 2017.
In the year 2017 and 2018, Uber has issued its long-awaited safety report, revealing that it acquired roughly 6,000 allegations of sexual assault. the accused includes 45 percent being the passenger whereas 54 percent, the drivers(McCarthy, 2019).
Specifying alternative courses of action
Uber is rapidly expanding and displacing private car ownership and taxi services. Lyft and other competitors have grown in popularity, although they are still modest in comparison to Uber. Uber is the leading company in every city and is continuing constantly to expand its market share and profit margins.However, given Uber’s massive expansion, the organization has been facing several issues that includes the safety issues, and majority being the cancellation issues.
According to Mišić & Perakis., Today’s enterprises are made up entirely by real-time eventsMišić and Perakis state that the data acquisition and operationalization are crucial factors. (2019).) Organizations could be differentiated, making them great or excellent.In that case comes the operational analytics, where it is process of collecting the data, monitoring it, and the utilizing the data is termed as operational analytics. This technique allows the organization to access and analyze the real time data of the customer like the user search habits, their demographic information and the web traffic etc. For informed decision making, using operational analytics will generate profits to the organization and increase the competitive advantage too.
Operational analytics tackles the problem by synchronizing data and ensuring that organizations use their operational routines and systems efficiently.
- Using predictive analysis to enhance passenger boarding services
- Using spark streaming machine learning procedures
- Using map reduce for performing analysis on data and determine
- Installing new safety technologies to ensure safety
Evaluation of each course of action
In this section, we will evaluate each specified course of action in detail.
Using predictive analysis to enhance passenger boarding services
Making use of predictive analysis within the organizational operations helps at enhancing boarding services of the passengers.Though the customers could board their service via the smart phone application and travel among the cities these also become difficult to the organization, the drivers, and the passengers too. Though Uber is playing an essential role in serving the passengers to reach out their destination when two passengershire a taxi and reach out their destination, it becomes difficult regarding the price distribution among both. According to Santosh., So as to make the passenger boarding services in a more improved way, making use of exploratory predictive analysis will help the organization(Santosh, 2021).
This is said because based on the data of the customer like how many times the customers boarded the service, which service, what product, what is the most went destination, how much time, distance, average lead time requesting the trip etc.
Developing The Machine Learning Model
We can analyze to provide better services. By prioritizing the data and business context together, we identify the better enhancement for developing the machine learning model. After evaluating the data, we can create a machine learning model in this way.
Figure: Machine Learning Model for Uberbased on predictive analysis(Santosh, 2021)
We identify the issue, develop a quick solution, implement it, and assess its effectiveness in the machine learning workflow. The many repeats of feedback collecting needed to establish a solution and conclude the issue.
Users who reserve a ride with a cash payment option frequently do not travel, resulting in losses both for Uber and the driver. The price without no earnings is driver to client distance.So, to avoid this, using predictive analytics that identifies the fraudulent users and their behavioral features and then disabling their cash payment option could be remedy.
Using map reduce for performing analysis on data and determine cancellations
According to Devika, et al., Mapreduce is one framework that is most widely used for processing massive data amounts as this majorly used in the place where there requires multiprocessing of large volumes of data(Devika, Prasanna, Swetha, & Babu, 2019). Since this framework is capable of analyzing large data volumes, using it for analyzing the Uber data and providing insights regarding the most used vehicles, their trips and determining why cancellations took place. According to Prasad, et al.,The primary goal of performing uber data analysis to determine which days most trips happened, what are the days where there are active vehicles. Utilizing Hadoop cluster will provide fastest results with zero errors(Prasad, Reddy, & Rama, 2018). This is to provide quicker outcomes while also improving the quality of social control options.MapReduce may be a modelling language which improves networked applications that manage huge data.
To put the concept of MapReduce into practice, we will divide the information into parts and then use mining approaches to evaluate each part. According to Devika, et al., This is the most important portion of the workflow in a Hadoop system since it provides the logic of the method. The two-unit functions of Map reduce are Map () and Reduce ()(Devika, Prasanna, Swetha, & Babu, 2019). The operations of the map function include grouping, sorting, and filtering. Then the reduce function will be responsible for aggregating and summarizes the result of map. The reduce function takes the map function’s output as its input.
Installing new safety technologies to ensure safety
The new safety technologies allow for the introduction of safety into the ridesharing platform due to technological advancements. As a part of ensuring safety and avoiding the sexual assaults faced by the people in the ridesharing of Uber, the following safety acts could be helpful.
With the use of operational analytics on the uber data, the organization could reveal the mostly occurring reasons for are more and could provide improvised safety measures for that particular demographical area. We can make decisions to enhance customer safety with the help of operational analytics. In such regard, creating the emergency button in the application itself and whenever the issue is faced, the user either the driver or the client may click on the button so that the sexual assaults, it could reveal the places where the sexual assaults the button itself the directs the indication about the emergency.
Once the emergency is observed then the details of the driver, the cab number, the trip details like the location of the trip, safety measures.Creating an indicator that detects the vehicle wrecks, its lengthy stops, and the unnecessary stops etc. Limiting the speed is also one safety option that ensures safety to the customers i.e., noticing the speed per kilometer and generating alerts in the form of notifications to its drivers and adjusting the setting will be helpful.
New data models to support the alternative courses of action
Machine learning model to support predictive analysis
Here, we will use predictive analytics to identify Uber pickups, completed trips, client-cancelled trips, and driver-cancelled trips.
Figure: Prediction of cancelled trips using predictive analytics(Santosh, 2021)
When we clearly see the graph, the completed trips number 541, the number of cancelled trips is 82.1% less, and the number of driver-cancelled trips is 1.8%. Santoshad had the highest cancellation rate among us. (Or, Santosh had the highest cancellation rate.) Whereas the adverse impact was seen because of Covid epidemic which is a concerning sign and which is the overall cancellation was observed as 17.9 percent includes both the driver and the rider cancellation. As previously mentioned, Covid has an impact on a wide range of services. Uber should alter their offerings. High pricing also contribute to service cancellation;thus, companies must cut their prices in these circumstances.
Figure: Heat Map representing demographic places where Uber is most widely used(Santosh, 2021)
The above is the heatmap with power which displays most popular locations in various hues and sizes. This might be essential criteria for Uber in terms of changing costs and increase demand within specific places, as well as including time-consuming statistics to follow user behavior.
Hadoop Map and Reduce model to support analysis of data
Map reduce models the input in two steps: the map stage, and the reduce stage. (The input is processed in two stages: map and reduce.) The following is the example to show the map reduce model with the help of Hadoop counts the occurrences of the terms Apache, Hadoop, track, and class in the provided dataset.
Figure: How MapReduce and Hadoop Work Together(Jevtic, 2020)
To clearly explain the example, the example consists of three nodes as a part of demonstration. The three map servers share all six documents among them. According to Jevtic., Then the map constructs key-value pair to represent how many times the word appears, which counts and represents the value(Jevtic, 2020). In the map-reduce process, the reduce stage merges the shuffled outputs from each map stage. Four tasks are designed for identification. And reduce one word each at the reduce stage. Each four reduce step tasks produces the final pair of keys at the same time.
Recommending the best course of action
Based on the data, Uber should take the following actions. Must apply predictive analytics and incorporate safety measures in with its business organizations in order to eliminate the highlighted difficulties such as cancellation, sexual assaults, and so on. The following reasons explain why the suggested course of action is necessary.
- Making use of analytics will help the organization in determining prices. The users who use the Uber will be able to estimate the cost while hiring the cab itself. There are certain algorithms that are sophisticated. That will be able to calculate the ride cost. Surge pricing is implemented automatically.
- Drivers match with users based on analytics. The Uber application will be able to use the user’s location. The passenger requests their location. You compare your request to that of the nearby driver. When making a ride request.
- Making use of routing and matching machine learning algorithms the customers could be able to select their drivers who are near to them.
- Implementing new safety technologies as per suggested will also ensure better services and also help the customers for reducing the motor crashes, interpersonal incidents, etc.
- The organization should make initiatives to provide sexual misconduct education and foster respect and safety.
Executive Summary
Uber is a ride-sharing service-basedorganization located in the United States. This prominent ride-sharing company was the subject of an operational analytics case study. An organization’s real-time performance. Operational analytics will assess it. The analysis could lead to better decision-making and improvement of the organization’s business.
Uber faces several problems, and this case study aims to provide multiple solutions. Also, it reveals certain crucial facts about Uber’s organization and multiple market concerns. We identify issues with cancellation, human resources, safety, and lobbying. It offers a variety of solutions to the problem as well as analytics data for each one.The study offers recommendations from the three variety of options and will assist in improving the business of the selected organization.
Conclusion
Uber, the most popular ride sharing organization, successfully identified the issues it was facing. Though the organization has widely spread its market presence over more than 72 countries across the world. this organization is also facing several issues and one that is mostly creating the impact for the organization is the cancellation issue. This ride hailing company is experiencing the cancellation issues in several countries either because of higher fuel prices, or payment schedules and the longest distance pickups.
Where as a part of the reducing the issues. Uber is facing those issues. The report provided three different alternative courses of action in which one is the predictive analysis, the other both are MapReduce model and new safety technologies to enhance the riding services to its customers. We evaluate each specified course of action in a deeper way and define it to reduce issues in the following ways: [1], [2], [3].The major benefits identified in predictive analytics make it the best course of action. Predictive analytics, with its major identified benefits, is the suggested action. As a result, the combination of predictive analytics and the new safety technologies will enhance the operations of Uber.
References
Borthakur, D. (2019). Operational Analytics: What every software engineer should know about low-latency queries on large data sets. Retrieved July 25, 2019, from https://rockset.com/blog/operational-analytics-what-every-software-engineer-should-know/#:~:text=Operational%20analytics%20is%20a%20very,transparent%20information%20for%20business%20planning.
Chakravarthi, N. (2021). Uber Supply-Demand Gap Analysis & Possible Solutions. Retrieved January 17, 2021, from https://medium.com/swlh/uber-case-study-8da589c6161b
Devika, P., Prasanna, Y., Swetha, P., & Babu, G. A. (2019). Uber Data Analysis using Map Reduce. International Journal of Recent Technology and Engineering (IJRTE), 8(4), 2511-2513.
Dhaddha, R., & Lalwani, A. (2021). Uber’s Growing Global Concerns and Solutions. Journal of Emerging Technologies and Innovative Research , 8(10), d804-d816.
Drum, K. (2019). About 2 Uber trips per million are reported to involve sexual assault. I retrieved the information on December 6, 2019 from https://www.motherjones.com/kevin-drum/2019/12/sexual-assault-is-reported-on-about-2-uber-trips-per-million/.
Irwin, N. (2014). Uber’s Secret Agents: When Poaching Becomes Unethical. Retrieved August 27, 2014, from https://www.nytimes.com/2014/08/28/upshot/ubers-secret-agents-was-poaching-from-lyft-unethical.html
Retrieved
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Mahapatra, S., & Telukoti, P. (2018). Challenges faced by Uber Drivers and Customers Satisfaction in Pune City. Global Journal of Research Analysis , 7(2), 358-360.
McCarthy, N. (2019). Sexual Assault Reports Received By Uber. Retrieved December 9, 2019, from https://www.statista.com/chart/20236/incidents-of-sexual-assault-received-by-uber/
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Prasad, K. S., Reddy, N. C., & Rama, B. (2018). So,Analyzing and predicting academic performance of students using data mining techniques. In Journal of Advanced Research in Dynamical and.
Santosh, S. (2021). So,End-to-End Predictive Analysis on Uber’s Data. Data Science Blogathon, 1-12.
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