MBIS4008 Business Process Management
Assessment 2 (Part B)
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Executive Summary
This report aims to provide the current business processes of Uber, which focus on the core ride-sharing activities. The major assignment is to reflect upon the AS-IS model regarding Uber. It identifies problems based on the framework emanating from BMP. This goes into how the ride requests are taken in, how drivers get matched up with riders, and how the payment flows.
It provides an overview of Uber's growth and market position in with other transporters to set the context within which the various assessments were made. The methodologies from the BPM are put into place to compile reports on operational efficiency and its limitations regarding driver allocation inefficiency, surge pricing issues, user satisfaction challenges, and safety concerns.
Findings have been highlighted that show the strengths and weaknesses of present processes in Uber, developing a case with data support and relevant BPM frameworks. Improve driver matching algorithms, surge price optimization, and enhancement of safety measures are recommended to further operational efficiency and customer experience. The report discusses how BPM tools can help in solving issues for Uber in improving its performance level. A subsequent analysis provides suggestions for further process improvements that assure the drivers and the users end up with a better experience of ride-sharing.
Introduction
This report aims to assess the prevailing business processes of Uber, one of the leading ride-sharing companies in the world. The project aims to review the AS-IS business processes about how Uber handles ride requests, matches up the drivers to the riders, and its integrated payment system. By identifying the key operational challenges, it tries to report improvements with the use of Business Process Management frameworks.
The analysis covers a thorough review of the core activities in operation at Uber, excluding additional service offerings such as Uber Eats or its autonomous vehicle initiatives. It focuses on the processes utilized in the context of Uber's ride-sharing operations that traditionally have been in this company's business model and success. Inequities in driver allocation, surge pricing issues, user satisfaction, and safety concerns are some of the aspects this project will explore. The methodology adopted in this report comes from the principles of BPM, which introduce order lines into the analysis and renovation of business processes. This covers process mapping, data analyses, and the application of various industry-specific BPM frameworks to appeal to actionable insights. The report shall focus on major business processes at Uber and indicate how well current systems support the company's operational goals while further highlighting areas for improvement. This contributes toward an in-depth understanding of how these business processes can be optimized for customer experience and operational gains at Uber.
Background
Uber Technologies Inc. was founded in 2009 in San Francisco (Arora, 2023) and disrupted the urban transportation model by making ride-sharing possible with its digital platform. Uber operates in more than 10,000 cities around the world and engages drivers with passengers through real-time GPS tracking and automated payment processing. As one of the pioneers in this economy, Uber changed people's approaches to transportation, making it more accessible and convenient (Arora, 2023).
Its industry is part of the greater transportation and mobility vertical, and rapid technological development has turned it into one that changes very fast. The traditional taxi industry was disrupted by ride-sharing service provider Uber and competitors like Lyft and DiDi, snatching market share with their cost-efficient, customer-oriented business models (Ramji et al, 2023). Uber introduced algorithm-based driver matching, dynamic pricing, and digital ways of paying for rides to differentiate its services from more conservative modes of transportation.
Some of the major milestones that Uber has achieved over the years include its expansion to various other countries of the world, launching UberPool for carpooling (Nwankwo, 2023), and offering premium services added to the portfolio, including Uber Black. Besides, the company has enjoyed a fair share of regulatory challenges, strikes by drivers, and lately competition from other ride-sharing companies. Some of the areas that still need attention are safety concerns, dissatisfaction by users about surge pricing, and driver allocation inefficiencies.
This background will provide an analysis of the business processes at Uber how it has developed core operations concerning ride requests through to driver allocation and payments, and where these operations have become crucial today.
Methodology
The techniques in this report are Business Process Management approaches fitted for an AS-IS analysis of the current business processes of Uber operating in the ride-sharing industry. Its purpose is to identify inefficiencies and problems within its operational framework where potential improvements can be made to further improve general performance and customer satisfaction (Nwankwo, 2023).
Process mapping was utilized to show the actual process Uber uses for ride request processing, driver matching algorithms, and payment (Mitrofanskiy, 2024). This methodology allows the development team to more easily ascertain the activity sequence and information flow between stakeholders. Mapping these business processes can expose bottlenecks, redundancies, and other potential causes of errors. Process mapping is suitable for the transport sector, where real-time decision-making stands for operational success.
The performance indicators including but not limited to average waiting time, cancellation rate of rides, and user satisfaction score were measured through data analysis. The fact that such an analysis draws upon existing data from operational reports and customer feedback concerning Uber reinforces this with empirical evidence of the state of the operations now (Uber, 2021). This approach is in line with the underlying principles of BPM, where metrics are considered vital in assessing how efficient is the process.
It was necessary to consider industry benchmarks for the performance comparison of Uber with competitors like Lyft and DiDi (Ramji et al, 2023). It is important to understand how other companies in the ride-sharing market address similar challenges, helping in identifying best practices and potential areas of improvement.
It also integrated qualitative studies like stakeholder interviews and surveys to capture useful information from drivers and riders. This becomes quite instrumental in helping understand user experiences and perceptions, highlighting aspects of the service that need attention.
AS-IS Model
Figure 1: AS-IS Model for Uber
(Source: Author, 2024)
Results
The analysis of the existing AS-IS Business Processes for Uber has many critical findings in process handling, such as requests for rides, drivers, and payments. Apply Business Process Management techniques principally about process mapping and data analysis with operational inefficiencies (Amblard-Ladurantie, 2023) and user challenges.
Processing of Current Ride Requests
This is the diagram showing the process for ride-sharing, starting with a customer opening the app and making a request for a ride (Chaudhry et al, 2018). Based on location, the system will set up a matching rider with an available driver based on driver rating. Once this match is affected, a notification may be provided to the driver to either accept or refuse.
Ride Requests: These would generate ride requests, and the system works rather efficiently to process the requests for rides to respond and deliver to the customers as soon as possible.
Driver Matching: Algorithmic matching of the drivers considering all the relevant factors most optimally.
Accept/ Reject Rides: The ability to accept/reject rides allows drivers to regulate their workloads and keep customers satisfied.
Driver Matching Process
According to the diagram, Matching by several elements such as location and availability is difficult. However, specific information that applies to matching is not availed.
Possible Improvements:
Real-time traffic data: It can be done by providing real-time or live traffic congestion data (Akhtar and Moridpour, 2021) and route time taken can further improve the matching accuracy.
Customer preferences: Maybe preferences related to driver ratings or vehicle types (Ashkrof et al, 2020) can make the matching process even better for the customers.
Payment Process Flow
The process of payment will entail the customer choosing either UPI/Card or cash and then completing the transaction when a receipt is provided.
Key Points:
Multiple means of payment: It gives the facility the system for the usage of flexibility (Bartinique and Hassol, 2019) in different ways of payment.
Generation of Receipt: In return, clients will receive an electronic receipt to confirm payment.
Improvements:
Contactless payment: More convenience and safety can be offered by using contactless payment.
Split payment: This could be helpful in a case where there are group rides or shared expenses.
Key Findings
Application to Ride Requests: The efficiency of ride requests handled by the process of sharing itself is via the method of matching available drivers. However, the present-day matching algorithm can be further optimized through real-time consideration of traffic data and customer preferences (Suhr et al, 2019). This is based on studies that prove that other than the factor of location and availability, matching would have increased accuracy in matches, along with better customer satisfaction.
Driver Matching: Drivers are matched in the system according to their location and ability to take up the ride request. However, there is still more room for improvement by making use of real-time traffic data and customer preference to come up with an increasingly viable match. This is based on evidence given by Suhr et al, 2019, about the worth of real-time information in driver optimizations.
Payment Process: The process of making the payment is rather simple since different modes are under consideration. However, in such scenarios, contactless (Luthuli, 2020) and split payments can increase convenience and speed. According to various scattered research, such features, result in higher user experience and reduced transaction time.
Challenges
Inefficient Driver Distribution: Uber faces inefficiency in driver distribution, especially in low-demand areas, which causes long waiting or cancellation of rides (Ashkrof et al, 2022). Sometimes, the existing algorithm is not effective in optimizing driver availability across all regions during off-peak times.
Issues with Surge Pricing: Surge pricing creates a balance between demand and supply, but it often upsets users because of its unfairness and unpredictability. It leads to poor experiences made by customers and feelings of price fluctuations (Ashkrof et al, 2022) during events or bad weather.
User Satisfaction: The inconsistency in the service quality, lack of transparency regarding the fares, and low response time from customer support (Vu, 2021) further add to the lower overall satisfaction of users. Riders often complain about how prices keep changing, and about drivers not always being available.
Safety Concerns: Other than the assurance of safety via measures like background checks, users and drivers alike are concerned about safety (Vaziri, 2024). It is rather the reported incidents and partial enforcement of the safety protocol that severely dent the trust in this network.
Recommendations
The analysis of the following recommendations is done based on Uber's present business processes, through the AS-IS representation, in overcoming the inefficiencies identified and enhancing overall delivered services.
Enhance Ride Request Algorithm: Improve how ride requests are handled, and this cannot be attained without improving the average waiting times (Morris et al, 2019). To this effect, advanced predictive analytics technologies must be availed to Uber to analyze past data for accurate demand forecasts and optimize driver dispatch in peak hours using machine learning algorithms. The eventual outcome will be improved customer satisfaction and a reduction in cancellations since drivers will be better-oriented.
Driver Allocation Algorithms: The mismatch in driver allocation is at 100 percent, and this needs improvement. Uber would do better to improve its algorithms for matching passengers and drivers. An advanced algorithm that prospects driver preferences, location, and even historical performance can help improve the efficiency of driver allocation (Morris et al, 2019). Using real-time traffic data to optimize driver assignment will enable Uber to make more calculated decisions (Arora, 2023) that reduce driver frustration and wait times for riders.
Smoothen Payment Processing: Payment processing remains a bottleneck and requires much attention to improve user experience. Uber should continue to refine their payment mechanisms by introducing more alternatives that users can use at their choice. It must invest in robust technology to handle volumes at peak transactions so that payment failures can be handled. On the user experience side, one-click payments, or automated billing (John, 2023) for rides taken smoothens the process of paying for a ride.
Improve Safety Features: Regarding safety, Uber needs to enhance its process of screening drivers and conduct periodic checkups of their cars (Vaziri, 2024). More thorough background checks and safety training courses for drivers would make the riders feel safer. The introduction of features like real-time tracking of the ride and an in-app emergency button would enable customers to feel far safer using the service.
5. Implement User Feedback Mechanisms: Establishing a system of formal feedback from both drivers and riders periodically through post-ride surveys and focus groups will help the company identify assessment areas. Generating such proactive outreach to users helps not only in understanding their needs but also introduces community and loyalty among users.
Conclusion
This report has presented a comprehensive analysis of Uber's existing As-Is business processes in the ridesharing industry. The salient points that have come up through the findings include inefficiency in the handling of ride requests, driver allocation, payment processing, and safety protocols. Thus, with predictive analytics for better demand forecasting and optimized algorithms for driver-to-passenger matching, Uber can simplification in the process of paying, and increasing safety features will provide a safer space for the riders and drivers as well. Regular mechanisms for feedback by users will make Uber always responsive to the needs of its customers. Addressing these key areas, Uber will be able to spotlight not just the enhancement of operational efficiency but its leading position in the ride-sharing market. Because of rapidly changing industries, continuous improvement, and innovation are required to sustain the firm's competitive advantages and engender customer loyalty in the long run.
References
? Ashkrof, P., de Almeida Correia, G.H., Cats, O. and Van Arem, B. (2022) ‘Ride acceptance behaviour of ride-sourcing drivers,’ Transportation Research Part C: Emerging Technologies, 142, p.1-19. https://www.sciencedirect.com/science/article/pii/S0968090X22002121
Akhtar, M. and Moridpour, S. (2021) ‘A review of traffic congestion prediction using artificial intelligence,’ Journal of Advanced Transportation, (1), pp.1-18. https://onlinelibrary.wiley.com/doi/pdf/10.1155/2021/8878011
Amblard-Ladurantie, C. (2023) ‘Business Process Analysis: A Step-by-Step Approach,’ MEGA. https://www.mega.com/blog/what-is-business-process-analysis#:~:text=To%20conduct%20a%20business%20process,changes%20to%20optimize%20the%20process (Accessed 24 September 2024).
Arora, B. (2023) ‘Uber Case Study: from Startup to Global Mission,’ Protocloud Technologies Pvt. Ltd. Available at: https://www.protocloudtechnologies.com/uber-case-study-the-drive-from-startup-vision-to-global-mission/ (Accessed 24 September 2024).
Ashkrof, P., de Almeida Correia, G.H., Cats, O. and Van Arem, B. (2020) ‘Understanding ride-sourcing drivers' behaviour and preferences: Insights from focus groups analysis,’ Research in Transportation Business & Management, 37, pp.1-15. https://www.sciencedirect.com/science/article/pii/S221053952030078X
Bartinique, I. and Hassol, J. (2019) ‘Mobility Payment Integration: State-of-the-Practice Scan,’ United States. Federal Transit Administration. Office of Research, Demonstration, and Innovation. https://rosap.ntl.bts.gov/view/dot/42672/dot_42672_DS1.pdf
Chaudhry, B., El-Amine, S. and Shakshuki, E. (2018) ‘Passenger safety in ride-sharing services,’ Procedia computer science, 130, pp.1044-1050. https://www.sciencedirect.com/science/article/pii/S1877050918305088/pdf?md5=bdcdff1d39169657b2323a96f1cbd99e&pid=1-s2.0-S1877050918305088-main.pdf
John, S. A. (2023) ‘Automate billing in just 5 simple steps,’ RackNap Blog - A Guide to Automate Web Hosting and Cloud Business. https://www.racknap.com/blog/automate-billing/ (Accessed 24 September 2024).
Luthuli, L. (2020) ‘Factors influencing payment convenience in the mini-bus taxi industry in Cape Town, South Africa,’ (Doctoral dissertation). https://etd.cput.ac.za/bitstream/20.500.11838/3441/1/Luthuli_Lolwethu_213303655.pdf?
Mitrofanskiy, K. (2024) ‘The Foundation of Uber: The Tech Stack and Software Architecture,’ Intellisoft. https://intellisoft.io/the-foundation-of-uber-the-tech-stack-and-software-architecture/ (Accessed 24 September 2024).
Morris, E.A., Pratt, A.N., Zhou, Y., Brown, A., Khan, S.M., Derochers, J.L., Campbell, H. and Chowdhury, M.R. (2019) ‘Assessing the experience of providers and users of transportation network company ridesharing services’ https://rosap.ntl.bts.gov/view/dot/53586/dot_53586_DS1.pdf
Nwankwo, E. C. (2023) ‘The Impact of Uber and Lyft on the Transportation Industry,’ Medium. https://ericnwankwo.medium.com/the-impact-of-uber-and-lyft-on-the-transportation-industry-cfab72151ee5 (Accessed 24 September 2024).
Ramji, A., Singh, A., Khatri, J., Rathi, V., Dey, S., Sachdev, S., Vyshnavi, D. and Student (2023) ‘The Impact of Ride-Sharing Companies on the Transportation Industry and the Evolution of Courier Delivery Services in India,’ International Journal of Research Publication and Reviews Journal homepage: www.ijrpr.com, 4, pp.1551–1557. https://ijrpr.com/uploads/V4ISSUE4/IJRPR11544.pdf (Accessed 24 September 2024).
Sühr, T., Biega, A.J., Zehlike, M., Gummadi, K.P. and Chakraborty, A. (2019) ‘Two-sided fairness for repeated matchings in two-sided markets: A case study of a ride-hailing platform,’ In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3082-3092. https://pure.mpg.de/rest/items/item_3217646/component/file_3346964/content
Uber Blog. (2021) ‘How Uber Achieves Operational Excellence in the Data Quality Experience’ https://www.uber.com/en-IN/blog/operational-excellence-data-quality/ (Accessed 24 September 2024).
Vaziri, S. (2024) ‘Uber vs Lyft Safety: A Comprehensive Comparison,’ Vaziri Law Group https://www.vazirilaw.com/uber-versus-lyft-safety (Accessed 24 September 2024).
Vu, T. (2021) ‘Service Quality And Its Impact On Customer Satisfaction,’ ResearchGate, pp.1-53. https://www.researchgate.net/publication/356592279_Service_Quality_And_Its_Impact_On_Customer_Satisfaction [Accessed 24 Sep. 2024].
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