Manchester Metropolitan University (MMU) is currently undergoing a large-scale organisational change initiative to improve the quality of service it provides to students.
The Student Journey Transformation programme (SJTP) is designed to enhance every aspect of the business administrative processes and systems and will provide more efficient, responsive support to students, from initial enquiry to becoming a member of the alumni community.
The University wanted to improve the student experience by leveraging Automated Intelligence technologies including Robotic Process Automation (RPA), Artificial Intelligence (AI) and Machine Learning (ML) to increase and improve the speed and availability of student services. To support this initiative, MMU needed to develop a technology strategy and build a compelling business case that would support the required investment.
MMU challenged Equantiis to:
- Engage and educate participants about RPA and related technologies.
- Allay any fears around the technology and the potential impact to staff numbers.
- Identify suitable processes that would be appropriate for RPA.
- Identify the correct technology options.
- Assist in delivering a proof of concept (POC) for the identified processes and use cases.
- Select the right processes to form part of the business case for investment that would;
- Bring tangible and measurable benefit to the student experience.
- Deliver a sound return on investment.
Equantiis conducted a thorough market analysis of RPA vendors and selected Automation Anywhere (AA) as the company showed a strong suite of offerings that aligned best with MMU technical and user requirements and the ability to deploy its platform quickly. Equantiis and MMU invited AA to demonstrate the platform’s capability with a ‘proof of concept’ (POC) of MMU’s ‘Student Confirmation letter’ process. The POC demonstrated that AA’s platform worked well with MMU’s core systems and automated a 4-minute manual process into a process taking less than one minute.
The success of the POC enabled Equantiis to conduct a more detailed analysis of processes and develop a business case for investment. Equantiis explored potential RPA user cases with key stakeholders in 6 areas within the University. The workshops were highly interactive, where user cases were critiqued for two aspects;
1. Whether it could?
Determining whether RPA would be applicable for the process using a robust assessment framework and exploring the potential pitfalls, risks and other inter-dependencies that may impact upon the process.
2. Whether MMU should?
Identifying the value that automation would deliver to the outcome, by outlining the baseline metrics (volume pa, average time taken) and measuring the benefits both tangible (operational) and intangible (student experience).
In total, 45 processes were identified and assessed by Equantiis, with individual complexity scores, benefits, investment costs and return on investment identified. The data was then fed into Equantiis’ proprietary business modelling tool which gave the user full flexibility to explore the return on investment and student experience impact for any combination of the 45 processes selected for automation.
Equantiis recommended MMU invest in the AA platform and automate 12 high value processes with an expected ROI of 110% over a forecast period of 3 years. In addition, automating the recommended processes is expected to drastically improve the student experience through increased availability of services and shorter turnaround for requests.
Equantiis now recommends that MMU expands the feasibility study to the wider programme and back office functions, investigating which business processes can be automated using the proposed platform now and in the future. A conservative estimate, based on uncovering 40 more high value processes during the expansion, is expected to deliver a 240% return on investment.
Using Equantiis’ Artificial Intelligence business modelling tool, the project sponsor had the capability to explore any configuration of processes, and understand what impact this may have on:
- initial investment required,
- return on investment,
- student experience,
- staff efficiency,
- staff hours recovered,
- remaining capacity for more processes to be automated in the future.
The model ensured that any change in the investment environment or organisation could be addressed without the need to re-engage.
In addition, the workshops provided an interactive opportunity to engage staff and articulate the importance of RPA to the student experience journey. Positive engagement from the workshops led to a high volume of processes being identified, forming a rich pipeline of processes that could be automated in the future.