How Does Merck Harness Robotic Process Automation for Operational Success?

Merck Harness Robotic Process Automation for Operational Success
Merck Harness Robotic Process Automation for Operational Success
Businesses across industries are constantly seeking innovative solutions to optimize their operations and drive efficiency. One such solution that has gained significant traction is Robotic Process Automation (RPA). In the era of digital transformation, RPA has become an indispensable tool as it offers the promise of streamlining repetitive and rule-based tasks, empowering organizations to focus their human capital on more strategic initiatives.

Merck India, the renowned pharmaceutical and life sciences company, has embraced this transformative technology to reshape their internal processes and amplify productivity. To gain deeper insights into the world of RPA and understand its implementation within Merck, we interviewed Radhika Mahadev, the head of Robotic Process Automation in Merck Life Sciences,

By examining real-world use cases and the perspective of an industry leader in RPA, we try to shed light on the power of automation and its impact on the pharmaceutical landscape – from revolutionizing data management to enhancing regulatory compliance.

1. What does your day-to-day routine look like as the head of RPA at Merck?

My work primarily revolves around overseeing the implementation and management of RPA initiatives within the Merck ecosystem. Every day we see unique business cases where integrating technology would be challenging, and we as a team try to solve those by intertwining digital toolboxes.

I collaborate with various business stakeholders from marketing, customer excellence, supply chain operations, regulatory business, etc. It is essential for us to be in sync with RPA initiatives and business objectives for successful adoption of automation in the company.

2. What should product leaders know about RPA, and how can they identify use cases for it in their organization?

RPA is not a one-stop solution for all repetitive tasks in an organization. A defined process to evaluate the ROI, hours saved post-automation and volume turnover must be considered. So, integrating with process mining, Kaizen, Macro, Power Automate, probability-based automation, and Power BI would bring in more synergy than directly hopping towards automation.

If an organization is in a very early phase of adopting RPA, I would suggest choosing repetitive and rule-based activities so that you can track the success of automation and synergy. In this process, most of these cases will have a well-defined standard operation procedure (SOP) and come with structured data.

3. How did Merck spot the need for RPA, and what problems does it solve in your organization?

Merck started its RPA journey in 2019, working with external implementation partners. Once we realized the benefits, we started our own in-house team. Today, we have much automation that supports order entries, web orders, tender cross-referencing, regulatory submissions, new product introduction, order screening, and printing certificates for product packing.

These use cases have enabled incremental sales revenue, on-time delivery (OTD), and improved go-to-market timelines. Additionally, peers can focus on customer-facing activities rather than being glued to repetitive tasks. Automation significantly frees up time for the top leadership to focus on more productive goals.

4. What are the tangible and intangible benefits that you see after implementing RPA in Merck?
Tangible benefits are in terms of hours given back to business, and we see three-time ROI vs investments. The intangible benefits are many. RPA has enabled a virtual task force for employees. Our staff can now focus on more challenging and cognitive tasks than mundane tasks. RPA has also enabled seamless operations, and the tech operates outside of office hours, which is a plus.
5. Can you give us examples of how Merck Life Science is using RPA?
Digitalization is no more an option but a necessity in any organization. We at Merck predominantly use automation in the regulatory field, which is a more critical and complex task. Order screening and block order release is the most complex regulatory process harmonized and automated across different geographies. Many Merck warehouses have automated certificate printing at every pack station. Customer account creation and credit release are some of the critical tasks where we have clubbed RPA along with other technologies.
6. Are there any unique challenges to implementing RPA in highly regulated sectors like the pharma industry?

Regulatory process automation comes with great risk and a huge responsibility where accuracy and data integrity are the most critical. Merck has a specific governance panel to ensure the RPA bots have restricted access to the production platforms and the four-eyes principle is adopted to give high priority to accuracy.

The IT team has clear visibility of every task these bots perform, and we ensure it is traceable with audit-proof logs. Every bot that moves to production is validated by the Global Validation team and follows the Quality Management System protocol (QMS).
7. What tools do you use for implementing and managing an enterprise-wide RPA program?

Merck uses Automation Anywhere, UI Path, and Blue Prism. However, there are many other automation tools available in the market. While implementing RPA, one must choose simple tasks at the beginning rather than engaging in complex endeavors. Ensure proper governance is in place before implementation.

Keep in mind scalability options so that the implementation of a similar process comes with less turnaround time. Importantly, work very closely with the Process Excellence team so that you have a structured process with more harmony and efficiency.

8. What was your learning curve from implementing RPA in Merck?

RPA implementation is more of an evolutionary journey than a destination. We saw both successful and failed programs on our journey. Initially, we fell short in most of the unstructured data-based processes by adopting cognitive capabilities. It didn’t work as expected for a complex process. While fragmenting those into probability-based cleansing and streamlining data approaches helped us to find better results.

I would advise leaders to walk before you run when it comes to automation. Focus on quick wins rather than complex programs. Avoid unstructured data automation at the initial phases of automation implementation. Ensure the code standards are well maintained, and reusable codes are handy.
9. Can RPA be set up in legacy systems?

Yes, it can be achieved. It is currently up and running at Merck. During implementation, one must make sure to have enough test data in a quality environment so that the hypercare and go-live are smooth.

While RPA can be implemented at different stages of a product or process, a new process or product with a set standard workflow suits RPA implementation better. If the process or the product fluctuates its workflow, holding on until the process is set is advisable. Once you notice that the workflow is regularized, it’s time to look for automation opportunities.
10. What are the best practices for RPA implementation?

There are many aspects that one should consider while deploying RPA. Some of them are:

  1. Start small and later expand.
  2. Find an alignment with your IT team before implementing RPA, so that you are prepared to handle any late surprises when trying to integrate with legacy systems.
  3. Build a repository of reusable codes for similar business functions.
  4. Proper Gate reviews and handovers are a must in BOT maintenance.
In the case of customer-facing process automation, ensure the process is fool-proof and has the best solution. Especially while automating FAQ-based queries and frontline mailers, the response should be close to customer expectations. Decide which categories can be handled by bots upon further improvisation.
11. What are some common misconceptions that people have about RPA?
People have misconceptions that implementing RPA directly leads to a reduction in headcount. I have also seen resistance and anxiety about the accuracy of the bot’s performance on certain critical tasks.
Working with operations teams and explaining the benefits and the efficiency gain has helped us remove these misconceptions. It’s change management for the operations teams as well. With four years of journey with RPA CoE (center of excellence), I now see that the acceptance level has increased, and they are open to coming up with more automation ideas.
12. What advice would you give product leaders in the pharma and life sciences industries considering implementing RPA in their organizations?

RPA has become a tool for realizing efficiencies, specifically in back-office processes across organizations in every sector. In the pharma industry, RPA has already penetrated processes like Pharmacovigilance, supply chain operations, commercial and marketing departments, regulatory and compliance, and finance. Some key factors that product leaders need to follow while implementing RPA are:

  • Map process suitable for RPA before implementation
  • Compliment with the Process Excellence team for better synergy savings
  • Stick to scalability and reusability for quick wins.
  • Don’t start with complex or cognitive processes.
  • RPA is not a sole cost-cutting exercise. ROI will be low during the learning exercise, and eventually, you will start realizing the benefits.
13. Have conversational NLPs like ChatGPT entered the world of enterprise RPA yet?

We do get to hear a lot about AI, ML, and NLPs like Chat GPT every day, but these are in the early stages of implementation for day-to-day operations.

14. How do you see RPA evolving in the pharma and life sciences industries?
The next line of innovation would be AI, image recognition, language capabilities, and free text handling to complement RPA. These innovations will elevate automation not just for rule-based tasks but also for cognitive processes.

Going forward, augmented intelligence will be the need of the hour. AI algorithms and machine learning methods that help to learn from existing activity would be playing a key role.

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