Has your organization thought through how to leverage automation in your Quality business operations?
We are in the midst of a digital revolution in the life sciences; where disciplines such as intelligent automation (IA) can bridge the gaps between complacency and innovation. In this era, innovation can take an organization from discouraged to empowered, by leveraging emerging technologies within their Quality Management Systems (QMS). Now more than ever, there is great urgency for organizations to digitally transform processes and become comfortable in a virtual world, as they look to rebound and reimagine their business models in light of global health and economic challenges. COVID-19 has successfully, or not so successfully for some, pushed many companies into all virtual collaboration, where they can possibly leverage some intelligent automation to compensate for no longer being in person. It’s no secret that technologies like Artificial Intelligence (AI) and Robotics Process Automation (RPA) can support organizations in their efforts to develop, manufacture and test quality products that meet or exceed customer and patient expectations. The challenge is figuring out how companies apply these technologies within their continuous improvement lifecycle to ensure compliance and gain improvements. Let’s explore that thought a bit!
While the intelligent automation spectrum provides value to organizations in all levels of automation, I want to focus on two main tools that can be leveraged in a Quality System to drive continuous improvement initiatives; RPA and AI. RPA is a great tool that can be used for replacing tasks in a system that are repetitious, high volume, well-defined, and administrative type tasks that contain little to no exceptions within the process. On the other hand, AI is a much more mature tool that can be used for more nuanced tasks that have large, complex data sets and exceptions to the process. Both tools can be used simultaneously in a process or separately, depending on the solution scenario. So how do organizations in the life sciences apply this to their QMS? Let’s take a closer look…
- Assess your QMS
- Define and prioritize automation goals through workshops and current state assessments of challenges or issues within your Quality organization.
- Develop a roadmap including governance frameworks, change management and security; select technology and document scenario steps creating a phased project plan for implementing innovative technologies within your organization.
- Pilot your plan
- Develop an automation pilot to help support your business case with real data to prove value.
- Implement your pilot into production
- Scale and transition the pilot into an operational environment where you can capture and program exceptions into the process and then implement certain aspects of your automation strategy from step 2.
- Operate at full scale and monitor your production intelligent automation tools at work. Conduct appropriate automation methodology and tools training and begin developing your automation center of excellence.
This typical implementation for a simple process can take about 3-6 months or so for proof of concept in your QMS. However, to truly leverage emerging technologies in an “ideal” QMS, you need to ensure two things within the business: You have harmonized, well-defined processes and good data that is readily available.
|01 Process definition and harmonization||02 Data quality and availability|
|RPA can only be implemented when a process is well-defined and repeatable with clear rules for exceptions.||RPA is limited by the data it receives and will only process data according to very specific rules, so if the data is not mastered, consistent and available, the RPA bot will fail or product “garbage”.|
|Machine Learning (ML) and other Intelligent Automation approaches work well within well-specified scope for a specific purpose.||ML and other approaches, as well as AI require large, high quality data sets to function and “learn” effectively and produce valuable results.|
|AI can handle more variation than RPA and ML, but is aspirational in the Quality sector, once RPA and ML mature.||Data must be standardized across the business to gain the most value out of the IA spectrum for CQI and transform your data to life in real time.|
Once you have these two items mastered and you are ready to embark on your digital transformation journey to enhance quality and CQI, you must consider a few choices and capabilities to truly bring your data to life across the enterprise:
|All-In-One Cloud Platform(s)
Reduces integration points & transformations: Provide a spectrum of component Modules (CTMS, RIMS, DMS, QMS) on a shared platform
Drives process harmonization: Workflows honed by market best practices with frequent enhancements and minimized customization
Expanded access and outsourced Disaster Recovery / Business Continuity burden
3rd Party is handling your data: Contracting for and building out the Push/Pull of data from the hosted solution to the firm’s data layer can be challenging
Boundary System challenges: Integrations between separate cloud platforms and cloud to on-premise integration can be difficult depending on the solution
Change Management: to a global platform with shared business processes requires significant up-front work on process harmonization and subsequent change management and training to achieve understanding and adoption
|On Premise Enterprise Platform(s)
Consolidates like processes and data into platform(s) shared across regions, sites, and functions: Reduces integrations and transformations
Drives process harmonization: The act of consolidating requires up-front workshops to define common workflows
IT Infrastructure & Lifecycle Maintenance Challenges: Firm incurs ongoing cost of maintaining applications/databases; upgrades can be difficult and infrequent
Change Management: Consolidation to a global platform with shared business processes requires significant up-front work on process harmonization and subsequent change management
The other two main areas for consideration when looking to begin a digital transformation for an ideal Quality Management System or to reach ideal Quality, is to look at reporting and analytics and how easy integrations will be for future reference.
|Native Reporting & Analytics Capabilities
Don’t overvalue native analytics and reporting features that work only within a given platform: Most platforms have searching, filtering, trending, and reporting capabilities which can be used to meet basic needs. However, to achieve a truly connected quality ecosystem, these capabilities are either not sufficiently advanced or only work within the platform and can’t ingest relevant and related data from other sources. Firms are better suited implementing fit-for-purpose analytics and reporting engines atop their integrated data layer.
|Ease of Integrations
Do place a high value on platforms offering proven Application Processing Interfaces (APIs) and flat data architectures: Most leading platform vendors are aware of the ecosystem into which their systems get implemented and have developed APIs to the most common edge ware and middleware systems they encounter (for example LIMS to SDMS or Instrument Management interfaces)
Once you have considered these points and have developed a strategy aligned with your business and quality values, you will be able to begin to fully visualize quality across your organization and implement CQI initiatives with ease using innovative technologies to support user experience. You will be able to pin point where improvement are needed and support predictive decision making within your organization.