Employ intelligent automation to drive value in life sciences quality operations
The biggest misconception around the use of intelligent automation (IA) is that its aim is to replace people. In fact, IA tools free people to focus on the critical elements of their work that require human mastery—by both handling tasks that do not require human intervention and by enhancing processes that do. In addition, as more experts are needed to perform IA oversight as well as train and advance learning algorithms, more IA-based jobs will be created.
Now more than ever, many organizations are realizing the need to begin their IA journey in earnest. The COVID-19 pandemic has been eye opening for the life sciences industry, as they had to turn quickly to virtual business collaboration and identify how unprecedented change would affect their business and client needs.
It is strategically important for life sciences organizations to increase efficiencies, reduce costs, improve customer service and minimize risk. IA offers innovative tools to help them achieve these outcomes and adapt to changing times. Advances once thought of as science fiction are becoming mainstream. The visual below shows the full spectrum of IA technologies, from simple robotic process automation (RPA) through complex artificial intelligence (AI). Depending on an organization’s requirements, usually a mix of these is used.
Intelligent Automation Spectrum
Five-stage chart showing the spectrum of automation from simple to complex (viewed left to right). Those five stages are basic automation, robotic process automation (RPA), enhanced process automation, algorithmic automation and artificial intelligence. The chart also categorizes these types of automation with an arrow from simple to complex (viewed right to left) as task automation, process automation and human augmentation.
Advancing your intelligent automation journey
To get started on your IA journey, there are three key success factors to keep in mind: goal definition, platform selection, and employee training and communications. First, it is very important to state clearly the intentions and expected achievements from using IA technologies. A second pivotal factor is collaborating with the right IA provider to develop and implement the right solutions for your organization. The third factor is to ensure employee training and communications are an integral part of the rollout process. IA solutions will not be successful unless employees fully understand the need for them (and how they will be affected by them), are trained properly, and have confidence in their abilities to work with the new technologies.
Quality areas where IA is driving value
Many IA-supported functions will reside in and enhance the quality unit. There is rapid progression in developing IA technologies, and quality leaders are identifying areas where IA will help the organization innovate and remain in compliance. Based on our work with life sciences organizations, here are several quality areas where we see IA starting to drive beneficial outcomes:
- Audits: IA technologies can help confront the trials of compliance effectively in today’s digital era. This includes the use of automated response tools, non-conformity detection and real-time monitoring. AI that uses natural language processing and speech recognition can help decrease audit cycles, allowing more time to focus on corrective and preventive action plans (CAPAs) for identified compliance issues.
- Training: Quality leaders today are starting to deploy machine learning for training to be more stimulating and resourceful for their employees. The use of machine learning algorithms can help to improve the instructional content and provide enhanced visual and auditory response, which then can provide enriched personalized performance coaching to the learner.
- Quality assurance: IA technologies such as machine learning and computer vision can help automate routine tasks, for instance, to identify defects and quality issues. Such tools can identify quality defects by using camera systems that are extremely sensitive, exceeding the capabilities of the human eye. The data gathered by these technologies can be analyzed in real time to support operational process improvements.
- Forecasting: IA technologies can assist quality leaders with constructing forecasts that are more precise. Deep learning and deep neural technology can proactively envisage the voice of customer. This further allows organizations to meet forecasted and urgent customer needs.
- Supply chain: Within the supply chain, machine learning can help identify patterns of product demand over time and by geographical location. The technology can then make automatic adjustments due to disruptions and market changes.
By building the proper framework, organizations can realize good return on their IA investments such as:
- Time and cost savings that also enable greater flexibly for environmental and client demand changes
- Reduced human error that in turn lowers overall compliance risks
- Process efficiencies that give employees more time to perform value-added activities and focus on their growth and development
A key lesson is to think big, start small, learn from failures and build upon successes.
RPA benefits at a glance
The average cost of implementing a robot can range from $80K – $150K and decreases with large-scale deployments
Speed and productivity
Automation typically is 2-3X faster average production times
Robots can work 24/7
Accuracy and compliance
Robots work to 100% accuracy levels and enable compliance
Reducing costs associated with errors
Scalability and flexibility
Robots can easily be scaled up and down to handle demand fluctuations and seasonal variations
Knowledge capture and transfer
Knowledge can be transferred from humans to bots to preserve it
When a biopharmaceutical firm desired to automate its request processing for contract research organization (CRO) new accounts, access changes and revocations, CGI implemented an effective RPA solution. Now, a bot now monitors the request inbox for new emails, uploads all data into a tracking system, and enters account details into various systems (such as SharePoint and Veeva). It also can update those systems with proper permission sets and report on all bot actions, capturing relevant metrics. With the bot handling 150 requests per week, the team now has 25 more hours per week to focus on support tasks that are more complex.
10 minutes to handle a request manually
<1 minute for bot to handle a request
25+ per week
1,300+ hours per year
Has your organization identified opportunities where IA can help you achieve more efficient and sustainable growth and compliance?
For more information on how CGI empowers quality, manufacturing and compliance teams to increase efficiency and safety standards while reducing costs and time to market, visit our Quality & Manufacturing Services page.