It is often said in life science and biopharmaceutical manufacturing that “the process is the product”. It should come as no surprise that Life Science manufacturers must meet substantial compliance challenges with increasing, ever-changing regulations. The need to improve quality and compliance, reduce costs, and optimize operational efficiencies should be at the forefront of any organization in this highly competitive industry. One of the most important aspects to a manufacturer of pharmaceuticals and biotherapeutics, particularly for those manufacturing new products, is proving compliance and adherence to established standard operating procedures (SOPs). Implementing a reliable and auditable monitoring program for life science samples, assets and equipment is an important step in enabling an organization’s success.
When monitoring valuable samples, assets and equipment, life science manufacturers must be much more focused on ensuring compliance. Implementing fail-safe procedures to ensure that samples or assets are used, packaged, maintained, stored, or incubated at the appropriate temperature and humidity, for the appropriate duration, and in the appropriate area, is critical for a well-functioning manufacturing facility.
Data collected on the ground floor of a manufacturing facility such as ambient parameters, sample storage conditions, and equipment functionality can be invaluable for root cause analysis and process optimization. However, there are often disconnects between what is recorded on the floor and what is reported to an operational or quality management team due to error-prone, manually intensive tasks or paper-based tracking systems. This leads to substantial risks in an organization’s ability to properly execute corrective action in the event of equipment deviations, anomalies, or failures.
There are many recent developments occurring in device connectivity and data collection from individual devices, OEMs, and machines in Life Science industries. Machine Learning, Artificial Intelligence and the Internet of Things (IoT) are being leveraged more so than ever before and can have an immense impact on optimizing overall operations. Manufacturing equipment requires constant monitoring and industry leaders are searching for robust, software platforms that can deliver better data quality and reduce human error. Meanwhile, as many as 30% of Life Science organizations are not utilizing such tools and are missing the crucial information that this data provides. This only compounds the potential risks of error-prone, paper-based procedures.
Implementing a total monitoring solution for all ambient parameters, assets, and equipment is the only fail-safe way to ensure that manufacturing operations are safeguarded. For example, on a manufacturing floor, air quality must be constantly tested for temperature, humidity and airborne particulates which can introduce contaminants during manufacturing processes. Sensitive equipment needs continuous calibration. If certain equipment is uncalibrated, data outputs may appear acceptable when in reality they are outside established limits. Manual reporting practices often exacerbate these problems exponentially. All of this could be alleviated with an implementation of a real-time monitoring system.
Real-time notifications for any deviations in air quality or equipment operations is the most effective way to mitigate risks. A comprehensive solution will store all relevant data and provide proper alerts to appropriate personnel. Knowing automatically if and when a device needs calibration or maintenance eliminates error-prone, manual tasks and enables an organization to optimize maintenance schedules, streamline workflows, and reduce operational costs.
Organizations that want to understand the benefits of implementing an autonomous, monitoring solution should look at the potential for increased productivity, improved data quality and acquisition, and reduced compliance risks. There are many monitoring solutions available for life science industries, but most are pinpoint solutions that are difficult to integrate into existing manufacturing processes. Simple, rudimentary sensors or data loggers on the market eventually encounter similar problems as time-consuming, manual data entry. The processes involved with these archaic systems are seldom superior to manual, paper-based tasks and are inadequate for proper review and reporting cycles. Monitoring systems that are robust and scalable are paramount for fast-growing companies. A cloud-based platform that is OEM agnostic and capable of monitoring both wired and wireless sensors will ensure that a company is well prepared for any potential problems. Autonomous, real-time monitoring closes gaps in procedures, increases the quality and traceability of an organization’s data, and mitigates common risks found in data loggers and paper-based monitoring practices.