In this article, we identify opportunities to minimize downtime with the use of predictive and prescriptive maintenance and the Internet of Things. We also share a biopharma case study.
Traditionally, equipment was fixed only after it broke down. Today, most industries plan maintenance at set intervals to boost performance and prevent failure. Recent advances in information and communication technology (ICT) and computer science (especially in the areas of artificial intelligence [AI] and machine learning [ML] technology), have enabled predictive and prescriptive maintenance approaches (1, 2, 3).
Predictive maintenance uses Internet of Things (IoT) sensors and data to predict equipment maintenance schedules and prevent failures (4, 5). By leveraging AI and ML, it analyzes data to spot issues before breakdowns occur – using supervised learning methods such as regression, decision trees, and neural networks. Prescriptive maintenance goes further, offering tailored solutions for error alerts based on AI, ML, diagnostics, and past failures (5).
Predictive and prescriptive maintenance is customized to each asset, process, organization, and industry. Organizations can expect to control operational risk by reducing equipment downtime and improving production uptime.
Maintenance challenges
Organizations that use traditional maintenance often need urgent support when equipment unexpectedly fails, causing downtime (6–11). Factors affecting prompt resolution include service level agreements (SLAs) with vendors, escalation efficiency, part availability (if not stocked in-house), and repair time. Many failures stem from operators' lack of knowledge about equipment use, maintenance, and troubleshooting; ongoing training challenges and staff turnover worsen the problem.
Impacts of downtime and process resolution
Unplanned downtime impacts business continuity and can lead to substantial loss of revenue and profits. In extreme cases, nonfinancial impacts can extend to patient outcomes. Some consequences, such as increased pressure on production staff and subsequent investigations, are not easy to measure in the short term; these factors can influence employee morale and retention.
With predictive and prescriptive maintenance, organizations can keep operations on-track by planning downtime to prevent equipment failures. This proactive approach helps to cut costs, reduce how long downtime lasts, rapidly fix problems, and get equipment up and running quickly.
Customer case study
In this success story, a fully integrated biopharmaceutical company located in Ahmedabad, India, turned to OptiRun™ Connect remote monitoring system to address their downstream process development challenges. They've installed five ÄKTA™ chromatography systems to develop advanced-stage biosimilars and other complex products for patients worldwide. They needed to plan their batch runs, optimize utilization of their ÄKTA™ systems, and keep a detailed record of instrument utilization throughout the process development workflow.
They began using OptiRun™ Connect remote monitoring system in December. For the next several months, their ÄKTA™ avant chromatography system was in "ready" status and was working fine. Then, during a batch run, six events were generated on the OptiRun™ Connect portal. After the Global Remote Service Engineer Nishith Soni received the events on the OptiRun™ Connect portal dashboard and reached out to the customer, Nishith performed a detailed analysis of the error and discovered that it was related to rusty monochromator grating bearings of the ultraviolet (UV) module. The monochromator is the heart of the system, and if not functioning properly, it leads to incorrect results and product loss.
The same day, a field engineer was dispatched to visit the customer's site to confirm the remote diagnostic assessment, and the engineer explained the error to the customer. As a predictive finding, our equipment service team advised the customer to keep a spare UV module in stock in order to avoid system downtime in the future.
The customers appreciated the predictive diagnosis, which will allow them to be alerted of an upcoming failure in advance – ultimately decreasing interruptions to their processing runs and increasing the speed at which they'll be able to deliver effective therapies.
Moving forward with digital maintenance
Organizations thinking about digital maintenance want speed and scalability, ease of use, and accurate equipment warnings and predictions. A predictive and prescriptive maintenance approach teaches in-house teams how system components affect production. It also saves money by reducing the lead time for repairs, uses on-hand spare parts, and schedules and performs preventive maintenance that minimizes downtime.
No matter what an organization needs, this type of maintenance can be tailored to help teams make better decisions, boost equipment performance, keep processes running smoothly, manage budgets more easily, and achieve strong business results.
Wherever you are in your digital transformation, we offer a variety of proactive ways to keep your equipment and your production running smoothly.
Related content
References
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