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Predictive Maintenance in Industry 4.0 for Life Sciences

In the life sciences industry, the importance of predictive maintenance is clear because lives depend on the quality and reliability of the product supply chain. And, to reliably deliver a quality product a company must ensure the proper functioning of their manufacturing equipment.

A new era is now beginning. The 4th industrial revolution, called Industry 4.0, is where cyber physical structure is disrupting industry and the lines blur between the physical, digital, and biological spheres.

Understanding Predictive Maintenance in Industry 4.0

For many years, the term predictive was linked to monitoring the condition of the equipment. A measuring instrument that predicted the current equipment situation with some empirical knowledge or calculation could, with a small projection, predict the time the equipment could run in that condition. New technologies, like SAP Leonardo, are going way beyond this simple model to deliver true predictive analytics.

The maintenance strategy in this new era is to use sensors connected to the machines that transmit data to the cloud through the internal network or internet of things (IoT), which feeds a huge database. Computers with advanced statistical methods in their algorithms and machine learning capabilities analyze the behavior of the machine based on the data generated by the sensors and provide statistical prediction. Here the word “predictive” makes perfect sense; predict when a machine will fail through statistics and with this information, maintenance can plan the jobs more safely and know the window of time to schedule accordingly.

This scenario is supported by SAP Leonardo with SAP Predictive Maintenance, Service On-Premise (PdMS), and Asset Intelligence Network (AIN) tools. PdMS is an out-of-box tool and can connect to any platform. The solution provides in its core a functionality called Insight. Insight is the differential of PdMS where data can be obtained not only from the sensors but also from CMMS, SAP ERP and so on. In addition, artificial intelligence and other algorithms are used to generate so-called derived signals.

Driving Transformation with Predictive Maintenance

The strategies of preventive and corrective maintenance by nature are expensive and risky activities. As we open an equipment to do some type of maintenance, we have created a risk which may include contamination and risk not being able to perform the job correctly. Additionally, there is always the impact of the downtime of production that affects productivity and increases production costs.

The evolution of Industry 4.0 in predictive maintenance aims to reduce the resources involved in preventive and corrective maintenance by increasing a portion of the condition monitoring strategy and greatly expanding the true predictive strategy using artificial intelligence and machine learning. The most interesting of these is that these strategies, despite being correlated, are independent. This means that a company can implement PdMS even though the preventive maintenance strategy still needs some improvement in its process.

The results of high volume data manipulation with the help of dashboards, mobile app, and analytics through artificial intelligence and machine learning will contribute significantly to improving the maintenance business process when supported by SAP PdMS. Integration with SAP 4/HANA, SAP CRM, SAP Plant Maintenance/Service Management, SAP Multiple Resource Scheduling (MRS) and the ability to integrate with SAP MII, SAP ARIBA/Fieldglass makes PdMS a key tool for the integrated maintenance.

Investments in LIMS, MES, and other systems that have a productive machine as the main factor are significantly affected if this machine is in constant breakdown. One of the challenges for engineers is to increase the plant’s productive capacity. This basically can be accomplished in two ways. The first is the physical by retrofitting the existing equipment or acquiring new equipment. The second is by improving the predictive maintenance process. This is the faster and less expensive way as SAP PdMS can make a significant contributions to this improvement process.

There are many quantifiable benefits of SAP PdMS On-premise including the improvement of asset reliability on manufacturing companies can achieve a 10-40% equipment maintenance cost reduction, up to 50% equipment downtime reduction, and a 3-5% reduction on capital investment by extending the useful life of the equipment. Other positive factors such as improvement of equipment effectiveness, increased security to prevent accidents, and optimized dynamic maintenance can be achieved with SAP PdMS.

More than purely technological, Industry 4.0 also impacts processes. New knowledge, organizational structure changes, relationship with suppliers, and other impacts will feed the area of change enablement and organizational performance.

Industry 4.0 has arrived quickly bringing with it technological innovations and forcing human nature for yet another significant change in our lives. Companies have tremendous opportunity to improve their manufacturing processes, and upgrading predictive maintenance capabilities is one of the best places for life sciences companies to start.

Co-author and contributions by: Tomas Marzullo

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Tags: Predictive Analytics, SAP Leonardo