M-Powered is a portfolio of easy-to-use observational, analytical and support services that gives customers a competitive advantage. Leveraging Industrial Internet of Things (IIoT) technology, M-Powered runs sophisticated algorithms that utilize real-time machine learning to monitor machine operations and alert before any potential issues.
M-Powered yields unique intelligence on:
- Current and Future Operations
- Sharp Manufacturing Quality
- Optimized Uptime and OEE
The addition to your company’s bottom line from implementing IIoT solutions are: maximum quality, availability, utilization.
M-Powered takes our entire suite of services delivered by our ServTek aftermarket brand to the next level of customer performance. M-Powered connects parts, service, rebuilds, retrofit and preventative maintenance service to our customers’ machines to provide a unified, stronger offering: optimized uptime. ServTek delivers performance. Now, with M-Powered, ServTek provides competitive advantage.
The M-Powered Advantage
- Improve quality in the cloud
- Reduce downtimes
- Manage operations proactively
- Increase machine utilization
- React quickly to problems
- DEKRA Certified secure & private cloud
- Data archiving + compliance
- Audit trails + reporting
- Predictive system and machinery maintenance
- Service + aftermarket programs
- Energy + resource savings
The first step to improving your OEE is measuring and tracking the data. More importantly, the value must be dependable and consistent to help drive action. Using real-time machine data, OEE can be calculated at your fingertips, anytime and anywhere.
It can be frustrating to spend hours on the phone troubleshooting and then be told that you need a technician on-site to perform additional troubleshooting steps. When this happens, you want to know that the best and brightest are assessing your situation and providing an accurate first-time fix.
Wear is part of the normal life of a molding machine. Deterioration can display itself in many forms, but two of the leading offenders show up in performance loss or quality escapes. When problems are discovered, you may ask ‘Was it a slip with the machine or the operator?’ and ‘could we have prevented it?’ A data repository to help answer these questions is often required to determine root cause.
How do you choose what to focus on to increase your availability? Utilize the downtime tracking application to understand what issues are causing setbacks, outages, and downtime and how they are impacting your OEE.
The M-Powered iMFLUX Module is the cutting edge technology of adaptive processing control. Backed by years of processing exploration, the M-Powered iMFLUX Module will not only help to improve part quality but also decrease the energy required to produce it. As part of the M-Powered suite of tools, the iMFLUX Module and molding technology will allow for a number of real-time adjustments to mold and materials changes. These real-time adjustments coupled with the other M-Powered analytical tools are designed to assist in improving OEE through adaptive process control. With iMFLUX, variations in process are a thing of the past. iMFLUX combines advance data collection with intentional solutions so that your assets are as effective as possible and further assisting your team in maximizing productivity. Using iMFLUX, molders can increase productivity by up to 50% on existing injection molding machines. The process is ideal for most molding applications, but is especially advantageous for wide specification materials, recycled materials, and can help a biomaterial work for many more applications. This will continue to be an increasing sustainability focus for molders and brand owners.
Measuring quality accurately takes a coordinated effort from quality managers, six-sigma black belts, machine operators and other various personnel. When a quality escape happens, it can be tricky to assess and coordinate with processing data after the fact to determine root cause. Molders need answers quickly to understand the possible affected product, what could have caused the issue and in some cases, quickly eliminate potential root causes.
Downtime is a costly combination of people, parts, unnecessary maintenance operations and unmade product. Machine data tells a story of a drifting component and sequences that can identify unique signatures. Studying this on a large scale can be difficult, but when combined with thousands of machine years, machinery expertise, and a team of data scientists, it is possible to prevent downtimes altogether.
Proactive preventative maintenance (PM) generally requires dedicated personnel and parts; however it can be difficult to showcase ROI with the hope that it shows up in performance improvements and uptime. Aggregated real-time usage reports are the next step to ensure equipment is maintained properly and cost-efficient.
Setting up a good process, validating and maintaining its integrity are critical to part quality and identifying changes in operation. Controls typically provide the ability to monitor set points and variation, but this can become difficult to manage and monitor on a machine by machine basis.