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Title: Do You Know Predictive End Cover Making Machine Maintenance is Important?
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Blog Entry: Predictive End Cover Making Machine maintenance helps optimize planned downtime Planned downtime can encompass everything from machine cleaning and oiling to replacement of parts that are known to fail on a regular basis. This kind of preventive maintenance reduces the risk of unplanned downtime. Just like taking care of your computer and sweeping it for viruses, or keeping other appliances clean in your home, you’ll get more efficient and better quality output from a well-serviced machine. Thanks to the data collected in machine operations, preventive maintenance can be scheduled regularly and at times that will have the least impact to order production. There is also the added benefit that adequate maintenance of this nature will invariably extend the life on a machine that would be difficult, and costly, to replace. Maximizing uptime and the life of a component will ultimately result in significant cost savings. Predictive maintenance limits unplanned downtime According to a Wall Street Journal post, “Unplanned downtime costs industrial manufacturers an estimated $50 billion annually.” Using predictive maintenance to limit this cost is critical in highly competitive manufacturing industries. Inasmuch as scheduled preventive maintenance can ensure that machines run smoothly most of the time, monitoring machines digitally collects reams of data that, when analyzed, will show patterns on any given machine. This kind of pattern detection, based on historical data, can help to identify a machine that is likely to experience an outage, and for which maintenance can be planned proactively. Predictive maintenance can help to optimize equipment lifetime Being able to monitor a machine’s efficiency, output and quality over time will reveal data that will identify when a machine requires maintenance, as noted above, but will also help identify when a machine is reaching the end of its life. As machines age and depending on their level of use, the maintenance schedule will change, which can be managed through predictive maintenance. Parts of the machine will respond to production stress differently over time. The eventual increase in maintenance that is predicted through data patterns will reveal when a machine is reaching a tipping point on cost vs. performance. The need to eventually replace large parts of a machine, or the entire unit, is made manageable by being able to forecast that need and plan for it, both from a cost / budget and time / effort point of view. Predictive maintenance can help optimize employee productivity There are many ways that predictive maintenance optimizes employee productivity. Firstly, let’s just look at the the cost of the labor itself: when repairs are scheduled, the amount of time needed for repair is reduced because of a smaller number of component replacements instead of entire equipment replacement. Also, the frequency of repair for critical failure of equipment will be reduced and the amount of “critical callouts” will be greatly reduced. From the employee’s perspective, reduced breakdowns and accident avoidance systems which can alert or even halt equipment when there is danger to a worker, can dramatically improve factory conditions and minimize worker injuries. Furthermore, down-times, operation with sub-optimal parameters not only impact output but also impact employee morale. It is stressful to rush to solve problems when they arise. Predictive maintenance minimizes such instances. Predictive maintenance can help increase revenue The advantages of predictive maintenance we’ve covered above in the end all have the same goal: increasing the bottom line. With less maintenance on good components and quicker repair of faulty components, repairs can be more effectively handled, thereby reducing repair time.One of the most comprehensive studies on potential of industrial analytics like predictive maintenance was conducted by McKinsey in 2015, and they uncovered the opportunity of for the following improvements: 10-40% reduction in maintenance costs: Since planned maintenance is based on a schedule, there will be cases when maintenance tasks will be performed when they are not needed. Predictive maintenance can prevent such inefficiencies. 10-20% reduced waste: Sub-optimal operation that is not detected, can result in wasteful production. Raw material, energy, labor costs and machine time get wasted in such instances. Predictive maintenance systems can uncover issues that can result in waste before they arise. 10-50% new improvement opportunities uncovered: Once data collection becomes automated, new insights on process optimization opportunities can be uncovered daily through advanced analytics. The end can is also one of our products. welcome to your come and purchase!