Increase uptime and reduce maintenance costs using data driven maintenance schedules.
Many companies still schedule their maintenance jobs based on fixed intervals. Or even worse: they are taken by surprise with machine or mold failure. Molds have an increasing maintenance requirement during their lifespan, and finally reaching the end of their predicted useability.
By applying IoT and Big Data technologies this process can be optimized. Our customers typically go through the following phases when implementing these technologies:
When an incident happens, maintenance is performed causing unexpected downtime.
During this phase, maintenance is scheduled using a fixed interval. For example, every three months.
Once enough data about the usage of a machine is gathered, machine learning is used to predict when breakdowns are about to occur. This information is being used to schedule maintenance in time to prevent this.
When more data is collected, a maintenance schedule can be created which prescribes specific maintenance jobs on an individual component level. Taking the following variables into account: the advised and actual measured lifespan of components, detected wear and tear, external influences such as weather conditions, type of resins used or executed operations, etc.
In order to realize this, many factors need to be considered, such as:
- Maintenance recommendations of manufacturers
- Actual runtime data from MES
- IoT data
- Machine learning models
- AI analytics
When you start to analyze these kinds of data, real-time alerts can be generated in order to reduce unexpected downtime while at the same time eliminating unneeded maintenance. In case of customer owned molds, replacement alerts or additional maintenance cost can be discussed with the customer.
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