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    Graham Immerman
    Graham Immerman MachineMetrics / August 08, 2018

    What's driving manufacturing innovation today? | MachineMetrics

    What is Driving Manufacturing Innovation Today?

    Innovation, in factories of all sizes, is coming down to technology. Industry 4.0, with the use of big data to manage and grow manufacturing, is where it’s at. With data from the shop floor being recorded and analyzed in greater quantity, production can be monitored throughout the cycle and deviations from established standards can be noted in real-time, by anyone who needs to be in the know, from anywhere ranging from the shop floor to the C-suite.

    Collecting data to monitor the production cycle has a lot of uses, but one of the main ones is to achieve predictive maintenance.

    How does predictive maintenance enhance manufacturing?

    By analyzing production data from the machines on the floor, patterns can be discovered in the operation of any one machine that will enable the prediction of when maintenance will be required. Rather than reacting by fixing a machine that is already down or which is producing faulty parts, the data being generated from the machine and run through analytics software will notify operators of deviations well before they can cause a disruption in production.

    In this way, not only is the problem quickly and easily analyzed—a task that used to take ages when being executed manually—but maintenance of the machine can be scheduled for a time that will cause the least disruption to production. This kind of planned downtime, versus reactive repairs, is far less expensive and more likely to lengthen the lifespan of any given machine.

    Not only is production efficiency better managed with predictive maintenance but quality control is also managed proactively.

    Data collection impacts quality control too

    The quality of the output can be better controlled, allowing a machine to produce an order with little waste from subpar output.

    Imagine this scenario before data-driven manufacturing: An order for 15,000 parts comes in with a short lead time. The machines needed for this production cycle are engaged but about halfway through production, a part in one of the machines begins to experience vibrations. It’s not visible to the operator and they allow the cycle to continue until some hours later, a quality control inspection reveals that about 1,000 of the parts produced are substandard. Those parts are discarded and the delivery time is delayed, resulting in an unsatisfied customer.

    Now imagine that scenario on a shop floor with data monitoring in place: That machine would be known to have this issue, thanks to the ongoing data collection and a preventive review of that part would have been engaged before the order was started, to ensure full function throughout the production cycle. However, even if that did not take place, when that vibration began, it would notify the operator that a part was behaving outside of normal parameters. The operator would be flagged to the problem and be able to rectify it before too many substandard parts were produced, avoiding a lot of waste and a delay in delivery of the completed order.

    Data-driven manufacturing is the next evolution

    The Internet of Things (IoT) and the collection of data for the purposes of analysis is how manufacturers will be able to remain on the front line of their respective industries. Collecting real-time data from independent machines on the factory floor and disseminating it to everyone from operators to the CEO, through cloud-based technology, enables decisions to be made quickly and accurately, resulting in lean manufacturing throughout the organization.

    The more data that is collected and analyzed, the better processes can be put in place to manage order from supply to production, including operations and maintenance, and through to delivery. On time delivery to customers, thanks to the avoidance of unplanned downtime and the ability to schedule maintenance for least production impact, is key to profitability.

    Add to that the reduction in waste because machine maintenance issues that affect the quality of the output are detected far before too much production time has lapsed, and you’ve got an important impact to the bottom line.

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