The first step to solving any problem is defining the problem. For downtime, knowing when, where, and how downtime occurs is essential to knowing how to prevent it. An early step toward reducing unexpected production backups or outright downtime can be achieved by carefully and accurately tracking when and where downtime occurs.
With MachineMetrics, we've made tracking, understanding, and improving processes that lead to downtime as easy and user-friendly as possible, so you can get to the bottom of your downtime issues and have a better understanding of how to address issues and improve processes.
Let’s take a real-life example from one of our customers.
The average time to solve a problem was hours and sometimes even days for Brian* and his team. He was struggling with his operators because they weren’t addressing issues quickly enough. Instead, they were constantly requesting his help to resolve the issues and at times that was often hours after the incident occurred. Further, Brian would not always be the right person to solve the problem, although his position as a factory floor manager allowed him to direct the right people to help. However, this lack of real-time problem-solving leads to something all factories fight daily: increased downtime.
What Brian needed was a way to alert different people for different reasons and in today's’ notification-driven technology world, we have the capability to do just that.
Using MachineMetrics, Brian and his team were able to set up notification workflows for virtually any issue that occurred on the shop floor. In the end, it was all about letting the right person know at the right time, not just what’s happening, but what exactly they could do about it to improve a process or the operation of the machine.
Brian even went a step further and worked with his team to purchase inexpensive mobile phones that could be used as a notification delivery system for the MachineMetrics notifications. For example, if a machine went down due to a tool break, the system would send a notification directly to the tooling engineer without the engineer having his own personal, potentially distracting phone on the shopfloor.
To make this a bit “gamified” and add some urgency to resolve problems, Brian’s team leveraged MachineMetrics’ workflows to create a cascading set of notifications that escalated problems based on time. So, if the problem wasn’t resolved within 10 minutes, the manufacturing engineer was notified. If it wasn’t resolved within 30 minutes, the floor manager was notified, and if there was no resolution after 60 minutes, the executives were notified.
For those familiar with lean manufacturing, this workflow is in reality, a modern twist on the classic andon system. The idea behind the andon cord was to stop the production line and call for attention, allowing the team to swarm the problem to fix it. In this instance, rather than shutting everything down, Brian set up a system with MachineMetrics to notify the right resources immediately to solve the problem at hand.
Of course, we are all used to notifications thanks to the social media age, and we need to be careful not to become numb to the notifications that are important for our successes. This smartly planned escalation system creates the right sense of urgency for the right people at the right time.
Brian and his team refer to these buzzing phones as the “hot potato,” so that they aren’t the one holding it and they get problems resolved as soon as possible. Technologies like MachineMetrics make those notifications possible in addition to providing historical reporting on what happened. Through the Downtime Pareto, Brian and his team can identify which reasons for downtime are most prevalent/persistent across machine types, shifts, etc. Also, looking at unplanned vs planned downtime historically can provide insight and enable additional actions to be taken to optimize machine operation.
This system created by Brian and his team by leveraging MachineMetrics’ industrial IoT platform has not only enabled insight into what is actually happening, but also real-time communication workflows that generate real, actionable process changes that help solve problems faster and improve their company’s bottom line. Using MachineMetrics notifications, this team was able to create notification systems that improve reaction times when downtime events occur, when setups run over expected times, or when symptoms of a potential downtime event is sensed. This is only one of many use cases for transforming analytics into action, improving systems, processes and communication, all of which in the end optimize machine operation and increase profitability
*Not his real name.
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