Industry Insights

Finding the extra production line(s) hidden in your factory (Part 3 of a series)

In two previous posts on the OEE metric and how to find the extra production line(s) hidden in your factory, we discussed the factors that go together to measure Overall Equipment Effectiveness. Restating a simple progression from the end of the last post, generally you monitor line performance first. Often, simply being more aware of what’s happening on your production lines will expose some opportunities for quick wins. As you improve your ability to monitor your production lines, you can begin to predict (and prevent) future issues, and finally optimize your production capability.

 

The gotcha with that progression is that manual monitoring mechanisms have to be maintained, or they will slip. Manual systems can be slow to react, losing minutes or even hours of production time. And manual monitoring systems don’t produce the data that will help you prevent future downtime, let alone predict it. We need data to be able to automatically detect and alert the production staff to anomalies, and we need to collect and organize that data so we can move past mere detection to prediction and optimization.

 

In my experience, a lot of companies have allowed themselves to get stopped right there. They recognize that real-time data from their production lines would let them react faster and more positively, improving their ability to predict and prevent the kind of situation that leads to downtime. They might even recognize that most of the data they need is already being generated by the equipment they’ve installed on their lines. (This is particularly true in electronics assembly, where modern pick-and-place equipment, soldering machines, and other production equipment often have dozens, if not hundreds, of sensors to keep the machines running and help diagnose failures.) But they haven’t acted to harness that data and make use of it.

 

Why don’t they take that next step – to build an IT backbone so that the data they are generating can be used to improve their operations? I think it boils down to a simple lack of knowledge – and, in some cases, perhaps a little fear and trepidation left over from early proposals when the technology was still new. The fact that many companies are missing is simple: one of the best use cases for the Internet of Things (IoT) is to improve factory performance.

 

And that’s what we’re talking about – IoT or IIoT, depending on whether you want to tack the word “industrial” on in front. A few years ago, IoT may have looked like “too much money for too little benefit,” but if so, the ratio has reversed itself. In fact, the cost/benefit ratio has shifted to where building an IoT infrastructure (and using it!) may be one of the best investments you make, especially if your OEE is well below world-class levels (in the mid-80% range). IoT data, and analytics that use that data, can enable a permanent improvement in OEE – freeing up as much capacity as you might get from an extra production line or two, and saving you the millions of dollars that new production lines would require.

 

Instrumenting existing equipments with wired or wireless sensors is the most important step in establishing an IoT Infrastructure as data producer. Once we have that, my colleague Peter Xu explains that “establishing an IoT Platform as the data consumer in a manufacturing setting could be as simple as setting up an IIoT gateway/box, which retrieves data with its built-in connectors for industrial equipments and sensors, performs some local analytics, and then feed the massive amounts of data generated either into an “on-premises” data lake/private cloud,  or into a public cloud like IBM’s Watson IoT platform.

 

Having said this, the process of establishing an IoT infrastructure is relatively straightforward (and not terribly expensive). Essentially, you need:

  • IoT sensors – as mentioned, since electronics manufacturing relies heavily on automated machinery, many electronics manufacturing lines will already have many of these. So will most other industries that have set up automated manufacturing lines in the last couple of decades. As necessary, a company can add a sensor here or there — to monitor conveyor speed, for instance, or temperature & humidity on the line, or count pieces as they pass by. Sensors are reasonably inexpensive — some are under $10, and even the most sophisticated are typically under $50. (There are a few exceptions, especially for monitoring things like fluid pressures.)
  • Routing This can be done in several different ways. Most companies will purchase an IoT router (see footnote[1] for some options). These work very much like the internet routers used to connect laptops in a home or business, but they’re focused on transferring the data from IoT sensors to a single destination. Some of them have up to 80 ports — which is usually enough for even a complex manufacturing line. Another option is to use wireless technology. This uses LTE technology, just like 4G mobile telephones. It’s a little more expensive but much more flexible (valuable if you reconfigure your production equipment frequently, or you just don’t want the bother of extra wiring.)
  • An IoT database. The data created by the sensors, and consolidated by the router, needs a destination. The easiest, and generally cheapest, way to provide this is to simply send the data to the cloud. IoT data in the cloud can be stored so that the data can be observed over time. A client could also store the IoT data on a server.

 

The easiest way to establish an IoT Platform, so that the data can be “consumed” and made useful, is to send the data to an existing cloud offering. There, the data will be harmonized and standardized, so you won’t have to worry about which supplier’s sensors are being used in which locations.  Once the data has been standardized and stored, it can be accessed by analytical tools on the IoT platform. Those tools can be descriptive, predictive or prescriptive; and we can also build a “digital twin” for each line so that plant managers can tell exactly what is going on down on the plant floor whether they are in their office, or halfway around the world.”

 

Of course, there isn’t much value in establishing an IoT infrastructure if the IoT data isn’t used for something, so let’s state that the purpose of setting up that infrastructure is to use the data from all of those sensors for one or several of these purposes:

  • Detection of an event or a “limit breach” (an “event” could be that a line has stopped, for instance; a “limit breach” might be that a temperature sensor is reading too high or too low).
  • Monitoring of Key Performance Indicators (this is really another sort of detection — but of a condition rather than an event. Examples might be that a production line is only producing 80% of expected volume, or that the temperature has been gradually increasing, even though it hasn’t gone past a limit yet).
  • Analytics built using the data from a sensor or a combination of sensors – there are a variety of advanced analytical tools that go past reacting to an event, or taking preventive actions due to monitoring, and begin to utilize predictive Advanced Manufacturing Analytics can tell you things like “your ____ machine is going to shut down in three hours unless you perform _____ maintenance on it.”
  • Cognitive tools that can go beyond even the algorithms used in analytics, and learn from watching the data over time. Cognitive requires that IoT data be available for an extended period of time, so that it’s possible to learn from it.

 

All of these purposes are accomplished on an IoT platform. The platform will “ingest” and process the data that is generated by the IoT infrastructure. The platform may include a symantic model, which can provide a unified view of the data generated by the sensors. The IoT infrastructure (the sensors, equipments and wired/wireless networking) essentially is the hardware Data Producer, and the IoT platform is the software Data Consumer.

 

The software tools on the IoT platform continually monitor and analyze the masses of data coming in from the IoT sensors. The tools available can help you:

  • Harmonize/ standardize the data. Not all sensors broadcast data in the same way, and different brands of machinery use different standards as well. For example, you’ll need to know whether temperature sensors are reading in ˚F or ˚C.)
  • Reduce downtime – so that actual production time comes close to planned production time.
  • Maintain expected cycle times – so that your manufacturing lines actually produce the planned number of products in the planned amount of time.
  • Avoid quality defects – so that the products manufactured meet requirements and can be shipped to customers.

 

All of which, of course, will lead you to world-class Overall Equipment Effectiveness.

 

[1] IoT gateways and routers are available from companies like MICA (https://www.harting-mica.com/en) Telit (https://www.telit.com/industries-solutions/smart-factoryindustry-4-0/), Cisco (https://www.cisco.com/c/en/us/solutions/internet-of-things/iot-routers.html ) and others. Wireless IoT infrastructure is available from companies like Sierra: (https://www.sierrawireless.com/products-and-solutions/sims-connectivity-and-cloud-services/iot-cloud-platform/ )

 

Sr. Managing Consultant, Electronics Center of Competence at IBM

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