The Need for Data and System Interoperability in Smart Manufacturing

As technology advances at a faster pace and the world becomes flatter, manufacturing operations are generally focused on efficient production to maximize profitability for the organization. In the new era of industrial automation and smart manufacturing, organizations are turning to data generated on their plant floors to make sound decisions about production and process improvements.

Smart manufacturing improvements can be divided roughly into six different segments: Predictive Analytics, Track and Trace, Error Proofing, Predictive Maintenance, Ease of Troubleshooting, and Remote Monitoring.IOLink-SmartManufacturing_blog-01To implement any or all of these improvements requires interoperable systems that can communicate effectively and sensors and devices with the ability to provide the data required to achieve the manufacturer’s goals. For example, if the goal is to have error free change-overs between production cycles, then feedback systems that include identification of change parts, measurements for machine alignment changes, or even point of use indication for operators may be required.  Similarly, to implement predictive maintenance, systems require devices that provide alerts or information about their health or overall system health.

Traditional control system integration methods that rely heavily on discrete or analog (or both) modes of communication are limited to specific operations. For example, a 4-20mA measurement device would only communicate a signal between 4-20mA. When it goes beyond those limits there is a failure in communication, in the device or in the system. Identifying that failure requires manual intervention for debugging the problem and wastes precious time on the manufacturing floor.

The question then becomes, why not utilize only sensors and devices with networking ability such as a fieldbus node? This could solve the data and interoperability problems, but it isn’t an ideal solution:

  • Most fieldbuses do not integrate power and hence require devices to have separate power drops making the devices bulkier.
  • Multiple fieldbuses in the plant on different machines requires the devices to support multiple fieldbus/network protocols. This can be cost prohibitive, otherwise the manufacturer will need to stock all varieties of the same sensor.
  • Several of the commonly used fieldbuses have limitations on the number nodes you can add — in general 256 nodes is capacity for a subnet. Additional nodes requires new expensive switches and other hardware.

IOLink-SmartManufacturing_blog-02IO-Link provides one standard device level communication that is smart in nature and network independent, thus it enables interoperability throughout the controls pyramid making it the most suitable choice for smart manufacturing.

We will go over more specific details on why IO-Link is the best suited technology for smart manufacturing in next week’s blog.


Predictive Maintenance for Zen State of Manufacturing

Industry4.0In a previous entry, Mission Industry 4.0 @ Balluff, I explained that the two primary objectives for Balluff’s work in the area of Industry 4.0 are to help customers achieve high production efficiencies in their  automation and achieve  ‘batch size one’ production.

There are several levers that can be adjusted to achieve high levels of manufacturing efficiencies in the realm of IIoT (Industrial Internet of Things). These levers may include selecting quality of production equipment, lean production processes, connectivity and interoperability of devices, and so on. Production efficiency in the short term can be measured by how fast row materials can be processed into the final product – or how fast we deliver goods from the time the order comes in. The later portion depends more on the entire value-chain of the organization. Let’s focus today’s discussion on manufacturing – inside the plant itself.  The long-term definition of production efficiency in the context of manufacturing incorporates the effectiveness of the production system or the automation at hand. What that means is the long-term production efficiency involves the health of the system and its components in harmony with the other levers mentioned above.

The Zen state of manufacturing – nothing important will come up on Google for this as I made this phrase up. It is the perfect state of the entire manufacturing plant that continues production without hiccups all days, all shifts, every day. Does it mean zero-maintenance? Absolutely not, regular maintenance is necessary. It is one of those ‘non-value added but necessary’ steps in the lean philosophy.  Everyone knows the benefits of maintenance, so what’s new?

Well, all manufacturing facilities have a good, in some cases very strictly followed maintenance schedule, but these plants still face unplanned downtimes ranging from minutes to hours. Of course I don’t need to dwell on the cost associated with unplanned downtime. In most cases, there are minor reasons for the downtime such as a bad sensor connection, or cloudy lens on the vision sensor, etc. What if these components could alert you well in advance so that you could fix it before they go down? This is where Predictive Maintenance (PdM) comes in. In a nutshell, PdM uses actual equipment-performance data to determine the condition of the equipment so that the maintenance can be scheduled, based on the state of the equipment. This approach promises cost savings over “time-based” preventive maintenance.

PowerSuppliesIt is not about choosing predictive maintenance over preventive maintenance. I doubt you could achieve the Zen state with just one or the other. Preventive and predictive maintenance are both important – like diet and exercise. While preventive maintenance focuses on eliminating common scenarios that could have dramatic impact on the production for long time, predictive maintenance focuses on prolonging the life of the system by reducing costs associated with unnecessary maintenance.  For example, it is common practice in manufacturing plants to routinely change power supplies every 10 years, even though the rated life of a power supply under prescribed conditions is 15 years. That means as a preventive measure the plants are throwing away 30% life left on the power supply. In other words, they are throwing away 30% of the money they spent on purchasing these power supplies. If the power supplies can talk, they could probably save you that money indicating that “Hey, I still have 30% life left, I can go until next time you stop the machine for changing oil/grease in that robot!”

In summary, to achieve the zen state of manufacturing, it is important to understand the virtues of predictive maintenance and condition monitoring of your equipment. To learn more visit