The IIoT Success Formula: Leverage software to convert new workplace capabilities into business value
Several years ago, the Industrial Internet of Things (IIoT) era launched with a big bang. Much talk was generated regarding how the industry would be forever changed. Although the speculation was somewhat overhyped, most of the industry had a sense that a new and different way of conducting business was about to emerge. However, most businesses were justifiably cautious and decided, from an implementation standpoint, to wait and see. Like any big innovation, the adoption takes longer than expected.
Now, a few years later, we see broader acceptance and are entering into a period of practical technology application. We are now seeing a significant change in the way automation modernization is being implemented. The traditional industrial mindset of the big project, with big cumbersome equipment, big disruption and high cost is beginning to take a back seat. Instead, plant managers are taking incremental steps and are being offered packages of focused functionality (at least in the initial stages) that offer a software platform and services on the cloud or on-premise. The start-up investment is smaller which helps reduce business risk. And new choices can now be made to manage the application either on or off-premise. In effect, what was once a CAPEX investment is now migrating into OPEX.
The benefits of IIoT are based on the ability of industrial equipment to connect to a network so that data can be collected and then analyzed. The analysis of that data then enables the execution of more informed decisions which then influences both efficiency and productivity. Industries need to choose and decide which partners can help them both monetize short-term IIoT productivity gains and manage long-term business model transition. Below are some first steps to consider when planning IIoT-related deployments:
Connect – Choose a partner with experience in the industry who can connect not only to their own products, but also to products throughout your target industrial site. Make sure the connected products are also cyber secure.
Collect – Choose software platforms capable of interoperability so that more can be done with the data that is gathered via sensors. Look for solutions that connect to edge controllers that are close in physical proximity to the local operation. Plan to collect data that has tactical value and that also supports higher level organization-wide analysis.
Analyze – Select solutions that involve both automated analysis and human analysis. From time-to-time you will encounter unusual situations that still require intervention from human experts to make sure problems are properly diagnosed and addressed. Select analysis tools that are advanced in terms of asset management, so that predictive maintenance can be performed. This reduces cost and limits unplanned downtime. And, don’t analyze what has already been analyzed before, in the sensor, on the edge or elsewhere.
Improve Existing Infrastructure – Real life examples of implementations by early adopters are now opening the door to others, seeking operational efficiency improvements or changing their business model.
Consider the example of a traditional boiler. A boiler typically has very few process critical measuring instruments that connect to the DCS system. In such a scenario, the temperature distribution within the boiler is never 100% optimized. Acquiring more data from inside that boiler will result in a more productive process. When some inexpensive sensors are placed on the boiler, the temperature profile is now sent to the cloud. Data analytics are performed and the data indicates that modifications are needed in the process control to increase efficiencies. This approach is called secondary sensing. This alters the original automation structure without a large capital investment. Start with a prototype and then, if successful, replicate across similar applications in the plants.
IIoT applications are now making industry more productive and efficient. The value lies in converting data into knowledge and then knowledge into process and business model improvement, thus enhancing competitiveness and business growth.
This article was written by Peter Herweck, EVP Industry Business, Schneider Electric. Peter began his career at Mitsubishi where he served as Software Development Engineer. In 1993, he joined Siemens in the Motion Control for Machine Tools unit where he led various R&D projects.