Leverage Your Existing OT System As You Create Your Digital Transformation Roadmap

Malisko Engineering's director of operations provides a guide to understanding your company's essential stepping stones to digital transformation and how to utilize the automation technologies already implemented in your operations.

Daniel C. Malyszko, Director of Operations and IIoT Consultant, Malisko Engineering
Daniel C. Malyszko, Director of Operations and IIoT Consultant, Malisko Engineering

Much has been done in manufacturing over the past several decades to noticeably improve production efficiency, product quality, and safety by deploying automation technologies to process and work cells. Nonetheless, manufacturers continue to seek out ways to gain the competitive edge in their respective industry by focusing on what happens on the plant floor through digitization. As they discuss, strategize, and create their “digital roadmap,” they sometimes neglect the stepping stones existing in the automation technologies already implemented in their manufacturing operations. Let’s examine just a few to consider and then build from there.

Many manufacturers, over the years, have incorporated a data historian(s) in their automation platform. Data historians can be an extremely useful tool for production, engineering, maintenance, and management. Data historians already contain a wealth of data, but it likely that the raw archived data is not yet contextualized. A good first step is modeling the plant floor assets along with their attribute and event data to build a more comprehensive picture of what is really going on based on actual raw process variables residing in the data historian. Data preparation at the source using traditional OT tools can and should be part of a digital strategy.  If you don’t prepare the data and contextualize it in an asset modeling effort, you most likely end up with the same old story of, “I’ve got tons of data, I just don’t know what to do with it.” In fact, there are some manufacturers developing tools to organize and contextualize data at the controller level where the original tags reside. 

An area we see all too commonplace in many processes is the continued reliance on paper records for production tracking and for meeting regulatory compliance, such as clean-in-place (CIP), sterilize-in-place (SIP), and pasteurized milk ordinance (PMO) in the dairy industry. In almost all cases the instrumentation and control programming are already in place to effectively provide the necessary CIP, SIP, and PMO functionality; yet operations continues to rely on traditional ink and paper-based chart recorders for verification. Replacing paper records with electronic records via harvesting data from the historian together with automated reporting can be the low hanging fruit of digitalization already sitting in your lap.        

A digital initiative cannot ignore the operational technology (OT) systems already in place. Often companies are only considering new purpose-built sensors like vibration, power consumption, and motor temperature as their digital strategy. While some IoT sensors can provide value, they should be considered just another data source in the larger digital transformation plan. Don’t discount the value your existing sensors—such as level, pressure, flow, conductivity, turbidity, and torque—already have to contribute to your digital strategy.

A common challenge to a digital strategy is deciding on a platform for data aggregation and visualization. This will likely not be the traditional SCADA system you already have in place. It will need to be a platform that can be readily accessed across the enterprise, is scalable, and can be secured. Often, these platforms require non-OT skillsets for data collection at scale and proficiency with advanced analytics. Most likely the collection and basic data pre-processing will reside on the edge of your automation platforms.  Applications of these non-OT skills and non-traditional computing platforms deserve a conscientious evaluation for supportability and sustainability.

Beyond deploying analytics at the edge, the industrial automation market is starting to catch up to the consumer market with regards to adoption of cloud data storage and computing. The cloud within the industrial segment is usually associated with digital transformation initiatives including a heavy emphasis on analytics, artificial intelligence (AI), and machine learning (ML). The application frameworks for these technologies are often hosted in the cloud, but more importantly is that these applications are built in a completely different technology stack than what the traditional industrial automation market is accustomed to. Fluency in full-stack web application development will help to navigate these opportunities.  Having deeper knowledge of data modeling and data ingestion at scale will be required to effectively apply these technologies to manufacturing operation improvements.

System Integrators knowledgeable in manufacturing and advanced technologies can be an asset to manufacturers navigating the many options to dealing with leveraging existing components of an automation system as well as helping create a digital transformation roadmap. The role of the SI is to understand the client’s business objectives so the SI can provide relevant guidance to the client on the “here and now,” “what’s next,” and “what’s after next.”

Daniel C. Malyszko is Director of Operations and IIoT Consultant at Malisko Engineering, a certified member of the Control System Integrators Association (CSIA). See Malisko Engineering’s profile on the CSIA Industrial Automation Exchange.

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