As they say, knowledge is power. Scouting out the upcoming industry trends is an important aspect of being a digital transformation leader as newer technologies disrupt the status quo and establish new norms in the manufacturing industry. Here are a few emerging trends in the industrial analytics space that can boost your current strategy and keep you ahead of the competition:
#1: HARNESSING THE POWER OF OT CONTEXT FOR IT/OT CONVERGENCE
While IT/OT convergence has been practiced for decades, manufacturing organizations have yet to master it. IT/OT convergence should start with capturing the right OT data context where the data is produced. Remember “garbage in, garbage out”? Often, data scientists spend significant cycles with engineers to get the right OT context—after that fact—while engaging in the data preparation phase of building analytical models. But it doesn’t have to be so difficult. To make it easy, OT engineers should be able to configure the OT data tags to be captured at runtime, indicate the frequency of collection, and setup a logical structure or a common information data model for packaging the OT data per the needs of data scientists.
#2: INCLUDING EDGE IN YOUR ANALYTICS STRATEGY
While the cloud gets all the glory in the technology curve, the edge has been relatively underutilized in the analytics story. Considering many important industrial analytics use cases require a hardware control system response time within milliseconds, sending data to the cloud, retrieving the insight back, and then acting isn’t enough—the network delay and data transmission costs are too high. So, it makes sense to offload data processing and analytics at the edge, so that appropriate prescriptive action can be taken in real-time at the hardware control layer—which can make all the difference. Leaders in analytics are also deploying intelligent edge gateway solutions — hardware or software – as part of their strategy.
#3: EMPOWERING OT PROFESSIONALS WITH VISUAL & INTUITIVE MACHINE LEARNING TOOLS
While Machine Learning has been the main stay of data scientists, a new crop of technology-savvy plant engineers and operators is emerging—one that is ready to apply their domain OT knowledge to the machine learning space. These citizen data scientists are comfortable with the basics of data management and are leveraging visual tools for preparing data pipelines, configuring ML models, deploying them, and ultimately scoring them at runtime operational data—all near the plant equipment. OT professionals are even starting to use off-the-shelf, targeted machine learning applications for predictive maintenance, predictive KPIs, or anomaly detection use cases to maximize business outcomes in manufacturing. Who needs an army of data scientists now?
#4: APPLYING MACHINE LEARNING TO PRODUCT LIFECYCLE INTELLIGENCE
Product lifecycle intelligence (PLI) is an evolution of Product Lifecycle Management (PLM) that applies artificial intelligence and automation to help PLM users extract meaningful insights from product data in the manufacturing plants. By tapping these meaningful product insights powered by AI/ML, industrial manufacturing organizations can bridge the gap in PLM analytics capability today—allowing them to understand current product performance, historical averages, and the variances across different business units and functions in plants. These product lifecycle insights can help manufacturing organizations to develop more meaningful customer experiences, while driving superior business objectives and product value.
Stay ahead of the curve
Beyond the potential of these new developments, engaging a trusted partner with proven credentials and successful customer implementations can unlock even higher organizational value from planned industrial analytics implementations. Rockwell Automation has a world class consulting arm that offers strategy, technology and implementation services, so that you don't have to deal with multiple players in a disconnected way. Our human centric process begins with identifying your “north star” and key stakeholders to chart out the right transformation roadmap, starting with the highest impact use cases.
