- August 20, 2019
By Michael Risse, Seeq Corporation As manufacturers transition to Industry 4.0, it becomes critical to accelerate the extraction of insights from ever-exploding quantities of process data.
By Michael Risse, CMO and Vice President, Seeq Corporation
The transition to Industry 4.0 is under way, the next stage of industrial automation maturity following decades of deploying SCADA and distributed control systems (DCSes). The next stage by any name—Industry 4.0, IIoT or Digital Transformation—will be enabled by the proliferation of cost-effective wireless sensors, wireless communication networks and flexible data storage in the cloud.
According to a recent McKinsey survey, most manufacturing companies have pilot projects in IIoT and other digital initiatives, and they are now charting strategies for how to scale up their efforts. Sensors, networks and the cloud are all pieces of the puzzle, but not the end-all, as the adoption of advanced analytics software will be necessary to unearth key insights from the mountains of process data being generated by these technologies.
The Promise of IIoT
Today, industry plants and operations are overwhelmingly hard-wired as process manufacturers are not giving up the wired sensors that monitor mission-critical functions and processes, nor should they. Wired connections enable sensors to directly and reliably connect to distributed control, SCADA and HMI systems. It took enormous resources to wire these facilities over the last few decades, both in terms of costs and manpower. There are billions and billions of wired sensors at work in the field today, and they are not going anywhere anytime soon.
At the same time, manufacturers have many more unconnected assets, along with a desire to expand monitoring into the supply chain and other disconnected resources. For many manufacturing companies, these unconnected assets are the focus of the IIoT opportunity. Gaining visibility into assets and connecting them to existing systems enables many potential benefits: predictive maintenance, process improvements, increased efficiency, optimized energy usage, etc.—all enabled by empowering employees to make better decisions, faster. With advances in wireless sensor hardware and network protocols (Figure 1), it is now affordable to connect many of these assets wirelessly. Tiny devices with long battery life can go anywhere. The potential benefits for manufacturing organizations, as listed above, are enormous.
Figure 1: WirelessHART and other wireless mesh networks are in widespread use, enabling fast and inexpensive connectivity for monitoring stranded assets. Courtesy of FieldComm
As Deloitte analyst Robert Schmid explained in a recent issue of Wired (ironically enough) magazine article, IIoT has the potential to transform manufacturing’s traditional linear supply chains into vibrant, interconnected systems—digital supply networks—that can more readily interact with ecosystem partners.
Fast adoption is expected. The global IIoT market is expected to reach a value of $922.62 billion by 2025, according to a Million Insights report. According to the report, factors contributing to this rapid growth are the usual suspects: cost-effectiveness and easy availability of wireless devices for processors, sensors, and other connected systems. Globally, the report predicts the industrial IoT market will grow at a compound annual growth rate of 27.8% between 2014 and 2025.
Beyond cost savings, wireless has a number of advantages over wired options. Installations are less complex and easy to expand, and wireless sensors require much less power and space. Wireless sensors can also work in hard-to-reach places, making them particularly well-suited to process environments such as oil rigs. They can improve worker safety by eliminating the need for people to monitor processes in dangerous settings. Wireless sensors don’t require change to the existing infrastructure, and they don’t disturb the wired assets essential for real-time control.
The good news is one doesn’t have to be an expert in sensors and sensor data to use these technologies. Vendors of these devices and connectivity systems are competing to offer services that will enable collection and storage of data, with the vendors or third parties providing additional data-related services. BCG, another consulting organization sees major potential in the monetization and sharing of collected data, writing in a recent report, “While digital ecosystems provide the underlying platforms, data ecosystems enable B2B companies in asset-heavy industries to generate additional revenues and build enduring competitive advantage with their IoT data.”
Cloud Storage Expands Elastically
The explosion in wireless sensors appeared on the scene at just about the same time as the option to store data in the cloud, a felicitous marriage enabling elastic storage that can shrink and grow as needed for maximum agility. Companies of all types, including process manufacturers, are moving their IT infrastructure and data to public and hybrid clouds to increase IT flexibility, speed responsiveness, and reduce complexity. Driving this growth are burgeoning data volumes and increased demand from compute-intensive workloads such as IIoT.
Storing large volumes of data in the cloud is increasingly, if not already, a “when” and not an “if” question for many companies. Consequently, the big public-cloud platform vendors like Microsoft, Amazon, and Google are paying more attention to the world’s largest sources of data. These enormous data volumes are attracting new players, and the storage location for an increasing amount of sensor data will be in the cloud versus on premise.
Going forward, the cloud will be the destination of choice for monitoring datasets collected from IIoT end points. This frequently is a more natural and easier option than trying to reroute data from carriers and wireless systems into IT systems and then to the cloud because data “born on the cloud” is the best option for many monitoring applications.
Monitoring data may be complemented or contextualized by connecting to other data sources—analytics applications, historians, MES, SCADA, databases, individual hard drives, and other systems—to get a complete view of all data. Accessing multiple sites from a cloud deployment of analytics software will facilitate cross-plant comparisons for yields, quality, and other key performance indicators.
Analytics workloads are particularly suited for migration because most use cases require the scalability, agility, time to market, and reduced costs provided by the cloud. Large process manufacturers will likely utilize a mix of public and private cloud offerings as well as on-premise components for analytics.
Using Advanced Analytics to Create Value from IIoT Data
Beyond wireless sensors and cloud storage as enablers of Industry 4.0 lies advanced analytics. McKinsey defines Industry 4.0 as “The next phase in the digitization of the manufacturing sector, driven by four disruptions: the astonishing rise in data volumes, computational power, and connectivity, especially new low-power wide-area networks; the emergence of analytics and business-intelligence capabilities; new forms of human-machine interaction such as touch interfaces and augmented-reality systems; and improvements in transferring digital instructions to the physical world, such as advanced robotics and 3-D printing.”
What this means in simple terms is manufacturers are already overwhelmed with the amount of data they are grappling with today, and they need a new generation of big data and machine learning enabled software to help their users get insights (Figure 2). This approach also can enable the integration of IT data science with OT expertise, a key component of digital transformation initiatives to drive business improvement.
Figure 2: Advanced analytics applications employ machine learning (ML), deep learning and other technologies to help users make sense of big data stored in the cloud.
The industry term for this type of solution, which leverages cognitive computing into the visualization and calculation offerings that have been used for years to accelerate insights for end users, is advanced analytics. McKinsey defines advanced analytics solutions this way:
“[Advanced analytics solutions]…provide easier access to data from multiple data sources, along with advanced modeling algorithms and easy-to-use visualization approaches and could finally give manufacturers new ways to control and optimize all processes throughout their entire operations.”
The introduction of ML and other analytics techniques accelerate an engineer's efforts when seeking correlations, clustering, or any other type of needle-in-a-haystack analysis of process data. With these features built on multidimensional models and enabled by assembling data from different sources, engineers gain an order-of-magnitude improvement in analytics capabilities, akin to moving from pen and paper to the spreadsheet 30 years ago.
Spreadsheets were the analysis tool of choice in process manufacturing for the past three decades, but this general-purpose tool is now too cumbersome and inflexible for complex analysis of the expanding volumes of time-series data. Advanced analytics applications (Figure 3) are needed to accelerate insights for engineers and other SMEs, helping them make decisions to drive positive business outcomes.
Figure 3: Seeq is an advanced analytics application designed for self-service use by process experts to obtain insights into large data sets.
IIoT presents an exciting opportunity for process manufacturers to connect previously standalone assets. The affordability of wireless sensors is now enabling visibility into whole new asset classes, and the value of connecting these assets is just beginning to emerge. IIoT will transform manufacturing by bringing it closer to attaining the promise of Industry 4.0, but only when used with the new class of advanced analytics software to create value from the burgeoning data volumes.
About the Author
Michael Risse is the CMO and Vice President at Seeq Corporation, a company building advanced analytics applications for engineers and analysts that accelerate insights into industrial process data. He was formerly a consultant with big data platform and application companies, and prior to that worked with Microsoft for 20 years. Michael is a graduate of the University of Wisconsin at Madison, and he lives in Seattle.
Figures, all courtesy of Seeq except as notedLearn More
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