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Demystifying Iiot And Its Value To Manufacturing
Craig Zedwick, Director Production Excellence And Automation, Cabot Microelectronics
In the late nineties, I worked for Eastman Kodak in their solvent recovery and synthetic chemicals division. They had a sophisticated Distributed Control System (DCS) so operators could run interconnected distillation towers from a central control room. The Supervisory Control and Data Acquisition (SCADA) system had graphics showing every valve status, which pumps were running, and whatever data we wanted to display from our thermocouples, flow meters, and online gas chromatographs. Back then, such systems were state-of-the-art, even though the internet was in its infancy, with AOL and FTP sites representing cutting-edge technology. Fast forward more than twenty years, and manufacturing control systems haven’t changed that much. PLC ladder logic is still used at many companies. SCADA systems look better in most cases, but the control mechanisms are basically the same. So, what’s the big deal about Industry 4.0 and the Industrial Internet of Things (IIoT)?
IIoT is a buzzword and can mean many things, depending on the context. For manufacturers, it describes an interconnected set of sensors, smart devices, and control systems that are also connected to the cloud and accessible via internet URL’s. The purpose of this interconnected system is to collect, structure, and utilize large amounts of data to provide manufacturing value. For certain industries, the advancements made in the last 10-15 years have enabled small, customized production runs that have transformed the customer ordering and fulfilment process. But for manufacturers that are not focused on discrete parts, or on direct-to-consumer production, IIoT has may not have been on their minds until recently. The question to answer in this article is why these companies should embrace IIoT.
If you are starting with a foundation of manufacturing processes controlled via DCS or advanced PLC systems, then IIoT may seem redundant. IIoT has many components, and several of them may already be in place at your company. The key is to understand the vision of IIoT and how you can integrate value-added elements into your current systems to achieve that vision.
The IIoT vision is to democratize data such that every person in the company has access to the data they need when they need it and in the format they require. Everything else flows from this. On the manufacturing floor, if operators have access to real-time data, then they make better decisions. If the engineers and quality team can immediately pull data on maintenance activities, yields, variances, and so on, then they can take effective action to optimize and troubleshoot. If management can track costs and see real-time accounts and dashboards rather than monthly reports, they can respond more quickly to situations as they arise. When operating models can predict failures before they occur, maintenance can be done proactively, avoiding downtime and costly repairs.
At its core, IIoT is about getting the data into people’s hands as close to real-time as possible. For many manufacturers, the instrumentation and sensor aspects of IIoT may already be in place. Most of us already have such instrumentation because safety, quality, and efficiency depend on it. We have internal industrial networks to allow communication across the plant such that this data can be displayed and used by people connected to that internal network. Where IIoT shines is by enabling those industrial networks to organize and send the data to the cloud, allowing broader access and more sophisticated analysis.
This democratization of data is the first IIoT-based transformation that delivers value to manufacturers. Installing a simple local server to act as an edge device allows real-time manufacturing data to be compiled, analyzed, and reduced on that edge server before being sent to the cloud. Once in the cloud, multiple people and apps can access the data. The most common use case is to create models enabling predictive maintenance, thus reducing maintenance downtime and costs for the plant. Other use cases include troubleshooting, process optimization using digital twins and process models, and implementing machine learning to automatically improve predictive maintenance or process models.
The second major IIoT-based transformation is the ability to combine manufacturing data with data from other systems. Combining historical and predicted manufacturing volumes with variable costs in the accounting system allows a deeper understanding of cost drivers. The sales team gathers demand forecasts, supply chain tracks safety stocks for key items, and the manufacturing team makes the items to meet both the forecasted demand and to keep inventories at target levels. For many of us, those data sets are in different, non-integrated systems. With most ERP and supply chain management software now moving to the cloud, it’s no longer a massive task to integrate those data sets to build optimization models. The merging of so many previously disparate data sets creates a new, seamless data set for analysis in seemingly infinite ways. Data mining and building analytical models deliver value in terms of maintenance, labor utilization, equipment efficiency, logistics, customer pricing and fulfilment strategies, and a host of other cost-saving and revenue-creating areas.
Although there is much more that can be said about the value of IIoT and how it enables more than just manufacturing excellence, space doesn’t allow for such an exhaustive review. Suffice to say that the IIoT vision can bring value to manufacturers, even when they feel that their internal control systems and networks are already state-of-the-art. Moving that data into the cloud and integrating with data from other systems can take your company to a new level of efficiency and data-based decision-making.