Processing Q&A: Digitalization and the Internet of Things in process manufacturing

The calling card of modern manufacturing continues to shift from analog to more digitalized processes. How end-users are navigating this transition is largely unique according to their throughput demands.

But as Bosch Rexroth’s Garrett Wagg explains in this Processing Q&A, there are universal trends and best practices that companies need to be aware of, as a connected network of digital components is serving as the foundation that successful manufacturing is being built on.

Q: How are manufacturing solutions continuing to evolve in an increasingly digital landscape, especially in the chemical, food and beverage, pharmaceutical and petrochemical industries?

A: Manufacturing solutions are evolving at a rapid rate in today’s digital age. Some of these solutions include, data centralization and integration of ERP, MES and SCADA systems through Internet of Things (IoT) devices. These devices allow process manufacturers to deploy sensors on nearly every piece of equipment on the factory floor to collect real-time data, data analysis, quality control, resource consumption and preventative maintenance.

All this data can tie in with upper-level management systems for a holistic view of operations and improved decision making. This is extremely valuable for a food or beverage manufacturer who may produce a multitude of products and want better insight on cost, efficiency and performance of each product made. Other solutions include incorporating a digital twin and simulation tool to create virtual models of physical machines, processes or entire factories. This allows for companies in the petrochemical or pharmaceutical world to test out manufacturing methods before making a substantial investment in expensive, high-precision machinery.   

Q: What are the biggest reasons that manufacturers might be hesitant to adopt fully digitalized/automated processes? And are there cybersecurity risks related to a more digital footprint? 

A: There are a few reasons why manufacturers may be hesitant to adopt full digitalized/automated processes. Some of those reasons include the upfront cost. Adding a plethora of sensors to your machine floor, updating your equipment and implementing an upper-level data management systems can be costly; however with the right plan of implementation, most of these costs can be outweighed overtime with reduced operating costs and labor costs. Another large hesitation includes the increased chance of a cybersecurity attack. As more data and operations are pushed to the cloud, remote access becomes a must, leaving vulnerable points in the system. These risks can be managed with additional security blankets such as firewalls, VPNs, security scanners and Linux-based controllers that were designed with cybersecurity in mind.

Q: How important is it to have cohesive integration between IT and OT components amid this digital transition and transformation?

A: The importance of cohesive integration between IT and OT components cannot be overstated. This integration is critical and foundational to the success of any advanced digital manufacturing strategy. OT systems such as PLCs and sensors generate vast amounts of raw data about the "what" and "how" of production — such as temperature or machine status. IT systems can provide the "why" and context such as maintenance schedules, cost of goods and customer demand. The marriage of these two unlocks advanced decision-making, faster responses to market changes, integration of artificial intelligence (AI) and machine learning, and end-to-end visibility that can ensure compliance and traceability for industries such as pharma and food and beverage.

Q: Data management continues to grow in importance within manufacturing. How is AI helping manufacturers manage the constant influx of data and translate it to actionable insights?

A: In today’s digital age, the sheer volume and variety of data that sensors, machines, supply chains and quality systems produce can overwhelm a company. AI tools are helping manufacturers manage, clean and sort the data in a variety of ways to make it relevant and insightful to manufacturers. Some of the tools AI offers includes automated data collection of PLCs, SCADA and MES systems through imbedded IoT devices that can collect, store and sort data no matter the format. AI can clean this data through anomaly detection and then compress it to only include relevant information. Other AI tools include pattern recognition for predictive quality, demand forecasting and preventative maintenance.

Q: What impact are third-party software apps having on manufacturing processes? How do they integrate with a manufacturer’s control system?

A: Third-party software apps are having a major impact on manufacturing processes and are a key factor in the shift to more collaborative and open control systems. Third-party software apps differ from proprietary, single-vendor solutions and offer a more modular, specialized and often cloud-based approach that can run on a variety of hardware platforms. These typically integrate with a manufacturer’s control system through layers of integration. The first layer includes the OT level where sensors, PLCs, PCs and other control critical hardware are integrated. The second layer is an MES layer that orchestrates production, manages work orders and tracks materials, and a third would be an enterprise layer such as an ERP system. With proven success and more demand for third-party software apps, more and more of the large automation players are developing app-based controllers to deploy these tools.        

Q: As manufacturers continue to shift towards fully automated processes, how will digital monitoring software guide process oversight?

A: As manufacturers increasingly shift towards fully automated processes, digital monitoring software becomes the central neural network that guides process oversight. These monitoring tools can offer real-time visualization in easy-to-read dashboards. These dashboards can give insight to overall equipment effectiveness, anomaly detection, proactive alerting, real-time utility consumption and a wide variety of important data. In an environment where human intervention in direct control is minimized, this software plays a critical role in ensuring efficiency, quality, safety and continuous improvement.