The Future of Process Controls and Automation: Embracing the Next Wave of Innovation

Process control in manufacturing involves the application of automated systems and advanced technology to oversee and manage production processes. This approach ensures the manufacturing process is completed accurately and consistently, maintaining required quality standards throughout the production cycle. By leveraging process and machine sensors, controllers, and software, process control systems can monitor and adjust process variables such as temperature, pressure, flow rates, weighments, and others to maintain processes within specified parameters.

Effective process control systems play an important role in optimizing efficiency in process manufacturing operations. They enable adjustments to be made quickly when conditions change, and they can prevent deviations that could affect quality. This proactive process management helps minimize waste, reduces the likelihood of defects, and ensures that the end products meet quality standards.

In addition to improving quality and reducing waste, process control enhances overall productivity. Automating routine tasks and providing valuable insights through data management proven techniques means that these systems streamline operations, allowing for quicker responses to production challenges. As a result of process control in manufacturing, higher throughput, better resource utilization, and increased profitability can be realized.

Ultimately, process control and automation are about using technology to your advantage and creating a more reliable, efficient, scalable, and adaptable manufacturing environment. This is essential for maintaining a competitive advantage in today’s fast-changing industrial market segments.

Integration of Artificial Intelligence (AI) and Machine Learning (ML): AI and ML offer the potential for significantly evolving process control and automation to a next-generation level by enabling predictive maintenance, real-time optimization, and advanced quality control. For example, AI algorithms could be utilized to predict equipment failures by monitoring machine sensors and status and using this information to track changing machine health and performance.  This allows for eliminating or significantly reducing downtime and enhancing process efficiency. In pet food manufacturing, ML might be used to adjust ingredient proportions in real time to ensure consistent product quality and nutrition. However, while these technologies hold promise and potential, their real-world impact is still evolving and much unrealized. Adopting key trends and technologies is often limited to specific sectors or companies with substantial resources. Challenges such as data quality, integration complexity, and cost can affect widespread success.

Industrial Internet of Things (IIoT): IIoT aims to create intelligent factories by connecting machines, sensors, data management, and integration systems. Consider a food processing operation where process control in manufacturing includes IIoT devices to monitor critical parameters such as temperature, moisture, and equipment performance to ensure perfection in food production and to prevent spoilage while ensuring personnel and consumer safety. Similarly, in the plastics and rubber compounding industries, IIoT sensors would track material flow and equipment efficiencies. Despite potential benefits in all industries, the practical implementation of IIoT can be complex and costly. Many industries are still in the early stages of adopting this technology. Integration with existing systems and managing the increased volume of data remain significant challenges.

Advanced Robotics and Automation: Robotics technology is advancing rapidly, with robots capable of precisely handling complex tasks. “Collaborative robots” (cobots) in discrete manufacturing enhance productivity and flexibility. In process control in pet food manufacturing, robotics can streamline packaging, improving speed and accuracy. While advanced robotics offer benefits, the extent of their impact varies. The use of cobots in automated systems often depends on the industry. Challenges exist, such as high costs and the need for specialized programming. Even considering the challenges for use of robots, cobots and AGVs (automated guided vehicles) have grown in popularity and use within process control in manufacturing.

Enhanced Data Analytics and Visualization: Data analytics platforms provide insights into manufacturing processes, helping businesses make informed decisions. In food processing, analytics can optimize parameters like baking times, and in plastics and rubber compounding it aids in understanding material behavior. While data analytics offers valuable insights, the practical application can be uneven. Many companies are still grappling with the complexities of integrating analytics into their operations and may not fully realize the potential benefits due to data quality issues or limited resources.

Digital Twins and Simulation: A Digital Twin is a virtual copy of a physical asset or system that enables a manufacturer to test and adjust production operations without affecting the actual real process. In discrete parts manufacturing, this has allowed the manufacturer to model their assembly lines to uncover and address challenges before they become a serious problem. Imagine, if you will, in pet food manufacturing, the use of digital twins to help identify production process bottlenecks. Further, the digital twins can allow the pet food manufacturing operation to adjust on a trial basis to improve production. While the use of digital twins presents significant potential for process manufacturing improvement, their broader integration is still underway. Key challenges include the costs associated with the development and maintenance of digital twins and the challenges associated with integrating them with current process systems. The advantages associated with the use of digital twins are encouraging but continue to develop.

Blockchain for Supply Chain Transparency: Blockchain technology is a decentralized digital record of transactions or activities across multiple computer operations. It can ensure that once information is recorded in a database, it cannot be altered or deleted. This provides both transparency and security of the data. Each record is grouped into a “block,” and blocks are connected in chronological order, forming a “chain,” so to speak. This type of structure makes it very difficult to access the data in an unauthorized manner.

While blockchain technology is being explored and beginning to be implemented in process manufacturing, its adoption is still evolving. Blockchain is believed to enhance supply chain transparency and product safety by providing a more secure way to track raw materials from their origin to the final product. This helps ensure quality and regulatory compliance. For example, blockchain technology could be used in food processing to verify ingredients and their freshness and to track their handling throughout the supply chain. It can also assist with compliance and traceability in the chemical and pharmaceutical industries, ensuring that materials meet regulatory standards.

While there are significant potential benefits in using blockchain technology, challenges such as integration with existing systems, costs, and the continued widespread adoption in the industry continue to be addressed. Blockchain promises to improve efficiency and trust in process manufacturing, but its full impact is in the future and continues to evolve.

Integration: When adopting advanced technologies, companies often face challenges integrating them with their existing systems and processes. One of the main obstacles is ensuring that the new systems are compatible with their existing ones, along with managing data security concerns. To navigate this, process manufacturers should carefully plan how these new technologies will work alongside their legacy systems. An increase in the interconnection between devices, systems, and networks occurs due to technological advancements. While this allows for seamless communication and data exchange between systems, enabling much smarter operations, it also comes with the necessity of dealing with potential cybersecurity risks.

Cost and ROI: Adopting the next wave of technological innovation can often involve significant upfront investment. Evaluating the return on investment (ROI) is crucial for justifying these expenditures. ROI considerations include reduced maintenance costs, decreased downtime, increased production capacity, enhanced product consistency and quality, more effective and easier regulatory compliance, and improved overall efficiency. Conducting a thorough cost-benefit analysis to assess long-term gains is recommended.

Real-World Impact: The technologies we’ve reviewed and discussed here certainly indicate a lot of promise, but their effects on transforming process manufacturing operations are still unfolding. The adoption of these innovative and advanced technologies is often confined to specific industries or applications, and the benefits can differ widely. Process manufacturers need to have a grounded perspective on both the potential of these technologies and their current limitations. Taking the time to assess readiness for implementation and the expected return on investment is key to making the decisions best for your company and operations.

Looking ahead, process controls and automation are positioned to make a big splash across various process industries such as food, pet food, animal feed, chemicals, plastic and rubber compounding, and much more. Technologies like AI, IIoT, advanced robotics, data management and analytics, digital twins, and blockchain are process manufacturing game changers. Embracing these innovations can boost efficiency, enhance product consistency, improve quality control, assist in ensuring regulatory compliance, allow process manufacturers to become more agile, and even positively impact competitiveness and brand loyalty. However, fully tapping into their potential may take some time, with different industries adopting at their own pace. The proven effects of these technologies to transform process manufacturing are still in the works, and their success hinges on overcoming integration hurdles, managing costs, and addressing specific industry challenges.

As these innovative technological tools develop, businesses must stay updated on the latest advancements. Process manufacturers can take full advantage of these innovations by making strategic investments and being flexible in their implementation strategies. This approach will help them maintain a competitive edge and thrive in an ever-evolving market.

The future is unpredictable. However, innovation continues to produce advanced technologies that promise to take process manufacturing to another higher level in various industries.  And this is a necessity. From feeding the estimated 9 billion humans that are projected to populate our planet by 2035 to feeding the fast-growing pet population drawing off the same sources for food as their human owners, to the production of the various chemical-based compounds and substances needed for a wide variety of purposes, advancing technology is essential to our growth and survival. We need to aid it, enable it.

With over 50 years of expertise in custom engineering process control and automation systems, automated ingredient batching systems, material weighing, liquid handling, and other bulk material handling solutions for a wide range of process industries, Sterling Systems & Controls proudly has the experience to provide you with expert guidance to achieve your automation and process improvement goals. Contact us today to discuss how our knowledge and expertise can align with your goals and how you can continue your journey toward manufacturing excellence!