How automation will make a big impact on manufacturing plants

Automation insights

• Manufacturers are using artificial intelligence (AI)-enabled smart machines to enhance operational efficiencies.
• Hands-on training during the installation of automation technology enhances the effectiveness of training.

Respondents

• Scott Dowell, senior vice president and general manager, U.S. Industrial and CIG, Wesco, Pittsburgh
• John Glenski, CPM, senior director, digital and automation, Salas O’Brien, Cincinnati
• Brandon Herrington, field applications engineer, Red Lion Controls, Bowling Green, Kentucky

Courtesy: WTWH Media

What are the key advantages of implementing automation in a manufacturing plant?

John Glenski: There are numerous advantages to implementing automation in a manufacturing facility. These include cost reduction, increased efficiency, improved quality, enhanced safety, flexibility, sustainability and workforce optimization. Every facility has an opportunity for enhancement with automation.

Brandon Herrington: With the complexities of processes and manufacturing applications, data has become the backbone of every aspect in the environment. Sometimes thousands of data points and calculations were formerly managed and analyzed by an individual or team. With automated systems, this data can quickly be visualized, accessed and decisions made while needing minimal interaction. Critical failures can almost be eliminated by designing in automatic alarm-based logic while still using the same devices and data to streamline a much more efficient process.

How can a company determine if it’s the right time to invest in plant automation?

John Glenski: If your facility has challenges in efficiency, quality or workforce staffing issues, it’s the right time.

Brandon Herrington: There really isn’t a bad time to invest in automation. Whether it’s investing your time to generate an automated Excel sheet to give the company its statistics for the shift or investing in automated machine systems to enhance the company’s operational capacity, it’s easy to establish a return on investment with automation even if it only saves time. Time saved in an industrial environment always equates to money earned in some way.

What are the emerging trends and technologies in plant automation that are shaping the future of manufacturing?

Scott Dowell: We’re seeing two major trends shaping the future of manufacturing ––  modernization and digital transformation.

On the digital transformation side, manufacturers are utilizing artificial intelligence (AI)-enabled smart machines to enhance operational efficiencies, and they’re incorporating predictive maintenance to help extend asset life and predict machine failure before it occurs.

From a modernization standpoint, companies are seeing that aging systems can hamper efficiency and expose them to risk. An effective modernization strategy will align with a manufacturer’s digital transformation goals, laying the groundwork for a smart factory. Businesses are also looking at their energy usage and safety programs, since both can significantly impact productivity and their bottom line.

John Glenski: Plant automation is advancing rapidly, and embracing trends and technologies is crucial for manufacturers to remain competitive. Three of the top emerging trends shaping the future of manufacturing are:

Data availability: Both edge computing and cloud platforms enable real-time data access, allowing for faster decision-making and process optimization.

• AI and machine learning (ML): Both technologies analyze vast amounts of data allowing real time and predictive process optimization, efficiency enhancement and predictive maintenance.
• Robotics: Advanced robotics leverage data to enhance production, automate tasks and increase precision, leading to greater productivity and flexibility.

The future of manufacturing is going to be driven by innovation and efficiency and those that can build solutions focused on those aspects.

Brandon Herrington: Probably the most talked about subject lately has been AI. Automation has historically been a type of AI, but in today’s manufacturing it has been adding in many ways. Anything from assisting the design team in writing the code through Q&A-style applications to human-machine interactions that are based. For example, the robot that feeds a human quality inspection may alert the inspector to a potential quality point to check based on tolerances trending close to being exceeded on the prior process. These AI systems can only be as good as the data they are given, which has driven the need for more advanced data collection systems that can access and collect all the data points for decision making.

How can plant managers ensure automated systems are adaptable and flexible to changing production demands?

Scott Dowell: Modernizing your installed base of industrial automation and controls infrastructure allows you to collect the relevant data needed to understand production needs and challenges. AI’s data analysis capabilities can help more accurately forecast demand, and it can identify potential opportunities to optimize production. It can also help present data in a way that’s relevant to make a business decision. However, it’s important to highlight that AI is only useful if there’s data to analyze. If you’re early in your automation journey, the best place to start is by creating a comprehensive plant modernization roadmap to help ensure that any automation solutions best meet your needs.

John Glenski: The best way to ensure adaptability and flexibility are clear upfront standards on those systems, aligned to business objectives.

Brandon Herrington: From the lowest level, dynamic programming will allow the operational level to always change based on production. From a hardware standpoint extra care in the architecture design and application discussions can ensure the systems can adapt to any needs. Some hardware may only speak one communication protocol where others may have hundreds natively embedded which can allow a wide versatility. This in addition to modular hardware that can have additional input/output (I/O), communication ports, and programming abilities added will streamline an always growing and adapting manufacturing environment to keep up with the latest technologies.

What are the most critical considerations when selecting automation technologies for a specific manufacturing process? Describe the challenge and solution.

John Glenski: Choose familiar technologies that fit the process requirements, integrate with enterprise systems and scale effectively within budget.

• Process type: Determine if the process is continuous, batch, discrete, high-speed or high-precision, and choose a control system accordingly.
• Existing technology and expertise: Stick with technologies the team is familiar with to reduce the learning curve and ensure smooth operations.
• System integration and scalability: Consider how the system needs to integrate with existing platforms (such as manufacturing execution systems and enterprise resource planning) and handle data requirements. Cost must be balanced with these factors to meet project needs efficiently.

Brandon Herrington: As with any decision in manufacturing, the most critical aspect is whether it meets the need. Considerations such as: do I need certified hardware? (Class 1 Div 2, UL, etc.) What will it be doing? Controller, data collection, remote access? What could it need in the future? (Additional IO, remote access, operator interface.) Careful review of device specs and research into the capabilities can help overcome all these scenarios. Some devices may hold multiple environmental design specs. Some may be single-purpose devices such as a programmable logic controller. Data collection, or protocol conversion, where some may have been multi-purpose devices with multiple built-in protocols and the ability to also control decision making. Then you can also use modular platforms to help adapt to some of those changes with IO additions, communications media and even screen addition abilities.

What are the main challenges faced when integrating automation into an existing manufacturing process?

Scott Dowell: Manufacturers need to take several things into consideration when looking to integrate automation solutions into their existing operations. Automation is increasingly integrating advanced smart technologies, leading to a surge in both data consumption and production. To effectively access and distribute this smart data, a robust network is essential. Naturally, organizations will need to keep cybersecurity top of mind as well. They’ll also need to ensure that any automation solutions can work within the physical footprint of the plant floor, and that new machines and systems can talk to any legacy systems. Training is essential; workers will need to understand how to properly use the machine to maximize its capabilities.

John Glenski: The main challenges of integrating automation into an existing manufacturing process can include:

• Client familiarity: Many clients may not be well-versed in the latest automation technologies, making adoption and effective use more difficult.
• Maintenance resources: Clients may lack the necessary resources or expertise to maintain new automated systems.
• Initial cost: The upfront investment in automation technology can be a significant barrier, especially for smaller operations.
• Harsh environments: In sectors like food and beverage, finding automation equipment that can withstand harsh conditions is a challenge.
• Timing: Determining the optimal time to integrate new technology is critical, especially as automation solutions evolve to address previously unsolvable challenges.

Brandon Herrington: Manufacturing processes are always evolving as well as the technology behind the process. Some facilities may be using hardware or software that is 20 to 30 years old in addition to newer systems that were recently installed so the need to still collect that old data might present some challenges. As they migrate these systems into their Supervisory Control and Data Acquisition (SCADA) systems or even newer decision-making systems, a protocol converter may be needed or even media convertors such as serial to ethernet to access this data.

How can knowledge transfer and training programs be effectively implemented to upskill the workforce for a more automated environment?

John Glenski: There are multiple ways to enhance the workforce as new automation and automated systems come online. This can include training programs implemented with new projects, engaging people in the project process so they feel ownership and enhance training effectiveness. Hands-on training during installation and commissioning also enhances the effectiveness of training. Having new employees with an individual development plan and associated technical training plans help them to see the long-term vision and how they are working towards their goals on a regular basis.

Figure 1: By removing the human element from dull, dirty or dangerous work, robots can help improve safety on the plant floor. Courtesy: Wesco

Brandon Herrington: As the technology evolves, the workforce must also evolve. The best way to ensure the new systems are utilized to their highest potential is through training. Engineers and maintainers of equipment often find themselves using many different software for troubleshooting and configuration. One of the best sources is through manufacturer provided training programs and internal scheduled training where the knowledge transfer and brainstorming sessions for process improvements can occur. These sessions may also introduce new features previously unknown to even the power users of a given software or hardware.

How has the rise of robots affected plant automation and what has the feedback been like from plant managers and workers?

Scott Dowell: Many manufacturers continue to contend with labor challenges. Staffing shortages can create unwanted downtime if there aren’t enough people to run the production line, and training –– or retraining –– workers can be time-consuming and costly. Robots can help businesses navigate these challenges by performing dull, dangerous or repetitive jobs, allowing human workers to focus on more critical tasks. In addition, there is growing interest in collaborative robot technology, which enables robots to safely work alongside plant personnel without the need for costly safety designs and barriers.

What are the ethical considerations when implementing automation in a manufacturing setting, particularly regarding workforce displacement?

John Glenski: Automation should be approached with a focus on enhancing, not replacing, the human element in manufacturing. The goal is to allow workers to use their skills and creativity rather than just physical labor. By making day-to-day tasks more engaging and meaningful, automation can improve the overall work experience. This human-centric approach ensures that technology serves to uplift the workforce, making their jobs more fulfilling.

We also must consider second-order effects — outcomes that aren’t immediately obvious. For example, McDonald’s experience with automation, like touchscreen kiosks, demonstrated that initial fears of job losses weren’t fully realized as workers shifted to more customer-centric roles. We need to think beyond immediate impacts when implementing automation, focusing instead on how it can enrich both the workplace and employee roles.

Maintenance and Safety

How can automation improve product quality and consistency in a manufacturing plant?

Scott Dowell: In many applications, like steel manufacturing, quality and consistency are critical. Poor-quality products are scrapped, and inconsistent products can either create additional waste or be inefficient. We had a global steel manufacturer that was struggling with excess waste during a production switchover process. They used AI to accurately predict quality degradation, which delivered more than $8 million per year in savings and greatly improved overall equipment effectiveness. It also had the added benefit of optimizing their energy usage during the production switchover, which improved overall sustainability and reduced their Environmental Protection Agency permitting requirements.

Brandon Herrington: One phrase I’ve heard many times before is the data doesn’t lie. When it relates to quality and consistency, this data can be fed into data-driven decision systems and reduce some of the common manufacturing mistakes by creating alerts or modifying a process via monitoring systems when a deviation occurs to prevent quality problems later in the process. An automated process can also ensure repeatability within a system by adhering to specific defined tolerances and setpoints within the process logic. Using trending and historical data these systems can also provide future predictions on capacity and run times to allow management to schedule more efficiently.

How does predictive maintenance play a role in optimizing plant automation efficiency and reliability?

Scott Dowell: Predictive maintenance has the potential to transform industrial operations. Traditional, or reactive, maintenance strategies can lead to machine failure, unplanned downtime and potential injuries. By leveraging the power of data, sensors and advanced analytics, manufacturers can identify and address potential issues before they become larger problems. Predictive maintenance can help extend equipment lifespan and enhance overall reliability. Shifting to proactive maintenance can lower overall costs by avoiding emergency repairs, and detecting potential hazards earlier minimizes the risk of accidents or injuries caused by equipment failures. And enabling data-driven decisions can help optimize the manufacturing process and improve overall efficiency.

How can we establish a comprehensive preventive maintenance program that complements our automated systems?

Brandon Herrington: A properly designed architecture can allow access to any data point down to the lowest level in a system. This data can then be used to provide trending and alerts to allow scheduled maintenance based on a manufacturer’s spec or by a trend that may show a possible future failure. This could allow maintenance teams to be much more data-driven proactive on their preventative maintenance vs reactive only. It can also help in the troubleshooting of systems by allowing addition of data points to the operator interface screen. A great example would be a motor drive status may be added to a diagnostic screen to allow personnel to view all data points at a glance. A notification alert could then also be automatically triggered via the human-machine interface visuals and a text sent to the maintenance team to alert of a fault or even added to a scheduled downtime for inspection.

What are the latest advancements in sensor technologies for monitoring plant equipment and processes?

Scott Dowell: The adoption of industrial internet of things (IoT) technologies has driven advancements in smart sensor technologies, resulting in a greater volume of data and insights generated by end devices. Sensors today can monitor and collect data on a wide variety of machine conditions that can impact performance or potentially indicate maintenance needs. They can collect data on temperature, vibration, voltage, energy usage, pressure and flow rate, among many others. Process-wise, they can also monitor the integrity of network communications and help troubleshoot issues. Taken together, today’s sensors and data collection capabilities can help manufacturers improve operational efficiency, spot machine issues before they become failures and minimize downtime.

What are the best practices for conducting risk assessments and implementing safety measures in automated production lines?

John Glenski: Assemble a multidisciplinary team to systematically assess machinery for potential hazards, such as moving parts or electrical risks. Document all identified hazards, evaluating both the likelihood and severity of each. Implement appropriate mitigation measures, such as engineering controls, administrative procedures or personal protective equipment (PPE). Regularly review and reassess risks to address any changes in machinery, processes or regulations, ensuring ongoing safety and compliance.

How can we implement a robust safety culture within a highly automated plant environment?

John Glenski: To implement a robust safety culture in a highly automated plant, prioritize clear and consistent communication about safety protocols and procedures. Pair this with regular, comprehensive safety training to ensure all team members have the knowledge and skills to identify and respond to potential hazards. Foster an environment of accountability and continuous learning, where safety is integrated into daily operations and reinforced through frequent updates, drills and employee engagement.

What are the considerations for integrating autonomous vehicles or robots into the plant workflow to enhance efficiency and safety?

Scott Dowell: By removing the human element from dull, dirty or dangerous work, robots can help improve safety on the plant floor. One application, for example, uses robots to take temperature readings and other measurements in extreme environments, eliminating the need to expose humans to those conditions in most cases. Robots are also effective at performing repetitive tasks, which can help reduce ergonomic injuries. Incorporating robots frees up human workers to focus on more critical or higher-value tasks, which can improve productivity and mitigate the impact of labor shortages.

Sustainability

What are the key benefits of implementing automation in a manufacturing or production plant from a sustainability standpoint?

Scott Dowell: Making your manufacturing or production processes more efficient and sustainable can have a significant impact on your bottom line. Older equipment generally consumes more energy, which drives operational costs up. Similarly, if equipment is running when it’s not needed, such as when demand is low, you’ll be wasting power and adding unnecessary wear and tear on your machines. Sensors, smart machines and other automation solutions can help ensure that your lines are running more sustainably and more cost effectively.

John Glenski: I often emphasize to clients that automation is key to bringing sustainability initiatives to life. It provides the real-time insights, data and visualization necessary to demonstrate to stakeholders that sustainability efforts are achieving the intended reductions and optimizations. Automation not only tracks performance but also validates the impact of these initiatives, ensuring that factories meet their sustainability goals effectively and transparently.

How can automation help in achieving compliance with environmental regulations and standards, and what are the best approaches to stay ahead of evolving regulatory requirements?

John Glenski: Automation not only allows the standardization of a process, but also enables a data-driven approach to reach sustainability goals. Environmental regulations and standards lean heavily on the ability to provide assurance. Automation allows for continuous monitoring of a process with data gathering. This provides the ability to have real-time insight to your performance against set standards as well as the backup data for assurance or audit purposes. Automation also allows for lifecycle monitoring, maintenance and replacement of equipment ensuring the efficient use of resources. An automated facility is one of the best approaches to staying ahead of evolving requirements as one can adjust to those changing requirements more nimbly in addition to giving real time feedback on compliance.

How important are sustainability and ESG initiatives becoming and how has this changed projects and future initiatives?

John Glenski: Salas O’Brien has a strong history, and extensive resources dedicated to environmental, social and governance (ESG) and sustainability, which we see as a key differentiator compared to most system integrators in the industrial sector. While sustainability and ESG initiatives are gaining importance on the plant floor, current discussions still largely focus on operational efficiencies and energy and water cost savings. When capital improvements are made, ESG considerations are often included, but prioritization is still primarily driven by business ROI, with decisions made at the corporate level. For most companies, ESG and sustainability are not yet the primary drivers of investment. However, in the longer term, market forces — especially in regions like Europe and Canada where sustainability is more integral to decision-making — are likely to shape these discussions more significantly.

How can we continuously monitor and improve the energy efficiency of automated plant operations?

Scott Dowell: Improving energy efficiency in automated plant operations starts with real-time monitoring through IoT sensors and energy management systems. These tools provide data to identify inefficiencies, allowing for adjustments to equipment and systems for optimal performance. Predictive maintenance also plays a crucial role in preventing energy waste due to equipment issues. AI and machine learning can then forecast energy needs and automate decision-making for better efficiency. Regular reviews of energy reports and setting clear goals help maintain continuous improvement, reduce costs and ensure alignment with sustainability efforts.

What are the challenges associated with integrating sustainability metrics into automation systems, and how can they be effectively addressed?

John Glenski: Integrating sustainability metrics into automation systems focuses on enhancing efficiency, but organizations may not fully realize improvement opportunities until after implementation. In the built environment and manufacturing sectors, these metrics aim to reduce energy, water, waste and materials, while prioritizing safety. Key challenges include managing data effectively, turning it into actionable insights and ensuring digital security. Without clear insight into the processes being automated, identifying sustainability opportunities can be difficult since improvement requires measurement, digitization and automation are essential for sustainability success.

AI/ML and Industry 4.0

How can artificial intelligence and machine learning (AI/ML) be leveraged in plant automation to drive process optimization?

Scott Dowell: Both tools are adept at analyzing massive amounts of data and developing insights that help businesses make more effective decisions. For example, AI can help better plan operations by forecasting demand more accurately. It can also help reduce unplanned downtime by predicting maintenance needs before a machine fails. Manufacturers can reduce operating costs and better manage inventory by using AI to streamline logistics and supply chain operations. AI can help identify the root cause of product quality issues, thereby minimizing defects and scrap rates. Furthermore, autonomous AI will play a crucial role as experienced operators retire. By capturing and leveraging that expertise, autonomous AI will help optimize plant performance.

What roles do data analytics and real-time monitoring play in enhancing plant performance and decision-making?

Scott Dowell: Monitoring machines in real time allows operators to gauge whether they are performing optimally and spot potential maintenance issues before they become failures that take the machine offline. Data analytics can help manufacturers in several ways as well. It can allow for better forecasting and inventory management. It can help improve overall product quality by letting operators identify and address defects earlier in the manufacturing process. And advanced data analytics can help plant managers better understand energy usage and make their operations more efficient.

Have you experienced challenges getting information and operational technology (IT/OT) to work together on a project? If so, describe the challenges and solutions reached.

John Glenski: Yes, our teams have encountered multiple challenges when integrating IT and OT teams. A key challenge is the cultural difference: IT prioritizes security while OT is focused on uptime and reliability. Legacy systems also pose an issue, as many OT systems run on outdated platforms that IT teams find difficult to manage. Additionally, there’s often a knowledge gap — IT may not fully grasp OT’s operational requirements, while OT teams may not be as familiar with security risks.

The solution, in our experience, is fostering collaboration between the teams to address these concerns. This might involve isolating legacy equipment, planning upgrades or using third-party solutions to maintain network separation while enhancing security. By working together, both teams can align their goals and find balanced solutions.

What cybersecurity measures should be in place to safeguard automated systems from potential threats or attacks?

Scott Dowell: The growth of internet-connected devices on the plant floor has unfortunately opened the door to bad actors who are continuously looking for vulnerabilities to exploit. Having an OT-specific cybersecurity plan is critical to protecting assets and mitigating risk. In addition to making sure product software and firmware is up to date, the NIST Cybersecurity Framework is a good playbook to follow. There are six key tenets to the framework but implementing it will help manufacturers understand which processes and assets need protection, identify and deploy the right safeguards for their application and monitor threat detection and response in real time.

Describe a success story in which AI was used to improve automation. Include data and statistics to prove the point.

Scott Dowell: We had a customer that produced large diesel engines for use in generator sets, naval and marine applications and military vehicles. Despite rigorous testing after the assembly process, subtle indicators of pending problems would often go unnoticed, even by experienced operators. This led to catastrophic failures that caused extensive damage and delayed shipments that cost millions of dollars and negatively impacted on-time delivery. We helped design an AI model that analyzed billions of data points to identify potentially at-risk engines. Within the first 90 days, the AI solution detected 20 real-time events, preventing more than $4.5 million in engine damage, resulting in a 10x project ROI.

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