Editorials
Robust Machine Design through Technology Fostered Collaboration
POSTED 01/21/2025 | By: Thomas Kuckhoff, Product Manager - Core Technology, Omron Automation
In today’s automation industry, successful machine integration depends on collaboration among plant management, operators, engineers, safety officers, and IT teams. This teamwork is crucial for balancing metrics, best practices, quality, risk, and security, especially with the unique nature of each project and the rapid technological changes.
New technologies, such as AI, are creating a gap between early adopters and those lagging behind. Many facilities have the necessary sensors but lack advanced process controls or AI. Machine builders can help bridge this gap by providing expertise and technology, enabling end users to make informed decisions and improve efficiency.
By using inclusive software and all-in-one automation platforms, machine builders can unify programming languages, create open networks for future edge computing, and deploy software digitally. These solutions enhance system robustness while maintaining the design modularity that machine builders value.
When integrating AI into an organization, it’s often noted that for every dollar spent on developing the algorithm, five dollars are spent on integrating it. This integration, referred to as the algorithm's EQ, can be expensive and is often hindered by cultural resistance. For example, some team members might be hesitant to share their knowledge with new automation systems, fearing job loss. To overcome this, fostering a data-first culture is crucial. This approach ensures that all users, from IT to OT, feel valued and see a future for themselves in the evolving landscape. By unifying high- and low-level programming languages in automation platforms, we can bridge the gap between new AI talent and existing factory workers. For instance, AI can help generate structured text for function blocks that can be easily integrated into ladder logic or create Python code that fits into a program flow chart. This flexibility enables both IT and OT teams to collaborate more effectively, focusing on data flow rather than the specifics of a single programming language. As machine learning roles continue to grow, this inclusive approach is becoming increasingly important.
Open industrial protocols, such as OPC-UA and EtherCAT, are crucial for advancing automation and predictive edge computing. These protocols make it easy to integrate different technologies, enabling data from devices like sensors, cameras, and servos to be shared seamlessly across various systems and manufacturers. This smooth data exchange helps identify and fix underlying issues, not just surface-level problems. It also speeds up the adoption of new technologies and improves overall efficiency. The growing membership in groups, like the OPC-UA Foundation and the EtherCAT Technology Group, shows that the industry recognizes the benefits of open networks. These networks provide detailed and accurate data at consistent speeds, which is essential for developing and optimizing predictive algorithms. In short, open networks are vital for building a scalable and efficient automation system.
In the manufacturing sector, ensuring that software is reliable and trustworthy is crucial. Factory workers need to trust the software to make reliable production choices. Software solutions are frequently adopted by automation companies during periods of rapid technological change, when there's doubt about long-term performance, or when the market demands quick product launches. It's vital to maintain the reliability of software access. Issues like missing license keys, expired subscriptions, and software incompatibilities can lead to production halts. By digitally deploying software and reducing these risks, we can speed up the setup of new machinery and enhance the scalability of future projects. This allows for faster design, setup, and installation of additional machines. Comprehensive platforms are designed to cater to different user preferences, whether they prefer pay-per-use or a one-time purchase. These platforms aim to build trust through software deployment, rather than causing frustration.
In the industrial sector, raw material prices are expected to continue fluctuating, and consumer buying habits will remain diverse until 2025. To stay competitive, companies should focus on improving their production processes. Many factories already have sensors that collect valuable data, but they often struggle to use this data effectively. Machine builders can assist their clients by using both advanced and basic programming languages to enhance production efficiency. As well as setting up open networks to support the integration of future technologies. Also, initiating pilots as functional proofs of concept to demonstrate the value and potential of new technologies. By successfully implementing these strategies, machine builders can become trusted advisors, helping their clients make the most of their data and maintain a competitive edge despite the challenges of fluctuating raw material prices and diverse consumer behaviors.
Start data collection pilots in settings that require minimal coding. These pilots can help build in-house expertise in advanced connectivity and foster a culture that values data. Connect a variety of equipment and collect data using different methods, such as time series CSV files and video recordings. Combining these data types provides a clear and objective view of processes, enabling confident predictions of operational improvements. Focus on the root causes of issues and build a data repository for future machine learning applications. Expand pilots strategically to reduce the variety of industrial protocols used on the factory floor. Using globally open industrial protocols can provide algorithms with detailed, real-time data, which can help lower the costs of implementing advanced process controls.
In the past, new machines were primarily evaluated based on their performance. However, by 2025, the focus has shifted to how well machines can connect, collect, visualize, and share data. The key value of new machines now lies in their ability to predict performance. Collaborate with automation manufacturers that can integrate emerging technologies with proven industry equipment. This partnership can enhance a machine builder's understanding of current equipment and demonstrate successful automation, giving a competitive edge through high first-pass yields, backed by data.