Süeda Asil
Corporate
- Thread Author
- #1
The definition of industrial automation used to be very clear: PLCs, sensors, robots, machine vision, control systems, and SCADA platforms. Anything that moved, measured, monitored, or controlled a physical process was within the scope of automation. This definition is still valid, but it is now incomplete.
bottleneck 🤯 Bottlenecks in Information Flow
Those working in manufacturing, engineering, or industrial operations are encountering a different kind of bottleneck: teams waiting for information, rather than machines waiting for materials. Reports are manually recreated, and data can only be used in another system after being exported and cleaned from one system. Information exists, but if it's not in the correct format, reviews are delayed. This is where the new opportunity for automation lies not just on the factory floor, but in software-driven workflows that enable information flow between engineering and operational systems.
⚙️ Manual Information Flow and Automated Equipment
Many industrial organizations have invested heavily in physical automation. Machines are faster, sensors are smarter, and equipment generates more data. However, the workflows around these systems often remain manual. One team might use an engineering platform, while another uses an asset management system. A third team might manage documentation, reporting, or approvals in separate tools. While each system works well on its own, the transition between them often relies on people. One person exports the data, another reformats it, someone else checks it and sends it via email. Although these steps may seem small individually, when repeated across teams, projects, and facilities, they lead to significant operational disruptions.
🚀 Small Automations, Big Gains
Workaround solutions are often created to address workflow friction: a spreadsheet is added, a checklist is created. However, these solutions are not scalable and rely on memory, discipline, and repetitive human effort. Many workflow problems are not large enough to justify a major enterprise software project, but they are still significant enough for automation. For example, tasks like checking for missing required fields, moving approved data from one system to another, or flagging inconsistent naming conventions. These are often not platform issues, but rather automation layer issues.
💻 The Role of .NET, APIs, and Databases
A practical automation layer does not replace existing industrial or engineering software. It sits between systems and eliminates repetitive tasks. This layer can be built using tools such as .NET, Python, SQL, REST APIs, cloud services, and workflow automation platforms. The value is not in the programming language itself, but in capturing the rule once and applying it consistently. A .NET application can provide users with a simple interface for a repetitive engineering task. An API can carry information between two systems without manual export and import. An SQL query can identify exceptions without causing delays. A script can generate a report that previously required hours of manual preparation.
💡 AI Removes Barriers
AI-powered coding tools are accelerating this change. Engineers who understand a process can now prototype automation ideas faster. They can generate sample code, debug errors, and test approaches with less friction. However, AI does not replace engineering judgment. It cannot fully understand which exception is important, which data field is critical, or why a workflow step poses a risk. This information still comes from people close to the work. The most powerful results emerge when domain experts use AI as an assistant, not as a decision-maker.
📈 What Should Industry Leaders Do?
For leaders in manufacturing and industrial operations, the takeaway is simple: move the automation conversation beyond equipment. Look for digital work around physical work. Where are people copying data? Where are they checking the same thing repeatedly? Where are reports being manually recreated? Where are approvals slowing down due to a lack of information? Where does one person 'just know' how to fix a problem? These are signals that a software-driven automation layer is needed. The next productivity improvement may not require a new machine, robot, or enterprise platform. It could come from connecting two existing systems, validating information earlier, or turning a repetitive manual check into a small application.
Industrial automation is no longer just about controlling physical processes. It's also about controlling the information flow that supports these processes. Teams that understand both operations and software will be better positioned to eliminate friction, increase consistency, and create more scalable workflows.
