Industrial automation has long been the driving force behind greater efficiency, reduced operational costs, and improved product quality in manufacturing. However, like any other field, automation technology continues to evolve rapidly. While some practices remain foundational, others are reaching obsolescence, replaced by more advanced and adaptive systems. With the rise of artificial intelligence (AI), machine learning (ML), and more sophisticated industrial equipment, certain automation methods will soon be phased out. This article explores these soon-to-be outdated practices and what technologies will likely replace them in the near future.
What is it? Ladder logic is a programming language used to develop software for programmable logic controllers (PLCs). It mimics electrical relay logic diagrams and has been the go-to language for PLC programming for decades. Ladder logic has been favored for its simplicity, allowing engineers without programming backgrounds to create automated workflows.
Why it’s becoming obsolete:
● Complexity in Modern Systems: Modern industrial processes are increasingly complex and require sophisticated control systems. Ladder logic is limited in its ability to handle the intricacies of today’s automation needs.
● Efficiency Issues: As production lines and machinery become more advanced, ladder logic lacks the flexibility and scalability required to efficiently manage large-scale, multi-faceted processes.
● Better Alternatives: More advanced programming languages like Structured Text (ST) and Function Block Diagram (FBD) are now being adopted due to their greater versatility, better handling of complex logic, and ability to interface seamlessly with advanced digital technologies, including AI and machine learning algorithms.
What will replace it? Graphical interfaces, higher-level programming languages, and AI-driven platforms that allow operators to program without in-depth coding knowledge are emerging as the replacements. These platforms also offer easier scalability and integration with IoT and cloud-based systems.
What are they? Robots in industrial automation have historically been programmed for specific tasks on fixed lines, performing repetitive actions based on pre-determined scripts. These systems operate in environments where their tasks rarely change, making them effective for mass production but inflexible.
Why they’re becoming obsolete:
● Inflexibility: Fixed robotic systems struggle to adapt to changing production requirements, making them unsuitable for industries where customization, agility, and frequent retooling are necessary.
● Increased Product Variation: Modern markets demand a high level of product variation and customization. Fixed systems are slow to adapt and costly to reprogram for different tasks.
● Advances in AI and Machine Learning: Robots now benefit from AI integration, allowing them to "learn" from data, adapt to new tasks, and optimize their actions in real time without the need for reprogramming by human operators.
What will replace them? Collaborative robots (cobots), flexible robotic arms, and AI-driven robots that can work alongside humans and adjust their behavior based on sensory feedback are already making fixed systems obsolete. These systems can reprogram themselves on the fly, responding to production changes dynamically without human intervention.
What are they? In traditional industrial automation, sensors and actuators are hardwired into a centralized control system. These sensors collect data and feed it to the control unit, which then directs actuators to perform physical tasks like opening valves or turning on motors.
Why they’re becoming obsolete:
● Inflexibility and Maintenance Issues: Hardwired systems are cumbersome to install, difficult to modify, and require extensive maintenance. Changes to production lines or sensor layouts necessitate physical rewiring, which can be costly and time-consuming.
● Limited Connectivity: Traditional sensors lack the intelligence needed to communicate autonomously or perform self-diagnostics, limiting their ability to interact with more advanced, interconnected systems like those enabled by IoT.
What will replace them? Wireless and smart sensors with integrated IoT capabilities are set to replace these hardwired systems. These smart devices can communicate wirelessly with central control systems, perform self-monitoring and diagnostics, and even update themselves remotely. This reduces maintenance costs, enables greater flexibility in production layouts, and supports real-time data analytics.
What is it? Historically, quality control has relied on human inspectors manually reviewing products to ensure they meet the desired specifications. Although automated quality checks have made inroads, many industries still rely on human oversight to catch defects and errors in production.
Why it’s becoming obsolete:
● Inconsistency and Human Error: Human inspectors are prone to fatigue, oversight, and inconsistency, leading to variability in product quality. As production speeds increase, the human capacity to inspect every product diminishes.
● Increased Automation and Accuracy Needs: Today’s industries demand higher standards of quality, especially in sectors such as automotive, aerospace, and electronics manufacturing, where precision is critical.
What will replace it? Automated inspection systems equipped with machine vision, AI, and deep learning are rapidly phasing out human-dependent quality control. These systems use high-speed cameras and sensors to inspect every item on the production line with pinpoint accuracy, identifying defects and abnormalities in real-time. Machine learning algorithms allow these systems to improve over time, increasing their reliability and speed.
What is it? Historically, much of the data in manufacturing environments was collected manually or through semi-automated means. Operators would record data from machines and processes, analyze it, and make adjustments based on their findings.
Why it’s becoming obsolete:
● Human Error and Inaccuracy: Manual data entry is prone to mistakes, leading to inaccurate records that can negatively impact decision-making.
● Delayed Response: Data collected manually is typically processed long after events occur, leading to reactive rather than proactive decision-making.
● Data Overload: Modern factories generate immense amounts of data that can no longer be effectively processed or interpreted manually.
What will replace it? Real-time data analytics systems, integrated with IoT and AI, are taking over manual data logging. These systems continuously collect, analyze, and act on data from connected machines and processes. They allow operators to make data-driven decisions in real time, predict maintenance needs, and optimize production flows on the go. Smart automation panels and interfaces enable operators to interact with these systems intuitively, focusing on decision-making rather than data entry.
What are they? Centralized control systems have traditionally served as the command center for manufacturing processes. All data and control signals are routed through a single location, which governs the operation of machines across the factory floor.
Why they’re becoming obsolete:
● Bottlenecks and Vulnerabilities: Centralized systems can create bottlenecks and single points of failure. If the central controller goes down, it can halt the entire production line.
● Lack of Scalability: Centralized systems struggle to scale as production demands increase, particularly with the rise of complex, multi-functional processes that require real-time responsiveness.
What will replace them? Decentralized control systems, distributed through edge computing and networked automation, are increasingly replacing centralized control models. These systems enable individual machines and devices to operate autonomously while still being connected to an overarching IoT network. With built-in intelligence, these decentralized systems can perform real-time adjustments without waiting for a central command, resulting in faster, more resilient, and more scalable manufacturing operations.
Industrial automation is undergoing significant changes as technologies like AI, machine learning, and IoT take center stage. Practices that were once cutting-edge are now reaching obsolescence due to their limitations in flexibility, efficiency, and scalability. By moving away from fixed robotic systems, hardwired sensors, and manual data collection, industries are embracing automation practices that enable smarter, more adaptive manufacturing environments. With innovations such as servo drive controllers like the DKC22.3-200-7-FWenabling precise and efficient motor control, and advanced sensors enhancing real-time data gathering, the future of automation is set to be more agile, intelligent, and connected than ever before.
