The Fortune Business Insights predicts that the industrial automation market will show a CARG of 9.2% to grow from $191.74 billion in 2021 to $355.44 in 2028. This is explainable since automation of all stages of a production cycle is the essential condition for developing the entire industrial sector.
The industrial automation agenda began actively unfolding in the last century to effectively utilize all available economic resources such as equipment, labor force, raw materials, energy, and investments.
Information technologies and total computerization added momentum to the process in the present century. Industrial automation keeps evolving to acquire new forms and technologies according to the never-fading technical progress.
The present post aims to provide a holistic picture of the contemporary realm of industrial automation to fill possible gaps in realizing which industrial automation implementations are worth special attention.
Mapping of industrial automation
Modern manufacturers have to continuously improve the quality of their products and services to stay competitive among their rivals. Advanced human skills were the means of quality improvements in the pre-automation era. Today, manufacturers prefer betting on machines. Multiple meaningful reasons lie behind such an approach. To grasp the causes and reasons for industrial automation, it is worth realizing what objectives modern manufacturers are trying to achieve.
Objectives of industrial automation
Any sort of industrial automation such as industrial IoT implementation, for example, entails extra capital expenditures and workforce costs. However, the automation measures result in the following:
- Elimination of hard manual labor;
- Reducing the defect rate caused by human errors;
- Improving quality amid expanding product range that, in turn, generates new customer flows;
- Increasing productivity i.e. producing more goods on shorter notice;
- Reducing staff and, consequently, payroll.
As a result, a company can achieve the primary goal: increasing profits. At the same time, such an approach has some particular drawbacks:
- Technological unemployment;
- The need for hiring highly-skilled staff as well as for requalification of the existing employers;
- The increased risk of hacking the system;
- Necessary extra measures for providing multi-source power supply.
Nonetheless, the benefits of industrial automation outweigh possible flaws.
Levels of industrial automation
Different industrial enterprises have different degrees of preparedness for industrial automation. That’s why, all things being equal, the more modernized the company, the easier the transition to fully automated production. All industrial enterprises can be conditionally divided into the following levels of industrial automation:
- Zero-automated production. The level implies staff availability at each stage of a production cycle. No human worker is replaced by either sensor or networked facility. This is when an old-style manufacturing process depends on humans to share information across the entire production cycle.
- Semi-automated production. A certain number of sensors, drives, industrial IoT, and other automation means are available, but their control does not rely on self-analyzing network systems. The human staff remains critical to keep such a production working.
- Highly-automated production. This is when a company introduces the full scope of industrial automation techniques and networked machinery, including autonomous vehicles and robots in all stages of the production cycle. At the same time, the human staff remains present at managerial and back-office positions to coordinate the process.
- Fully-automated production. This is a human-free fully-autonomous production where the entire infrastructure is completely robotized. Such a level implies the manufacturing control and management functions transferred to the AI-enabled self-sustaining systems that do not require human intervention unless a severe emergency happens.
Planning of industrial automation
The process of complex industrial automation requires a deep analysis of the existing infrastructure to realize both the level at which a company is currently staying and the measures to be implemented. This is about planning to avoid redundant expenses and reduce a preparation period. Peculiar nuances of planning may be available in each particular case, but the main regularities are the following:
- All stages of automation are coherent;
- All steps follow each other with minimum gaps;
- Automation stages run in a certain rhythm;
- Automation measures can be executed in parallel.
The planning helps figure out what processes can and should be automated. As a rule, all procedures can be summarized in manufacturing, management, and planning. They rely on specific software for data collection and forecasting that, in turn, is based on some predefined parameters that allow analyzing data and achieving expected results.
Managerial processes remain the most automation-resistant since decision-making relies heavily on human intuition.
Desired outcomes of industrial automation
Industrial automation never implies following fashion trends: particular benefits are expected at the end of the process. Direct financial profits are not the only outcomes, however. What else can industrial automation bring to enterprises?
Manufacturers start understanding their processes clearer when preparing to implement one or another industrial automation solution. Weak points and gaps of the production cycle appear more apparent while hitherto hidden ways of efficiency improvement come to light.
Unlike human workers, machines do not demand lunchtime, an eight-hour working day, sick leave, vacations, as well as other labor protection measures. Machines never tire of working while making no human-inherent errors and mistakes. Highly automated production can continuously run 24/7 to boost productivity by orders of magnitude.
When quality control in real time is implemented in highly automated industries, little to no defective rates occur. The early identification of improperly functioning links of a production line allows minimizing factors that can impact the quality of final products.
Machines are deprived of any sort of spontaneity beyond their predefined functions. Hence, automated industrial enterprises can rely on more consistent long-term planning with no “surprises” caused by human factors.
Industrial automation makes enterprises data-driven. Big data processing, machine learning, AI-powered predictive analytics, and other advanced data-related technologies augment human capabilities in making much wiser decisions.
The fewer the human workers, the simpler the security compliance of a production. Occupational injuries are reduced to almost zero at highly automated enterprises. It has a positive effect on labor insurance costs and other human-related expenses.
Despite larger pre-investment required by automated production, the buyback period appears to be shorter and, therefore, the return on investment is significantly higher. Industrial automation boosts ROI due to lower defective rates, higher productivity, and reduced human factors.
General trends of industrial automation
Industrial automation implies replacing human labor with various sorts of machines and robotics as totally as possible. They can include a wide array of equipment, beginning from primitive motion sensors and up to sophisticated hardware-software complexes.
Every production link, including transportation of the feedstock and quality monitoring of the final products, requires specific means of automation to be implemented. A great variety of the existing production cycles can hardly let us list out all available means of industrial automation. However, some common trends under which industrial automation is typically carried out are worth mentioning as the following:
Automated big data analytics
Since data is the primary driver of the 4th industrial revolution, data processing and big data analytics are critical for industrial automation companies. The number of signals generated by various equipment of an automated production does not allow processing the data manually. That’s why automated big data processing algorithms have to be a compulsory element of any industrial automation service. In general, the following layers of data analytics are considered:
- Data derived from monitoring systems. It helps describe and visualize what is happening with the machinery and processes in real time. Algorithms of descriptive analytics are required to make the current status of the production graspable and operable. The more accurate the collected data, the more efficient the production management;
- Data obtained during diagnostics. Diagnostic analytics reveal why processes run differently from what is expected. Such data helps figure out the cause of the machinery and equipment failure. Timely executed diagnostics enhance the overall efficiency of a production process and, therefore, diagnostic analytics meet the industrial automation paradigm;
- Predictive analytics provide the next level of big data processing. It is based on both descriptive and diagnostic analytics. Recognizing patterns and forecasting trends are essential for the complex management of any automated production. Continuous efficiency improvements in both manufacturing and commerce are possible if insight-providing data appear in the disposal of industrial automation specialists;
- Successful development strategies become implementable with prescriptive data analytics. Even a fully automated, highly efficient production is not an end in itself: an enterprise should know where to move to run a profitable business. Prescriptive analytics provide the company management with such knowledge.
Mobile devices are not limited by consumer-oriented gadgets such as mobile phones and tablets. Any automated production can hardly work well without mobile devices for industrial use. In addition to such industrial mobile gadgets as barcode readers and protected smartphones, the following elements of the industrial mobility infrastructure facilitate industrial automation:
- Enterprise mobility management (EMM) systems that prevent data loss at any production link;
- Mobile device management (MDM) systems that provide network access control;
- Mobile content management (CMS) systems that relate to commerce operations and online presence;
- Mobile application management (MAM) systems that organize interactions between endpoints and networks;
- Client management tools (CMT) that provide mobile devices with related connectivity.
A unified endpoint management system combines all mobile facilities to provide staff with freedom of movement within production infrastructures and untie workers from computerized on-floor workstations.
Industrial Internet of Things
Many experts consider industrial IoT the very essence of industrial automation. Interactive, highly networked devices transform an ordinary production into a smart one. The IoT allows production operations to be integrated with business activities.
The industrial IoT facilities provide manufacturers with real-time data on the machinery status and overall performance. Monitoring, diagnostics, predictive maintenance, and remote security control all rely on the IoT. The following industrial IoT implementations are typical for industrial automation:
- Sensors and PLCs. Controllers with programmable logic (PLC) are industrial microcomputers that provide automated operation of equipment via signals received from sensors as well as commands sent to drives and actuators;
- SCADA. Supervisory control and data acquisition systems collect data from IoT devices to display such information on industrial user interfaces. Optimization of processes in real time is what SCADA systems are aimed at;
- The next level of industrial IoT sophistication is the human-machine interface (HMI). This type of software provides industrial staff with control over production processes to a greater extent and more flexible manner;
- Centralized internal networks are operated by distributed control systems (DCS) that combine various control and monitoring IoT elements under unified automated management.
Artificial intelligence and machine learning in industrial automation
Artificial intelligence and machine learning algorithms have already been adopted by many companies on the path towards industrial automation. Both technologies are based on big data that drives automated industries.
More intelligent decisions, effective production management, agile processes, and continuous operation constitute the positive outcomes of AI and machine learning implementation. Moreover, fully automated production implies human-free autonomous workflows that can hardly be achieved without some sort of self-educating machine.
Besides, an industrial IoT infrastructure starts working much better if processing of the IoT signals is handled by machine learning algorithms. They can find particular patterns in the manufacturing process more precisely than human operators. It improves diagnostics and predictions that make automated production more secure.
Robotization is a broad definition to interpret when it comes to industrial automation. It is not easy to identify what can be called a robot and what cannot. Some experts consider robots as autonomous machines capable of completing certain pre-programmed functions from A to Z. Such a definition implies an excessively wide range of equipment in terms of its functional sophistication, however.
Nonetheless, various robots explicitly represent how industrial automation works as little else does. Robots have replaced human workers completely in some sectors (automotive, electronics, etc). The highest performance and quality are inherent in robotized production. Despite requiring significant initial investments (the cost of robotized systems is decreasing over time, at the same time), robotics provide the fastest ROI. Hence, robots will continue playing a critical role in industrial automation.
Global providers of industrial automation
The global market of industrial automation is based on hundreds and thousands of diverse providers of industrial automation solutions. However, only a small fraction of them can be called full-service industrial automation companies. They are the global leaders that can offer a wide array of special hardware such as sensors, PLCs, drives, and IoT devices compatible with various industrial machinery.
Besides, the leading automation providers can supply specialized software in addition to their hardware components for industrial automation. The following industrial automation providers are recognized as the top global leaders in the automation sector:
The European industrial giant offers its own digital platform ABB Ability divided into four segments: electrical components, electric drives, robotics, and industrial automation. Besides, the company is one of the world’s SCADA leaders with such original products as Symphony Plus and SCADAdvantage. Also, ABB occupies the first position on the global robotics market, outperforming such famous robot suppliers as Midea Group (KUKA), The Fanuc Corporation, and Kawasaki Heavy Industries. ABB Group shows its annual revenue of EUR 28.9 billion in 2021.
The old German corporation is the undisputed leader of the European industrial automation sector while giving way to Emerson in the Americas. Siemens is famous for its wide product range of high-quality automation components such as controllers, CNC, sensors, drives, and the like. Distributed control systems and SCADA software are also in the scope of Siemens. Siemens is the fifth-largest corporation globally with a market value of $139.8 billion. According to Statista, the total revenue of this leading automation vendor reached about EUR 62 billion in 2021.
This is a direct competitor of both ABB and Siemens in the full range of industrial automation services and products. The company is famous for its SCADA system OASyS. Schneider operates globally, sharing the world market with a dozen top industrial giants. Besides, according to the Oil & Gas Journal, Schneider was awarded as The Company of The 2021 Year in the nomination of safety instrumented systems.
In addition to the industrial automation providers above, the following manufacturers are worth mentioning as the largest industrial automation companies:
- Rockwell Automation,
- Mitsubishi Electric,
- Fortive (Danaher),
- Yokogawa Electric,
Any up-to-date production is data-driven however wide the data can be interpreted. Modern industrial equipment generates a lot of data when working. Big data processing is becoming critical for modern enterprises, therefore. Since manual processing of big industrial data is hardly possible, various methods and technologies of industrial automation are to the rescue.
The more autonomous and human-free the production, the higher the quality of goods and the better the production performance. Numerous industrial automation providers are available to deliver any sort of special hardware such as sensors, PLCs, drives, CNCs, etc. Industrial automation software is also available with various products beginning from SCADA systems supplied by the leading automation giants and up to custom-made industrial automation platforms from a variety of small and middle-sized automation software vendors.
AI and machine learning go hand in hand with other industrial automation technologies. Industrial IoT implementations and robotics are becoming a norm for those companies that seek the highest ROI and unbeatable competitive power.
Contact us today to get professional support if complex industrial automation is on your corporate agenda.