Challenges and Solutions in Implementing Industry 4.0, Automation, and IoT on the Factory Floor.
Technological evolution has propelled the digital transformation of the industry, paving the way for the era of Industry 4.0. Automation and the Internet of Things (IoT) have become fundamental pillars to optimize processes, enhance efficiency, and improve productivity in factories. However, the need for low-latency communication between equipment on the factory floor and the computing systems responsible for intelligence, automation, and control has presented significant challenges.
Industry 4.0 aims for seamless connectivity and collaboration between machines and systems, enabling real-time exchange of information. Advanced automation and IoT enable continuous monitoring of equipment, detailed data collection, and real-time analysis for quick and accurate decision-making. However, these processes rely on highly agile and efficient communication between devices and computing systems responsible for processing and interpreting this information.
The major challenge arises when considering the impracticality of utilizing resources from public cloud computing to meet this need for low latency. Cloud Data Centers are typically situated in regions distant from industrial plants, resulting in delays in data transmission between the factory floor and the cloud. These high latencies can hinder process efficiency, affect synchronization between systems, and compromise real-time decision-making.
The solution to this dilemma lies in the implementation of modular Data Centers locally — within the factory premises. This approach brings computational capacity to the same production locality, removing physical barriers and ensuring low-latency communication between devices on the factory floor and control and automation systems.
The installation of modular Data Centers in an industrial plant brings several benefits:
Latency Reduction: The physical proximity of modular Data Centers to equipment on the factory floor eliminates high latencies, ensuring swift and reliable information exchange. This allows real-time process execution, enhancing system response and operational efficiency.
Maximum Efficiency: With locally installed computational capacity, data can be processed, analyzed, and utilized optimally, enabling intelligent use of high-cost industrial assets such as machines and robots to achieve maximum production efficiency.
Optimization of Production Processes: With real-time data availability, it’s possible to swiftly identify faults, bottlenecks, and improvement opportunities in production processes. This facilitates immediate adjustments, enhancing overall quality and productivity.
Data Security: Keeping data within the industrial plant’s environment reduces security and privacy risks associated with sending sensitive information to public cloud data centers.
Cost Savings: Modular Data Centers can be scaled according to the specific needs of the factory, avoiding excessive expenses on unnecessary computational resources.
The application of Industry 4.0, automation, and IoT on the factory proper is a crucial strategy to tackle the challenges of competitiveness in today’s industrial landscape. Modular Data Centers emerge as an efficient solution to ensure agile and reliable connectivity between industrial equipment and control systems, enabling the full realization of Industry 4.0’s potential and driving efficiency, productivity, and innovation in the factories of the future.
Alinie Mendes is CEO at MODULAR Data Centers and a partner at Lemniscata Ventures, a private investment company specializing in engineering and technology assets.
With a degree in Administration and a postgraduate degree in Processes and Quality, she holds an MBA in Production and Quality Engineering from the School of Engineering at USP. She began her career in the automotive industry, involved in cost optimization and quality projects in production lines. Since 2010 in the technology industry, Alinie has led high-performance teams in managing and applying organizational transformation models and quality assurance.