Aaron Ricadela | Content Strategist | December 19, 2023
The convergence of widespread internet coverage, low-cost wireless sensors, 5G communication, and more powerful data processing is empowering manufacturers to digitize their factories to lower production costs, raise output capacity, and boost product quality. This movement is commonly known as Industry 4.0, referring to the fourth industrial revolution—after the three earlier industrial eras ushered in big advancements around steam, electricity, and electronic automation. It entails blending machine and information technologies to improve how goods are sourced, produced, and shipped worldwide.
Industry 4.0 factories feature sensor-equipped machines and tools; a new generation of automated robots; and augmented reality, virtual reality, and video tools that communicate with each other to increase production accuracy and throughput. Coupled with machine learning (ML) software and big data analytics, these “smart factories” wring more efficiencies from processes by monitoring and automatically adjusting production runs. They also help manufacturers schedule production lines according to predicted bottlenecks, maintenance schedules, costs, and production line setup. Manufacturers with these smart factories can respond more quickly to changes in demand or production requirements than those with conventional assembly lines, and they can more easily identify both potential problems and opportunities to maximize revenue.
The effects extend beyond production. Industry 4.0 has the potential to substitute more automation for human work, requiring manufacturers to retrain many of their employees for higher-level, technology-oriented work.
Industry 4.0 is the use of automation and data analysis technologies to create smart factories where machines communicate with one another and with workers to automate more production processes, reduce defects, and predict equipment failures. An Industry 4.0 approach to manufacturing can raise production throughput, lower costs, boost product quality, speed time to market, and help ensure that production lines don’t break down.
For example, Austria’s Alcar Ruote, which makes steel aftermarket wheels for European cars, implemented supply chain and manufacturing software to analyze sensor data in its Swiss plant. With a smart factory, Alcar Ruote can now better anticipate machine failures and compare product quality to customer requests, and it has dramatically reduced lost production time, or the periods that workers spend idle or redoing tasks.
Smart factories use sensors mounted on production equipment that collect data and transmit it to ERP software, which analyzes the data in real time. Industry 4.0 production techniques let manufacturers use this data to create digital representations of equipment and processes. Using these “digital twins,” manufacturers such as BMW and Siemens virtually test new factory layouts—for example, adding a robot to a given workspace while accounting for the building’s lighting and human ergonomics—and deploy them in less time than usual.
“Lighthouse” factories, which multinationals use to showcase their adoption of Industry 4.0 methods, include
Still, as of 2020, about three-quarters of manufacturers implementing Industry 4.0 technologies were “stuck in pilot purgatory,” unable to deliver satisfactory ROI, according to a McKinsey study. Such programs remain difficult to scale, and teams tend to get stuck analyzing an entire operation before implementing quick wins.
Leading manufacturers increase production, reduce material losses, and improve delivery times with Industry 4.0 methods. That’s especially important now, amid materials shortages and disrupted supply chains. Facing mounting pressure from regulators and investors to reach sustainability targets, manufacturers also use Industry 4.0 practices to reduce energy consumption and waste by fine-tuning production lines. Industry 4.0 can also improve productivity in day-to-day factory operations, a necessity for manufacturing industries in many countries struggling with labor shortages.
Successful Industry 4.0 projects depend on several technologies working together in an integrated, secure fashion.
Networks of machines with sensors that transmit information about the health and operations of equipment, combined with software using AI and ML to analyze results on the spot, yield gains in productivity and product quality as companies use them to spot bottlenecks, reconfigure lines as needed, and detect defects.
Smart factories integrate production-line monitoring and predictive maintenance with technologies such as cloud computing, blockchain, and 3D printing. Some of these factories serve as “lighthouse facilities,” which show other manufacturers the gains they might realize with an Industry 4.0 approach. In addition to more quality control, benefits of smart factories include the ability to virtually reconfigure machinery on the production floor and observe the potential effects through digital simulations of processes, as BMW does in its Regensburg, Germany, plant.
Machines equipped with sensors that connect to one another and the internet help monitor and control factory processes such as assembly and painting. For example, a smart manufacturer might outfit machines at a handful of plants with internet-connected sensors, then use a single software system to remotely evaluate whether the machines need maintenance instead of visiting each one in person to make that evaluation. Advantages of cyber-physical systems include more efficiency, safety, and the ability to tailor processes to changing conditions, as product realization company Noble Plastics found after implementing IoT technology to drive machine performance and configuration remotely.
This concept is an extension of IoT. Some companies build analytical software services for their machinery and sell them to customers. For example, Siemens offers customers its software called Insights Hub, which lets them monitor individual factory machines with metrics including daily yield, time spent idle, and maintenance status. Manufacturers can use these insights to, for example, choose the right mix of machines to complete an unexpected, urgent order. In another example, remote monitoring software from Swiss power and control vendor ABB lets technicians at solar power facilities forecast production and manage equipment remotely. Customers can buy both the plant machinery and the software from ABB.
Taking advantage of IoT data requires the integration of applications and the exchange of that data among various systems, including databases, ERP applications, and analytics software. Conclusions derived from analyses of that data need to be shared among departments, including procurement, production, sales, and service—for example, letting assembly and painting lines coordinate with shipping or keeping supervisors current on machines’ status. Titan Wheel, which makes heavy-duty wheels for farm and construction equipment, uses IoT technology with cloud ERP and supply chain management software to record parts coming off a line, offering teams a real-time view into inventory availability and removing a shipping bottleneck.
Industrial manufacturers use cloud-based computing and data storage services to process and store the reams of data coming from connected machines and apply advanced analytics to that data to make better decisions. The next generation of edge computing solutions, such as Oracle Roving Edge Infrastructure, brings cloud capabilities closer to production lines.
Big data analytics, with the aid of AI and ML, derive insights from the huge amounts of data that smart factories generate, and manufacturers use these insights to make decisions. For example, a manufacturer might analyze maintenance patterns for machines of a given make and model or over a given year, along with reams of sensor data from a particular machine in its factory, to decide when to send a maintenance worker to that machine. AI can also help reconfigure production lines based on order planning and releasing.
The open exchange of data among IT systems and industrial equipment is the cornerstone of Industry 4.0. Software in factories and cloud computing data centers needs to be able to read information from sensor networks to fuel autonomous decision-making and improve production.
The accuracy and utility of robots are improving in a variety of factory settings that require machines and humans to work side by side or robots to handle delicate tasks. In industrial settings, collaborative robotic arms can speed up for efficiency during certain times of the assembly process, then slow down when operators approach to place new parts on a pallet. Industrial robots can run for back-to-back shifts, helping companies address a shortage of assembly-line workers amid rising order volumes.
Machines on factory floors get replaced only once or twice a decade, while IT systems are updated much more frequently. That disparity creates potential security issues as machines are exposed to the open internet. However, cloud computing can shore up security since software delivered as a service is continually kept up to date. Cloud service providers can deliver security services that analyze potential vulnerabilities of connected machines and recommend remedies. LTE and 5G private network setups, in which a manufacturer owns and controls the radio spectrum used to transmit sensor data, can also strengthen a company’s IoT security.
Manufacturers can gain more insights into how to improve their operations using Industry 4.0 software from Oracle. Supply chain management and ERP applications let manufacturers capture and analyze shop floor data to support their smart factory setups. The applications let teams visually design production processes and give technicians mobile dashboards for tracking work progress. Mixed-mode manufacturing software handles both discrete and bulk processing in the same facility. Production supervisors can see overviews of work progress, problems, and quality reports on a PC, tablet, or phone and generate parts lists or quality histories with ease.
Oracle Fusion Cloud Internet of Things Intelligent Applications include modules that let manufacturers use sensor data to monitor work in progress and prevent unplanned downtime; predictive maintenance to support uptime; connected logistics to monitor transportation systems and warehouses; and workplace safety. Oracle's manufacturing execution system (MES) lets companies monitor their production activities with KPIs and measures of shop-floor status. The system helps teams boost productivity by prioritizing important activities and helping spot wasteful ones. Automatic data capturing helps operators on the production floor spend more time on manufacturing and less on reporting.
What’s the difference between Industry 4.0 and IoT?
The Internet of Things connects machines and processes over an IT network. It’s just one component of an Industry 4.0 setup, which also includes powerful software for analyzing the data coming off the network and automating decisions based on that analysis.
How can Industry 4.0 help my business?
The technologies we’ve discussed, when implemented efficiently, can improve product quality and delivery times, help reduce operational costs, lead to faster and better decisions, support companies’ environmental goals, and support new manufacturing industries, processes, and business models.
How does Industry 4.0 differ from lean production?
Lean manufacturing, in which companies receive raw materials at the time they need them and continually work to eliminate waste in their processes, traces its roots to Toyota’s postwar manufacturing systems. It has been widely adopted since. Industry 4.0 expands on that principle, incorporating networked machines and software-based analytics to apply learnings in an approach that supports both business and industrial goals.