Unmanned trucks, IoT sensors for supply chain management, industrial augmented reality… Do these things still sound like a science fiction to you? Well, advanced industrial intelligence is already closer than you might think. From advanced robotics in factories to computer vision in warehouses, technology is making an impact on every step of the manufacturing process.
With access to the cross-market data, the future factory can predict, respond, and adapt to market trends to optimize business processes and personalize the consumer’s experience. Little by little, a future factory starts resembling Lego bricks, with plug and play modules that fit together and connect with each other. So, it gets much easier to set up complex systems, but at the same time it also reduces the overall complexity of a production platform by using new integrated technologies.
How will futuristic factories be organized and managed?
Everything in such a future factory will be operated by computer-controlled robots. The technical staff will monitor activity of these machines via a central control system. The experts in the field have already labelled the upcoming wave of as “Industry 4.0”, the 4th industrial revolution.
For example, in Operations Technology Monitoring/Machine Data, factories will get basic digitization, and further a greater predictive power. In Warehousing, we’ll see “lights-out” fully automated warehouses even faster than an unmanned factory, with the help of robotics and vision tracking. Manufacturing is deeply changing with the new technology, and nearly every manufacturing vertical is implicated.
Machine data metamorphosisIt’s highly probable that tomorrow’s manufacturing process will look like one huge, self-sustaining cyber-physical organism that only intermittently requires human intervention. But across sectors, manufacturing process has a long way to go before we get there, especially considering the existent IT and OT gap. Initially, we’ll see some machines becoming more digital-friendly. Later on, that digitization could translate into predictive maintenance and true predictive intelligence.
Large capital goods have evolved to a “power by the hour” business model that guarantees uptime. Power by the hour (or performance-based contracting) is now fairly common in the manufacturing world, especially in mission-critical areas like semiconductors, aerospace, and defense. The truth is that shop floors typically contain old machines that still have decades of production left in them. In addition to significant cost, sensors tracking temperature and vibration aren’t made with a typical machine in mind, lengthening the calibration period and efficacy.
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From initial digitalization to predictive digitalization
As manufacturing gets better digitized, the boundaries between IT and OT will continue to blur. This will create plenty of opportunities for startups to bring off-the-shelf computing hardware that can plug into most machines directly, or integrate existing PLCs. This, in its turn, will allow small- and medium-sized businesses to be leaner and analyze their efficiency in real time.
In the near future, advances in AI and hardware will allow IoT as we know it to be nearly independent of centralized clouds. Mission critical-systems such as connected factories can’t afford the delay of sending packets to off-site cloud databases. Cutting power to a machine split-seconds too late is the difference between preventing and incurring physical damage.
Moreover, edge computing lays down the rails for the . The AI software underpinning the edge will be the infrastructure that allows future factory machines to make decisions independently. Cyber security of the future is another issue to consider: managing the ICS and IIoT systems securely will continue to be a critical area for investment.
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Automation for production and assembly
It’s quite logic that automation will come for dirty, dull, and dangerous jobs first. The concepts for Industry 4.0 involve a completely intelligent factory where networked machines and products communicate through IoT technology, and not only prototype and assemble a specific series of products, but also iterate on those products based on consumer feedback and predictive information.
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Robotics substitute previously unpopular jobs
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Manufacturing jobs, which have been on the decline for decades, have extensively been taken by industrial robotics in factories. Thus, cobots (collaborative robots) are programmable through assisted movement. They “learn” by first being moved manually and then copying the movement moving forward. These robots are considered collaborative because they can work alongside humans. To illustrate, in heavy-duty machining, a significant market share is taken by big industrials players like ABB, Mitsubishi, Fanuc, and Yaskawa.
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Manufacturing is becoming increasingly more efficient, customized, modular, and automated.
But factories still undergo the transitional stage. Manufacturers are known to be slow adopters of technology, and many may still resist making new investments. Yet, the most powerful levers manufacturers can pull will come in the form of robotics in factories, AI, and basic IoT digitization. Richer data and smart robotics will maximize a factory’s output, while minimizing cost and defects.
As Henry Ford once said, “If you always do what you always did, you’ll always get what you always got.” To reach its full potential, the manufacturing industry will need to continue to embrace new, at times, risky technology.
Creating new organizational and architectural infrastructures are a key aspects of this data-driven trend. The infrastructure with which data is managed, both within companies and at a broader industry level, needs to change dramatically to provide the ability to bring data together effectively, so it can be analysed and deliver value in different ways. Companies are increasingly seeing value in interconnecting, ingesting, provisioning, and orchestrating data for manufacturing action up and down the value chain.
Transformational change has to be disruptive, otherwise it’ll take forever if you just try to tackle it one piece at a time. It’s important to tackle the training and education, the investment, the IP, and the technology in a comprehensive and coordinated way. It is better to take small steps in all areas together, than to take a large step in one area and then, eventually, try to take on another. If you try to do the technology without the others, you just won’t get there.