Lately, AI and machine learning algorithms have become a vital part of manufacturing processes in many industries. Artificial Intelligence is also one of the pillars of the so-called fourth industrial (or Industry 4.0). Let’s take a closer look at the role of AI in today’s industry and how a factory can benefit from it.
Artificial Intelligence as part of Industry 4.0
Manufacturing and logistic industries have to deal with the volatility, uncertainty, complexity, and ambiguity (or VUCA for short) of the global market. Thus, many business leaders decide to invest in new business models and technologies that can mitigate the unpredictability and uncertainty of the world.
You can learn more about the ‘VUCA World’ and its effects on industries in the post at https://versabox.eu/ai-in-industry/.
Artificial intelligence is one of the most prominent examples of such technologies – it’s at the foundations of Industry 4.0 (others are e.g. Internet of Things/IoT, machine learning, blockchain, and automation). Thanks to AI, machines used in a production process or logistics can perform much more complex tasks and ‘think’ for themselves. This plus other technologies (e.g. computer vision) makes robots fully autonomous and gives them far higher flexibility – an AMR (autonomous mobile robot) can be a substitute for many other machines and fill in for human workers on various workstations.
Which parts of manufacturing can benefit from AI systems?
AI systems can be applied to both management and operations. Here are some of the most popular AI applications and the benefits they bring to manufacturing plants:
- Quality control – such a system uses machine learning algorithms and image processing enables robots to detect anomalies/defects in products based on given quality standards. Such real-time testing is effective and helps in reducing waste and improving overall product quality.
- Autonomous vehicles – probably the most common industrial artificial intelligence example. AMRs with AI on board can make their own decisions, based on the current situation, and modify their route ‘on the fly’ if such a need arises. Of course, simulating human intelligence wouldn’t be enough without the right optics – image recognition and machine vision. Therefore, AI-powered robots are capable of handling logistics without human intervention (excluding minimal supervision). If you would like to know more about AMRs and their functionalities, visit https://versabox.eu/en/.
- Production planning and optimising performance – AI algorithms are often used as optimization tools for production schedules. They can help reduce downtime, minimise waste, maximise equipment utilisation, or introducing/expanding robotic process automation.
- Augmented reality (AR) – AR, AI-powered systems can provide assistance for workers in interacting with robots, repairs, maintenance, and other tasks on assembly lines. AR interfaces can lead to fewer errors and improved efficiency.
- Virtual reality/Digital simulation (Digital Twin) – thanks to AI systems and machine learning algorithms, companies can get advanced performance analysis and create digital models (digital twins) of various physical assets. The former takes data from various sensors to predict e.g. most likely time of equipment failure and prevent it with predictive maintenance. The latter includes simulations of everything from a single product, through a complete production process, to whole factories/manufacturing plants. Such digital models allow you to test various solutions and machines before their physical implementation.
- Inventory management – Artificial Intelligence systems can be used for analysing all sorts of data that surrounds a company. Such in-depth analysis can help in e.g. supply chain management and keeping an optimised stock of raw materials and components, thus mitigating the risks of stock-outs and overstocking.
These few examples of AI applications should give you a rough idea of the possibilities opened up by Artificial Intelligence and technologies surrounding it. Of course, this is just the tip of the industrial AI iceberg – Artificial Intelligence will keep evolving, giving us even more innovative solutions in the foreseeable future.
Undoubtedly, applying AI systems into your factory can provide tremendously beneficial boons. Tools such as Digital Twins and AI-based advanced data science are a great first step for completely autonomous intralogistics and fully optimised production capacity.
Smart factories and their challenges
A smart factory can be summed up as a modern manufacturing plant that embraces new technologies and constantly strives to better its production processes. The main goal of ‘smarting-up’ a factory is to maximise speed and quality of production, while lowering its costs. Many challenges are standing before the implementation of smart, AI-based factories – let’s take a closer look at the most common among them:
Technical complexity
Integrating new and advanced technologies into factories can be a complex task and requires specialised expertise. Thankfully, although far from being easy to deal with, problems of technical nature can be solved by technology itself. In this case – in the form of standardised digital platforms. Such platforms can work on a multitude of end user devices and provide a wide array of digital services from various vendors.
For clarity, let’s look at an example. Standardised digital platforms work similarly to the automotive industry – they use a shared set of major components, common designs, and tools to deliver various effects tailored to your needs. Just like different models of cars of the same brand. This also means that such platforms are highly adjustable and can fit in various industries.
The best choice of platforms for manufacturing should be based on a shop floor operations platform or Industrial IoT platform. This allows you to link all smart devices/processes into a shared network, which in turn makes it easier (or even makes it possible) to integrate them into a master factory system, like ERP, MES, or WMS. It’s worth noting that many industries accept digital platforms as the solution to integrating legacy systems – thanks to the standardised nature of these platforms, legacy systems can still be accessed through digital ‘adapters’.
Another wall caused by the so-called legacy thinking and work culture stagnation (unfortunately quite common in the manufacturing industry) is a reluctance to embrace new technologies, such as modern digital platforms deployed as part of global cloud infrastructure. Manufacturing companies that cling to the past often focus on point solutions and miss the bigger picture, discarding highly beneficial solutions.
Data integration and management
Data integration and management is, arguably, a matter of even higher importance, as smart factories are a data-driven ecosystem of humans and technology. It shouldn’t be a surprise that such a place generates copious amounts of data – collecting and managing it all effectively requires a robust infrastructure and skilled specialists.
But that’s not all – in data integration, clear assignment of business roles and responsibilities is a must. Such matters as data stewardship and ownership must be as clear as day. Fortunately, clear data structures are a part of lean management practices, which can be a valuable aid in introducing new smart solutions.
Again, legacy management and culture is the greatest obstacle in harnessing the power of Big Data – clinging to the ‘good old’ methods can, and most likely will, create a wall that would stop factory digitisation in its tracks.
Cybersecurity
Security in cyberspace is definitely a complex problem, especially for smart factories – such production plants rely heavily on interconnected systems, which can be very vulnerable to cyber threats. Thus, many factory owners and managers tend to reject digitisation, based on fear of insufficient security. Overcoming this obstacle requires addressing basic human psychology, enterprise culture, and, of course, technology.
These concerns are often baseless – many professional digital platform providers prioritise data safety and offer the highest grades of cybersecurity currently available. This means that high-quality cloud data centres ensure much better protection than your local servers.
Work culture adjustment
Implementing AI systems into a factory also brings about significant changes in work culture – e.g. the way the work is done changes, and the role of human workers will be reduced in some areas. Artificial Intelligence and the advanced automation it brings typically means that workers will have to acquire new skills, which can be troublesome in companies with a diverse and large workforce or with deeply rooted legacy culture.
High initial costs
There’s no denying that making a factory ‘smart’ will require significant investment in new infrastructure, processes, and technology. Although it can be challenging, the results are definitely worth it – when done right, a smart factory will work far more efficiently and in time will generate significantly higher profit. You should keep in mind that ‘smarting up’ a factory can be done in stages, spreading the expenditure over time.
AI in industry - summary
Despite the challenges it brings, implementing AI and other smart factory technologies bring in significant benefits like making better informed decisions (based on solid data analysis), increased efficiency, improved product quality, optimised production, and significant cost reduction. Based on this, it shouldn’t be a surprise that many manufacturers find it compelling – according to a 2022 World CEO survey done by IDC, over 80% of respondents are willing to initiate a ‘Digital First’ programme. Half are already doing it, and 27% have started their digital transformation before the events of 2020.
So if you want to avoid being left behind and reap the benefits of these technologies, start going smart with your factory today and join the market giants in the digital era.
