For manufacturing and logistics companies that want to stay competitive going forward, smart factories are regarded as indispensable. A smart factory uses flexible automation solutions and allows products to be tailored to individual customers. But it also changes the way humans and machines interact. Alongside a host of benefits, there are also risks inherent in such a high degree of digitalisation.
What is a smart factory?
In a smart (or intelligent) factory, production is autonomous, connected and digitalised thanks to the use of cyber-physical systems (CPSs). These systems combine components such as machines, robots and production plant with software and information technology. Equipped with sensor, processor and radio frequency technology, they communicate with each other via the Internet of Things (IoT). Cyber-physical systems store digital information and share it with each other. The resultant smart production environments, comprising production equipment, driverless transport systems, industrial robots and tools, organise themselves: They control their production steps in such a way that a product cycles through each one without humans having to intervene in the production process. This approach is referred to as smart manufacturing.
The concept of smart factories is intimately intertwined with the Fourth Industrial Revolution (4IR), for which the use of the Internet is central. Information and communication technologies, connected machinery, plant and workflows together cause production processes to converge with IT.
What part do people play in smart factories, and how do humans and machines work together?
In a smart factory, people do not become surplus to requirements. On the contrary, humans and machines complement each other. In place of active involvement in the production process, humans take on a monitoring, optimising and controlling function. That is true of electronic engineers for building and infrastructure systems and for plant technology, for example, but it is equally true for mechatronics, mechanical engineers and IT experts. Additionally, in the context of developing and improving products, people naturally remain important in designing smart factories themselves and in solving technical problems in the course of smart production. Humans also assume an interface role: Even a smart factory needs some measure of support when communicating with external systems such as other smart production facilities. To meet these demands, the experts who staff a smart factory – such as IT specialists for digital connectivity – require an in-depth knowledge of applications around the Fourth Industrial Revolution. These include the cyber-physical systems (CPSs) referred to above, radio frequency identification (RFID), near-field communication (NFC) and augmented reality (AR). The latter in particular assists smart factory staff by digitally supplying images and videos to enhance their visual perception of reality. For example, if a machine needs maintenance, repairs or adjustments to its settings, the experts responsible do not necessarily have to be physically present on site. Thanks to augmented reality, they can support or guide colleagues in accomplishing these tasks from anywhere.
Opportunities and risks inherent in smart factories
Smart factories deliver a number of advantages compared to traditional production methods. Greater efficiency and adaptability enables them to optimise the value chain. For example, production processes can be designed in such a way that small series of products can be manufactured at short notice at the same low cost as conventional factories could only have managed with high volumes. This combination of flexibility and cost-effectiveness in turn facilitates rapid implementation of innovations and allows companies to quickly respond to the changing needs, preferences and demands of the market. To fully exploit the opportunities afforded by smart factories, every aspect of the company should ideally be digitalised. And this is where the element of risk comes in, because extensive digitalisation makes companies a bigger target for hackers. Cyber-attacks can sabotage, spy on or paralyse entire factories. Data can be stolen and machines manipulated. Companies must therefore take precautionary measures and invest in both a secure technical infrastructure and suitably skilled cyber-security experts.
What distinguishes traditionally automated production from a smart factory?
The crucial difference is that a smart factory controls the steps in production autonomously. It does so on the basis of production information that the product itself contains in machine-readable form (such as in RFID chips) in conjunction with seamless communication across all components and logistical procedures in the entire production process. Thanks to intelligent systems, the processes involved remain lean and production cycles short: These systems draw on relevant information about the target product – its dimensions, function and the number of units – to independently specify the optimal steps in production. In a traditional factory, this role is assumed by humans who serve more as the production “memory bank”. By contrast, machinery and equipment handles this task in a smart factory by gathering data and contextual information, learning over time and thereby making production processes more efficient.
How the automotive industry uses smart factories
Many observers describe smart factories as the “factories of the future”. Yet this kind of production is already present reality, at least in part. The automotive industry, for instance, deploys self-learning production environments. Generally regarded as a smart factory pioneer, this sector plans to invest in corresponding solutions in the years ahead with the aim of significantly improving both productivity and quality. The prospect of blending customisation with mass production is what makes this development so attractive to carmakers. A smart commissioning system, for example, makes its own decisions about what parts are needed. Autonomous transport systems deliver the parts to the right robots, which then begin to assemble them. In parallel, data from the machines and systems is collected, analysed and used to identify potential ways to optimise the workflow, predict any possible disruptions and schedule maintenance work.