INDUSTRY 4.0
Marrying AI and manual labor for more effective assembly lines By Carl Staël von Holstein, Axis Communications Digitization, automation, and Industry 4.0 are driving far-reaching changes in the manufacturing industry. But most of the mass production in industries such as automotive is still done by manual labor.
granted based on your work and frame of reference. So I was pretty surprised to learn that some 72 percent of assembly in mass production is still carried out by manual labor.
When it comes to manufacturing, there is quite naturally much talk about Industry 4.0. As you know, the industrial internet of things (IIoT), along with automation, artificial intelligence (AI), machine learning, augmented and virtual reality (AR and VR), are frequent buzzwords.
I came across this number when talking to a new partner. They are called Drishti and aim to improve productivity, quality, and training in various industries where manual assembly is still frequent and where lean production is generally the norm. They have a solid position within the automotive industry but also in medical devices and electronics and other long-tail discrete manufacturing industries. Drishti is also a valued member of the Axis Application Development Partner (ADP) Program.
And with good reason. These are potent tools that are already transforming manufacturing, and this development shows no signs whatsoever of slowing down. As one of the persons at Axis in charge of driving Industry 4.0 solutions, this area is obviously close to my heart. And believe me, many exciting innovations are going on. Quite recently, however, I was reminded how easy it is to start taking things for
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Connecting manual labor and AI When talking about Industry 4.0, we mainly focus on connecting smart components, letting the machines communicate to reap the benefits. In this
perspective, Drishti offers a kind of hybrid solution. They use videos to connect the manual labor at the assembly line with AI. This way, they gather masses of data in the process, which is analyzed and form the foundation for future improvements. It is a huge step forward since most mass production units still use the traditional way of measuring productivity by using a stopwatch and then writing down the results. However, this is a very limiting model because you miss out on valuable data that can be used to improve the operation. On the other hand, Drishti places cameras above each workstation, which quickly and continuously measures the process. The analytics on top of the cameras and AI translate the video streams into actionable data. You will, for example, get information on how long each moment takes and if there are any anomalies, bottlenecks, or repetitive
