Connected Production Eases Burden on Skilled Workers

Programming production facilities, replacing machine components, controlling material flows and ensuring quality standards, skilled production workers are genuine multitasking talents who coordinate many tasks simultaneously and must maintain a high level of concentration at all times. They are in charge of ensuring a smooth flow of production processes, are responsible for responding promptly to irregularities and must keep an eye on many consecutive processes. That is easier said than done because operatives as a rule work at the machines – hardly an ideal location for maintaining a continuous overview of the manufacturing process chain.

If they also have to perform many tasks manually, such as taking samples or switching components for different production lots, an exciting job can quickly become stressful. The good news is that the Internet of Things (IoT) offers many approaches to ease the burden on production workers, to inform them better and to optimize processes in the long term.

Providing skilled production workers with more information and automating recurring activities efficiently presuppose availability of data. Companies acquire data by efficiently connecting critical plant components such as valves, pumps, drills and presses. Everything that makes a significant contribution to a smooth and trouble-free production process provides important information for on-site operatives. The sensors of these components, for example, can be equipped with wireless modules to enable them to send key indicators such as tool wear, temperature, pressure or humidity to an IoT platform in the cloud. Employees at the production facility can access this information on a tablet or smartphone and see all key values at a glance. They can then tell when important tool or machine components are not working properly and can remedy any problems before workflow stoppages occur.

With the right information at their disposal skilled machine operatives will much less frequently need to perform routine checks; they can always check the status of components on the IoT platform. In combination with predictive maintenance applications factory workers can even identify imminent problems well in advance. On the basis of the data the technology anticipates specific values such as wear and tear of a machine component and automatically notifies the technicians of imminent maintenance requirements or outages. As a result, the risk of outages is much lower and skilled workers will much less frequently need to carry out standard maintenance work.

Companies can not only integrate production employees into processes of all kinds and ensure intelligent support by means of IoT-based monitoring solutions; with a Digital Production Twin they can also ensure greater transparency and more efficient processes. A digital twin is a virtual map of the entire production process that is based on real IoT data from plant and machinery. With a digital twin entrepreneurs can, for example, simulate changes in the production process and analyze repercussions without running the real risks that the changes might entail. A digital twin also improves the information situation of machine operatives when, for example, the findings of simulations indicate imminent material bottlenecks or process chain disruptions.

Easing the burden of monotonous activities on skilled workers requires both greater transparency on the shopfloor and specific automation measures. In manufacturing, for example, they may be in quality assurance. In many factories skilled workers still have to take samples by hand to check products for irregularities; in modern production processes optical sensors perform this task. They generate images of the products, send the data via the IoT network to an AI-assisted analytics platform and identify production faults in next to no time.

An outage that occurs despite real-time monitoring and predictive maintenance can quickly prove really costly for the company in question. Depending on the damage the repair itself can be expensive and the outage can also have a negative effect on the balance sheet. Cosmetics manufacturer Schwan Cosmetics created a pragmatic, IoT-based solution to this problem. Rather than wait for a service technician to arrive, skilled workers now remedy malfunctions themselves on-site. To do so they wear Augmented Reality glasses that enable them to consult external specialists by live stream as they carry out maintenance work. Production employees receive important advice on how to remedy the fault and are ideally able to solve the problem without costly waiting times.

IoT solutions can not only assist management and employees in manufacturing with their core tasks but also be very helpful with organizational issues. With the right technology companies can digitize their time recording, for example. In practice, missing timesheets or breaks and overtime that are not recorded regularly cause problems. According to a survey by HR service provider SD Worx fewer than 50 percent of German firms record hours worked, and many of those that do still rely on paper records.

That’s how it was at Dekoran until it digitized its time recording with tools from ZMI. On larger construction sites its employees now clock in and out at a terminal in the site container. The terminal is equipped with a Telekom M2M-SIM and an IoT data tariff to send the hours worked via the cellular network directly to the time recording solution, which takes over the evaluation of the hours and then sends them to its payroll department via a DATEV interface. Work instructions for each site are deposited at the terminal. On smaller sites or for quick jobs employees record their hours via a smartphone app. That too is an efficient and time-saving approach many manufacturing companies can use.

Practice shows that there are many ways in which manufacturing companies can use smart IoT solutions to increase value creation in the digital factory and put the skills of machine operatives to use more efficiently. Digital twins and IoT-assisted monitoring improve machine transparency so that skilled workers are aware at an early stage of possible outages or bottlenecks. Data glasses make employees more independent in the smart factory and reduce both outage times and service costs. If firms also automate manual activities, skilled production workers can put their skills to much better use and contribute in the long term to higher value creation.