Human-Machine Interface


Augmented Reality

The technology behind Augmented Reality (AR) enables bridging the physical environment of a shop’s floorwith its virtual counterpart in the form of a digital twin. The virtual layer provides immediate access and visualization of the right data on the site of production process directly. As a result of this, production personnel could aptly dispose situations with an up-to-the-minute overview of production processes OR have at hand the information necessary to efficiently manage maintenance operations or interventions per a particular machine or equipment.

Telemanipulation – Gesture Control

We monitor moving objects via a set of software solutions based on state-of-the-art technologies that process images obtained from an online video. Thanks to advancements in motion and position detection, both of which could sense an operator’s movement in space, we have introduced new possibilities that can be used to control production lines with an operator’s gestures. Besides the immense advantages of contactless control, this technology makes it possible to perform 3D scanning of an operator’s motions, which is applicable in the training of an assembly line’s operations at a factory’s training center(s).

Voice Technology in Manufacturing

We are developing an automatic identification technology based on the recognition and digitization of human voice. What’s more, we have made the system’s interpretation of the data from a human voice feasible, thanks to the specialized mobile terminal.

Users are not receiving information visually, but aurally. Voice commands confirm their activities. We are widely using the system PickByVoice, which enables employees to move freely.

Navigation Through Dynamic Visual Elements

It’s a projection of a technological procedure or navigational information into the operational space of an employee directly. Eventually, we will be installing Head-Up displays onto a car’s front windscreen or another reflex glass; and thereafter, signal lights in designated positions.

This way, we would achieve a more precise navigation system; hence an employee would be afforded a free hand and do not need to take eyes off their display devices while working. An employee could ably interpret a change of color as an alteration in the state of a machine, equipment, or material. The examples of such navigation are PickByLight and PickByPoint systems or different variation of ANDON system.

Big Data for Smart Industry


Prediction of Demand in Logistics

Big Data presents an inspiration to develop new business models for logistics services. As a result of it, we can analyze large volumes of heterogeneous data and unveil patterns of behavior and relations. It lets us identify anomalies in automatized production’s data. We could anticipate delays in supply chain and devise an appropriate workaround to tackle them during logistics services, thereby achieving higher client’s satisfaction.

Predictive Maintenance

We are working with a tool that extends the existing maintenance plan through statistics and data mining methods. This tool notifies the operator of when the next interference would occur because it detects when a machinery or equipment is liable to succumb to high-risk failure. On the reverse, the tool, based on predictive maintenance, evaluates an equipment’s condition and can call off planned maintenance if the equipment does not neccesitate one.

Next generation automation enables us to integrate a machine or equipment’s diagnosis into the operational systems directly. The industrial Internet of Things (IIoT) provides a platform through which information can be shared, while Big Data analyzes and stores the information.

Production Planning and Predictive Manufacturing

Based on the analysis of production data, we can easily predict the origin of scraps, downtimes, and delays in the manufacturing process. This tool notifies supervisors upfront about the possibility of an emergency or reclassifies the order of production to minimalize the risk of incident.

Artificial Intelligence


Operative Dispatching/ Manufacturing Resource Scheduling

Each machine or equipment operating in a factory or enterprise communicates, thus they generate an extensive amount of data. Fittingly, employees are unable to keep track of all those information and notifications.

We are analyzing these Big Data directly in our systems to control manufacturing operatively. Implemented self-learning algorithms and artificial neural networks provide already consolidated information to users—in the right amount and moment—so that they can take practical and apposite decisions.

Warehouse Positioning Optimization

We predict the most appropriate positioning of goods in a warehouse in proportion to the expected quantity in a way that makes the best use of the warehouse’s space.

Autonomous, self-learning, and genetic algorithms and artificial neural networks are the leading concepts of production automation in managing manufacturing operations and processes in real time. The algorithms are also serviceable in planning in the form of forecasts and prognosis for the optimization of industrial processes.

Autonomous Logistics Solution Based on the Multi-Agent System

Each unit (i.e., equipment, accessory, vehicle) is assigned a virtual agent in the cyber system. The system, based on the principles of interconnectivity and decentralization, synchronizes and coordinates units as a whole entity. These aggregations (regarding their mutual information and availability) perform their tasks in an especially optimal fashion.


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