## 2 A Method for Assessing the Maturity Level of Key Areas in Manufacturing Companies
### Characteristics of the Key Area
\begin{table}
\begin{tabular}{c c} \hline \hline
**Level Characteristic** & & **Description and Recommendations** \\ \hline \hline \multicolumn{3}{c}{Comprehensive robotization of the factory with robots as IIoT devices:} \\ \(\bullet\) & Robot variation—AMRs, conventional industrial robots, and cobots with IIoT functionalities, \\ \(\bullet\) & Characteristics of processes’ support activities (machining, assembly, manipulation)—flexible automation with robots as IIoT components (Cloud Computing, Big Data, multi-level connectivity, SSoT, automated control without human intervention, etc.), wireless connectivity and an agile production management model, \\ \(\bullet\) & Staff knowledge, comprences, and skills in the field of robots—knowledge of robots aided by the application of AI algorithms; knowledge in planning and integrating the robot into the plant’s digital assets, simulation, and programming in the Digital Twin environment and hyper-automation, \\ \(\bullet\) & Staff knowledge, comprences, and skills in the field of industrial process robotization—ability to think \\  & systemically, agile process thinking, ability to design autonomous production systems, \\  & Robot–employee interaction—responsive collaboration (shared tasks and workspace) with Augmented Reality support, online or offline programming (using AI-assisted methods), \\ \(\bullet\) & Interaction and integration at robot-machine level—as in the previous case, Multi-Cell Flexible Manufacturing Systems, \\ \(\bullet\) & Robot features and parameters: \\ \(\bullet\) & Sensors—intelligent sensors (embedded in robots, machines, and process sensors), RTLS systems, \\  & Communication interfaces—Internet, IO-Link, OPC UA, ZigBee, LoRa/LoRaWAN, Bluetooth, BLE, eLowPan, \\  & DUST, NB-IoT, Thread, SG campus networks, \\ Current & \(\bullet\) & Safety features—extended by advanced AI and ML methods, \\ state & \(\bullet\) & Possibility of remote configuration, programming, monitoring, diagnosis and optimization—network services (M2M software); real-time monitoring, control and diagnostics; prediction based on AI/ML methods (datasets generated by a set of robots—operational and environmental data, exploitation patterns; prediction at individual and holistic level); overview of circuits, location, and status; activation and deactivation of the standby function during periods of inactivity (energy saving); monitoring and identifying incidents to enable immediate action and resolving problems; application of statistical methods to optimize the production process; management of equipment tasks and their optimization according to set priorities; creation, configuration, and modification of magnetic navigation circuits’ tag certification, user supervision, and revision managemently; tools for creating, configuring, and modifying navigation paths, \\ \(\bullet\) & Mobility mechanisms—full autonomy in the working environment, \\ \(\bullet\) & Vision systems—optional (depending on the required functionality), possible data acquisition from external sources (e.g., CCTV), \\ \(\bullet\) & Environmental adaptation—supported by AI/ML, data exchange with WMS, WCS, WES, ERP systems, \\ \(\bullet\) & Data sharing tools and methods for remote maintenance and repair—software for cooperation with integrators and service providers; creation of digital checklists and maintenance scenarios; definition and assignment of maintenance intervals; managing, recording, and documenting primary data to optimize inspections; digital files of the robot (robot life cycle documentation, data management, and decision making); smart notifications, \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The fifth maturity level of process robotization (KA2-ML4)—autonomous/stationary robots as IIoT devices.