## 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** & \multicolumn{2}{c}{**Description and Recommendations**} \\ \hline  & \(\bullet\) & Data storage and recovery—clouds, servers, magnetic tapes, \\  & \(\bullet\) & Man–Machine Interfaces—dashboards and APIs, \\ Current & \(\bullet\) & Redundancy—hardware, software, data, network, and power, \\ state & \(\bullet\) & Application of AI methods—support for monitoring, control, steering, and optimization; decision making; data processing, \\  & \(\bullet\) & Methods for optimizing energy efficiency—optimization of operating and routing parameters. \\ \hline  & Machinery, infrastructure, and equipment: \\  & \(\bullet\) & Autonomous intralogistics system combined with automated and robotic production, storage, and digital \\  & information flow throughout the company, \\  & \(\bullet\) & Connectivity (transparent process analysis, optimization of programming methods, real-time programming, \\  & process data monitoring), \\  & \(\bullet\) & Mandatory diagnostics and prediction (scheduling of maintenance and repairs based on current operational status; \\  & improving safety by identifying irregularities and implementing safe behavior scenarios, reduced downtime, \\  & \(\bullet\) & increased productivity, and OEE). \\  & Human resources: \\  & \(\bullet\) & Minimization of employee participation, \\  & \(\bullet\) & Reduction or total elimination of human error (including cyber security risks). \\ Processes: \\  & \(\bullet\) & Increase quality, safety, reliability (improving internal and external supply chains), and flexibility (adaptation to \\  & changes in demand—in real time), \\ Advantages & Monitoring, controlling, and maximizing operational efficiency (the elimination of manual tasks and functions in \\  & favor of automated and digital solutions; data-driven decision making; tracking of individual objects and their \\  & groups—full utilization of the resource potential; process optimization—in terms of performance and scheduling, \\  & consumption of utilities, other costs), \\  & Reduced time to market (more effective decision making in response to market fluctuations, better insight into \\  & supply chains, reduced response time to disturbances, identification, and elimination of inefficiencies), \\  & \(\bullet\) & Holistic standardization within the application of Digital Process Twins, \\  & \(\bullet\) & Streamlining maintenance and repair processes (provision of parts, sub-assemblies, components, consumables on \\  & time; dynamic estimation of budgets), \\  & Advanced production monitoring methods (maintaining the serviceability of resources, quality management, \\  & safety and health of workers, production planning and scheduling, process optimization), \\  & \(\bullet\) & Intralogistics and storage operations—monitoring asset parameters (status, current and total efficiency, failure rate, \\  & location) and processes (compliance with agreed schedules, bottlenecks, delays), \\  & \(\bullet\) & Supply chains—monitoring deliveries, stocks, and products on the shop floor, control of storage and the required \\  & \(\bullet\) & External process operations—integration of external suppliers (stock stability), monitoring quality standards, and timeliness of tasks. \\ \hline  & Machinery, infrastructure, and equipment: \\  & \(\bullet\) & High purchase, implementation, and operating costs (sensors, devices, communication protocols, cloud storage, IT \\  & staff, technical support, and IIoT maintenance—including software), \\  & \(\bullet\) & The need to ensure reliable connection and communication (machines, equipment, safety systems, controllers, \\  & application software, and other required components) and communication infrastructure (ensuring consistent, \\  & reliable, and real-time data transmission, supervision, and monitoring), \\  & \(\bullet\) & Optimization of the number and type of data collected (data range, data structures, plugins, and fusion methods) \\  & for KPI evaluation purposes, \\  & \(\bullet\) & Data collection and processing required (algorithms, continuous optimization of data storage infrastructure, \\  & prioritization of safety and security through the implementation of cyber security rules). \\  & Human resources: \\ Disadvantages & \(\bullet\) & Highly skilled robot programmers and integrators and employees with interdisciplinary skills are required. \\ Processes: \\  & \(\bullet\) & The need to develop a data security policy and provide the necessary technical measures, \\  & \(\bullet\) & Required specification of data management methods—scalability, upgrade management, troubleshooting without \\  & interrupting workflow, integration of internal or external resources (including Cloud Computing), standardization \\  & of interfaces (elimination of information silos), holistic and centralized policy (integration, associativity of existing \\  & resources and elimination of the risk of inconsistency and data loss), \\  & \(\bullet\) & Defining the scope of data management policies to create value for the company (minimum number of datasets, \\  & the frequency of collection, and the volume and cost of archiving), \\  & \(\bullet\) & The need for advanced software and analytics—scalable platforms, Big Data analytics, data management, and \\  & interpretation to provide reliable information. \\ \hline \hline \end{tabular}
\end{table}