A Method for Evaluating the Maturity Level of Production Process Automation in the Context of Digital Transformation—Polish Case Study

Abstract

This paper puts forth a systematic approach for evaluating the maturity level of production process automation in the context of digital transformation for manufacturing companies. The method was developed to address the absence of a sector-specific framework for assessing automation maturity growth, in line with the Industry 5.0 guidelines (incorporating sustainability, the circular economy, and human-centeredness). The survey covers six core areas for manufacturing companies: automation, robotization of production processes, digitalization of warehouse processes, flexibility, intralogistics, and end-to-end integration of key data management processes. The study aimed to advance digitalization through improved process automation maturity. The study surveyed 200 small- and medium-sized businesses operating in Poland from 2022 to 2024. The study presents a method for assessing enterprise operational maturity, covering the current and planned levels and the development plans for the next three years.

1. Introduction

Automation of industrial processes remains a dominant development area in the modern industry due to its advantages in increasing efficiency, cost savings, consistency, and quality, enhancing security and scalability [1,2,3,4,5]. The advantages of automation go beyond its direct benefits, including the ability to collect and process data, move to a real-time data-based management model, implement sustainability policies by eliminating production waste, and optimize energy consumption. Industry 4.0 implementation relies heavily on automation, an essential pillar [6,7,8,9,10,11,12,13,14,15].

However, Industry 4.0 was a technological paradigm centered around cyber–physical systems, digital connectivity, and Artificial Intelligence, resulting in increased efficiency [14,15]. The guidelines and principles of Industry 4.0 were mainly focused on increasing the efficiency of production systems and replacing human labor with connected machines. As the concept grew more widespread, it became clear that it was associated with revolutionary changes, significant investments, and the need for extensive awareness among the teams implementing it.

Increasing automation, digitalization, growing machine autonomy, and globalism (in the sphere of production, trade in services, and consumer goods) have necessitated the development of solutions to strengthen the competitiveness of the industry and national economies [16]. Industry 4.0 in its current form has lost value, especially in applications addressing the climate crisis, the global economic situation, and deep social tensions. Comprehensive system transformation was prevented due to the need for more coverage of essential design and action aspects. This hindered resource and material use, decoupling from negative environmental, climate, and societal impacts. In this approach, modern technologies are applied to achieve the desired outcome. Figure 1 shows the main areas developed within the Smart Industry concept [17,18].

From the end of 2022, the perception of the manufacturing industry has changed with the development of Industry 5.0 [19,20,21,22,23]. The emphasis on circular economy and sustainability has not diminished the need for advanced industrial process automation systems. Automation remains a leading force in modern industry, driving digital transformation towards sustainability [24,25,26,27,28,29,30,31,32,33]. Automation is gradually adopting many solutions to support pro-ecological development:

• A shift from the traditional hierarchical automation pyramid model to the connectivity pillar, which necessitates significant digitalization and both horizontal and vertical integration [34,35,36,37,38,39,40,41] but allows the operation of reliable data,
• Enhancing employee safety and optimizing operational excellence through new control quality methods and technologies [42],
• Inclusion of sustainability—in particular, monitoring machine utilization rates, diagnosing and predicting operating states, sustainable energy management, optimizing processes, and reducing energy consumption [43,44,45],
• Production systems can be made more flexible while reducing interruptions and reallocating employees to higher-value tasks, such as optimizing products and processes, developing innovative product and process improvements, and implementing continuous improvement methods [46,47],
• Sustainable product and process design (supported by simulation methods, Digital Twins, aided by real-time data analysis) [48,49,50,51],
• Integration of production data into a company’s business development planning system is a crucial task for inclusive growth and development [52,53,54,55],
• Progressive promotion is implementing digital operator support and a paperless policy [56].

Due to the need to meet the imposed requirements, many state-of-the-art solutions from modern technology and management methods were adapted to automation. Figure 2 shows the primary advancements in industrial process automation. It should be noted that the current solutions exhibit a high degree of validity in implementation. This is due to the increased awareness of entrepreneurs and the availability of experience from previous stages.

Investment in solutions causing technological debt, development barriers, and blockages due to a lack of consideration of standards, particularly for industrial data exchange networks, persists because of an apparent knowledge gap [57].

Given the current trends, automation development, especially for medium and small enterprises, requires incorporating the fundamentals of digital transformation and sustainability.

The author has developed a method to evaluate the automation maturity level of manufacturing companies, considering the digital transformation process and environmental factors.

2. A Method for Assessing the Maturity Level of Key Areas in Manufacturing Companies

2.1. The Main Obstacles to Automating Technological Processes

After analyzing the existing methods for assessing the level of maturity of companies, the author found some common shortcomings, among other things [58,59,60,61,62,63,64,65,66,67,68,69]:

• A focus on evaluating only the digital maturity level [58,59,60,61,62,63,64,65,66],
• A lack of definition of how changes in the core business areas of manufacturing companies impact their maturity level,
• Some approaches propose comprehensive digitization without justifying the technological solutions, and they fail to define the current level of maturity through a set of key factors that entrepreneurs understand well,
• A lack of focus on continuous improvement and sustainability [67,68,69],
• An inability to identify the advantages and disadvantages of the respective maturity level, which often leads to a willingness to adapt technological solutions that have untapped potential, resulting in incurring technological debt,
• The need for recommendations as requirements for moving to a higher level of maturity [70,71,72,73,74,75,76], which often leads to getting stuck or choosing the wrong solutions.

The author’s proposed method for assessing the level of maturity is based on the key areas of operation of manufacturing companies, where digitalization is an inclusive development factor necessary to meet the requirements of the relevant maturity level.

The approach used to assess the development of defined areas includes staff competency development, sustainability, continual improvement, modern technologies, and management methods. The guidelines were created by the European Union’s current market trends and policies.

2.2. Aims, Objectives, and Structure of the Proposed Method

The author developed a method that uses a five-point scale to evaluate the maturity of a manufacturing company’s operations in six key areas, which include:

• Automation of production processes—a key area of development that determines the possibility of the digitalization of production, as well as integration of production and management processes,
• Robotization of production processes—a key area of development for increasing the quality, reproducibility, productivity, and autonomy of manufacturing workstations, as well as the redeployment of employees to higher-value tasks,
• Digitization of intralogistics warehouse processes—a key area of development determining the ability to efficiently manage and optimize inventory levels, including inputs, finished products, equipment, and tools,
• Flexibility of production systems—a key area of development for achieving agility relies on improving internal processes such as dynamic path routing and increasing machine Overall Equipment Effectiveness ratios, as well as external processes such as responding quickly to market changes,
• Intralogistics of production processes (inter-station and inter-department transport)—a key development area for achieving agile automation in material and product flow streams,
• Integration of management, production, quality control, intralogistics, and warehousing systems—a key area of development for achieving Single Source of Truth data exchange status between all systems for managing and controlling company processes.

Key areas of the maturity level assessment method for manufacturing companies are illustrated in Figure 3.

Figure 4 shows a matrix of the manufacturing company’s key areas and maturity levels. The rows correspond to the current and target maturity levels, while the columns represent the individual key areas.

Guidelines and recommendations were defined for each maturity level for all the areas to identify current and target states, whereby it specified:

• Current state—a set of characteristics defining the minimum requirements of a selected key area, divided into categories representing the characteristics of a specific maturity level,
• Advantages—three independent sub-areas (machinery, infrastructure, and equipment; human resources; processes) defining strengths in each maturity level of a specific key area,
• Disadvantages—three independent sub-areas (machinery, infrastructure, and equipment; human resources; processes) defining weaknesses in each maturity level of a specific key area,
• Growth opportunities—a set of necessary directions to identify technological deficiencies (machinery sub-area), skills gaps (human resources sub-area), and process optimization (processes sub-area),
• Recommendations—recommendations including the implementation of measures to achieve a higher level of maturity.

The method involves selecting a specific key area’s current and target states. Defining the initial and target states requires meeting the requirements of lower maturity levels.

The key areas are closely interlinked and intersect at the development planning and implementation stages. While it is possible to evaluate a company’s level of maturity in a specific area that is considered a priority, implementing the recommendations suggested to reach the target state will always increase the overall maturity level.

The maturity level assessment is conducted separately in the specified key areas. The area chosen for analysis depends on personal preferences and current development needs. The first step is to assess the compatibility of the solutions used with the elements in the current state scope to identify the initial level of maturity.

As shown in

Table 1. Scale of implementation of the defined maturity levels.

Implementation Scale	Identifier	Characteristics
Level 1	Chaotic	The indicated solutions are either not being applied, or they are being implemented without proper definition, planning, structure, and management before and during implementation; this includes the level of service, training, and utilization of the potential of machinery and equipment.
Level 2	Defined	The activity areas were accurately defined for applying key solutions, and conscious implementation was carried out in selected areas; however, a long-term plan for integration and further development has not been established; meeting development needs is currently carried out on an ad hoc basis.
Level 3	Planned	A multi-stage implementation process was developed; for example, the implementation of the proposed solutions was planned in the short term; an implementation timetable and milestones were developed, and training and staff deployment processes were planned.
Level 4	Managed	The solutions identified are implemented, and the ability to manage implementation and use is achieved professionally; procedures are in place to identify and implement changes, address needs, and assess their validity.
Level 5	Optimized	The level of optimization was achieved with the solutions used, supported by mature change management processes in the focal area.

, each of the five maturity levels within a specific key area is rated on a five-point implementation scale.

The level of optimization was achieved with the solutions used, supported by mature change management processes in the focal area.

The scale was designed to indicate increasing levels of advancement. The highest level of advancement allows for the optimization of a specific key area, leading to increased maturity.

The author defined the scale in terms of criteria for ownership, management, and utilization of specific technological and IT solutions. This allows for the evaluation to encompass the organizational and planning methods, competencies, and knowledge and utilization level of the identified solutions.

Figure 5 shows the algorithm for evaluating the defined maturity levels across key areas. Reaching the fifth level of implementation makes moving to the next level of maturity achievable.

The author’s fundamental premise is that the acquisition and utilization of modern technology, advanced machinery, and equipment without proper management to ensure optimization hinders the development of the issues identified in the respective key areas. Continuous improvement solutions should be implemented in each case.

The following section delves into the characteristics of the primary area addressed in the article.

2.3. Characteristics of the Key Area—Automation of Production Processes

The stages of automation in production processes are classified into maturity levels, ranging from ML1 to ML5 (as shown in Figure 6). These levels represent increasing degrees of advancement while considering the general trends and assumptions regarding the modern trends of automation and the concept of the Factory of the Future.

Nine key factors were identified to estimate the level of advancement in the maturity level under consideration. Figure 7 illustrates the key factors involved in automating production processes.

Table 2. The first maturity level of automation in production processes (KA1–ML1)—conventional production machinery; manual quality control processes; manual support of auxiliary and maintenance operations.

Level Characteristic	Description and Recommendations
Current state	Lack of automation of production processes: Control devices; Industrial sensors—N/A (manual control based on staff knowledge, skills, and competencies), Safety systems—safety is assured through compulsory solutions in the Machinery Directive and CE marking (physical barriers—fencing, E-STOP, Cable-Pull Safety Switches, Disconnect Switches, Safety Interlock Switches, Safety Two-Hand Control Switches, Safety Limit Switches, Stack Lights, Machine Guarding Alarms, enclosures for wiring and electrical components, cable trays, mechanical shields), overload protection (electric circuits—fuses, circuit breakers, overload relays; mechanical devices—clutches) and overcurrent protection (magnetic circuit breakers, fuses, overcurrent relays, Surge Protective Devices, thermistors), individual employee training for occupational safety (includes safety manuals, procedures, risk assessments, safety standards, and Personal Protective Equipment PPE), Machine-to-Machine Communication; Man–Machine Interfaces; Application of process data—N/A, Monitoring and prediction (of equipment, machines, processes)—N/A, or based on experience of employee; predominantly Run-to-Failure Maintenance, Preventive Maintenance PM or Corrective Maintenance CM (Emergency Corrective Maintenance—ECM, Deferred Corrective Maintenance—DCM, Total Productive Maintenance—TPM), Application of IT infrastructure in production departments—N/A or possible exchange of essential information by e-mail, Application of integrated IT infrastructure in management and business processes—lack of connection between departments,
Advantages	Machinery, infrastructure, and equipment: Lack of expenditure on equipment, instrumentation, and software for process and machine monitoring (in the areas of automation, digitization, and integration—machinery, equipment, and employees), High machine reliability (no failures due to malfunctions of the control electronics or software), simple maintenance, immunity from interference and operator interference (simplicity of operation determined by the construction), Cost-effectiveness for smaller batches or complex and challenging unitary tasks, Flexibility achieved using dedicated tooling or simple adjustments, Lack of restrictions related to the need to access advanced technology or infrastructure, Simple and quick repair due to low complexity, Human resources: Highly qualified staff aware of the requirements, scope of work, and responsibilities (continuity and quality of production ensured by the knowledge, competencies, and skills of employees), Knowledge and proficiency with machinery (derived from experience) increasing the sense of control over the process, Increase in work dynamics and elimination of monotony by performing a variety of tasks within a single job position, Encouraging critical thinking and adaptability, A stronger sense of teamwork through direct interaction and collaboration with other employees, Reduction of technology stress by elimination of constant adaptation to new software, systems, or interfaces, The ability to shape employment levels to quality requirements and production capacity, Processes: Production tasks entirely entrusted to employees (high maturity of teams with collective self-organization), High flexibility and customization of tasks through manual work and unit production, Adaptation to local or sustainable production practices (support for local economies and reduction of environmental impact by using more environmentally friendly production methods—especially in the case of artisans), Direct quality control and provision of standards by employees, The dominant pull method (customer orders) as a model for controlling and scheduling production; less inventory, waste, and overproduction,
Disadvantages	Machinery, infrastructure, and equipment: The need to maintain an extensive fleet of machinery (including special equipment), Lower efficiency and productivity compared to automated manufacturing lines and cells, Limited scalability (lack of automation and robotization as well as strategic performance planning), Lengthy and complex changeover of certain machines (due to design and technological reasons), Difficulties in monitoring the quality of machine operation, The occurrence of unplanned downtimes and failures, Possible delays in the delivery of spare parts resulting in extended production downtimes, Limited dynamics or a lack of technical progress (lack of process improvement and cost reduction through the implementation of innovative technologies), Higher energy consumption due to the outdated technology, Insufficient assistance for surveillance, monitoring, and diagnostics, Human resources: The need to maintain an excellent organization and high employee qualifications, Flow, continuity, and timeliness of production dependent on employees (sensitivity to turnover, absenteeism, skills, wellbeing, etc.), The need to develop incentives to retain highly competent and qualified staff, High labor costs due to manual work and long lead times, High risk of occupational diseases, Strong dependence on quality indicators on the experience and skills of employees (rework, defects resulting from human errors), Safety hazards (resulting from manual handling and operation of machinery that increases the risk of accidents or injuries in the workplace), Processes: Difficulties in the establishment of the permanent production schedule (due to the adopted methods of obtaining production orders and limitations of the machinery), decentralization of operational planning and production management, The need for maintenance planning and a good organization of the machinery repair process (including stocks and availability of spare parts), Lack of Machine-to-Machine Communication and Man–Machine Interfaces—siloed structure preventing data-driven management (lack of collection, processing, and formulating conclusions from production data; lack of IT connection with other departments for work planning and production optimization), Lack of failure prediction (no monitoring of operating parameters from machines),
Growth opportunities	Machinery, infrastructure, and equipment: Development of a plan for modernization (short-term perspective) and the gradual replacement of machinery (long-term perspective)—with emphasis on automation and robotization, The deliberate replacement of manual labor with automated equipment and machinery in specific areas Human resources: Development of the staff competence matrix, Identification of staff shortages and competencies to be replaced by automation and robotization of processes, Analysis of the labor market and forecasts for the supply of specialists, Processes: Mapping, analysis, and optimization of processes (identification of redundancies and streamlining of workflows, analysis of process efficiency and contribution to lead times), Operational improvements and process optimization (5S, Kaizen, Kanban, SMED, TPM, VSA, Six Sigma, Poka-Yoke, Heijunka, DFMA), Risk assessment and mitigation at workplaces, implementing risk reduction strategies in processes, developing management strategies to ensure smooth operations and prevent disruptions, Analysis of processes suitable to automation (criteria: failure rate, bottlenecks, quality, time consumption, cost-effectiveness, production deficiencies), Analysis of critical information in automation and robotization areas,
Development recommendations	Process analysis and optimization (elimination of unnecessary activities, improving workflows and communication, forecasting changes; identification and elimination of bottlenecks or redundancies in processes; improving cooperation and linkages between processes (at fundamental, strategic, and management levels); streamlining activities to enhance the efficiency and productivity of processes, reducing risk by avoiding errors and rework through better process management, strengthening competitive advantage through more efficient operations and performance), Analysis of areas where CNC machines and equipment can replace manual work, Immediately and unconditionally removing employees from activities that are dangerous and endanger health or life, Implementation of machines to replace the manual or mental activities of employees, Implementation of procedures and standards (e.g., Standard Operating Procedures in technical and administrative areas including Step-by-Step Instructions, roles and responsibilities, equipment and resources, safety procedures, quality control measures, troubleshooting and problem resolution, approval and revision, training and compliance, documentation, and record-keeping), Introduction of simple digitization tools to improve the planning, execution, and control of processes, Development of the implementation plan and an ROI analysis for the automation case (identification of objectives, analysis and simplification of processes, analysis of areas for automation, focus on sustainability), Development of a comprehensive plan to replace outdated machinery and equipment from both short- and long-term perspectives.

,

Table 3. The second maturity level of automation in production processes (KA1–ML2)—automatic or numerically controlled production machines; manual quality control processes; and manual support of auxiliary and maintenance operations.

Level Characteristic	Description and Recommendations
Current state	Automation of individual machines or devices: Control devices—dedicated embedded machine controllers or PLCs, Industrial sensors—sensors integrated into machines (monitoring critical technological and operational parameters—protection against damage and failures, loss of the assumed quality level, or decrease in operational safety), Safety systems—as in the previous case, and built-in safety systems for individual machines, light curtains, and sensors, Fault Detection Systems, compliance with standards, Machine-to-Machine Communication—independent or connected machines via industrial fieldbus interfaces (e.g., Fountain Fieldbus, Device Net, ModBus/ ModBus TCP, ProfiBus, InterBus, ControlNet, HART, EtherCAT, ProfiNet, CAN, Varan, AS-Interface, DeviceNet, EtherNet/IP, CIP, IO-Link), Man–Machine Interfaces—integrated devices and user interfaces (e.g., HMI, SCADA), Application of process data—data storage mainly in the form of design documentation (CAD software) and machining programs (CAM software), Monitoring and prediction (in the domain of equipment, machines, processes)—essential monitoring of operating parameters (indication of error codes based on machine documentation; self-diagnostic functions: power-on self-diagnosis, monitoring, signal display interfaces, internal status and fault information display, offline test); mostly PM maintenance method, less often CBM or TPM, Application of IT infrastructure in production departments—possible connection of machines to the company’s network and internal servers, Application of integrated IT infrastructure in management and business processes—information exchange depending on the automation method adopted (most often a siloed structure for information acquisition),
Advantages	Machinery, infrastructure, and equipment: High-volume stability—the ability to reliably produce batches of products over an extended period without significant fluctuations, interruptions, and downtimes, High and repeatable quality of technological parameters (high precision, accuracy, repeatability, and failure-free operation time), efficiency and scalability, Reduced downtime through the elimination of failures and detuning based on the monitoring of operational parameters, Less dependence on labor and human error, maintaining repeatable patterns, the possibility of digital simulations, lower operating costs (less expenditure on repairs following major breakdowns), increased operational safety, Support of the control process and monitoring of the workflow, the use of local user interfaces (e.g., HMI, M2M), and introduction of condition monitoring (partial or complete insight into the operational status of machinery and its parameters), A faster return on investment with properly planned production, optimization of the supply and distribution chain and a reduction in repairs caused by unforeseen factors, Human resources: Redeployment of staff to higher value work (development of technical skills in the operation and programming of machines, proposing improvements, and creative problem-solving), An increase in job satisfaction and productivity reduced physical and mental strain, Reduction of the task’s complexity and relieving the burden of repetitive or tedious activities, Increased work safety (elimination of dangerous and repetitive activities, limited access to machine work zones), Processes: Partial transition to electronic documentation (e.g., design and technological documentation, control programs, etc.), Increased certainty and quality of planning (repetitive work cycles, better insight, and improved production control), Reduction of production and maintenance costs (operational planning, monitoring of machinery and equipment, partial elimination of the human factor, increased productivity, waste minimization), Simplification of quality control procedures due to increased repeatability,
Disadvantages	Machinery, infrastructure, and equipment: Limited flexibility due to functionality and intended use (no possibility of changes, need for significant reconfiguration, replacement, or reprogramming if the production profile changes), The need to invest in software, necessary hardware components, and system maintenance, Possibility of getting stuck in the current situation (lack of standards enabling interoperability of existing facilities or production systems planned to be implemented—combining machines into specialized production cells), Strong dependence on technology—lack or temporary unavailability of electronic components, Human resources: The need to employ (or outsource) skilled programmers (CNC machines, PLCs), technologists, and maintenance staff, The possibility of fear of technology, staff errors and productivity losses (especially during the initial implementation period), Possible strong dependence on technology, loss of manual skills, and reduced creative thinking, Processes: Most often, a lack of connection between OT/IT levels (no recording of KPIs, metrics, and statistics), Flexibility depending on machinery, the efficiency of production planning, and the workload of production systems, The impact of programming and equipment costs on the unit price (profitability of production for a specific batch size),
Growth opportunities	Machinery, infrastructure, and equipment: Production without operators (a complex robotization of machine handling processes), Analysis of the functionality of available machinery and equipment to apply systems for monitoring and prediction of operational status, Human resources: Training of employees in new technologies, management methods, and production organization, Talent search and management, redesign of team structures (competencies and autonomy, tasks, division of responsibilities), Processes: Integration of production processes, quality control, intralogistics, and machine maintenance activities,
Development recommendations	Forecast of the expected level of flexibility and interaction rules (MMI, M2M), Analysis of the validity of robot implementation (conventional robot or cobot), Implementation of machines to replace handling operations (loading, unloading, fastening, reorientation), Complex robotization of processes with a negative impact on human health (e.g., painting, welding, bonding, etc.) using industrial conventional robots, The use of cobots in processes supporting employees with no negative impact on health (e.g., flexible assembly, loading, unloading, etc.), The use of scalable industrial network standards susceptible to expansion, Analysis and development of a robot implementation plan (criteria: operational parameters and general features, security requirements, energy efficiency, the complexity of the planned solution, maintenance strategy, cybersecurity), ROI analysis.

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Table 4. The third maturity level of automation in production processes (KA1–ML3)—automatic or numerically controlled production machines interoperating with robots; manual quality control processes; automated support of auxiliary and maintenance operations (implemented in selected areas).

Level Characteristic	Description and Recommendations
Current state	Automation and robotization of individual machines/devices or their groups: Control devices—individual controllers (of machines, robots, equipment) and master PLCs (depending on configuration), Industrial sensors—sensors embedded in machines and robots (protection against damage, breakdowns, and quality deterioration) and for monitoring the interaction of machines and robots (safety systems and sensors), Safety systems—dedicated systems and devices for human, machine, infrastructure, and environment safety (including robot–human interaction analysis; risk assessment and mitigation; mechanical fencing where required; safe position monitoring with sensor-position and proximity switches; safety switches—mechanical, magnetic, coded, safety bolts, safe hinges; gate systems; light curtains; sensors technology; intrinsically safe isolators; safety relay modules; safety mats and edges; safety bumpers; safety controllers; software tools), Machine-to-Machine Communication—as in the previous case with an emphasis on industrial Ethernet-type interfaces, Man–Machine Interfaces—centralized/distributed monitoring and control of production, Application of process data—data analysis to monitor the correct workflow and process status, Monitoring and prediction (of equipment, machines, processes)—essential monitoring of operating parameters with display of alarms and error codes (including the entire automated system), possible use of systems that integrate data from controllers, use of SCADA systems and local HMI panels, Application of IT infrastructure in production departments—web-based shared data ports, Application of integrated IT infrastructure in management and business processes—uniform data formats and data exchange rules,
Advantages	Machinery, infrastructure, and equipment: Maintaining continuity of work and basic machine operations during the process without the involvement of employees (loading, unloading, reorientation), High (but limited) flexibility depending on the functionality and purpose of machinery and equipment (production lines or cells with variable scope and volume of production), proper management and production planning, Minimization of the number of machine failures due to operator errors, Increasing productivity, repeatability, and quality, Integration of machines and robots through network infrastructure and efficient communication protocols, Human resources: Physically relieving the employees’ workload, reorganizing work methods (inclusion in processes that require manual labor with less intensity and physical strain), Increased safety and reduced number of injuries and accidents, The possibility of operating several machines or production cells simultaneously, Processes: Full availability of machine information (locally—HMIs, SCADA; remotely—industrial and enterprise networks), Advanced shift capacity planning (efficiency, coordination of feeding and pick-up from storage fields), Structured warehouse, supply, and intralogistics management, Reduction of waste and increase in production efficiency through planning, scheduling, and production preparation,
Disadvantages	Machinery, infrastructure, and equipment: Additional robot tooling required (gripper changers, tools, or effectors needed in the process), additional infrastructure (e.g., compressed air generation and distribution facilities), Additional measures and safety systems required for conventional robots (fences, locks, curtains, safety controllers, Lockout/Tagout systems, etc.), The need for additional warehouses, storage fields, and the reorganization of methods and strategies for the circulation of materials and finished products, Human resources: Required qualifications in robot operation and programming (in-house resources or outsourcing), Required application of dedicated security measures and the development of access procedures, Processes: The need for accurate planning and scheduling of production (including the allocation of resources and materials, considering the risk of unplanned downtimes and random factors), The need to develop and implement procedures (retooling; removing detuning, damages, and failures; storage or rapid purchase of spare parts; integrator or service companies involved), Required development of stringent procedures (depending on the type of robot–human collaboration: fenced robot, coexistence, sequential collaboration, cooperation, responsive collaboration) for safety in case of presence in the work area, maintenance, and repairs,
Growth opportunities	Machinery, infrastructure, and equipment: Use of aided or automated quality control systems (inspection, testing, statistical process control, documentation and records, corrective actions, training and education, continuous improvement), Integration of robotic processes with automated quality control systems, Human resources: Reskilling employees for tasks with required new competencies, Processes: Application of data-driven decision support systems (e.g., in the assessment of operational conditions, quality control, KPI indicators), Integration of robots with quality control systems and defect correction stations (in line with sustainability principles),
Development recommendations	Use of future-oriented communication network standards with support for development (e.g., Ethernet, OPC UA), Planning the implementation of machines with the possibility of future integration into complex production systems (automation, robotization, digitization, Cloud Computing, IIoT), Analysis of the feasibility of implementing integrated systems for quality control (e.g., vision systems, measuring machines, AI/ML, etc.), Integration of the horizontal and vertical value chains (production and management).

,

Table 5. The fourth maturity level of automation in production processes (KA1–ML4)—automated processes enabling long-term operation without personnel intervention (e.g., workpiece change, reorientation, etc.); automatic quality control (identification and classification—repair or elimination of defective products).

Level Characteristic	Description and Recommendations
Current state	Integrated automation and robotization: Control devices—Edge Computing machine and robot controllers, master PLCs or industrial computers with Edge Computing or Fog Computing functionality, readiness to support Cloud Computing, Industrial sensors—as in the previous cases, automatic processes, systems for continuous quality monitoring of products and equipment (visual inspection, thermal imaging, ultrasonic or vibration diagnostics, chromatography, etc.), Safety systems—as in the previous case, Machine-to-Machine Communication—access of selected machines or devices to the Internet (IIoT functionalities), Man–Machine Interfaces—as in the previous case, with the additional use of mobile user interfaces, Application of process data—use of data for ongoing control, planning, and optimization of work, quality, and critical KPIs (process- and product-oriented), Monitoring and prediction (of equipment, machines, processes)—process monitoring, possible prediction of the operating state of machinery and equipment, automatic notification of emergency conditions, processing of qualitative data (strategic, operational, and financial KPIs), Application of IT infrastructure in production departments—automated information exchange (e.g., order tracking, production flow), Application of integrated IT infrastructure in management and business processes—uniform data formats, shared data servers, the possibility of introducing SSoT,
Advantages	Machinery, infrastructure, and equipment: Elimination of continuous involvement of machine and plant operators and achievement of autonomy in the work cycle (excluding the supply of input materials and the receipt of finished products), The possibility of implementing Predictive Maintenance (PdM) with the support of AI/ML algorithms, Support the defects and errors identification at each stage of the process according to the First Time Right method, Human resources: Increased occupational safety, Possible implementation of digital operator support (e.g., On picking and assembly lines), Possible reduction of human resources to the level required by processes not subject to automation and robotization, Processes: Improved process control (at the planning and execution stages), The possibility of planning through simulation (Digital Twin of the production process, offline programming of robots using 3D environment), Reduction of waste by monitoring and optimizing production processes; formalized procedures for reporting, evaluating, and implementing improvements and error prevention, The ability to define, monitor, and validate KPIs on an ongoing basis, Rapid identification of error sources and shorter lead times through the minimization of delays associated with quality errors, Aiming for a level that supports quality assurance through reliable and trustworthy systems (Quality 4.0—organizational excellence in the context of Industry 4.0),
Disadvantages	Machinery, infrastructure, and equipment: Costs of storage equipment and warehouses (inter-station, buffers, pallets, bins etc.), machine tooling (automatic tool changers and magazines, tool holders), robot and cobot tooling, The need for a quality management policy, the implementation of quality control equipment, instrumentation, and software, Human resources: The possibility of an inefficient workload (changing the scope of responsibilities, elimination of manual tasks, difficulties in integrating workers into automated and robotic processes); training and changing procedures for human–machine collaboration, The need to employ specialists in quality control process automation (full-time or outsourced), Additional technical competencies required (in definition, monitoring, and analysis of qualitative data), Processes: The need to coordinate the planning and use of resources as well as processes (orders, production, logistics, intralogistics, and distribution) while maximizing OEE, Configuration of production lines/cells (sequence of operations, flows, control programs) performed about the planned production profile, which results in limited flexibility, inability to change the production profile, or downtime in the changeover phase,
Growth opportunities	Machinery, infrastructure, and equipment: Optimization of the production process through automation of intralogistics, Human resources: Development of creative and innovative thinking, The acquisition of multidisciplinary knowledge, Openness to change and a desire for continuous learning, Processes: Use of AI tools to support processes, planning, decision-making and data analysis, Use of Cloud Computing tools for data collection from edge devices (analytics at business decision level; optimization, planning and scheduling of production processes),
Development recommendations	Multi-level planning (various departments and areas) with digital support, Focus on particular attention on the cybersecurity of networks and assets, Implementation and use of the full capabilities of ERP systems with complementary modules, Application of the SSoT at the level of data analytics and reporting, Improvement of the traceability of products, equipment, and other resources, Improving knowledge of Intelligent Manufacturing, Factories of the Future, Dark Factories, Manufacturing-X.

and

Table 6. The fifth maturity level of automation in production processes (KA1–ML5)—integrated production machines, equipment, and quality control stations operating continuously and automatically (complete process autonomy), autonomous intralogistics, or flexible Plug&Produce configuration.

Level Characteristic	Description and Recommendations
Current state	Integrated automation and robotization of machines and equipment with dynamic product flow paths: Control devices—control process hierarchy (PLCs, industrial computers, Edge Computing, Fog Computing—operational control), Cloud Computing (business process control), Industrial sensors—embedded and distributed sensors (wired or IIoT), Safety systems—as in the previous case; advanced machine and personnel safety systems (laser scanners, radars, and vision systems), Machine-to-Machine Communication—network services focusing on the OPC UA standard and wireless networks (LTE-M, NB-IoT, Sigfox, LoRa, BTLE Mesh, Zigbee, WiFI, LTE, 5G campus networks), Man–Machine Interfaces—as in the previous case; Augmented Reality (less preferred Virtual Reality), MMI software available for mobile devices, Application of process data—automatic planning, scheduling, control, and optimization of process parameters, Monitoring and prediction (of equipment, machines, processes)—PdM or Reliability-Centered Maintenance based on AI/ML, Application of IT infrastructure in production departments—full integration, Application of integrated IT infrastructure in management and business processes—interdepartmental, fully networked IT solutions (based on the SSoT),
Advantages	Machinery, infrastructure, and equipment: Configuration in accordance with Cyber–Physical Systems requirements, The use of Asset Administration Shells at the level of defining digital representations of functionality-oriented data models, Plug&Produce configuration—specialized modules (technological, assembly, quality control) susceptible to relocation, the appropriate setting and shaping of stages and process flow, Autonomous intralogistics for the operation of flexible machines, specialized cells (machining, assembly, quality control) or production lines, Full automation with diagnostics and prediction of the operating state of machinery and equipment, Integration of intralogistics units with production machinery and equipment, Bi-directional data exchange enabling monitoring, reporting, change or modification of control programs (also adaptation to the production batch), development of Digital Twins, remote diagnostics, and repair (within the possible functional range), Human resources: Safe and friendly work environment, Decision support at process, operational, and business levels (short- and long-term perspectives), Processes: Structure and data exchange in accordance with Reference Architectural Model Industrie 4.0 (RAMI4.0) [77,78,79,80,81,82], Readiness to implement Common European Data Spaces and GaiaX, Use of ERP systems with the required complementary modules and other systems offering process support (design, testing, validation, quality control, customer relations, etc.), Achieving automation at the production level and exchange of business data (combination of horizontal and vertical value chains), Use of data for business process optimization (inclusion of KPIs from machines; full traceability of processes, products, and resources; the possibility of comprehensive reporting—not only production KPIs but also the ESG Sustainability Reporting), Application of the SSoT for planning, change and process optimization, Dynamic task allocation to free or idle machines (OEE optimization),
Disadvantages	Machinery, infrastructure, and equipment: Providing a fault-tolerant, efficient, reliable, and secure infrastructure and data exchange standards, Human resources: Required staff with the highest multidisciplinary skills and knowledge, The need for continuous improvement and competence development, Processes: The need to integrate IT/OT or ICT areas, The need to ensure cybersecurity (hardware, software, and security policies),
Growth opportunities	Highest level of maturity (automation combined with ICT systems for production planning and scheduling, orders, intralogistics, and logistics).
Development recommendations	Constantly monitoring of changes in market and technological trends, Continuous development of employee competencies, System maintenance, updating, and control, Periodic carrying out of penetration tests and cybersecurity audits.

present the assumptions used to evaluate the maturity of production process automation.

The first level of maturity involves no automation of industrial processes and relies on conventional production machinery, manual quality control processes, and auxiliary and maintenance support operations. The workers manually carry out the entire technological process without using automated equipment or industrial robots. The main recommendations for moving to a higher maturity level are to structure and optimize processes to reduce the risks and costs of implementing automation. One way to improve production efficiency is by implementing Standard Operating Procedures and replacing manual processes with automation. It is crucial to divert workers from life- and health-threatening activities and develop plans for automation while estimating the return on investment. Before deciding, conducting a technological audit to analyze production processes, work organization methods, and production activity planning is essential. Each time unstructured processes are automated, it creates technological debt, reduces operational efficiency, or disrupts continuous improvement, which is necessary to reach higher maturity levels. Before implementing automation, it is recommended to apply classic methods of organizing processes in both production and business areas to structure and organize them. Using process management software integrated with industrial automation systems is an essential step toward achieving the full functionality of cyber–physical systems. This involves the convergence of Operational Technology, Industrial Control Systems, the Industrial Internet of Things, and Information Technology.

The second level of maturity involves using automated or numerically controlled production machines, with manual support for quality control processes and other production activities. Machines often use embedded controllers at this maturity level, but the available technology requires advanced safety systems.

Information exchange between machines is possible but depends on the network interfaces’ method and functional parameters. Modern industrial networks complying with Industry 4.0 standards are required for classification and future growth. The proposed network standards were targeted at upgrading existing machines or replacing them with new ones, assuming future integration of resources at the OT/IT level.

At this level of maturity, it is uncommon to utilize solutions based on the Industrial Internet of Things (IIoT) and Cloud Computing technology. In addition, HMI panels enable the setting and monitoring of current processes and operating parameters. SCADA systems can be utilized for remote process monitoring and control. The following factors are recommended for achieving higher maturity levels:

• Implementation of programmable, flexible, process or integrated automation (with simultaneous phasing out of rigid automation),
• Use of automated machines and equipment to replace manual work (processing and assembly workstations), the introduction of monitoring and diagnostics through data collection via industrial networks,
• After conducting a technology audit, the required level of production flexibility should be analyzed and correlated with development plans from both short- and long-term perspectives.

The third level of maturity involves using automated production machines integrated with industrial robots. A manual quality control process is assumed, but automatic support for auxiliary technological operations (e.g., automatic change of grippers, tool change, automatic diagnostics via embedded controllers, etc.) may be provided in certain areas.

Integrating robots and machines requires PLCs, additional industrial sensors, and embedded machine solutions. Utilizing safety systems and selecting an appropriate standard for machine and robot data exchange are crucial factors. The possibility of implementing predictive monitoring via local or remote user interfaces exists when exchanging data. At this stage, the development of standardized data exchange formats, rules for exchanging information between devices, monitoring, diagnostic systems, and business management systems planned for future integration are required. To advance to the next stage of development, it is advisable to adopt data exchange protocols that are forward-looking, prioritize open structures to eliminate information silos, contemplate the potential for implementing AI/ML in the future, and ensure the comprehensive integration of both horizontal and vertical value chains.

The fourth level of maturity involves carrying out long-term technological operations without operator support. This includes the automatic and programmed change, reorientation, and removal of defective parts. It is worth noting that the mentioned condition necessitates a thorough overhaul or reorganization of the processes. This includes ensuring the smooth flow of input materials to the machining stations and inter-operational warehouses, receiving products at specific times and in particular quantities, and so on. Both classic PLC solutions and Edge/Cloud/Fog Computing solutions are employed to achieve the stated objectives.

The latter solutions come from integrating operational planning systems, such as production scheduling and planning, warehouse and logistics management, and machine maintenance and repair planning. Introducing KPIs for data use, progress evaluation, and continuous improvement is critical to achieving the demand maturity level.

Implementing Industrial Internet of Things (IIoT) systems enables real-time production tracking and optimizing relocation, storage, and operations. Integrated cloud solutions are being used to create a Single Source of Truth architecture, eliminating data silos and increasing digital maturity.

Moving to the next level of maturity requires a multi-level planning system that links production with other business areas (e.g., supply chain management, quality management, engineering and maintenance, risk management, strategy, and planning, etc.). The following factors were also identified for implementation:

• Implementation of protection of computers, servers, mobile devices, electronic systems, networks, and data against malicious attacks (emphasis is placed on network and application security, information security, operational security, disaster recovery, business continuity, and user education),
• The implementation of digital systems for the management of individual production areas, with a focus on integration in data management (achieving full functionality requires upgrading or equipping the production facility with a scalable, secure, reliable, flexible, interoperable, and fully manageable IT infrastructure),
• Complex transition to a Single Source of Truth—the primary objective is to process high-volume, high-speed, or highly variable information resources cost-effectively, enabling better insight, decision-making, and process automation while transitioning to a Single Source of Truth.
• Horizontal systems integration—including integrated Exchange to Exchange tools, real-time planning and execution, logistics process transparency, prescriptive supply chain analytics, intelligent warehousing, intralogistics, and intelligent spare parts workflow management,
• Vertical systems integration—coordination of digital production activities including E2E product lifecycle management, digital factories, machine automation, Manufacturing Execution System, Enterprise Resources Planning, and more,
• The solutions are gradually adopted in the Manufacturing, Factories of the Future, Dark Factories, and Manufacturing-X approaches.

The fifth level of maturity comprises the complete integration of production machinery, equipment, and quality control. In this case, the factory incorporates full automation and autonomous operation. Achieving a higher level of maturity in intralogistics operations requires implementing autonomous transport between workstations and warehouses. Wireless networks are necessary for exchanging data and automating process parameter planning, scheduling, control, and optimization. The data are utilized to diagnose and predict machine conditions, manage inventory, and streamline business processes. The Single Source of Truth integrates data from all areas to ensure correct operation. It guarantees data consistency and accuracy, ease of integration, improved efficiency, the highest level of compliance, data security, simple analytics and reporting, facilitation of communication and change, and reduced data redundancy and false information. Advanced analytics for Big Data and an easy-to-read interface are necessary for obtaining high-quality data.

Table 7. Key links of the Industry 4.0/5.0 concepts and the proposed maturity assessment method [83,84,85,86].

Area	Subarea	Application in the Proposed Method
Synergistic integration of trends in engineering and technology (A1)	Artificial Intelligence (A1.1)	Integration of AI in automated systems for process optimization, including reduction of energy and input materials consumption, elimination of production downtime, and efficient resource allocation,
	Real-time connectivity and data exchange (A1.2)	Increasing machine reliability and lifespan by monitoring operating parameters, providing digital support for operators, and implementing standards for future machine upgrades,
	Flexible automation (A1.3)	Increased speed of response to market demands with fewer specialized units, elimination of the need for continuous machinery and equipment replacement, and increased quality of work through digital support,
Market development(A2)	The circular economy (A2.1)	Conscious implementation of automation to reduce energy usage, extend machine life, minimize errors, and design processes for waste reintegration,
	Personalization and customization (A2.2)	Increasing response dynamics to market needs, developing flexible production close to customers to reduce transport costs and carbon footprint,
	The sharing economy (A2.3)	Using the maturity level evaluation method while retrofitting equipment in As-a-Service reduces the threshold for entry into new technologies and allows testing solutions without the need to purchase,
Social and environmental changes (A3)	Global warming (A3.1)	Increasing the importance of informed machinery and equipment selection, considering extended service life, retrofitting options, and future integration into management systems,
	The digitally connected society (A3.2)	Implementing employee-friendly technology to reduce information overload and increase comfort by generating clear and readable reports without significantly increasing employees’ workload,
	Lifestyle and demographic changes (A3.3)	Application of flexible automation in processes with staff shortages, and the automation of monotonous and repetitive activities.

summarizes the main development trends of Industry 4.0/5.0 in the context of the developed method.

Section 3 presents the research results conducted to assess the level of maturity in production process automation.

3. Results

The method was used to evaluate the maturity of 200 Polish manufacturing companies. The surveyed entities belonged to small- and medium-sized enterprises (SMEs) and operated in diverse sectors. These sectors included food, chemicals, textiles and clothing, electronics, machinery, wood and furniture, packaging, household appliance manufacturing, and suppliers of components to the automotive industry.

SMEs are the largest employers and contribute the most to EU development. However, their development needs help accessing capital, external funding sources, modern technology, innovation, and a highly skilled workforce. A disruptive factor is more familiarity with modern process management methods.

In each case, the company was classified into the described group based on fulfilling the following conditions: having less than 250 employees and an annual turnover of not less than EUR 50 million or an annual balance sheet total of not more than EUR 43 million.

The participants who responded were individuals with managerial or decision-making positions in investment planning, corporate digital vision, and strategy creation. During the survey, we identified the current level of maturity in a key area and defined a target state for the next three years.

Table 8. Summary survey of the initial and target maturity levels in automating production processes.

Maturity Level	Initial Maturity Level	Target Maturity Level
		Maintaining the Current Level	Moving to the Next Level
KA1-ML1	38%	6%	-
KA1-ML2	44%	10%	18%
KA1-ML3	12%	2%	36%
KA1-ML4	6%	6%	22%
KA1-ML5	-	-	-

summarizes the study on initial and target maturity levels for automating production processes.

In total, 38% of enterprises in the survey are at the KA1-ML1 maturity level, using conventional machinery. A total of 6% of enterprises plan to maintain the status quo due to the craft-based nature of operations and the need for more potential to automate production processes. Eighteen percent of respondents plan to use automated or numerically controlled machines at the KA1-ML2 level without implementing technological process robotization. Moreover, 14% of respondents at the KA1-ML1 level plan to implement automated machines or production lines operated by industrial robots.

A total of 44% of the surveyed companies are at the second maturity level (KA1-ML2), indicating that they own automated or numerically controlled production machinery. In this case, manual quality control and support for auxiliary operations are used to identify machine failures or detuning. In total, 10% of respondents reported no change in their achieved level, while 22% upgraded to KA1-ML3 by integrating industrial robots into their technology, assembly, and handling processes. Within the next three years, 12% of respondents plan to expand their infrastructure to include fully automated production lines or cells that can operate for extended periods without human intervention.

Twelve percent of respondents use robotic stations in automated cycles (KA1-ML3), and only 2% have no plans to change. However, 10% of the respondents in this group declared moving to the KA1-ML4 level (i.e., implementing uncrewed generation stations operating long-term without operator intervention).

Only 6% of surveyed companies have reached KA1-ML4, but they have no intention of increasing their level.

None of the surveyed companies reported achieving KA1-ML5 levels, indicating complete integration of production machinery, equipment, and quality control systems that operate in an automated mode without operator involvement. The need for an adequate level of digital maturity is the main reason for comprehensively integrating automation, robotics, and process management software.

Production process automation maturity level measures are summarized in Figure 8.

Conclusions can also be drawn from the transition rate between different maturity levels. The behavior of the companies that were interviewed can be characterized as cautious and risk-averse. A total of 24% of the respondents in the general population plan to maintain their current level of maturity in terms of technological process automation. While 26% of SMEs assume an increase of two levels on the maturity scale, 50% plan to upscale by one level.

Planning for a maturity increase of more than one level requires maintaining the recommendations of the skipped level. It also entails higher investment risk and involving larger or more experienced teams.

Table 9. Summary of the transition rate between different maturity levels.

Maturity Level	KA1-ML1	KA1-ML2	KA1-ML3	KA1-ML4	KA1-ML5
KA1-ML1	6%	18%	14%	-	-
KA1-ML2	-	10%	22%	12%	-
KA1-ML3	-	-	2%	10%	-
KA1-ML4	-	-	-	6%	-
KA1-ML5	-	-	-	-	-

summarizes the percentage transition rate between the different maturity levels.

Figure 9 shows the transition rate between different maturity levels based on the data from Table 9.

Table 10. The fundamental development directions of the industries being surveyed.

Industry	The Focus for Automation Development
Food	The development of process automation systems for monitoring and supervising production parameters, automation of quality control, traceability,
Chemicals
Textiles and clothing	Automation of fabric cutting and format preparation,
Electronics	Automation of product testing and quality control,
Machinery	Integration of industrial robots with CNC machine tools and autonomous transport,
Wood and furniture	Automation of storage, cutting of formats and milling of furniture fronts,
Packaging	Palletization, integration of vision-based quality control systems, and complex and automated production lines.
Household appliance manufacturing	Automated and configurable production lines, automation and robotization of bonding and welding processes (with integration of quality control),
Suppliers of automotive components	Automation of CNC machines and assembly stations, quality control, and marking.

presents the primary development directions of the companies within the surveyed industries.

Taking a comprehensive approach to the research findings, small and medium-sized enterprises are moving towards a logical process automation strategy while ensuring that the infrastructure and equipment are open to change, expansion, and future modernization.

4. Discussion

It is worth mentioning that all participating companies have familiarized themselves with the ADvanced MAnufacturing digital maturity survey methodology [87]. The ADMA covers seven areas of transformation, including those shown in Figure 10, to provide a comprehensive analysis.

ADMA offers insights into a manufacturing company’s processes and opportunities for digitalization, streamlining, and optimization. The diagram in Figure 11 shows the main issues in the transformation areas of the ADMA methodology.

The methodology emphasizes digitalizing manufacturing companies by implementing specialized IT solutions. When small and medium-sized enterprises (SMEs) perform an ADMA scan, they often try to focus on all aspects of the transformation process. However, they tend to overlook the workload and costs associated with upgrading their employees’ competencies, integrating hardware and software, ensuring adequate cyber-security, dealing with technology dependencies, managing changes in employee roles, maintaining new machinery and equipment, and complying with environmental regulations, managing sensitive data, and protecting the company’s intellectual property.

The author’s method outlines key areas of development for companies to ensure business continuity and gain a competitive advantage while adhering to the ADMA methodology. The method proposed in the article provides a targeted approach to specific areas of factories that complement ADMA.

In the next stage, the study analyzed the impact of automation on the development of other areas. The main goal was to pinpoint the key areas related to the automation of production processes. Entrepreneurs have identified areas where automation maturity is forecast to increase and should be included in development plans.

Figure 12 shows the analysis results of the key enterprise areas being developed due to the growing advancement of automation of production processes (KA1).

Ninety-four percent of entrepreneurs decided to develop production process automation (KA1) further. The second area identified is increasing the share of robot integration in automated production processes (78%; KA2), and the third is implementing automated solutions to improve the maturity of digitization of warehouse processes (76%; KA3).

The area of business management systems integration (KA6) showed the lowest correlation rate and 75% of those surveyed stated a lack of interest in the complex integration of factory management systems in favor of investment in production automation. Most respondents, around 70%, delayed the pursuit of flexibility (KA4) in their production systems to a later period. Only 32% of the respondents prioritized intralogistics (KA5) due to the abandonment of the development of manufacturing flexibility with mobile robots.

Figure 13 shows the interest levels in each ADMA transformation, indicating a long-term (5-year) perspective.

Entrepreneurs have initiated activities to streamline the current flow of materials and finished products. The presented plans show that small and medium-sized enterprises concentrate on specific areas of development, neglecting essential aspects of the impact that newly introduced solutions may have on other areas of activity.

According to respondents, the maturity level in production automation requires an interest in advanced manufacturing technologies (T1, 90%), factory digitalization (T2, 84%), and smart manufacturing (T6, 79%).

The indicated transformations are mutually complementary and reinforce the development of production process automation. The survey results testify to a high entrepreneurial awareness and orientation towards sustainable development (also regarding eliminating technical debt). There is less interest in customer-focused engineering, human-centered organizations, and green factories.

Entrepreneurs are aware of the sustainability issues within their businesses and now recognize the importance of long-term assumptions that were previously disregarded. Enterprises are realizing the importance of employee development and adapting to meet the expectations of a new generation of workers.

Entrepreneurs were surveyed to identify the principal risks and barriers associated with digital transformation:

• Lack of clear strategy and vision—lack of a defined direction for change, tools, and methods to set short and long-term goals,
• Resistance to change—employees’ fear of being replaced by modern technology, fear of increasing productivity and quality levels,
• Lack of understanding or acceptance of the culture of innovation—fear of creative and out-of-the-box thinking, lack of open communication and team collaboration,
• Lack of consistency in change management and communication—lack of adequate change management, competency gaps, inadequately mapped staff resources, lack of teams responsible for the different stages of the transformation and their scope, lack of measurable indicators and assessment of unforeseen risks,
• Lack of resources and support—lack of a change leader, staff with the right competencies and skills, lack of integrators or subcontractors,
• Changes in areas of the company’s operation—new production technologies, changing management methods (disintermediation, data-driven decision-making, reorientation of revenue streams, regulatory challenges, need for continuous learning and development, ensuring cyber security and data privacy, introduction of performance indicators and KPIs for evaluation purposes), employees (automation of tasks, remote and flexible working arrangements, reskilling and upskilling, digital channels for collaboration and communication, changing job roles and responsibilities, emphasis on innovation and creativity, greater professional autonomy, changes in work culture, potential job change),
• Breach of data confidentiality and security, loss of know-how—lack of security policies, software, and hardware protection,
• Difficulties in integration with existing systems—lack of use of standards and consideration of scalability requirements of systems under development,
• Budgetary constraints—lack of funds or purchase of solutions with redundant and underutilized functionality,
• Regulatory and compliance restrictions—maintaining the guidelines of norms, regulations, and standards,
• Existing systems and infrastructure—constraints related to integration and incorporation of newly designed technical systems,
• Supplier dependency and interoperability issues—problems of factory locations, lack of support, and local integrators.

The development of modern industry heavily relies on automating production processes. Adopting automation is crucial in transitioning towards a data-based management approach.

Polish entrepreneurs rely on conventional machines with little to no automation or numerically controlled machines characterized by siloed architecture. The prevailing trend in the industrial sector is the installation of industrial robots in already existing machinery, retrofitted at higher maturity levels with automated stations for quality control, production error correction, and scrap removal. This allows the machines to run for more extended periods without operators. Increased working comfort and thoughtful integration of machines and people align with Industry 5.0.

Currently, the deployment of mobile units is less widespread than conventional industrial robots and cobots, even though most emerging investments are based on autonomous robots. Wireless data exchange dominates, and facilities are integrated with industrial automation equipment and cloud systems for advanced data analytics.

Recognizing the need for future growth and scalability in the architecture and systems they build, businesses are increasingly turning to established standards for automation. A significant misunderstanding of Industry 4.0 and Industry 5.0 leads to ill-considered investments. At this stage, achieving Dark Factories requires a continuous change planning process. This involves combining individual implementation stages to form a single entire after ensuring the systems are interoperable.

The approach to production floor tasks is changing, but the automation of production processes is not threatened. AI and ML tools are increasingly prevalent in making important decisions and controlling machines.

Entrepreneurs can use methodologies to study digital maturity or the level of technology development, which helps introduce good practices to their companies amid rapid change.

5. Conclusions

The presented approach aims to help entrepreneurs achieve digital maturity for their company by improving the automation of the production process. The digital transformation process involves designing, managing, optimizing, and monitoring business, production, and logistics processes to implement continuous improvement and sustainability practices. The objective of digital transformation is to attain a state of digital maturity. The term can be described as a measure of an organization’s ability to create value by integrating organizational operations and human capital with digital processes, leading to increased responsiveness to the development of products, processes, business models, and marketing innovations. The correct implementation of change requires digital awareness, which determines the possession of the knowledge, skills, and attitudes needed to use digital tools effectively.

The method presented in the article was developed for small- and medium-sized enterprises but is not limited to this type of entity. Polish entrepreneurs are part of the research results presented, and the method is not limited to the territory of this country. The approach can be applied to all companies aiming to lead digital transformation by consciously automating production processes, regardless of location. The primary objective was to address the issues in the first ADMA transformation (i.e., Advanced Manufacturing Technologies).

The method’s strength lies in its comprehensive approach to the processes of manufacturing companies, which necessitates an analysis of the impact of automation on other areas of the company’s operations. Another advantage is the emphasis on suggesting solutions aligning with mainstream automation development, such as network standards and separating control systems into process and enterprise systems. These assumptions also consider the subjectivity, requirements, and development of employees while rejecting the treatment of the employee as part of automated processes. Disadvantages include the need to fulfill multiple requirements to achieve a certain level of maturity.

Further research involves expanding the method with tools to automate the survey process and implementing the Cost–Benefit Analysis approach.