The application of industry 4.0 into the company’s production activities through effective decision-making

Abstract

The successful implementation of Industry 4.0 (I4.0) technologies is capable of revolutionizing the production process. However, the realization of this potential largely depends on the effectiveness of the decisions taken. This article examines the key differences between the traditional approach to lean manufacturing and the so—called “Industry 4.0” - comprehensive methods that take into account several key factors contributing to increased enterprise productivity. The main results of the introduction of Industry 4.0 include a significant increase in operational efficiency, improved forecasting capabilities for equipment maintenance, increased flexibility in product customization, and overall increased competitiveness. The study highlights that the successful implementation of Industry 4.0 is not just a technology upgrade, but a real organizational transformation based on a reliable decision—making system. This case is a valuable source of information for organizations facing difficulties in the process of digital transformation. This article reveals the close relationship between effective decision-making and the successful implementation of Industry 4.0 in the company’s production activities. The article notes that the company’s ability to successfully adapt to the conditions of Industry 4.0 and significantly improve its operations largely depends on a well-structured, well-informed and flexible decision-making process using artificial intelligence. The article discusses the key aspects related to the use of artificial intelligence in the decision-making process. In particular, potential risks and ways to overcome them are discussed, as well as problems that can be solved with the help of AI. In addition, the article describes the benefits of implementing Industry 4.0 and their impact on production efficiency. The author emphasizes the close relationship between technology and strategy, and also provides recommendations for manufacturing enterprises that are just beginning their journey towards digital transformation. He emphasizes that to succeed, it is necessary to combine technological expertise with determination at both the strategic and operational levels.

Introduction

The Fourth Industrial Revolution, commonly termed Industry 4.0 (I4.0), represents a paradigm shift in manufacturing, characterized by the deep integration of cyber-physical systems, the Internet of Things (IoT), big data analytics, artificial intelligence (AI), cloud computing, and advanced robotics. This convergence promises unprecedented levels of automation, interconnectivity, real-time data exchange, and decentralized decision-making, fundamentally reshaping production processes, supply chains, and business models. The potential benefits – including hyper-efficiency, mass customization, predictive maintenance, enhanced flexibility, and the creation of new value streams – are compelling drivers for adoption.

However, translating the theoretical promise of I4.0 into tangible operational success presents significant challenges. Many organizations encounter hurdles such as technological complexity, high investment costs, integration with legacy systems, cybersecurity threats, cultural resistance, and a pervasive skills gap¹.Consequently, simply deploying I4.0 technologies is insufficient; their effective implementation demands a holistic and strategic approach.

Central to navigating these complexities and unlocking the value of I4.0 is effective decision-making². The journey from conceptualization to full-scale operation requires a continuous stream of critical decisions across all organizational levels. Strategic choices define the vision, investment priorities, and roadmap. Tactical decisions govern technology selection, partner ecosystems, and organizational restructuring. Operational decisions focus on real-time process optimization, data utilization, and workforce adaptation. The quality, timeliness, and alignment of these decisions ultimately determine whether an I4.0 initiative delivers its promised value or becomes a costly underutilized asset.

In this article, we will briefly outline the key arguments on which our research is based. To achieve this goal, we will conduct a thorough analysis of the existing literature on our topic. In the analysis process, we hope to identify the key features of Industry 4.0 and its potential advantages.

Then we will propose a systematic approach to creating an effective business model based on the principles of lean manufacturing and artificial intelligence (AI) technologies. We will develop a scheme aimed at optimizing work using AI, describe what problems it solves, and discuss the benefits that the company will receive from its implementation.

In conclusion, we will summarize the main results of our research and suggest directions for future research in the field of Industry 4.0 and its role in management decision-making.

Result

Development concepts "industry 4.0’’

To understand how the concept of “Industry 4.0” came about, let’s look at the history of the term and its creators. The term “Industry 4.0” was first mentioned at the Hanover Exhibition in 2011. Its appearance is connected with the High-tech Strategy of the German Federal Government, in which “Industry 4.0” was considered as a key factor for attracting production to the country. The government consulted with the Research Union Economy-Science, which includes Robert Bosch GmbH and Acatech (the German Academy of Sciences and Engineering). It was these organizations that proposed the German term “INDUSTRIE 4.0”. In 2013, German consulting firms at the CeBIT trade fair in Hanover proposed the term “Economy 4.0” or “Industry 4.0” to describe the new economic landscape. This term encompasses a set of economic, business, and social factors that arise with the advent and development of various technologies connected via the Internet. A more precise definition of Industry 4.0 is as follows: “This is a fundamentally new approach to managing production processes, which ensures synchronization of all stages in real time. This approach allows us to create unique products, taking into account their individual characteristics.”³.

The concept of Industry 4.0 represents a paradigm shift in industrial production, encompassing the agile generation and analysis of vast amounts of data in real-time. This development holds immense potential for enhancing strategic and operational decision-making processes. At its core, Industry 4.0 leverages data and connectivity to foster intelligent networks characterized by a high level of collaboration, ultimately contributing to a substantial boost in overall industrial productivity⁴.

Based on the results of data analysis, it is possible to enhance the load at specific points, thereby improving performance. Previously, statistical data was either manually collected or not collected at all, and was used for analysis. This made it challenging to access reliable information to assess the current situation and forecast the future state of production. Now, all this can be viewed in real time through the Internet of Things (IoT). Today, promising areas of development such as smart cities, home systems, predictive diagnostic systems in industry, robotics, and medicine are prominent on the IoT market in Russia. Scientific advancements in the field of artificial intelligence are also noteworthy^5–8. The IoT is also actively used in the IT segment responsible for the software part of the hardware objects that are connected to the internet. Some IoT technologies simulate reality, test various digital scenarios and use a digital twin. The digital twin of an industrial enterprise is a copy of it in a virtual environment. The function of the digital twin is to reproduce processes and operations in the enterprise with high accuracy, which allows one to solve a wide range of business problems.

Thus, Industry 4.0 differs from previous industrial revolutions in that technology leads to the integration of physical (material) and digital aspects of production and consumption. The key technologies are the Internet of Things, advanced materials, digital platforms, robotics, artificial intelligence, the interface of things, and big data analytics. The integration of the physical and digital worlds leads to mass customization through 3D printing (additive manufacturing), manufacturing as a service, and new business models such as sharing and on-demand economics. Reducing the cost of computing power, data storage, and bandwidth facilitates the integration and diffusion of technologies. Industry 4.0 also differs from previous industrial revolutions in that the rate of change is exponential rather than linear.

Artificial intelligence development in "industry 4.0’’

Let’s turn to the history of the emergence of AI. In 1936, Alan Turing proposed the universal Turing machine, which marked the beginning of modern computers. Later in 1950, he published an article titled “Computing and Intelligence.” In the same year, Isaac Asimov published his three laws of robotics. The first use of the term artificial intelligence is attributed to John McCarthy at the famous 1956 Dartmouth Conference, the first conference dedicated to AI⁹. His vision was for machines that could think logically and acquire true knowledge by following instructions and providing appropriate answers. However, this approach created problems for programmers, requiring precise guidelines and definitions. As a result, artificial intelligence has evolved from this traditional approach, and machine learning has become a more popular strategy. Machine learning allows machines to extract information from large datasets, gradually improving their capabilities. This method eliminates the need for carefully crafted programming instructions. This shift has led to revolutionary developments in various fields, especially in image recognition and natural language processing. Artificial intelligence refers to machines that exhibit intelligence through the use of computing resources and algorithms. These machines undergo a learning process, gaining knowledge and increasing their productivity, which leads to significant achievements in various fields. The advent of AI has led to a significant change in the way industries operate, replacing manual labor and traditional methods with digital solutions and automation. AI allows machines to make intelligent decisions, which leads to increased efficiency and innovation¹⁰.

As we have previously discussed, this technology is intersectoral in nature and allows for the unification of requirements for verifying the functionality of AI algorithms and coordinating the development of evaluation methods and tools. This helps to avoid duplication of effort and ensures that uniform standards are followed when creating test data sets.

Based on the information provided, we can draw some conclusions regarding the use of artificial intelligence (AI) in the context of Industry 4.0 and its potential for automation in various sectors. AI has the potential to perform intellectual tasks more efficiently, but it is important to objectively assess its performance in specific operational conditions. This is crucial for the development of a national conformity assessment system for products and services that use AI technologies. At present, AI is already being used to address challenges in areas such as transportation, healthcare, industry, education, and agriculture, demonstrating its potential. However, there are risks associated with its use that could have significant economic, social, and political implications. The use of AI technologies, including machine learning methods, has several essential characteristics for practical application, but it also requires careful consideration of potential risks and benefits.

In the light of these considerations, we can now delve into the realm of artificial intelligence (AI) development and explore its multifaceted benefits. Let us define a term that is pertinent to this discussion. The development of technologies that enable automatons and robots to make decisions represents a significant advance. This allows machines to collect data from their surroundings, analyze it for decision-making, learn from past experiences, and tackle increasingly complex challenges. This technological breakthrough has the potential to revolutionize various aspects of human life^11–14.

Implementation of the project using for industry 4.0.in the enterprise

Any manufacturer is constantly seeking to optimize their production processes with the goal of achieving the best possible outcomes. In this pursuit, companies across various industries invest heavily in tools and strategies designed to manage and enhance their manufacturing operations noteworthy^15–18.

In an era marked by digital transformation, the significance of reliable equipment assumes even greater importance. This aspect stands out as the most apparent area for digitalization, given that equipment malfunctions can result in substantial losses and unplanned downtime, irrespective of the specific product being manufactured.

Nevertheless, implementing digital technologies to anticipate and prevent equipment malfunctions would represent a significant advantage for any company. From a strategic standpoint, research papers underscore the fact that Lean Manufacturing serves as the foundation for Industry 4.0. It is anticipated that Industry 4.0 technologies will build upon the comprehensive approach inherent in Lean Manufacturing.

The research conducted by Tortorella and Fettermann demonstrates that the successful integration of Industry 4.0 technologies is more likely to occur in settings where Lean Manufacturing principles are already well-entrenched¹⁹.

A fundamental prerequisite for the successful deployment of Industry 4.0 solutions is the existence of a highly process-centric approach, characterized by meticulously defined processes, precise specifications for customers, suppliers, tasks, and timelines²⁰.

Recent research conducted using the latest technologies demonstrates that the introduction of digital technologies based on the principles of Industry 4.0 plays a key role in improving the efficiency of production and economic systems. This is confirmed by numerous studies that emphasize the importance of integrating Industry 4.0 digital technologies into the operation of such systems.

Most companies use specialized software products when making management decisions. These systems provide management with information on various aspects of the company’s activities. However, it is worth noting that these systems do not always provide up-to-date data necessary for quick decision-making. Often, making a comprehensive decision can be difficult.

This is because each system — be it an accounting system, a production system, or a logistics system — operates independently and performs its tasks. As a result, there is no interaction between the systems that could increase their efficiency.

The problem is that processing the large amount of data required for decision-making requires high system speed and efficiency. This can lead to increased data processing costs and complicate the decision-making process. The company’s computing power does not allow efficient processing of large amounts of data in real time. The maximum performance is 120,000 operations per second, and the failure recovery rate is 90%.

To solve this problem, many authors suggest using Industry 4.0 technologies. For example, one study suggests using a number of digital tools in the field of cargo handling and logistics, such as RFID sensors, electronic document management, cloud systems and a single window system.

From the application of advanced Internet of Things (IoT) sensors to the strategic use of artificial intelligence (AI) and blockchain, here is a range of technologies that can be used^21,23.

In another paper, it is proposed to use cloud computing, artificial intelligence, machine learning, the industrial Internet of Things and other technologies in various sectors of the economy. The combined use of these technologies can not only change individual business processes, but also completely restructure the industry, creating a product that did not exist before.

One of the scientific publications discusses the concept of digital transformation of production — Industry 4.0. The concept of a “smart” (digital, virtual) factory acts as the technological core of this concept.

In the course of our investigation, we will delve deep into the intricate details of the company’s operations during the transition to the Fourth Industrial Revolution. We will examine a well-established process within the framework of this industry.

The process starts with the collection of data from numerous sensors, which is then subjected to a rigorous process of refinement using sophisticated algorithms implemented through software. This process takes into account both technological processes and the entire production line. Moreover, various mechanisms are swiftly activated to ensure the smooth operation of the entire system^21–24.

More specifically, two research questions are posed:

RQ1.What concepts and technologies allow businesses to take an integrated approach to problem-solving in the digital world?

RQ2: What strategies can managers develop that can be seamlessly integrated into the work of an organization to use digital production technologies with artificial intelligence to achieve its goals?

Considering the questions raised, we will move forward with this solution through an optimization process aimed at enhancing the operation of the company and its processes. Implementing Industry 4.0 technologies optimally involves developing and implementing methods and strategies to maximize opportunities provided by new digital and automated lean management technologies.

The goal of this process is to enhance the efficiency, quality, and competitiveness of the company by utilizing advanced technologies and improving production processes. This includes analyzing and optimizing current production procedures, deploying artificial intelligence algorithms, implementing production management systems, and managing customer relationships effectively. By doing so, businesses can improve their overall performance and remain competitive in a rapidly evolving business environment.

In the first part, we will describe the pilot project and its objectives, as well as explain how the introduction of modern technologies, including artificial intelligence (AI), allows businesses to take a holistic approach to problem-solving in the digital world. We will also discuss how these concepts and technologies relate to the first research question (RQ1).

In the second part, we will present the results of the project and discuss how modern concepts such as lean digital production have enabled us to achieve our goals (RQ2).

As a first result, we will describe the process of the company’s work when switching to digital production. We will look at a well-known process within the enterprise. Let us consider a well-known process at the enterprise. At the beginning of the process, data are collected from sensors. Then, their complex processing by algorithms via software is carried out, taking into account processes and production. Furthermore, to ensure the correct operation of the entire circuit, the actuators are immediately activated. Enterprises have other technological processes that do not fit into the framework of such a closed cycle. The amount of process data can be limited, information collection can be carried out manually, and the analysis is practically not carried out. An example of such a process is monitoring the reliability of the equipment. Monitoring the security of processes, employees, and energy management¹⁴.

Using artificial intelligence s in this case, we understand that it is necessary to close the circle or automate all these areas (Fig. 1).

Fig. 1.

The scheme of the cycle for improving the efficiency of the production system using artificial intelligence in enterprise management.

This diagram shows that in order to make informed decisions, we need accurate and reliable data in real-time. This data can come from new measurement methods or entirely new data collection techniques, such as sensors using artificial intelligence. The data is then transferred to a computer or application, or to an employee, anywhere in the world, via the internet. It is then analyzed using digital technologies, such as digital twins or analytics with machine learning, or with the help of employees or experts. Thanks to cloud storage, there is almost unlimited potential for analyzing, storing, and processing this data using analytical tools. All this work aims to implement digital transformation projects. Thanks to digital tools, people can work remotely, even if the data they are working with is located far away. Secondly, we are changing the way we approach production processes and behaviors, taking into account data and recommendations from analytical tools. These technologies allow us to design, create, and evaluate new business models. Traditionally, employees in manufacturing companies have been involved in optimizing technological processes. They gather data independently and use software to process it or involve their own engineers to solve problems. However, in the past ten years, a number of remote locations have emerged, including test centers, landfills, mines, and oil and gas production platforms. This has led to the idea of a centralized control center for integrated or “digital” production. It has become clear that experts can be gathered in one central location where all data can be collected and shared, instead of having them travel to different sites. However, this still happens within the information systems of a single company and is supported only by its employees. Due to the digital transformation and the industrial internet of things concept, the company has adopted a new approach to services. The presented system utilizes artificial intelligence (AI), which allows innovative products to learn, adapt, and continuously improve. Digital devices gather information from their environment and interact with each other to accelerate their development. The ability to transfer knowledge is impressive and remarkable, as it allows experts to share their knowledge on various topics and issues¹⁸.

To better understand how the scheme works, let’s look at a general description of the AI-based digital transformation cycle to improve production efficiency.

AI-Driven digital transformation cycle for production efficiency

The presented scheme effectively reflects an integrated approach to modern production systems that is data-driven and supported by artificial intelligence. This approach focuses on real-time analytics and digital transformation, as well as centralized management.

Below is a structured description of the key concepts and a detailed explanation of the artificial intelligence-driven cycle for digital transformation to improve production efficiency.

1. Data Acquisition & IoT Integration.

Sources: Sensors, AI-powered cameras, SCADA, ERP, MES, and edge devices.

Innovations:

AI-enabled sensors for adaptive data collection (e.g., vibration, thermal, or spectral analysis).

Real-time streaming via Industrial IoT (IIoT) to cloud or on-premise systems.

Legacy system integration through APIs or middleware.

2. Secure Data Transmission & Cloud Storage.

Methods:

5G/low-latency networks for real-time transfer.

Blockchain for tamper-proof logs (e.g., in oil/gas or mining).

Storage:

Cloud platforms (AWS, Azure, Google Cloud) for scalable, global access.

Edge computing for latency-sensitive processes (e.g., robotic control).

3. Centralized Control & AI Analytics.

Tools:

Digital Twins: Virtual replicas for simulation and predictive adjustments.

Machine Learning: Anomaly detection, prescriptive maintenance, demand forecasting.

Collaborative AI: Human-in-the-loop systems where experts validate AI insights.

Remote Expertise:

Centralized hubs with domain specialists analyzing global data streams.

AR/VR for remote troubleshooting (e.g., offshore rigs or mines).

4. Decision-Making & Action.

Automation: Closed-loop systems where AI auto-adjusts parameters (e.g., energy use).

Human Oversight: Employees review AI recommendations via dashboards (e.g., Siemens MindSphere).

Business Model Innovation:

AI-driven servitization (e.g., predictive maintenance as a service).

Dynamic resource allocation across distributed sites.

5. Continuous Learning & Adaptation.

Feedback Loops:

AI retraining with new production data (e.g., reinforcement learning).

Knowledge transfer between systems (e.g., federated learning for multi-site ops)¹⁹.

Behavioral Change:

Workforce upskilling to interpret AI outputs.

Shift from reactive to proactive decision-making.

Key Innovations in Your Workflow.

Democratized Data Access.

Cloud + IIoT enables real-time collaboration across geographies (e.g., engineers in Germany optimizing)²⁰.

AI as a Co-Pilot.

Combines human expertise (e.g., veteran process engineers) with AI’s scalability.

Breaking Silos.

Centralized control rooms replace fragmented site visits, reducing downtime.

Self-Optimizing Systems.

AI learns from cross-facility data to suggest universal improvements (e.g., reducing waste in automotive supply chains).

Let’s look at how our scheme works and what problems it solves:

- Remote Monitoring:

There is no need to visit dangerous or remote sites such as oil rigs, which significantly saves time and resources.

- Preservation of knowledge: artificial intelligence systematizes accumulated experience, reducing dependence on outgoing employees.

- Scalability: Even small businesses can access cloud-based analytics tools that provide enterprise-grade capabilities.

The benefits of using our scheme:

- Quick Solutions: Real-time alerts reduce the average repair time, which significantly improves work efficiency.

- Cost Reduction: Preventive maintenance reduces unplanned downtime by approximately 30%, resulting in significant cost savings.

- Environmental friendliness: the use of artificial intelligence to optimize energy consumption and rational use of resources contributes to a more careful attitude towards the environment.

In the course of our research, we have developed a scheme that clearly demonstrates a modern approach to production systems based on data and artificial intelligence support. This approach is focused on real-time analysis and includes key steps necessary for the digital transformation of the management decision-making process.

Performance indicators, technical specifications, and risk assessment related to the use of artificial intelligence

Our research is based on the results obtained in the course of our work, as well as the latest achievements of Industry 4.0 specialists^26–30.

We will identify the key characteristics and technical features of the platforms, analyze potential threats and propose effective measures to minimize them in the management decision-making process.

In this section, we will discuss the key performance indicators (KPIs) of artificial intelligence platforms, their technical characteristics, and risk analysis.

1. Specific AI performance indicators.

Key AI performance indicators cover three key aspects: model accuracy, operational efficiency, and business impact. Let’s look at some specific examples:

• A.Model quality indicators:

- Accuracy: Measures the accuracy of forecasts. For example, fraud detection models typically have 95% accuracy.

- Accuracy/Recall: Accuracy (minimizing false alarms) and recall (capturing all true alarms) are crucial for medical diagnosis.

- Score: It provides a balance of accuracy and memorability, which is especially useful for unbalanced datasets, such as spam filtering.

- Delay: The time required to generate responses. For example, Chabot’s with a delay of less than 500 milliseconds improve user interaction.

• 2.Technical characteristics of AI platforms.

Artificial intelligence platforms have a number of technical characteristics that ensure their efficiency and productivity.

These include:

- Learning rate: The rate at which models learn and adapt to new data.

- Data quality: The quality of the source data on the basis of which the models are formed.

- Scalability: The ability to process large amounts of data and operate in real time.

- Reliability: The quality of the platform’s operation in conditions of uncertainty and possible errors.

- Security: The presence of security mechanisms that ensure data protection and confidentiality.

• 3.Risk analysis.

In addition to the key performance indicators and technical characteristics of artificial intelligence platforms, it is also necessary to consider the potential risks they may face.

These risks include:

Unpredictability: The potential for the model to draw unexpected conclusions.

Infidelity: The ability of the model to produce false results.

Vulnerability: Lack of protection against malicious code.

• B.Operational efficiency indicators:

1. Cost savings: Automation of processes using artificial intelligence reduces customer service costs by 30–50%.

2. Time Savings: AI-based logistics optimization reduces delivery planning time by 40%.

3. Throughput: Artificial intelligence systems are capable of processing up to 10,000 requests per minute, making them indispensable for making recommendations in real time.

• C.Business impact indicators:

1. Sales Growth: Personalized recommendations based on artificial intelligence increase e-commerce sales by 15%.

2. Customer Satisfaction (CSAT): Chabot with artificial intelligence contribute to a 20% improvement in CSAT scores due to faster resolution of emerging issues.

3. Employee Productivity: Artificial intelligence tools reduce the time spent on reporting by 60%, allowing staff to focus on solving strategic tasks.

• 2. Technical features of artificial intelligence platforms.

Leading platforms offer a variety of tools and functions for developing, implementing, and scaling artificial intelligence solutions.

Key platforms and features.

Scalability: Automatic scaling allows you to perform high-performance output, for example, more than 1 million forecasts per hour.

Ready-made algorithms: Includes XGBoost for working with tabular data and BlazingText for natural language processing.

- Integration with MLflow: Tracks experiments and model versions.

AI risk Analysis:

- Attacks using an inversion risk model for data privacy:

Hackers can recover training data, which can be especially dangerous if biometric data leaks from facial recognition systems.

- Gaps in compliance: Artificial intelligence systems often violate GDPR by storing unnecessary personal data.

Ways to minimize risks:

1. Collect only the necessary data: Collect only the data that is really needed. For example, you can anonymize training datasets to protect your personal information.

2. Encryption: Use homomorphic encryption to process sensitive data.

Risks associated with the movement of labor.

Low-skilled workers face higher risks of dismissal, while the demand for artificial intelligence specialists is growing rapidly.

Comparative analysis of traditional integrated lean and lean-I4.0

In accordance with the results obtained, we present the data in Table 1. It reflects the performance indicators Pre-Implementation and Post-Implementation of Industry 4.0. using the principles of lean manufacturing.

Table 1.

The performance indicators Pre-Implementation and Post-Implementation of industry 4.0.

KPI	Pre-Implementation	Post-Implementation	Δ
OEE= Availability x Performance x Quality	71%	89%	+ 25%
Lead Time	16 days	9 days	-44%
Energy Waste	18%	7%	-61%
Defect Rate	3.1%	0.8%	-74%

As can be seen from the Table 1, the average efficiency has improved.

As the main conclusion, we present a comparative analysis of traditional and modern integrated approaches related to traditional Lean, AI-Lean-I4.0 integration in Table 2.

Table 2.

Comparative analysis: traditional vs. Integrated Approaches.

Aspect	Traditional Lean	Lean-I4.0 Integration
Problem-Solving	Reactive (e.g., root-cause analysis post-defect)	Proactive (AI predicts failures via digital twin simulations)
Data Utilization	Manual collection; delayed VSM updates	Real-time IoT streams; AI-driven dynamic VSM
Human Role	Operator as executor	Operator as AI validator/decision controller
Scalability	Limited by physical audits	Cloud-based Kaizen algorithms for multi-plant optimization
Limitations	Information lag; subjective analysis	Risk of “black box” decisions; cybersecurity vulnerabilities

Traditional lean manufacturing

Focuses on eliminating waste (muda) through human-centric continuous improvement (kaizen).

Emphasizes value stream mapping, just-in-time production, and standardized work processes.

Relies heavily on manual observation, paper-based systems, and worker experience to identify inefficiencies.

Aims to reduce operational complexity and improve productivity through cultural transformation.

Industry 4.0-Integrated lean

Combines lean principles with digital technologies to create “Lean 4.0” or “Smart Lean” systems.

Uses cyber-physical systems and IoT to automate waste identification and process optimization.

Achieves operational excellence through real-time data analytics and predictive capabilities.

In this comparative Table 2, we will look at the key differences between traditional lean manufacturing and Industry 4.0, integrated approaches to lean manufacturing that address several important aspects.

In the course of our research, we analyzed the key performance indicators of artificial intelligence platforms, examined their technical features, as well as the potential risks associated with their implementation. Artificial intelligence opens up many new opportunities for us, but at the same time it comes with certain risks that must be carefully controlled in order to take full advantage of its potential.

In order to fully exploit the capabilities of this powerful tool, it is important to carefully consider these risks and effectively manage them. Traditional approaches such as lean manufacturing and Industry 4.0 offer comprehensive solutions to optimize production processes and solve various tasks. In our main conclusion, we present a comparative analysis of these traditional and modern approaches in Table 2, and also propose effective methods for minimizing risks in making managerial decisions.

Discussion

Summing up, it can be stated that this article makes a significant contribution both to the scientific community in terms of theory development and to the business sphere in terms of practical implementation. Previous studies have identified the advantages of integrating digital technologies into traditional logistics process management systems. More recent research has focused on the implementation of the principles of Industry 4.0, which include the use of well-known principles of lean manufacturing. These principles, such as the 5 S system, Kanban, Kaizen, TPM, JIT, SMED, TPM, Poka-Yoke, U-shaped cells and visualization, are integral components of this approach^21,22.

The research we reviewed in this article demonstrates the benefits of integrating digital technologies into existing enterprise management systems^15,16,23. We also explored the implementation of Industry 4.0 using advanced artificial intelligence techniques in an industrial setting. Our research has shown that it is essential to prioritize objectives in order to achieve success. To do so, it is important to establish regular communication channels throughout the organization. This includes those who are leading the transformation and those who use the system on a daily basis. This approach to fostering communication and collaboration differs from traditional practices in financial institutions. However, cooperation remains a crucial aspect of any digital transformation effort^24,25.

The author’s concept, developed through integrated cooperation, allows for the systematization of theoretical and methodological principles in enterprise management, as well as the improvement of organizational changes aimed at making management decisions¹⁵, or²⁶.

The results of the study are consistent with data on other manufacturing processes described in the literature. By examining these results in more detail and comparing them with data from other manufacturers, we can better understand why lean manufacturing (LM) methods are becoming so popular. Additionally, we will be able to evaluate their impact on production in the context of Industry 4.0. Given that methods such as 5 S and standardization, which are already widely used in many companies, have been found to be effective, the findings from this study may encourage other companies to adopt solutions based on similar principles^27,28.

The author has developed a theory that constitutes a comprehensive approach to the classification of theoretical and methodological foundations of corporate governance. Within this framework, he has addressed all the contentious aspects. This theoretical construct serves as a roadmap for organizational transformation, facilitating the enhancement of decision-making processes in the context of implementing lean manufacturing practices and integrating artificial intelligence into industrial operations.

In accordance with the previously discussed concepts, Industry 4.0 and the related technologies of the Fourth Industrial Revolution represent a broad spectrum of approaches that extend beyond the traditional frameworks of connectivity and digital automation in manufacturing. These technologies aim to enhance production efficiency and adaptability through self-monitoring capabilities and automated data exchange among components^1,15.

Moreover, enterprises can reap substantial benefits from the deployment of AI-powered processes. These processes appear to be more flexible and scalable than their conventional counterparts, enabling them to seamlessly integrate with a multitude of digital enterprises. This fosters a culture of continuous learning and improvement, transforming manufacturing processes and market interactions. The Fourth Industrial Revolution empowers companies to reinvent their operations and redefine their approach to market engagement, departing from the status quo²⁹.

This is achieved through a variety of techniques, encompassing big data analysis, the Internet of Things (IoT) integration, cybernetic systems, computer networking, robotics applications, collaborative robotics systems, and artificial intelligence integration. Industry 4.0 represents an evolving domain that necessitates a comprehensive understanding of its diverse aspects. Addressing the specific challenges within this field requires a deep comprehension of the underlying concepts and research domains. Such knowledge serves as the foundation for future research endeavors and development projects³⁰.

This section includes several examples of the successful implementation of AI in Industry 4.0^31–37.

Doxel robots use AI to improve accuracy and efficiency on large construction projects. A new robot, created by Doxel, can use AI and LIDAR to check that building projects are progressing as planned. After the construction site closes for the day, these robots can start work. Using LIDAR, they scan the construction site and feed the data into deep learning algorithms. These algorithms identify any deviations from the building plans, so the management team can address the issues the next day. If errors are not noticed on the construction site, they can lead to more complex problems that take time and money to solve. Rapid problem resolution leads to significant cost savings. In a recent test on an office building project, the approach improved labor productivity by about 38%.

Anomaly detection of bearings at Altair engineering

Bearings play a crucial role in the automotive industry. This example utilizes sensor data from four bearings, sampled at 20 kHz for a period of nine days, resulting in a one-second sample every ten minutes. The dataset was obtained from NASA’s Prognostic Center of Excellence. The initial sample represents a new bearing, serving as a reference point for anomaly detection. The goal is to monitor the health of the bearings as they age, predicting the onset of degradation and flagging it as an anomaly to the user. Initially, Principal Component Analysis (PCA) is used for dimension reduction. After that, the samples are compared to a healthy sample to assess the current health of the bearing. The comparison is represented as a Health Index (HI). If there is a 95% decrease in correlation in 5 or more consecutive samples, an anomaly is detected. The entire machine learning model runs on an edge device and sends the HI and anomaly status of each vibration pattern from the sensor to Altair’s SmartSight in real-time. The user can view the information graphically, and if an anomaly is detected, Altair’s SmartCore will send an email alert.

AI in the automotive industry

The widespread use of AI in automotive manufacturing is well documented, with OEMs incorporating it into all aspects of their business, including sales. AI insights allow companies to determine the best products to sell, to whom, and when, based on internal and external data such as registration numbers, macroeconomic indicators, local laws, and sales information. Mercedes-Benz, for example, uses Azure Machine Learning at its large-scale truck and bus production facility in Brazil to transform its sales process. This tool combines internal data with external data to provide sales representatives with tailored offers at the right time, based on factors such as local laws and sales statistics. The system continues to improve with each monthly data input from dealers, resulting in more accurate recommendations for the brand.

Conclusion

In conclusion, we would like to highlight a few key points from our research. The study we conducted has identified gaps in the current knowledge of a particular field and allowed us to answer the questions we raised through scientific reasoning and practical experience. Our proposed methodological approach is based on scientific principles and comprises two main components: expanding theoretical knowledge in the field and developing methodological and practical techniques for implementing Industry 4.0.

The first component, expanding theoretical knowledge, aids us in gaining a deeper understanding of the subject matter and discovering new insights. Through this, we can identify the potential advantages of artificial intelligence and its implications for the industry. By comprehending these benefits, we can enhance our skills and improve our mutual understanding. The second aspect, the practical application, suggests new methods and strategies for incorporating Industry 4.0 into existing systems. This involves the use of artificial intelligence and transfer learning, which can aid in solving problems and achieving goals. By combining these two elements, we aim to demonstrate the practical implications of our research and advocate for implementing our approach in managing production systems. Additionally, the study considers a practical example of an organization’s journey towards digital transformation through the implementation of Industry 4.0 technologies. In order to enhance operational efficiency and address the issues discussed in the research, the author proposes a model for enhancing the production system by integrating digital components, such as artificial intelligence. This integration has enabled the achievement of desired goals.

In general, the research results obtained represent an assessment of the feasibility and desirability of implementing a proposed cycle for improving the production process through the use of digital technologies within a company. This approach has clear benefits due to its evolutionary nature, as it does not replace existing control systems but rather complements them with practical methods for process control. Additionally, business leaders gain a better understanding of the need to integrate Industry 4.0 technologies, such as the Internet of Things and digital twins, which significantly facilitates their involvement in process management. This approach contributes to the development of procedures that cater to the diverse needs of industrial enterprises and reflect the efforts of many generations, representing significant intellectual value. An integrated management approach, incorporating lean and digital manufacturing as well as artificial intelligence and machine learning, provides a reliable path for enterprises to transition to digital methods. This approach can be applied in various business sectors.

According to the author, future research on artificial intelligence in Industry 4.0 should be expanded to include a wider range of industries and regions, as well as more diverse applications. This will help to increase the generalizability of the results and ensure that the research is relevant to a wider audience. To improve the quality of data and ensure better access to it, efforts should be made to collect more complete data sets from various sources. This can be achieved by using new data collection methods and technologies. In addition, it is important to study advanced artificial intelligence techniques, develop interdisciplinary collaboration, and conduct research that is in demand by industry. This will allow researchers to better understand the dynamic nature of the industrial environment and ensure the responsible use of AI.

Based on the results obtained, we have presented the data in Table 1, which reflects the performance indicators before and after the implementation of Industry 4.0 using the principles of lean manufacturing. We have also conducted a comparative analysis of traditional and modern approaches in Table 2 and proposed effective methods for minimizing risks when making managerial decisions.

The limitations of this research

The limitations and implications of this research provide valuable insights for both scholars and practitioners. They serve as a roadmap for future investigations and serve as inspiration for innovative approaches. This paper underscores the need for further exploration into the practical implementation of these guidelines, examining their variations across industries and their impact on the integration of Industry 4.0 into manufacturing processes. The success of these efforts hinges on effective decision-making, which contributes to the overall success of digital transformation projects.

This article provides an in-depth analysis of a wide range of problems and management solutions related to the development and implementation of artificial intelligence models in industrial systems. Despite the significance of the results obtained, it is necessary to note several key limitations.

Our conclusions are based on experience gained in specific industries, which may limit their applicability in other contexts. In addition, the data used in the study is limited to specific examples due to difficulties in the availability and completeness of relevant information sets. Future research on Industry 4.0 needs to expand its reach to include more industries, regions, and applications. This will make the results more generalizable and applicable in various settings. To achieve this goal, we should focus on improving the quality of data and ensuring free access to it. This can be achieved by collecting more extensive and diverse sets of information, as well as using new data sources. In addition, studying modern methods and approaches to Industry 4.0, as well as developing interdisciplinary collaboration and conducting long-term research, will allow us to better understand the dynamic nature of industrial environments. This, in turn, will help ensure responsible and ethical use of the artificial intelligence technologies we have reviewed.

Author contributions

AB Conceptualization, methodology, practical implementation of the project.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.