CC BY
0UDC 65.012.3
Smoliarchuk Vladimir
specialist degree, Moscow Aviation Institute Russian Federation, Moscow
METHODS AND TECHNIQUES FOR IMPROVING THE EFFICIENCY OF BUSINESS PROCESSES IN MANUFACTURING COMPANIES
Abstract: The article discusses modern technologies and methods aimed at improving business processes efficiency in manufacturing companies. It describes key areas of digital transformation, such as automation, artificial intelligence, the Internet of Things, and cloud computing. The article focuses on the benefits of these technologies in optimizing production processes, reducing costs, and increasing flexibility. It also addresses the challenges companies face when implementing innovative solutions, such as employee adaptation and significant initial investments. Successful digitalization examples from leading American companies are also presented.
Keywords: digital transformation, automation, artificial intelligence, Internet of Things, cloud computing, manufacturing companies.
INTRODUCTION
Nowadays, production businesses are faced with the need to adjust to a fast-evolving economic environment, increased competition and more complex supply chains. In the age of globalization and digitalization, traditional methods of managing production processes are no longer effective, which necessitates the need to look for new ways of optimizing business activities. Among the most significant areas of change is the introduction of new technologies such as automation, artificial intelligence (AI), Internet of Things (IoT) and cloud computing, which enable one to improve operational efficiency, reduce the cost and attain the flexibility of production systems. The significance of digital solutions in industry is attested to by their practical application in different industries, from mechanical engineering to pharmacy, which justifies the study of this subject as relevant and timely.
While the advantages of taking on digital technologies are clear, the process of adaptation is also associated with a set of issues, including high initial costs, complexity of integration with the current systems, and the need for qualified personnel. In this regard, scientific research in the field of digitalization of industrial
enterprises should not only identify the most effective technologies, but also investigate the impact of them on the most critical business processes.
The aim of this study is to explore new technologies and methods for the purpose of improving business process effectiveness for manufacturing companies. The article is researching concepts of digitalization, technological conditions for innovation implementation, and determines best practices of industrial enterprise digitalization.
MAIN PART. CONCEPTUAL FOUNDATIONS OF DIGITAL TRANSFORMATION IN INDUSTRY
In the context of global competition and accelerated technological development, digital transformation has become a strategically important direction for the development of industrial companies. Digital transformation is understood as the comprehensive implementation of advanced digital technologies in business processes, which leads to changes in production models, increased efficiency and the formation of new competitive advantages. Unlike traditional automation, aimed at partial improvement of individual operations, digital transformation covers all levels of the enterprise, including production management, logistics, quality control and interaction with suppliers.
One of the key factors of digital transformation is automation of production processes, which reduces the influence of the human factor, increases the accuracy of operations and minimizes costs. Modern enterprises are actively introducing robotic systems, automated process control systems (APCS) and adaptive production systems that allow them to flexibly respond to changes in demand. According to research, the use of robotic solutions can reduce production costs by an average of 20-30% and increase labor productivity by 40% [1]. In the context of growing competition and the need to improve operational efficiency, enterprises are increasingly turning to robotics as one of the key tools for optimizing production processes. This leads to the widespread use of automated solutions at various stages of production, from assembly to packaging and quality control (fig. 1).
300 200 100 O
2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
Figure 1. Annual installations of industrial robots 2017-2024 and 2025-2026 a
forecast, thousand units [2] Another key aspect of digitalization is AI and machine learning (ML), used in predictive maintenance of equipment failure, production optimization planning, and increasing the accuracy of resource management. ML is used for analyzing large data sets, finding latent dependencies, and predicting the behavior of production systems in real-time. For example, the use of AI in repair and maintenance processes reduces the likelihood of breakdowns in emergency equipment by 50-60%, which is critical for firms that have a cycle of continuous production [3].
The IoT is the basis for developing smart manufacturing systems, unifying physical objects (equipment, sensors, vehicles) into a single digital space. IoT networks provide constant monitoring of equipment condition, automated data collection and processing, allowing enterprises to respond in a timely manner to deviations in the technological process.
Finally, cloud technology and digital twin concept facilitate the creation of highly accurate virtual models of manufacturing processes so that businesses can try out new strategies without risking existing systems. Cloud platforms allow businesses to centrally manage data, monitor production indicators, and provide remote access to management systems. However, the use of cloud technologies can be effective only with a high-quality cybersecurity system because the centralized storage of data increases the risks of data leaks and information system attacks [4]. Digital twins allow for the virtual replication of production lines, equipment, and even entire enterprises.
It is used to simulate various modes of operation, forecast faults and test new strategies without any effect on ongoing production.
Introduction of digital technologies into industrial production requires a complex approach, including technical modernization, integration of new systems into existing infrastructure and adaptation of business processes to new conditions. Successful digitalization is dependent on the right choice of technologies, competent data processing and ensuring their security. Modern enterprises face the challenge of creating intelligent production systems that are not only able to automate tasks, but also to process data in order to optimize work in real time.
One of the most significant aspects of digitalization is the introduction of cyber-physical systems (CPS) providing communication between physical and digital space. These systems combine sensor technologies, IoT devices and analytical platforms, allowing companies to track production process parameters in real time.
BENEFITS AND CHALLENGES OF IMPLEMENTING DIGITAL TECHNOLOGIES IN INDUSTRY
The introduction of digital technologies into production processes opens up significant opportunities for companies to increase efficiency and reduce costs. Table 1 provides an overview of the key features and impacts of digital technology adoption across various industries.
Table 1. Features of the introduction of digital technologies
Aspect Advantages Challenges
Automation and robotics Reduction in production costs, increased speed and accuracy of operations. High implementation costs, need for infrastructure modernization.
AI Process optimization, predictive analytics to reduce downtime. Need for high-quality data, complexity of integration with existing systems.
IoT Reduced maintenance costs, improved safety. Cybersecurity risks, complexity of managing large volumes of data.
Cloud technologies Scalability flexibility, reduced IT infrastructure costs. Data leakage risks, dependency on cloud service providers.
Augmented Reality (AR) and Virtual Reality (VR) Improved employee training, faster adaptation of new staff. High cost of equipment and software.
COLD SCIENCE №13/2025 ХОЛОДНАЯ НАУКА
Technologies AI make it possible to process huge quantities of data, uncover latent patterns, and predict, thus improving planning, calculation accuracy, and product quality [5]. IoT devices, by contrast, allow the networking of equipment and sensors for monitoring equipment condition in real-time, leading to reduced downtime and reduced unexpected failure. For example, IoT solution deployment in a company can reduce equipment maintenance expenses by 20-30% due to early detection and fault fixing [6].
It is also significant to mention that digitalization helps in reducing operating costs. Utilization of automation and digital platforms to manage inventory, logistics, and production processes can significantly reduce the labor cost and improve the utilization of resources. Data analytics also helps one to streamline the procurement process, reducing the cost of materials and maximizing the inventory in the warehouse, which also has an effect on cost reduction.
However, the deployment of digital technologies is associated with a chain of difficulties. Among them is adapting workers to novel work conditions. Transition to digital systems requires a significant rise in the qualification level of staff [7]. Usage of such technologies as IoT and AI also requires not only information on how to utilize new systems on the employees' part, but also to possess the ability to manage bulk amounts of data, which becomes all the more difficult for workers without corresponding skills.
In addition, large initial investments in IT infrastructure and security systems are also a hindrance to most companies. While implementation does bring along long-term benefits that include low cost and more flexibility, most businesses face a challenge during the initial stage of digitalization where massive amounts of resources need to be invested for installing and implementing new solutions.
ANALYSIS OF SUCCESSFUL DIGITAL TRANSFORMATION CASES Inserting digital technologies into industries signifies that companies do the best if they take a holistic approach in transforming their business processes. Large industrial companies in the USA utilize automation, AI, and IoT to enhance efficiency, reduce costs, and deliver better quality products.
One of the most illustrative examples is General Electric (GE), which launched the Predix platform, specialized in industrial IoT solutions. With Predix, GE created an intelligent system of equipment monitoring based on real-time data analysis. To be precise, in the gas turbine stations of the company, predictive analytics improved equipment uptime and performance metrics. In addition, GE implemented digital twins, allowing for more effective asset management and fuel consumption optimization on aircraft engines [8].
Another example of digital transformation is given by Ford, which uses AI to manage how it manufactures cars. The Ford plant used the IBM Maximo Visual Inspection system, a system that uses computer vision and ML to scan the quality of car assembly. It cut down on defects and sped up the detection of defects early in the manufacturing process. The company incorporated AR technology into its staff training, which allowed new staff to adjust faster [9].
The success story of top USA companies suggests that the use of new technologies can yield tremendous enhancement in the manner in which they produce goods, manage quality, and reduce costs.
CONCLUSION
Digital transformation is a key driver in improving the efficiency of manufacturing companies in the context of global competition and technological advancement. The introduction of advanced technologies such as automation, AI, IoT and cloud computing results in a significant reduction in costs, business process improvement, and greater flexibility in the production system. The effective use of digital solutions implies a comprehensive approach involving the training of personnel, the introduction of new infrastructural solutions, and the provision of information security. The analysis of successful case studies demonstrates that companies that implement innovations at all levels of production at an early stage achieve the highest level of success. This integration not only enhances existing performance metrics but also fosters long-term competitive advantages.
REFERENCES
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