THE IMPACT OF AUTOMATION ON MODELING THE ECONOMIC DEVELOPMENT OF MODERN MANUFACTURING ENTERPRISES

Анотація

In the modern world, the economy is rapidly changing under the influence of digital
technologies and automation. Enterprises face the need to increase productivity, reduce costs, and
improve product quality, which makes automation a key factor in shaping the competitiveness of
manufacturing enterprises. The automation of production processes has become a crucial factor in the
competitiveness of enterprises in the modern economy. The implementation of automated systems
fundamentally changes approaches to modeling and forecasting the economic development of
production structures.
Automation encompasses various areas such as production processes, resource management,
logistics, finance, and HR processes [1]. The use of robotic systems and software for business process
automation allows for the reduction of human error, increases the accuracy and speed of operations,
and enables more flexible adaptation to market changes. Automation allows for the optimization of
resource utilization, reduction of production cycles, and improvement of product quality. This creates
new parameters for economic modeling, where traditional labor productivity indicators are
complemented by machine time efficiency metrics and automation coefficient calculations. The
implementation of automation tools changes the ratio of fixed and variable costs of an enterprise,
while also increasing capital investments in equipment and software with simultaneous reduction of
operational labor costs. In turn, this requires a revision of classical break-even and profitability
models. Automated data collection and analysis systems provide more accurate modeling of
economic processes. Big Data and real-time systems enable the creation of dynamic models that
account for the variability of market conditions.
The economic efficiency of implementing automation measures is manifested in increased labor
productivity, reduced production costs, and improved product quality [1, 3, 4]. For example, the
implementation of automated production management systems (MES, ERP) allows enterprises to
more accurately plan resource requirements and optimize logistics processes [2]. It is also important
to note the social and economic consequences of business process automation at manufacturing
enterprises. On one hand, automation may reduce the number of low-skilled jobs, but on the other
hand, it creates demand for highly qualified specialists and enables enterprises to invest resources in
the development of new technologies and new business models [1, 2]. Thus, the necessity of studying
the economic effects of automation and digital transformation, as well as developing strategies for
integrating modern technologies into enterprise business processes, is an extremely important issue
for contemporary manufacturing enterprises.
The aim of this study is to determine the economic effects of implementing automation tools at
enterprises and to formulate practical recommendations for the optimal integration of automated
systems.
The research objectives are as follows: (1) to analyze modern automation technologies in
industry and business, including ERP, MES, and robotic systems [2]; (2) to identify the economic
benefits (cost reduction, increased productivity, competitiveness) and risks (initial investments,
changes in the labor market) of implementing automation [3, 4]; (3) to assess the impact of
automation on the labor market and employment structure; (4) to develop recommendations for
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enterprises regarding the phased implementation of automation and the evaluation of economic
effects. Automation in manufacturing currently involves the implementation and use of robotic
assembly lines and automated production systems to increase productivity. At the same time, ERP
and MES systems allow for the optimization of resource planning, inventory management, and
production process control [3]. An example of successful automation implementation can be noted in
the experience of the company "Nova Poshta". The application of an ERP system at "Nova Poshta"
led to a 30% reduction in cargo storage time at warehouses, a 20% decrease in delivery times to
customers, and a 15% reduction in warehouse logistics management costs [2].
Research by domestic scholars confirms the significant impact of automation on enterprise
performance efficiency. The implementation of digital platforms and automated management systems
increases the efficiency of enterprises, especially in the manufacturing and IT sectors [4]. Under
martial law conditions, automation has become a strategic response to workforce challenges, allowing
companies to compensate for labor shortages and increase the productivity of production processes
[1, 5, 6]. Successful examples of digital transformation are demonstrated by Ukrainian companies
such as Interpipe, Fozzy Group, and Silpo, which have implemented comprehensive ERP systems for
managing production and business processes [2].
At the same time, among the challenges of implementing automation tools for economic
modeling of further enterprise development, the following can be identified. The assessment of the
effectiveness of investments in automation requires consideration not only of direct financial results
but also of indirect effects such as increased production flexibility, speed of response to demand
changes, and improved product quality. Moreover, the rapid development of technologies creates
additional risks of equipment obsolescence, which complicates long-term planning and modeling of
enterprise development. The automation of production processes affects the employment structure at
enterprises and requires staff retraining, which must be taken into account in comprehensive
enterprise development models. Thus, modern modeling of enterprise economic development
requires the integration of classical economic models with cutting-edge digital tools. Traditional
financial analysis methods, such as discounted cash flow, net present value, and internal rate of return,
must be complemented by dynamic models that account for the specifics of automated production.
The system dynamics methodology allows for the representation of complex cause-and-effect
relationships between the level of automation, productivity, costs, and the financial results of an
enterprise. The use of specialized software environments enables the creation of simulation models
that reflect the feedback loop between investments in production process automation and the technical
and economic indicators of the enterprise.
It should be noted that the agent-based approach is particularly effective for modeling
decentralized production systems with a high level of automation. In this approach, each agent
(machine tool, workstation, automated line) is modeled as an autonomous unit with its own behavioral
rules. This allows for the analysis of specific system properties, optimization of equipment utilization,
and prediction of production bottlenecks. The implementation of machine learning algorithms
expands the possibilities for economic modeling. Regression models, neural networks, and other
methods are capable of identifying non-linear dependencies between the level of automation and
economic results based on historical data. Predictive models allow for demand forecasting,
optimization of production schedules, and anticipation of equipment maintenance needs.
Mathematical optimization methods (linear, non-linear, integer programming) are applied to
determine the optimal level of enterprise automation considering budget constraints, technological
capabilities, and market conditions. Multi-criteria optimization allows for finding a compromise
between alternative objectives: cost minimization, productivity maximization, ensuring production
flexibility, and so on.
We believe that given the rapid technological changes and instability of the market
environment, the application of scenario planning methods is critically important. Modeling various
development scenarios (optimistic, pessimistic, baseline) using the Monte Carlo method allows for
the assessment of automation investment risks and the development of strategies to minimize them.