Production Process Management Research Articles

DIGITALIZATION OF SHOP FLOOR MANAGEMENT AT SELECTED SEGMENT IN THE INDUSTRIAL ENTERPRISE IN THE CONTEXT OF INDUSTRY 4.0

As businesses gradually transition to the concept of Industry 4.0, they strive to digitize all necessary data. The aim of this article is to demonstrate how the digitalization of shop floor management (dSFM) can enhance the efficiency of production process management, and to propose steps for faster and more effective implementation of dSFM in industrial enterprises. The digitalization of shop floor management increases agility, responsiveness, and overall improves decision-making processes. In the analyzed enterprise, the traditional shop floor management was overly burdensome, accompanied by excessive documentation and inefficient processes. It was necessary to analyze three areas: data collection, information visualization, and the flow of shop floor meetings. Based on this analysis, shortcomings were identified, and measures were proposed to automate routine activities and streamline processes. This case study thus fills a gap in the research by focusing on specific challenges in implementing dSFM in an industrial enterprise.

Automated methodology for calculating key production indicators of oil field development for business planning model

Background: In order to secure the stable and efficient operations of oil-producing companies, it is essential to forecast the profitability and ROI of the enterprise, as well as to regulate development for the next 5 years. Accurate forecasts facilitate more informed planning and decision-making, directly influencing the economic sustainability and competitiveness of the enterprise. Aim: The purpose of this study is to develop an innovative approach to automate the methodology for calculating key development indicators in a business planning model. Materials and methods: The study utilizes methods for collecting and analyzing production and geological data, empirical forecasting models, and statistical analysis techniques to enchance the accuracy and reliability of forecasts. This approach employs modern algorithms and technologies to process large volumes of data, which allows for more accurate and reasonable forecasts of key production indicators of the field development. Results: This methodology can be applied to forecast a five-year business plan and evaluate its expected implementation. It is integrated into the ‘Production planning and monitoring’ of the ABAI information system, which allows direct data export from the database, automates the monthly monitoring of production indicators, and generate reports for further export. Conclusion: The proposed method for automated planning of key production indicators of the development enhances the accuracy and efficiency of forecasting, thereby improving the quality of planning and evaluating the implementation of the business plan. This contributes to more informed and strategically validated management of oil production processes. Automation of planning processes reduces the labor costs traditionally associated with manual analysis and calculations, freeing up resources for more strategic purposes. This enables rapid responses to changes in production conditions and prompt adjustments of plans. As a result, managers can allocate resources more efficiently, minimize risks, and increase the overall productivity of oil production operations.

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

The Digital Environment and Its Impact on Labour Market Development Regulation

The article considers the influence of digitalisation in the management of production processes. As a result of the introduction of digital technologies and the transfer of the mechanism for the management of production processes into a digital environment, approaches and methods for regulation of the development of the labour market are being transformed. On the basis of the conducted analysis of research and regulatory documents, the author’s interpretation of the term “digital environment” is offered, which is interpreted from two points of view: on the one hand, as an integrated environment, providing information to all users of the labour market; on the other hand, as an environment for digital interaction between economic actors and the entire digital infrastructure. Using this approach, the authors have identified the system-forming components of the digital environment in the sphere of labour market development regulation, which are grouped into three blocks: the system of digital environment management, the system of labour relations management, and, as a result of the interconnection of these systems, a block of ecosystem directions of labour regulation, which in relation to grouped system blocks allow to influence the basic economic indicators of labour in the digital environment, has been allocated.

Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications

Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way to realize the accurate management and decision support of agricultural production processes using modern information technology, is becoming an effective method of solving these challenges. In particular, the combination of remote sensing technology and machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive and systematic reviews on the integrated application of these two technologies. For this reason, this study conducts a systematic literature search using the Web of Science, Scopus, Google Scholar, and PubMed databases and analyzes the integrated application of remote sensing technology and machine learning algorithms in PA over the last 10 years. The study found that: (1) because of their varied characteristics, different types of remote sensing data exhibit significant differences in meeting the needs of PA, in which hyperspectral remote sensing is the most widely used method, accounting for more than 30% of the results. The application of UAV remote sensing offers the greatest potential, accounting for about 24% of data, and showing an upward trend. (2) Machine learning algorithms displays obvious advantages in promoting the development of PA, in which the support vector machine algorithm is the most widely used method, accounting for more than 20%, followed by random forest algorithm, accounting for about 18% of the methods used. In addition, this study also discusses the main challenges faced currently, such as the difficult problems regarding the acquisition and processing of high-quality remote sensing data, model interpretation, and generalization ability, and considers future development trends, such as promoting agricultural intelligence and automation, strengthening international cooperation and sharing, and the sustainable transformation of achievements. In summary, this study can provide new ideas and references for remote sensing combined with machine learning to promote the development of PA.

Improving the Quality of Production Management Processes Based on Neural Network and Neuro-Fuzzy Models and Tools

Improving the Quality of Production Management Processes Based on Neural Network and Neuro-Fuzzy Models and Tools

ARTIFICIAL INTELLIGENCE IN LABOUR RELATIONS: A THREAT TO HUMAN RIGHTS OR NEW OPPORTUNITIES?

This article explores the possibility of using artificial intelligence in the field of labour relations. Modern technologies provide new opportunities, but at the same time, they give rise to a number of complex issues, solving new approaches to the realization of the right to work and proper social security by able-bodied citizens, which is important today, when Ukraine is defending its sovereignty and independence from military aggression by the Russian Federation. The use of machine learning algorithms and systems can lead to significant improvements related to the professional training of labour resources, management of production processes, labour protection and other aspects of labour relations.We came to the conclusion that the modernization of labour law involves expanding the circle of participants in labour relations and revising the meaning of the term "employee". Thus, the presence or absence of access to technology will create new forms of inequality in the interaction between "employee-to-employee" and "worker-robot (AI)".A proactive approach is proposed to mitigate the consequences of possible threats to the use of artificial intelligence in labour relations through an in-depth study of all possible threats arising in connection with the use of modern technologies. A proposal is made to take as a basis the international experience of using AI in the social structure of the state and adapt it to the life of the state, which, in turn, will contribute to the promotion of important theses on the quality and accessibility of data at the country level, and in cities in particular, promoting participation in data exchange schemes at the level of the private and public sectors.

Machine Learning Analysis Using the Black Oil Model and Parallel Algorithms in Oil Recovery Forecasting

The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to enhance traditional reservoir simulation frameworks using black oil models. This research involves four main steps: collecting a synthetic dataset, preprocessing it, modeling and predicting the oil recovery factors with various machine learning techniques, and evaluating the model’s performance. The analysis was carried out on a synthetic dataset containing parameters such as porosity, pressure, and the viscosity of oil and gas. By utilizing parallel computing, particularly GPUs, this study demonstrates significant improvements in processing efficiency and prediction accuracy. While maintaining the value of the R2 metric in the range of 0.97, using data parallelism sped up the learning process by, at best, 10.54 times. Neural network training was accelerated almost 8 times when running on a GPU. These findings underscore the potential of parallel machine learning algorithms to revolutionize the decision-making processes in reservoir management, offering faster and more precise predictive tools. This work not only contributes to computational sciences and reservoir engineering but also opens new avenues for the integration of advanced machine learning and parallel computing methods in optimizing oil recovery.

Determining inventory, purchasing and cutting policies to manage imperfect and perishable raw material

ABSTRACT Several industries deal with imperfect and perishable raw materials that need to be cut in order to assemble products. Managing the purchasing, cutting, and sequencing decisions is a challenging problem that spans both inventory control and production process management, where decisions are usually optimised separately. In this research we develop a dynamic programming model that determines these joint decisions. The inspiration comes from furniture companies where the raw material are sheets of plywood that may contain imperfections that need to be avoided when cutting pieces to build the assembled furniture. We evaluate decisions regarding the layout of pieces onto the plywood, dispatching policies of ordered furniture , and the quality of the raw material. The model is useful to perform cost analysis for different scenarios. Variations on the expected demand, purchasing, ordering, disposal, holding costs and initial inventory are considered. Experiments conclude that focusing in quality becomes more important than the age of the plywood sheet if the companies implement a production flow that cuts and sorts all pieces before assembling the furniture. Cost analysis confirms that a just-in-time policy in which little inventory is kept results in important savings when compared with the standard operation of the companies.

Longitudinal analysis of production process variability: an organizational information processing theory perspective

PurposeThis study applies organizational information processing theory (OIPT) to investigate managing production process variability in uncertain environments using information technology (IT) capabilities.Design/methodology/approachWe conduct an empirical analysis using the three-stage least squares (3SLS) technique on 1,612 manufacturing firms over ten years.FindingsThe findings show that IT capability plays a dual role: it reduces the positive impact of environmental uncertainty on production process variability and mitigates the negative relationship between production process variability and operational performance.Practical implicationsOur findings suggest that managers should focus on reducing production process variability by strengthening their firms' IT capabilities. This is particularly crucial in volatile environments where external uncertainties can significantly impact operational processes.Originality/valueVariability in the production process is a significant source of inefficiency and disruption within business processes. Using OIPT, our study contributes to the field by empirically analyzing the role of IT capabilities in reducing production process variability under environmental uncertainty.

Management of Production Processes in a Heating Company

Management of Production Processes in a Heating Company

ОЩАДЛИВЕ ВИРОБНИЦТВО: ІННОВАЦІЇ УПРАВЛІННЯ ПРОМИСЛОВИМ ПІДПРИЄМСТВОМ

The main problem is that the implementation of lean production requires not only technical changes, but also significant transformations in the organizational culture of the enterprise. Many Kazakhstani industrial enterprises face difficulties in the integration of lean principles due to the low level of awareness of management and personnel, insufficient training of personnel, as well as resistance to change, which is characteristic of many organizations. The purpose of the article is to research innovative approaches to the management of industrial enterprises by implementing the principles of lean production, as well as to develop a methodology that would facilitate the effective application of these principles in practice. The object of the study is the management processes of industrial enterprises in the context of the implementation of the principles of lean production. The methodological basis of the research was the logical methods of information processing, statistical analysis, comparison. The main hypothesis of the study is that innovations in the management of industrial enterprises, based on the principles of lean production, ensure the effective functioning of the enterprise in the long term due to the integration of the latest management practices that take into account the specifics of the domestic market and available resources. Presenting main material. The study covers the study of existing management practices, personnel training methods, and organizational culture transformation necessary for the successful implementation of lean manufacturing. Lean manufacturing is a modern concept of production process management aimed at reducing costs, improving product quality, and satisfying consumer needs. This approach, which was developed in Japan, in particular at the enterprises of the Toyota company, is now widely used all over the world, including Ukrainian industrial enterprises. Implementation of the principles of lean production requires not only technical changes, but also significant transformations in the organizational culture of enterprises, which is a complex and multi-stage process. Originality and practical significance of the research. In practice, the successful implementation of lean manufacturing requires a comprehensive approach that includes both technical and organizational measures. One of the key aspects is the adaptation of the best global practices to the specifics of a particular enterprise. For example, heavy industry may require longer staff training and a more thorough approach to change planning. Conclusions. The implementation of lean production is a strategic step that allows to increase the competitiveness of the enterprise, to ensure its sustainable development and successful functioning in the conditions of the modern economy.

Features and Perspectives of the Market of Information and Communication Technologies’ Functioning in Data-Driven Society

The article is intended to define the conceptual prospects for the development of the information and communication technologies market in a data-driven society. According to the results of the study, it was established that the implementation of innovative technological startups for the reorganization of information and communication networks using modern switching equipment, characterized by high performance, remains a relevant and global task for the development of the target market. This will provide potential opportunities for providing a high level of service to several numbering zones at once and maintaining the availability of a wide range of information and communication services in the modern conditions of digitalization of society. It was determined that the sphere of communication and information occupies an important place in the economic and social revival of Ukraine. Broad access to significant amounts of information and database management ensures the efficiency of organization and management of state, production and non-production processes of economic activity, thereby expanding the possibilities of successful business, optimizing costs and ensuring a high level of economic and information security of the business entities. The information and telecommunications market were analyzed and the activity of participating operators was diagnosed. It has been studied that in a data-driven society, the success of achieving goals in the digital development of information and communication enterprises directly depends on global information influence. A significant reduction in the number of telecommunications operators and providers was analyzed. It was determined that small operators of the target market are not able to ensure the appropriate level of quality, availability and continuity of service, in connection with which a significant part of the target audience switches to the services of large providers. Based on the analysis of the main indicators of the development of the information and communication technologies market, the main promising directions of development based on the formation of income from a diverse pool of services are determined.

Community Tourism Organization in the Parish Of Settlement, Cantón Riobamba

The research was conducted to understand why the Flores settlement has not developed community tourism, despite its significant natural and cultural attractions, such as the "Sisay Pacha" carnival, festivals, rituals, astronomy, and activities like green baskets. The settlement faces issues with poor organization of community tourism, underutilization of its heritage, and insufficient tourist services, limiting its growth. The study aims to identify the causes of this situation and propose solutions to strengthen community tourism. The research addresses the problem, the formulation of the problem, the justifications, the objectives, and the hypotheses. It presents a theoretical framework that includes background, theoretical bases, philosophical framework, and state of the art. It describes the methodology used, detailing the type and design of the research, the unit of analysis, the study population, the sample size and selection, the operationalization of variables, the consistency matrix, the research techniques and instruments, as well as the validity and reliability of these instruments, and the analysis and presentation of the information. It focuses on the results and discussion, covering the analysis, interpretation, hypothesis testing, conclusions, and recommendations. Finally, it presents the design of the Food and Beverage Production Management Process under the guidelines of the ISO 9001-2015 standard for the Flores parish. These findings reveal that the main obstacles to the development of community tourism in Flores are the lack of organization and training, poor destination promotion, deficient infrastructure, and limited tourist services. It is recommended to strengthen community organization, train local actors, promote the destination, improve infrastructure, and expand the range of services. The adoption of the ISO 9001-2015 standard is proposed to improve the quality of tourist services and the competitiveness of the destination.

Aspek Penerapan Manajemen Risiko Untuk Industri 4.0

Industry 4.0 is a relatively new method of managing production processes. In the field of risk management, as a result of new approaches, modified frameworks, more complex IT infrastructure, and so on, new types of risks may appear. In many cases, the application of Industry 4.0 has shown that the relationship between people, systems, and objects has become a more complex, dynamic, and real-time optimized network. On the other hand, there is the fact that the increase in the volume and availability of data in real time has led to new requirements on infrastructure, management, technology and so on. The purpose of this paper is to conduct research on Industry 4.0 related to the main aspects and present a draft framework for implementing risk management in the Industry 4.0 concept.

Management of Production Processes in a Heating Company

This paper is focused on researching the behaviour of heating companies in connection with current developments in the electricity market and flexibility in the context of market behaviour. The work assesses the increase in profitability through the creation of a technical–economic model using an objective function with profit maximization. The objective of the paper is to present the procedure and methodology for creating a model using the basic scheme of production processes integrated into the system platform. The result of the work is a comparative analysis of modelled cases of implemented operation deployment according to a defined period and modelling modes on selected time series. The description of individual outputs demonstrates the economic advantage of using combinations of modes of combined electricity and heat production, and non-combined electricity and heat production, including the use of heat-suppression mode because of overproduction of electricity.

Development of Differential Evolution Algorithm for Simple Assembly Line Balancing Problem Type 1

The Simple Assembly Line Balancing Problem Type 1 (SALBP-1) is a widely embraced method in the industry for its simplicity in organizing production processes and enhancing efficiency. Consequently, a Differential Evolution Algorithm (DDE) using a backward task sequence was developed in this study to assist in production process management by determining the optimal number of stations. The efficacy of this method was assessed by juxtaposing it with heuristic approaches, including Longest Operation Time (LOT), Most Following Tasks (MFT), Ranked Positional Weight (RPW), Shortest Operation Time (SOT), Fewest Following Tasks (FFT), Ant Colony Optimization (ACO), Differential Evolution (DE), and Immune Genetic Algorithm (IGA). The findings reveal that DDE outperforms LOT, MFT, RPW, SOT, and FFT in discovering superior solutions and consistently matches solutions achieved by ACO, DE, and IGA methods across all problems. Notably, the DDE method exhibits a shorter time frame for solution discovery.

Effects Of Lean Manufacturing Practices On Operational Performance

Lean manufacturing is a concept of process improvement in production management which focuses on waste elimination. This paper explores relationships between lean manufacturing practices and operational performance. Using data collected from 55 manufacturing companies in Tasikmalaya city. Lean manufacturing were measured by five dimensions: production flow management, customer focus, process management, workforce management, and supplier management. Operational performance comprises four dimensions: cost, quality, lead-time, and flexibility. The findings are that all dimensions of lean manufacturing are significantly related to on operational performance. Production flow management has a higher level of significance in large enterprises compared with SMEs, whereas for customer focus there is a higher level of significance in SMEs compared with large enterprises. Process management, supplier management and workforce management have much lower level of significance for both SMEs and large enterprises.

APPLICATION OF INFORMATION TECHNOLOGY IN THE PRODUCTION PROCESS

Due to the strong processes of globalization and the dynamics of market changes in the conditions of fierce competition, today's leading companies are forced to constantly develop in order to ensure their efficient operation. It is modern information systems that are responsible for efficient planning and management of company resources and allow companies to expand their business. Information system implementation is a real challenge for managers, operations staff, customers and subcontractors, especially in manufacturing enterprises, so it must be carefully planned and carefully executed. In case of absence of any fixed IT solution, the company can make a decision about the creation of new and individual IT system which meets all its demands. However, such a decision can be made by large companies or groups of companies with the same or similar profile from the point of view of high costs, extended implementation time, as well as high complexity and significant risk. At the present time, there is no economic reasoning for small and medium-sized companies to create a complex IT system from zero. Only one solution is to integrate the current production system (which has been developed throughout years in another company) or system, obtained from the external supplier specialized in the separate field with ERP system that is in charge of managing the other company activities. In addition, comprehensive and actual calculation of production expenditure requires the integration of process management systems, financial systems and payroll systems. Data provided by these systems, including the cost of raw materials consumed, cost of labor force, mass media and equipment, distribution switching mechanisms, cost distribution lists and automatic accounting procedures benefit system users to precisely calculate production costs. Detailed and up-to-date information about the production process and product costs, together with complex logistics information allows us to make reasonable and rational decisions about the future activity of the company, to increase the scale of production of the most profitable products or re-organize some business processes which may lead to a deceleration in the production of low-margin products. Empirical proof shows that there are not any complicated IT solutions that enable production companies to satisfy their needs in terms of logistics, finance, accounting and production process management. Certainly this is related to the fact that even though some procedures like finance, accounting, logistics, reclamation are very alike in different companies at the cost of legal requirements, production processes are individual and peculiar for each one. Thus, it is usually difficult, possibly, to apply certain production process procedures on the available wide-spread IT software. Keywords: Information technologies, company, production process, business activity

Green finance and food production: Evidence from cities in China

In the domain of global socio-economic dynamics, the pivotal role of food production (FP) cannot be overstated. It constitutes the bedrock of fundamental human necessities while being inexorably linked with critical aspects such as economic expansion, societal equilibrium, and geopolitical interplays. The multifaceted nature of FP appears through its direct impact on macroeconomic indicators and its latent influence on international relations and policy-making. This critical nexus underscores the imperative for a comprehensive understanding and strategic management of food production processes to sustain and enhance global socio-economic stability and growth. Investigating the nexus between green finance (GF) and FP is pivotal for ensuring national security and promoting sustainable economic development. This research thus paper examines GF's impact on FP in China as outlined in the 2017 green finance policies. It employs the difference-in-differences (DID) method and analyzes a balanced panel dataset comprising 282 cities in China from 2009 to 2020. The results show that GF has an inhibitory effect on FP, and this remains valid after a range of reliability checks, including parallel trend assessments and placebo tests. Further findings of transmission mechanism analysis indicate that non-agricultural factors and financial exclusion are critical for GF's inhibitory effect on FP. Heterogeneity analyses show that GF's inhibitory effect on FP is more pronounced in the main food-producing areas, the main food-marketing areas, and cities with better infrastructure. The empirical results of this paper comprehensively illustrate GF's impact on FP and how it contributes to the achievement of food security objectives.