This paper highlights the storage charging and discharging issue. The study objective is to manage the energy inputs and outputs of the principal grid at the same time in order to maximize profit while decreasing costs, as well as to ensure the availability of energy according to demand and the decisions to either save or search for energy. A fuzzy logic control model is applied in MATLAB Simulink to deal with the system’s uncertainties in scheduling the storage battery technology and the charging- discharging. The results proved that the fuzzy logic model has the potential to efficiently lower fluctuations and prolong the lifecycle.
The research aims to determine the effect of green maintenance “energy efficiency, health and safety risks, and environmental compatibility“ on environmental performance “environmental condition, operational performance, and management performance“ as analytical research in oil products distribution company by distributing a questionnaire distributed to 46 people. The research problem lies in the weak use of traditional maintenance technology, which negatively affects the environment. The research found that the research company’s reliance on green maintenance is weak in adopting new mechanisms and technologies to enhance environmental performance and the weakness of the efforts exerted by management in the field of environmental performance and the failure to involve its staff in training programs to create a culture of preserving the environment within the culture of the organization. The research recommends the need to use machines with low energy consumption and reduce the volume of pollution.
Increased competition has led businesses to compete with each other in streamlining supply chain processes, especially in the manufacturing sector. Supply Chain Management (SCM) determines the success of industrial business processes because it regulates product flow regarding integration, performance, and information. However, several problems have emerged in the supply chain process, such as a lack of coordination in the production queue, difficulties in forecasting trending products, and suboptimal production capacity. To address these issues, the role of information technology is crucial for implementing a Decision Support System (DSS). This study aims to develop a DSS to improve the supply chain processes. The research method used is Extreme Programming (XP) with a qualitative approach through a questionnaire. The research process involves collecting data, defining boundaries and problems, and designing, coding, and testing the system. As a final step, evaluation is carried out by distributing surveys to obtain valid satisfaction results. This research produces a DSS that has applicability in marketing, accounting, and production processes. The application of DSS in the furniture manufacturing industry can help manage the movement of resources, optimize strategic networks, and assist decision-making in the supply chain process.
This paper aims to enhance the productivity of a chilled beef production line by comparing two techniques; standard time calculation and simulation. The best improvement method was obtained using the work-study principle, a network diagram, and bottleneck identification. Two methods for improvement are proposed based on the ECRS, the Theory of Constraint (TOC), and line balancing concepts. A simulation model is developed to mimic the actual production line. The simulation results are verified, validated, and compared. Some workstations were combined, and the allocation of the workers was arranged. The present production line efficiency was 46.21%, which increased to 67.09% and 79.71% from the suggested methods. It showed that using the standard time calculation gives a different result from the simulation. In summary, the simulation model along with the application of TOC and ECRS, provides accurate information and improves overall productivity.
Improving product quality while making decisions remains a challenge. The objective of this research was to develop a model that supports the precise enhancement of product quality through comprehensive analysis of possibilities, product incompatibilities, root causes, and recommended improvement actions. The model incorporated various tools and methods such as the SMARTER method, expert team selection, brainstorming, Ishikawa diagram, 5M+E rule, FAHP, and FTOPSIS methods. The study demonstrated that integrating quality management tools and decision-making methods into a unified model enables the accurate prioritization of activities for product quality management. This integrated approach represents the novelty of this research. The model was evaluated using a mechanical seal made of 410 alloy. The research findings can be valuable to enterprises seeking to enhance product quality at any stage of production, particularly for modified or new products.
Currently, we live in a culture of being overly busy, but this does not translate into efficiency, speed of implementation of the actions taken. Enterprises are constantly looking for methods and tools to make them more efficient. The most popular method of production management is Lean Manufacturing, less known is Theory of Constraints. This work is a continuation of the research on the comparison of these methods with apply a computer simulation, which the analyzed production process in the selected enterprise, after 24 hours and week. An attempt was made to simplify the comparison of the methods based on the obtained simulation in terms of costs. In analyzed case, more advantageous solution is to use the DBR method. To produce various orders that do not require 100% production on the bottleneck position, the use of Kanban is a frequent practice as it provides greater flexibility in order execution.
Quality profiling seeks to know the quality characteristics of products and processes to improve customer satisfaction and business competitiveness. It is required to develop new techniques and tools that upgrade and complement the traditional analysis of process variables. This article proposes a new methodology to model quality control of the process and product quality characteristics by applying optimization and simulation tools. The application in the production process of carbonated beverages allowed us to identify the most influential variables on the gas content and the degrees Brix of beverage.
Electric energy systems need constant modernization and updating to solve such problems as distributed management, self-sealing, improving the quality of electricity, demand management, and integration of renewable energy systems. Currently, energy systems need advanced and intelligent technologies to perform various system-level tasks. The purpose of this research is to analyze the existing control systems of electric energy systems, as well as to consider the possibility of using multi-agent systems to control electric energy systems. To achieve the aim of the research, the following scientific approaches were implemented: method of direct research, experimental method, questioning, comparative method, analysis method, and method of observation. The primary value of the research is in the novelty of the work and the fact, that functional components in multi-agent systems act as independent agents, which can interact with each other through a message communication system. This provides a simple connection between the components, which can benefit complex systems designed for an intelligent network. The intelligent network provides an efficient energy management system, and the modernization of the existing power system using a multi-agent system provides solutions to many problems. The best implementation of a multi-agent system can be achieved through the employment of fast and protected communication protocols. The authors of the research have conducted research and presented key statistical data on electricity usage in Kazakhstan over the past few years. The practical significance of the research is determined by the applied results, and their scientific significance, which is conditioned upon the use of deep, modern mathematical results and the development of an optimal control system. This research is a part of a universal model and optimal system of emergency quick response, conducting a quick preliminary prognosis as well as ensuring more lasting planning in electricity consumption.
The objective of this research is to minimize product defects based on labor performance and prove the hypothesis on how labor performance affects the quality of a product through a scientific calculation using Overall Labor Effectiveness (OLE). The primary data is obtained by interviewing the supervisor and labor directly. For secondary data is obtained from the company, such as labor working time, machine scheduled downtime, total production, and defective products. The approach to extract the data is using OLE and the continued regression method. Furthermore, it proceeds to Six Sigma using the DMAIC approach since the results show a significant correlation. The result from Failure Mode and Effects Analysis (FMEA) shows four of six potential failures caused by product defects are coming from labor. To prevent failure mode, it is recommended to have the regular machine checked by labor, check the temperature of the machine, and provide Standard Operating Procedures.
The Job Shop scheduling problem is widely used in industry and has been the subject of study by several researchers with the aim of optimizing work sequences. This case study provides an overview of genetic algorithms, which have great potential for solving this type of combinatorial problem. The method will be applied manually during this study to understand the procedure and process of executing programs based on genetic algorithms. This problem requires strong decision analysis throughout the process due to the numerous choices and allocations of jobs to machines at specific times, in a specific order, and over a given duration. This operation is carried out at the operational level, and research must find an intelligent method to identify the best and most optimal combination. This article presents genetic algorithms in detail to explain their usage and to understand the compilation method of an intelligent program based on genetic algorithms. By the end of the article, the genetic algorithm method will have proven its performance in the search for the optimal solution to achieve the most optimal job sequence scenario.
The dynamically changing environment forces companies to introduce changes in production processes and the need for employees to adapt quickly to new tasks. Therefore, it is expected to implement solutions to support employees. The system that will manage the work on a manufacturing line should work in real time to support the ongoing activities and, to be implemented in SMEs, must not be expensive. The authors identified important system components and expected functionalities. The methodology of the work is based on humancentered design. A concept of a cyber-physical system is proposed. The aim of the proposed edge computing-based system is to manage the work on the manufacturing line in which certain elements communicate with each other to achieve common goals. The paper presents what the system can consist of, how information and knowledge are managed in the system, and what can be the benefits for enterprises from its implementation.
This article focuses on the issue of innovation within enterprise strategies, with a particular emphasis on the value-creation process and the multidimensional nature of innovation. To analyze this, the focal point was the overall structure of business operations, with a particular emphasis on the involvement of employees. It was also noted that economic entities are subject to generic, market, and HR conditions. The main objective of this article is to highlight the relationships between innovation within enterprises and the conditions under which they operate. This includes the challenges faced by employees in generating creative and innovative ideas and implementing new solutions. Two research hypotheses were put forward in this article. The first hypothesis, H1, assumes that a company’s overall profile is a crucial determinant in creating value through innovation within organizations. The second hypothesis, H2, assumes that market requirements in terms of creating customer value will drive employees to implement innovation defined in the company’s strategies. The importance of open innovation in business strategic decisions was also highlighted. Using statistical methods, these hypotheses were verified through a survey conducted in various enterprises.
The article presents the results of the simulation studies concerning the impact of random production interruptions on the efficiency of multi-spindle machining centers. Four different machining center configuration models were developed using a dedicated class of stochastic Petri nets. In addition to the number of machine spindles, the number of simultaneously mounted parts, loading time of parts, their machining time, and reliability parameters regarding the frequency of machine interruptions caused by random factors were also taken into account as model parameters. A series of virtual tests was carried out for machining processes over a period of 1000 hours of operation. Analysis of the results confirmed the purpose of conducting simulation tests prior to making a decision regarding the purchase of a multispindle milling center. This work fills the existing research gap, as there are no examples in the technical literature of evaluating the effectiveness of multi-spindle machining centers.
The article deals with a widely used method of measuring the overall efficiency of equipment (OEE), which in combination with technologies and software tools is gaining in importance. The overall efficiency of OEE equipment is a key performance metric for machines and equipment to identify hidden capacities and increase production productivity. The intensification of Industry 4.0 in traditional manufacturing companies supports and creates the conditions for their transformation into a smart factory. The integration of intelligent machines and devices with complex human-machine communication network systems requires a new direction in measuring and increasing OEE. Mass customization, resp. personalization of production raises a high need to monitor, improve and further maintain productivity. The aim of the article is to create a simulation model of the production process and test the energy consumption of selected equipment using TX Plant Simulation software with a proposal of measures to increase the OEE of the company.
Nowadays, it is necessary to develop a conceptual framework for analysing the relationship between the implementation of Additive Manufacturing (AM) and Supply Chain Management (SCM). In this context, a gap in the research has been observed in the new approach to designing the importance of AM in SCM. The main contribution of this paper, therefore, is a new framework to formulate the role in adopting AM in SCM. The research methodology is based on detailed literature studies of AM in relation to the SCM process within a manufacturing company, as well on a case study, namely the COWAN GmbH manufacturing company who specialise in producing homewares for motorhome enthusiasts. As highlighted in the state-of-the-art analysis, no work, currently available, supports all the features presented.
This paper aims to improve understanding of the drivers and barriers to digital transformation in asset management. Accordingly, this paper contributes to the literature by conducting a qualitative Delphi study with 15 experts (including academia, consultancy and industry) to identify, validate, and classify the drivers and barriers affecting digital transformation in asset management. As a result of the experts’ interactions, 20 barriers were identified. The main barriers to digital transformation in asset management are the following: Misunderstanding of the strategic importance of asset management, no clear vision/strategy, existing mindset and culture, inadequate asset management system, lack of understanding of digital trends, and lack of employee knowledge and skills. The study also highlights 12 drivers that are critical to the digital transformation of asset management. These include cost reductions, opportunities in condition monitoring of assets, expected benefits in asset management processes, expected benefits in risk management and others.
The rapid development of digital technologies have created unprecedented opportunities for the industrial world. Enterprises, especially small and medium sized companies, struggle to successfully implement these technologies, and there is scant literature to support this endeavor. The authors hypothesize that ERP (Enterprise Resource Management) implementation, being a mature field, can guide digital technology implementation, taking into considerations the similarities. A systematic literature review was conducted to determine the critical success factors (CSF) of ERP implementation in SMEs that were used to derive guidelines for digital technology implementation case study. The results of the case study is another list of CSF that more correctly mirror the digital technology implementation needs. They are: “digitalization strategic plan”; “project sponsor/leader”; “commitment to the workplace”; “involvement of top management”; “reasonable project scope”; “compatibility with existing processes/systems”; “progressing with small steps”; “use of correct competencies”; and “involving the users”.
Nondestructive and contactless online approaches for detecting defects in polymer films are of significant interest in manufacturing. This paper develops vision-based quality metrics for detecting the defects of width consistency, film edge straightness, and specks in a polymeric film production process. The three metrics are calculated from an online low-cost grayscale camera positioned over the moving film before the final collection roller and can be implemented in real-time to monitor the film manufacturing for process and quality control. The objective metrics are calibrated to correlate with an expert ranking of test samples, and results show that they can be used to detect defects and measure the quality of polymer films with satisfactory accuracy.
Appropriate product categorization in distribution centres is important for business success because of the possibility of intuitive product finding by the picker and increased product movement. Both of these factors result in the operational efficiency of the distribution centre. The goal of this paper is to explore a model of shelf space dimensioning of storage location on a rack with vertical and horizontal product categorization in a distribution centre, where the aim is to increase total product movement/profit from all shelves of the rack. This is controlled by a packer who must complete orders by getting the goods from shelves and picking them to the container. In this problem, we develop two heuristics and compare the archived results to the CPLEX solver. The average profit ratios of both heuristics are high and approximately equal to 99%. In 10 cases, optimal solutions have been found by heuristics.
The purpose of this article is to present a proposal for an expert’s model of managerial competencies in the era of the fourth industrial revolution, called Industry 4.0. This revolution results in the emergence of new competency requirements for employees at every organizational level. In the article, we focused on the requirements for Engineers 4.0, in connection with managerial competencies expected from them. In order to answer the research questions, we conducted expert research by referring to our previous studies. Findings: The conducted research allowed to develop an expert’s model of managerial competencies for Engineer 4.0 (EMMCE). The results of the study allowed to determine the scope of managerial competencies for an engineer in the age of Industry 4.0, thus contributing, in a practical scope, to the creation of requirements for candidates applying for a managerial position in manufacturing enterprises. The model makes it possible for educational and training entities to adapt their teaching programmes and training offer to the modern requirements of the industry.
Value stream mapping (VSM) is a well-known lean analytical tool in identifying wastes, value, value stream, and flow of materials and information. However, process variability is a waste that traditional VSM cannot define or measure since it is considered as a static tool. For that, a new model named Variable Value Stream Mapping (V-VSM) was developed in this study to integrate VSM with risk management (RM) using Monte Carlo simulation. This model is capable of generating performance statistics to define, analyze, and show the impact of variability within VSM. The platform of this integration is under Deming’s Plan-Do-Check-Act (PDCA) cycle to systematically implement and conduct V-VSM model. The model has been developed and designed through literature investigation and reports that lead in defining the main four concepts named as; Continuous Improvement, Data Variability, Decision-Making, and Data Estimation. These concepts can be considered as connecting points between VSM, RM and PDCA.
The paper considers the production scheduling problem in a hybrid flow shop environment with sequence-dependent setup times and the objectives of minimizing both the makespan and the total tardiness. The multi-objective genetic algorithm is applied to solve this problem, which belongs to the non-deterministic polynomial-time (NP)-hard class. In the structure of the proposed algorithm, the initial population, neighborhood search structures and dispatching rules are studied to achieve more efficient solutions. The performance of the proposed algorithm compared to the efficient algorithm available in literature (known as NSGA-II) is expressed in terms of the data envelopment analysis method. The computational results confirm that the set of efficient solutions of the proposed algorithm is more efficient than the other algorithm.
Leadership research is an essential part of all areas of organisational science worldwide, and there is still a lack of studies in this research area. The paper aims to determine leadership competency perceptions and their sub-competencies characteristics and determinants in the fourth industrial revolution era. The research survey, conducted in 2018-2021, covered a sample of 100 respondents from organisations from the Czech Republic. The most important competencies for leadership are effective communication, innovation, cooperation, creativity, solving problems, lifelong learning, Information and Communication Technology (ICT) and motivation and support of others. We selected statistical methods ANOVA and linear regression for the characteristics of the respondents and the cluster analysis for the leaders’ 4.0 types determination. The linear regression results showed that age, the field of education, position in the organisation and tenure in the organisation of the respondents affect their assessment of the level of leadership competency. We identified three management types that are currently facing the challenges of Industry 4.0: ICT-oriented Junior Managers, Top 4.0 Prepared Leaders, and Non-Creative Unmotivated Senior Directors. The contribution of this paper is the in-depth study in the area of perceived levels of partial competencies for leadership for different criteria of respondents.
University of South Bohemia: Jihoceska Univerzita v Ceskych Budejovicich, Department of Management, Faculty of Economics, Czech Republic
University of South Bohemia: Jihoceska Univerzita v Ceskych Budejovicich, Department of Applied Mathematicsand Informatics, Faculty of Economics, Czech Republic
Maintenance is a key manufacturing function that contributes to a company’s productivity, profitability and sustainability. Unfortunately, many aspects of the contribution of maintenance to sustainability in manufacturing remain unexplored, and many enterprises are not yet ready to assess the maintenance impacts on their sustainability. Maturity models are useful tools for assessing maintenance practices; however, no maintenance maturity model that allows the evaluation of the contribution of maintenance to sustainable performance was found in literature. This paper proposes a model for assessing the maturity and sustainability of maintenance processes. The model outputs are: a measure of the maintenance and sustainability maturity level; recommendations for improvement to undertake to enhance maintenance maturity and, thus, meet sustainability standards. The model was applied in three manufacturing enterprises: the calculation of their maintenance maturity and sustainability indices made the maintenance stakeholders more aware of the need to implement effective strategies for more sustainable maintenance performance.
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