This study uses statistical quality control (SQC) and overall equipment effectiveness (OEE) to examine quality at a porcelain production firm. The study is motivated by the most frequently broken machines in 2019, is the Jigger 01 machine. This paper aims to evaluate the machine’s effectiveness using the OEE method. The OEE determines the scope of the problem to be solved using the SQC method. The average OEE value in 2019 was 70%. Based on the SQC method, the product defect produced is still under control. However, the average defect is still above the company’s tolerance limit of 10%. Consequently, this study offers enhancements utilizing the Failure Mode Effect Analysis (FMEA) technique. The results indicate that human resources and machines caused defective products. This paper contributes to providing several improvements that the company can apply to maximize its quality control analysis. After implementing the improvement, the OEE value increases to 74%.
With continuous and rapid change in both technology and businesses due to climate change and governments regulations, Green Supply Chain Management (GSCM) is receiving increasing attentions during last two decades. GSCM refers to the abilities of businesses to enhance operations and sustain environment at the same time and efficiently. In this paper, a comprehensive review of GSCM development and applications is conducted. This review aims to explore the recent developments of GSCM and necessity of adoption GSCM applications in businesses to insure sustainable development. First, a descriptive analysis of 796 paper adopted from Scopus database was conducted to review research on GSCM. Next, a bibliometric analysis of 235 paper was conducted to determine the recent trends and areas of GSCM and related keywords by using VOSviewer software. Then, a content analysis was carried out of 53 review papers by adopting WordStat software to analyze the impact of GSCM on related subjects and trends of GSCM. Finally, a summary analysis of GSCM advantages and challenges was conducted based on fundamental GSCM’s objectives. Results reveal that researches on GSCM are in upward curve with limitations in implementing GSCM applications due to high cost, and lack of sufficient experiences. Results also reveal positive moderate correlation of GSCM with environment, performance, sustainability and management practices indicating the limited implementation of GSCM applications. To further explore these limitations, this study summarized the recent challenges and future potential directions of GSCM in both businesses and environments.
The Controlled Atmosphere Brazing (CAB) process together with NOCOLOKr flux is associated with the occurrence of potassium fluoroaluminate residue inside the cooler. Excess of this flux residue is known to cause gelation of the coolant, which deteriorates the efficiency of the cooler. The flux residue amount is most often measured via Atomic Absorption Spectroscopy (AAS), in accordance with DIN ISO 9964-3. This is a time-consuming measurement that requires the use of specialized equipment and costly solvents. The following article presents two innovative methods for flux residue measurement after CAB process. They include Scanning Electron Microscopy (SEM) with Energy-Dispersive X-ray Spectroscopy (EDS) and Reflected Light Microscopy (RLM) with Differential Interference Contrast (DIC) module. The accuracy of these methods has been compared to the reference AAS method to evaluate their potential as alternative, less expensive, and quicker measurement methods for determining the quantity of flux residue.
This work is interested to optimize the job shop scheduling problem with a no wait constraint. This constraint occurs when two consecutive operations in a job must be processed without any waiting time either on or between machines. The no wait job shop scheduling problem is a combinatorial optimization problem. Therefore, the study presented here is focused on solving this problem by proposing strategy for making Jaya algorithm applicable for handling optimization of this type of problems and to find processing sequence that minimizes the makespan (Cmax). Several benchmarks are used to analyze the performance of this algorithm compared to the best-known solutions.
The article presents the possibility of increasing the production efficiency in an enterprise dealing in the production of lounge furniture. The literature review shows that Poland is the sixth country in the world in the amount of furniture production. It is also one of the leading exporters of upholstered furniture in Europe. Each year, the value of furniture sales production increases by a few percentage points. Due to the constantly growing customer orders, many companies in this industry are struggling with delays in delivering products to customers. This problem also occurs in the analyzed company. While analyzing the production process, the occurrence of a large number of non-conformities was also noticed. An analysis was carried out to indicate the number of non-conformities and the place of their occurrence. The FMEA analysis was used, which indicated which non-conformities are the most important, and the 5Why method, which allowed to indicate the cause of their occurrence. The analysis of production processes showed which activities do not bring added value and how they can be eliminated at no cost. All these measures contributed to increasing the efficiency of the production of lounge furniture.
The severe consequences of climate change have left many unanswered questions about the environmental performance of businesses. Appropriate measures must be taken to tackle the problems conquered due to climate change. This study supports exploring the factors needed to get the Employees Environmental Performance (EEP). It includes Green Human Resources (GHR), Green Creativity (GC) and Proactive Environmental Management (PEM). The relationship among the variables was explored in the existing literature. This study aims to test the relationship between GHR and GC. Further, it also examined the mediating role of GC and moderating role of PEM. For this purpose, data were collected from employees of manufacturing companies (automobiles, food processing, chemicals, textiles, machinery and equipment) in India. More specifically, ISO 14001-certified companies were selected. Among the different institutions, 35 are manufacturing in India. Out of 35, 24 companies agreed to conduct a survey in their company. Totally 5376 employees were considered as a population for the study. With 95% confidence interval, 400 samples were selected, and a questionnaire was distributed among them using simple random sampling. 305 (76.25%) cases finalized for further analysis. The collected data analyzed using PLS-SEM with R software. The results highlighted the essential role of GHR practices in the manufacturing industry to enhance EEP since GHR positively influences EEP. Moreover, it was found that GC partially mediates the relationship between GHR and EEP. It indicates the importance of GC’s existence in increasing EEP through GHR. Finally, it was also found that PEM moderates the relationship between GHR and EEP. It suggested the role of pro-environmental management and their active pace in improving EEP. Managerial implications and future scope were discussed in this study.
Maintenance involves a large amount of data management. Although many tools, strategies, and systems, have been developed to organize the maintenance information resources, SMEs have not found the same benefits as large companies due to their inherent characteristics and, above all, the maturity level of the maintenance department. Maturity models are useful tools for assessing the maturity of maintenance information practices; however, existing models are not suitable for any type of business context, as they required companies to have a clear organizational structure and definite informative infrastructure. Moreover, they do not assist in identifying and defining actions to reach the highest level. This paper proposes a model for assessing and improving maintenance management information practices. It allows a clear measure of the maturity of the maintenance information management practices in industrial contexts and provides improvement actions identifying the information and data needed to enhance maintenance management information practices.
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.
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
Agile Project Management is a topic that has become popular both in business and academia, since the publication of the Agile Manifesto – a historic landmark in this subject. In the next 20 years, there was a relevant scientific production that must be analyzed to provoke reflection about the knowledge built up in this period. In this sense, this study aims to analyze the relevant scientific literature on Agile Project Management through a systematic review and a bibliometric analysis of articles published in scientific journals with Digital Object Identifier, in English, from the Web of Science and Scopus databases, from 2001 to 2021. The research results enable us to gain insights into the characteristics of this knowledge domain, regarding its volume and evolutionary trend, main contributors (i.e. scientific journals, authors, and their affiliations), main studies, methods used, and its central thematic axes.
The fourth industrial revolution has resulted in technology advancements in the manufacturing industry. However, the innovation potential embedded in these technologies should be unlocked by a viable application, i.e., the business model (BM). The BM as a holistic concept featuring different interacting elements is thus emerging as a promising vehicle for innovation. Current BM research describes the entire domain but lacks depth in the characterization of its individual components. This paper investigates the available manufacturing literature through the lens of the BM concept performing a scientometric analysis. The results are presented in a relational framework that provides an in-depth characterization of the manufacturing element of the BM and highlights identified connections that link the BM components. This is the basis for tools that will support firms in developing manufacturing portfolios aligned with their strategic goals.
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