Process Management Research Articles

Enhanced magnon thermal transport in yttrium-doped spin ladder compounds Sr14−xYxCu24O41

Magnons are quasiparticles of spin waves, carrying both thermal energy and spin information. Controlling magnon transport processes is critical for developing innovative magnonic devices used in data processing and thermal management applications in microelectronics. The spin ladder compound Sr14Cu24O41 with large magnon thermal conductivity offers a valuable platform for investigating magnon transport. However, there are limited studies on enhancing its magnon thermal conductivity. Herein, we report the modification of magnon thermal transport through partial substitution of strontium with yttrium (Y) in both polycrystalline and single crystalline Sr14−xYxCu24O41. At room temperature, the lightly Y-doped polycrystalline sample exhibits 430% enhancement in thermal conductivity compared to the undoped sample. This large enhancement can be attributed to reduced magnon-hole scattering, as confirmed by the Seebeck coefficient measurement. Further increasing the doping level results in negligible change and eventually suppression of magnon thermal transport due to increased magnon-defect and magnon-hole scattering. By minimizing defect and boundary scattering, the single crystal sample with x = 2 demonstrates a further enhanced room-temperature magnon thermal conductivity of 19Wm−1K−1, which is more than ten times larger than that of the undoped polycrystalline material. This study reveals the interplay between magnon-hole scattering and magnon-defect scattering in modifying magnon thermal transport, providing valuable insights into the control of magnon transport properties in magnetic materials.

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.

Managing next generation BP-x initiatives

AbstractPast generations of BPM involved the efficient and effective management of business processes. Yet, we currently face a turning point. The technological facets of data-driven BPM add complexity to traditional BPM applications. As a result, organizations face intended and unintended technology-related changes across all business process initiatives. Using the term BP-x, we summarize the recent changes in BPM knowledge that has hitherto been fragmented across academic literature. To address the challenges of BP-x initiatives, we envision a holistic model that focuses on managing related cutting-edge technologies and BPM. Thus, we propose the operationalized BP-x management model as a valuable IT meta-artifact. We develop the model using a two-cycled Design Science Research methodology and conduct a threefold summative evaluation. The results of our study indicate that creating awareness of potentials and opportunities accelerates the process toward action and fosters new business outcomes in terms of performance and innovation. By strategically aligning BP-x endeavors, organizations promote visibility, shared understanding, and culture among stakeholders. Our model guides managers throughout the BP-x adoption journey in conjunction with organizational, managerial, and technological prerequisites.

Data-driven natural capital accounting model in Indonesia: Impacts of environmentally related economic activities on ecological processes and services

In this study, natural capital accounting (NCA) is enhanced by constructing a data-driven hierarchical model that can help improve policy decision-making. NCA enables the management and transparency of natural capital and ecological processes and services, resulting in better societal activities and well-being in Indonesia's regions; however, it does not account for environmentally related economic activities that may negatively influence natural capital, processes and services. In this study, a data-driven approach is used to address this issue. Valid attributes are identified, and a thorough NCA hierarchical model that incorporates synchronized accounting on ecology, environment-related economic activities and societal well-being is developed. This approach utilizes content and bibliographic analyses, the entropy weighted method (EWM) and the fuzzy Delphi method (FDM), which are used to validate the data-driven NCA measurements. Exploratory factor analysis (EFA) is utilized to cluster the measurements and assess their reliability. The fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is used to construct the NCA hierarchical structure by considering the cause-and-effect interrelationships among attributes. The valid prioritized attributes are determined, and a hierarchical structure is built to support government decision-making processes. The findings suggest that water resource accounting, forestry production, raw material extraction, resource allocation management and resource consumption management are the criteria that fluctuate the most for practitioners and governments.

The classification algorithms to support the management of the patient with femur fracture

Effectiveness in health care is a specific characteristic of each intervention and outcome evaluated. Especially with regard to surgical interventions, organization, structure and processes play a key role in determining this parameter. In addition, health care services by definition operate in a context of limited resources, so rationalization of service organization becomes the primary goal for health care management. This aspect becomes even more relevant for those surgical services for which there are high volumes. Therefore, in order to support and optimize the management of patients undergoing surgical procedures, the data analysis could play a significant role. To this end, in this study used different classification algorithms for characterizing the process of patients undergoing surgery for a femoral neck fracture. The models showed significant accuracy with values of 81%, and parameters such as Anaemia and Gender proved to be determined risk factors for the patient’s length of stay. The predictive power of the implemented model is assessed and discussed in view of its capability to support the management and optimisation of the hospitalisation process for femoral neck fracture, and is compared with different model in order to identify the most promising algorithms. In the end, the support of artificial intelligence algorithms laying the basis for building more accurate decision-support tools for healthcare practitioners.

Project Management Anal yses in a Poul try Farm: Critical Path Anal yses Approach

Abstract: This research work explored the application of Critical Path Analysis (CPA) in the production cycle of broiler chickens, aimed at identifying bottlenecks and inefficiencies that could be mitigated to enhance productivity and profitability. Broiler chicken production, a critical component of the global agricultural sector, demands efficient management of various stages, including breeding, hatching, growing, and processing, to meet the increasing demand for poultry meat. The research utilized CPA, a time-tested project management tool traditionally applied in construction and manufacturing industries, to map out the critical and non-critical activities within the broiler production cycle. By identifying the longest path through these sequential activities, the study highlighted the minimum time required for a complete production cycle and pinpointed stages where delays could extend overall production time. This approach allowed for the precise identification of bottlenecks that impede productivity, such as the brooding phase and health management practices, which are critical for ensuring the growth and survival of broiler chickens.

Sustainable Human Resource Management with a Focus on Corporate Employee Recruitment

Sustainability principles are becoming an integral part of all aspects of business operations, including human resource management (HRM). Despite extensive research in the field of sustainability, there is a lack of focus on sustainable practices in the recruitment process. This article aims to identify opportunities for sustainable HRM with a focus on the recruitment of new employees in a company. The authors defined three research questions using the PICO method and subsequently applied PRISMA, bibliometric analysis, and content analysis methods to address them. The result is a proposal for a sustainable recruitment model, illustrated using the Milky Way Map framework. The model highlights the key areas of recruitment that need to be considered to achieve sustainable management of the recruitment process. The research emphasizes the importance of implementing a sustainable approach to recruitment. Information systems/information technology (IS/IT) plays a crucial role in optimizing recruitment processes and reducing negative environmental, social, and economic impacts. The implementation of the proposed model can bring benefits such as more efficient talent management, cost reduction, and enhanced sustainability.

Evaluating Business Efficiency: The Role of Digital Transfor-mation in Modern Enterprises

Digital transformation, renowned for its capacity to stimulate economic expansion and enhance business landscapes, requires a supportive ecosystem comprising universal digital infrastructure, skilled workforce, appropriate legal frameworks, adequate investment, effective governance, educational initiatives, robust security measures, and other conducive environments. This transformation presents governments with strategic opportunities to shape various economic sectors, encompassing finance, retail, healthcare, agriculture, manufacturing, education, tourism, media, and culture. Existing literature extensively investi-gates digital transformation in academic and practical spheres, yet a consensus on its fundamental principles remains elusive. This study contributes by summarizing the effects of digital technologies on business and management, stressing the need to broaden existing business domains and explore novel areas. Advocating for pro-social objectives, sustainable business mod-els, and widespread adoption of artificial intelligence (AI) are suggested strategies for navigating digital transformation. Fur-thermore, the research scrutinizes the concept of digital disruption, focusing on how emerging digital technologies and inno-vative business models reshape established value propositions. Business process management (BPM) is examined for its role in facilitating these changes, despite historical challenges in terminology and methodological coherence. The study un-derscores the importance of a structured approach to change management, advocating for flexibility and real-time decision-making to address complex business activities. Additionally, the research evaluates the performance of multiple companies using key performance indicators such as customer satisfaction, operational efficiency, employee productivity, and IT infra-structure cost reduction. Employing the Weighted Sum Method (WSM) to rank these companies, the study offers insights in-to their relative performance. These findings aim to guide stakeholders in strategic decision-making by providing a holistic assessment of company performance across various dimensions, identifying areas for potential enhancement, and deepening understanding of digital transformation's ramifications.

Методи обґрунтування запасів запасних частин у системі управління транспортним процесом

The article formulates directions and analyzes the possibilities of substantiating and choosing the most optimal variant of applying mathematical models for forecasting the number and range of spare parts. The provided recommendations allow to take into account the multi-brand fleets of motor transport enterprises and trends for extrapolation in the short term, identified by the results of observations in real operating conditions. The main reasons for the occurrence of prolonged vehicle downtime due to technical malfunctions are considered. Traditional methods of planning spare parts inventories based on the use of reliability theory and results of mileage forecasting, standard time to failure; inventory management theory and statistical methods for forecasting random processes; methods of operations research; economic and mathematical methods; stochastic methods of transformation of random variables are analyzed. The article formalizes the grouping of factors that affect the size of stocks and the range of spare parts in the operation of rolling stock of enterprises, depending on the state of the system of organization of work of services for maintenance and repair of vehicles; structure of the fleet and technical characteristics of vehicles; level of development of the production base; availability, interest and qualification of personnel; operating conditions of automotive equipment. It is proposed to improve the logistics of spare parts inventory management and to formulate measures to improve the performance of carriers based on an assessment of the total costs incurred by the enterprise to maintain the reliability of rolling stock in operation and taking into account the amount of lost profit due to vehicle downtime in the technical service units. At the same time, it is recommended to use the mathematical apparatus of fuzzy set theory for decision-making, which allows formalizing the parameters of vaguely defined expectations of consumers of motor transport services and reducing the subjectivity of decision-making when solving problems of optimizing spare parts stocks in the transport process management system based on the analysis of the structure of consumer properties and service quality indicators.

Reading between the Lines: Process Mining on OPC UA Network Data.

The introduction of the Industrial Internet of Things (IIoT) has led to major changes in the industry. Thanks to machine data, business process management methods and techniques could also be applied to them. However, one data source has so far remained untouched: The network data of the machines. In the business environment, process mining, for example, has already been carried out based on network data, but the IIoT, with its particular protocols such as OPC UA, has yet to be investigated. With the help of design science research and on the shoulders of CRISP-DM, we first develop a framework for process mining in the IIoT in this paper. We then apply the framework to real-world IIoT network traffic data and evaluate the outcome and performance of our approach in detail. We find tremendous potential in network traffic data but also limitations. Among other things, due to the dependence on process experts and the existence of case IDs.

Intelligent smelting process, management system: Efficient and intelligent management strategy by incorporating large language model

In the steelmaking industry, enhancing production cost-effectiveness and operational efficiency requires the integration of intelligent systems to support production activities. Thus, effectively integrating various production modules is crucial to enable collaborative operations throughout the entire production chain, reducing management costs and complexities. This paper proposes, for the first time, the integration of Vision-Language Model (VLM) and Large Language Model (LLM) technologies in the steel manufacturing domain, creating a novel steelmaking process management system. The system facilitates data collection, analysis, visualization, and intelligent dialogue for the steelmaking process. The VLM module provides textual descriptions for slab defect detection, while LLM technology supports the analysis of production data and intelligent question-answering. The feasibility, superiority, and effectiveness of the system are demonstrated through production data and comparative experiments. The system has significantly lowered costs and enhanced operational understanding, marking a critical step toward intelligent and cost-effective management in the steelmaking domain.

The innovation and challenge of artificial intelligence in the whole process management of fundus disease

Artificial intelligence (AI) has demonstrated revolutionary potential and wide-ranging applications in the comprehensive management of fundus diseases, yet it faces challenges in clinical translation, data quality, algorithm interpretability, and cross-cultural adaptability. AI has proven effective in the efficient screening, accurate diagnosis, personalized treatment recommendations, and prognosis prediction for conditions such as diabetic retinopathy, age-related macular degeneration, and other fundus diseases. However, there is a significant gap between the need for large-scale, high-quality, and diverse datasets and the limitations of current research data. Additionally, the black-box nature of AI algorithms, the acceptance by clinicians and patients, and the generalizability of these algorithms pose barriers to their widespread clinical adoption. Researchers are addressing these challenges through approaches such as federated learning, standardized data collection, and prospective trials to enhance the robustness, interpretability, and practicality of AI systems. Despite these obstacles, the benefits of AI in fundus disease management are substantial. These include improved screening efficiency, support for personalized treatment, the discovery of novel disease characteristics, and the development of precise treatment strategies. Moreover, AI facilitates the advancement of telemedicine through 5G and the Internet of Things. Future research should continue to tackle existing issues, fully leverage the potential of AI in the prevention and treatment of fundus diseases, and advance intelligent, precise, and remote ophthalmic services to meet global eye health needs.

Metrics for software process simulation modeling

AbstractSoftware process simulation (SPS) has become an effective tool for software process management and improvement. However, its adoption in industry is less than what the research community expected due to the burden of measurement cost and the high demand for domain knowledge. The difficulty of extracting appropriate metrics with real data from process enactment is one of the great challenges. We aim to provide evidence‐based support of the process metrics for software process (simulation) modeling. A systematic literature review was performed by extending our previous review series to draw a comprehensive understanding of the metrics for process modeling following our proposed ontology of metrics in SPS. We identify 131 process modeling studies that collectively involve 1975 raw metrics and classified them into 21 categories using the coding technique. We found product and process external metrics are not used frequently in SPS modeling while resource external metrics are widely used. We analyze the causal relationships between metrics. We find that the models exhibit significant diversity, as no pairwise relationship between metrics accounts for more than 10% SPS models. We identify 17 data issues may encounter in measurement and 10 coping strategies. The results of this study provide process modelers with an evidence‐based reference of the identification and the use of metrics in SPS modeling and further contribute to the development of the body of knowledge on software metrics in the context of process modeling. Furthermore, this study is not limited to process simulation but can be extended to software process modeling, in general. Taking simulation metrics as standards and references can further motivate and guide software developers to improve the collection, governance, and application of process data in practice.

Green business process management in a Polish municipal waste treatment plant-regional case study

Despite COVID-19, the world economy still contributes to the growth of production and consumption worldwide. Waste disposal, recycling management and energy generation are challenges for many companies in developing economies, including Poland. This article aims to assess the operation of a municipal waste treatment plant (MWTP) from the perspective of green business process management (BMP) solutions. The processes implemented in the MWTP were discussed, with specific consideration of the mechanical waste processing (sorting) process, including the reuse and recycling of materials, composting, energy production (anaerobic process), landfill storage and efficiency parameters of the sorting line. A sustainable waste management system was identified; the cost as well as social and environmental perspectives were analyzed. Also, strategic goals and key performance indicators were considered. The performed analysis included costs, environmental criteria and key environmental indicators. This paper has shown the successful implementation of green BPM, with potential cost and material savings results. The findings of this case study are expected to inspire other waste management companies to adopt green BPM. The presented case study might help raise awareness and promote the implementation of green BPM in municipal plants in Eastern and Southern Europe.

The Application of a Surgery Supporting Computer System Does Not Seem to Influence the Surgeons' Radiation Exposure: A Retrospective Analysis.

Since 2019, a surgery supporting system (SPM, surgical process manager; Johnson & Johnson, New Brunswick, New Jersey, Vereinigte Staaten) has been used in a Level I trauma center for common trauma surgery procedures (distal radius fractures, proximal femur fractures, ankle fractures, proximal humerus fractures, and spine fractures). The hypothesis of this study was that implementing standardized procedures (by using SPM) may reduce radiation exposure, especially for unexperienced surgeons.Workflows were developed for different surgical procedures (distal radius fractures, proximal femur fractures, ankle fractures, proximal humerus fractures, and spine fractures) and added into the SPM. Between October 2019 and June 2022, 90 surgeries using the SPM were included in the study. A control group was included with 107 surgeries using the same surgical technique. The values measured were the radiation exposure during the surgical procedure, the experience of the head surgeon, and whether or not the SPM was used. A statistical analysis was performed by using the chi square and Fischer exact tests, with significance set at a p value <0.05.SPM was applicated in 51 cases for the distal radius (control group 54 patients), 20 cases for distal fibula fractures (control group 21 patients), 9 cases for the proximal femur (control group 19 patients), 5 cases for vertebral fractures (control group 7 patients), and 5 cases for the proximal humerus (control group 6 patients). No difference concerning the median radiation exposure was observed by plating distal radius fractures with 5.7Gy/cm² in the SPM group and a median radiation exposure of 6.4Gy/cm² in the control group (p=0.96). The distal fibula fractures showed no significant difference in the intraoperative radiation (17.4Gy/cm² vs. 6.4Gy/cm², p value 0.53). Radiation exposure was lower when a consultant performed surgery without showing any significance, independent if SPM was used or not.In this study, no significant difference in the intraoperative radiation dose was observed when using a surgery supporting computer system. The experience of the surgeon showed no influence as well, regardless if SPM was used or not. Additional data should be collected questioning these findings.

The role of total quality management in enhancing customer satisfaction in Gulf Cooperation Council (GCC) countries

This study examined the role of Total Quality Management (TQM) practices, specifically continuous improvement, customer focus, process management, and employee engagement, in advancing sustainability and enhancing customer satisfaction in the telecommunications sector, focusing on three firms in Gulf Cooperation Council (GCC) countries. Secondary quantitative data from quarterly reports (2019–2023) were analyzed using descriptive, correlation, and regression methods with STATA software.•The findings indicated an increase in net promoter score over the study period, reflecting firms' commitment to addressing changing customer needs.•Employee engagement and process management had a positive and statistically significant effect on customer satisfaction.•Integrating TQM practices to enhance customer satisfaction in telecommunications.

TECHNOLOGICAL SOLUTIONS BASED ON ARTIFICIAL INTELLIGENCE IN TEACHING FOREIGN LANGUAGES: AN ANALYTICAL REVIEW

AI technologies are already beginning to change the learning process, they contribute to the creation of a personalized learning path; help to analyze educational metadata almost instantly; provide access to big data, helping in solving various professional tasks and problems. The purpose of this article is to provide an analytical review of examples of the use of artificial intelligence technologies in language teaching and to create a typology of AI-based technological solutions that have been actively used in the educational process over the past five years. The proposed typology of AI-based technological solutions used in teaching a foreign language is based on areas of didactic activity and didactic tasks that can be effectively solved with the help of AI. Under the directions of didactic activity, we mean: teaching and learning of foreign languages; management of the educational process, including the analysis of educational analytics, the development of an individual learning path, the creation of recommender systems; organization of the educational process, that is, control and assessment, providing feedback and reflection, developing didactic materials for adaptive online courses and intelligent learning systems.

Management of Production Processes in a Heating Company

Management of Production Processes in a Heating Company

Increasing productivity by using smart gas for optimal management of the gas lift process in a cluster of wells

In the face of the escalating global energy demand, the challenge lies in enhancing the extraction of oil from low-pressure underground reservoirs. The conventional artificial gas lift method is constrained by the limited availability of high-pressure gas for injection, which is essential for reducing hydrostatic bottom hole pressure and facilitating fluid transfer to the surface. This study proposes a novel ‘smart gas’ concept, which involves injecting a gas mixture with an optimized fraction of CO2 and N2 into each well. The research introduces a dual optimization strategy that not only determines the optimal gas composition but also allocates the limited available gas among wells to achieve multiple objectives. An extensive optimization process was conducted to identify the optimal gas injection rate for each well, considering the limited gas supply. The study examined the impact of reducing available gas from 20 to 10 MMSCFD and the implications of water production restrictions on oil recovery. The introduction of smart gas resulted in a 3.1% increase in overall oil production compared to using natural gas. The optimization of smart gas allocation proved effective in mitigating the decline in oil production, with a 25% reduction in gas supply leading to only a 10% decrease in oil output, and a 33% reduction resulting in a 26.8% decrease. The study demonstrates that the smart gas approach can significantly enhance oil production efficiency in low-pressure reservoirs, even with a substantial reduction in gas supply. It also shows that imposing water production limits has a minimal impact on oil production, highlighting the potential of smart gas in achieving environmentally sustainable oil extraction. Furthermore, the implementation of the smart gas approach aligns with global environmental goals by potentially reducing greenhouse gas emissions, thereby contributing to the broader objective of environmental sustainability in the energy sector.

Critical success factors in Malaysian housing projects: examining contractual, process and human management with knowledge sharing moderation

Critical success factors in Malaysian housing projects: examining contractual, process and human management with knowledge sharing moderation