Quality Management Tools Research Articles

Prediction of Diffuse Attenuation Coefficient Based on Informer: A Case Study of Hangzhou Bay and Beibu Gulf

Marine water quality significantly impacts human livelihoods and production such as fisheries, aquaculture, and tourism. Satellite remote sensing facilitates the predictions of large-area marine water quality without the need for frequent field work and sampling. Prediction of diffuse attenuation coefficient (Kd), which describes the speed at which light decays as it travels through water, obtained from satellite-derived ocean color products can reflect the overall water quality trends. However, current models inadequately explore the complex nonlinear features of Kd, and there are difficulties in achieving accurate long-term predictions and optimal computational efficiency. This study innovatively proposes a model called Remote Sensing-Informer-based Kd Prediction (RSIKP). The proposed RSIKP is characterized by a distinctive Multi-head ProbSparse self-attention mechanism and generative decoding structure. It is designed to comprehensively and accurately capture the long-term variation characteristics of Kd in complex water environments while avoiding error accumulation, which has a significant advantage in multi-dataset experiments due to its high efficiency in long-term prediction. A multi-dataset experiment is conducted at different prediction steps, using 70 datasets corresponding to 70 study areas in Hangzhou Bay and Beibu Gulf. The results show that RSIKP outperforms the five prediction models based on Artificial Neural Networks (ANN, Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Long Short-Term Memory Networks (LSTM)). RSIKP captures the complex influences on Kd more effectively to achieve higher prediction accuracy compared to other models. It shows a mean improvement of 20.6%, 31.1%, and 22.9% on Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Particularly notable is its outstanding performance in the long time-series predictions of 60 days. This study develops a cost-effective and accurate method of marine water quality prediction, providing an effective prediction tool for marine water quality management.

Complex affine arithmetic based uncertain sensitivity analysis of voltage fluctuations in active distribution networks

The uncertainties of distribution generations (DGs) and loads lead to severe voltage fluctuations in active distribution networks (ADNs). Meanwhile, energy storage systems (ESSs) and static var compensators (SVCs) can mitigate the uncertainties of power injections by regulating the active and reactive power. Considering the variations of multiple uncertain factors, this paper proposes a complex affine arithmetic (CAA) based uncertain sensitivity analysis method of voltage fluctuations in ADNs. First, affine models of active and reactive power injections are established. The correlations of noisy symbols are used to reflect the mitigation effects of ESSs and SVCs on the uncertainties introduced by DGs and loads. Next, sensitivity indicators of voltage fluctuations are defined based on the transitivity of noisy symbols. Then, a calculation method for sensitivity indicators based on the micro-increments of coefficients is proposed. Combined with the obtained indicators, a fast sensitivity method for calculating interval values of voltages is further proposed. The modified IEEE 33-bus system is tested to validate the accuracy and efficiency of the proposed method by comparison with the continuous utilization of power flow method. Moreover, the 292-bus system is tested to validate its applicability in a large distribution system. Facts have proved that this method improves the efficiency and reliability of calculations, and in different scenarios, it can achieve fast calculation of nodes and online analysis of the voltage fluctuation range in uncertain environments, provides an effective tool for voltage quality management in active distribution networks.

A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit.

Dissolved oxygen is one of the important comprehensive indicators of river water quality, which reflects the degree of pollution in the water body. Monitoring and predicting dissolved oxygen are an important tool for water quality management, which helps to effectively maintain water ecological balance and prevent environmental problems. A single model cannot describe the dynamic characteristics of dissolved oxygen sequence, which affects the prediction accuracy. In order to obtain more accurate dissolved oxygen prediction results, decomposition techniques are commonly used to extract the main fluctuations and trends of water quality sequences. However, the high-frequency modes obtained from decomposition are still unstable. To solve this problem, this paper proposed a hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit. Firstly, dissolved oxygen sequence is preliminarily decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and obtain several intrinsic mode functions (IMF). The fuzzy entropy (FE) is calculated to quantify the complexity of the IMF. Then, variational mode decomposition improved by northern goshawk optimization is used to decompose the IMF with higher entropy. The nonlinearity and instability of the sequence are further weakened. Finally, the bidirectional gate recurrent unit (BiGRU) neural network is used to predict each IMF component, and the final prediction result is obtained by reconstructing the prediction results of each component. In order to verify the effectiveness of the proposed model, this paper selects the dissolved oxygen data of Xin'anjiang Reservoir as the research object. The experimental results show that the RMSE, MAE, MAPE, and R2 of the proposed model are 0.1164, 0.0894, 1.0403%, and 0.9939, respectively, which is best among other comparative prediction models (BP, LSTM, GRU, BiGRU, EMD-BiGRU, CEEMDAN-BiGRU, VMD-BiGRU, and GNO-VMD-BiGRU). Therefore, this model effectively deals with high volatility and nonlinear dissolved oxygen data and provides reference for water environment management and ecological protection.

Total Quality Management Tools and Techniques: Brainstorming

Total Quality Management Tools and Techniques: Brainstorming

Total Quality Management Tools and Techniques: Flowchart

Total Quality Management Tools and Techniques: Flowchart

Quality Management in Pharmaceutical Development of Biotechnology-Derived Medicinal Products: Implementation and Evaluation

SCIENTIFIC RELEVANCE. One of the challenges associated with the development of medicines lies in creating a quality management system (QMS) and tools to assess its performance.AIM. The study aimed to propose a QMS model for the development of biotechnology-derived medicinal products and a performance evaluation tool for this QMS universally applicable in the research centre of the company regardless of the specific activities of its individual divisions.MATERIALS AND METHODS. The study analysed internal audit outcomes, noncompliance responses, and the document flow of the research centre. Parameter values for quality index (QI) calculations were entered into validated Microsoft Excel spreadsheets. Data analysis and visualisation involved using the Microsoft Power Business Intelligence (BI) business analytics platform (mainly, the Power BI Desktop and Power BI Service components).RESULTS. The QMS was implemented, and the authors proposed their QMS performance evaluation tool universally applicable to all the divisions of the research centre. CONCLUSIONS. The authors proposed their QMS model for the development of biotechnology-derived medicinal products and the QI tool for collecting digital data, carrying out standardised monitoring, and tracking QMS status changes. The QI tool is universal for all company departments regardless of their requirements, easy to use, and customisable by adding individual company-specific quality parameters. This makes the QI tool applicable not only to drug development departments but also to other research units.

Eliminating Non-Value-Added Activities and Optimizing Manufacturing Processes Using Process Mining: A Stock of Challenges for Family SMEs

Family small and medium enterprises (FSMEs) differ from non-family SMEs regarding leadership type, human resource management practices, innovation orientation, change management, information and communication technology deployment, process maturity, and resource availability. These differences present challenges when leading any change. Process mining (PM) tools can optimize process value and eliminate non-added-value activities in FSMEs based on “Event Logs”. The present study investigates how a PM project is implemented in an FSME operating in the agri-food sector, focusing on challenges faced in every project phase to extract the most appropriate process that eliminates all sources of waste and bottleneck cases. Drawing upon the L*Lifecycle methodology combined with quality and lean management tools such as the fishbone diagram, Pareto diagram, and overall equipment efficiency (OEE), this study applied a PM project to a manufacturing process for an FSME operating in the agri-food sector. To achieve theoretical production capacity (TPC) and customer satisfaction, the method was analyzed and optimized using Disco and ProM toolkits. The results analysis using Disco and ProM toolkits gave clues about the organizational and technical causes behind the manufacturing process’s inefficiency. First, OEE showed that the studied FSME is struggling with equipment availability. Then, the implementation of the L*Lifecycle methodology allowed for the identification of five critical causes. An action plan to eliminate causes was proposed to the FSME managers.

Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data

Accurately predicting air pollutants, especially in urban areas with well-defined spatial structures, is crucial. Over the past decade, machine learning techniques have been widely used to forecast urban air quality. However, traditional machine learning approaches have limitations in accuracy and interpretability for predicting pollutants. In this study, we propose a convolutional neural network (CNN) model to predict the spatial distribution of CO concentration in Nanjing urban area at 10 m resolution. Our model incorporates various factors as input, such as building height, topography, emissions, and is trained against the outputs simulated by the parallelized large-eddy simulation model (PALM). The PALM model has 48 different scenarios that varied in emissions, wind speeds, and wind directions. The results display a strong consistency between the two models. Furthermore, we evaluate the performance of our model using a 10-fold cross-validation and out-of-sample cross-validation approach. This yields a robust correlation (with both R2 > 0.8) and a low RMSE between the CO predicted by the PALM and CNN models, which demonstrates the generalization capability of our CNN model. The CNN can extract crucial features from the resulted weight contribution map. This map indicates that the CO concentration at a location is more influenced by nearby buildings and emissions than distant ones. The interpretable patterns uncovered by our model are related to neighborhood effects, wind speeds, directions, and the impact of orientation on urban CO distribution. The model also shows high prediction accuracy (R > 0.8) when applied to another city. Overall, the integration of our CNN framework with the PALM model enhances the accuracy of air quality predictions, while enabling a fluid dynamic laws interpretation, providing effective tools for air quality management.

The Impact of Quality Management Tools & Techniques and Innovative Emerging Technology on Smes Quality Orientation and Firm Performance

The Impact of Quality Management Tools & Techniques and Innovative Emerging Technology on Smes Quality Orientation and Firm Performance

E-FARMER Agriculture and Rural Development Using Block Chain Technology

The efficiency, validity, and health of many essential criteria in the food and agricultural supply chain are receiving more attention as a result of the globalisation of industrial and agricultural production. Due to the increasing risks to food safety and corruption, there is a great need for effective traceability solutions. These solutions serve as crucial quality management tools, guaranteeing the safety of the products across the agricultural supply chain. Block chain is a cutting-edge technological approach that yields groundbreaking results for commodity traceability in food supply chains and agriculture. Today's agricultural supply chains are intricate ecosystems that involve a number of stakeholders, making it challenging to validate a number of important needs, mostly related to yield monitoring, crop growth stages, compliance with quality standards, and the nation of first origin. In order to manage crop prices and traceability, this study suggests a system that levitates the block chain and efficiently executes business operations across the agricultural supply chain. The suggested framework method improves effective science and safety with high integrity and dependability by doing away with the necessity for intermediaries, centralised authorities, and records of the transactions. All transactions are first recorded, after which they are kept in the immutable ledger of the block chain and connected to a decentralised network. This ensures a robust, dependable, and effective supply chain ecosystem with a high degree of traceability and transparency. Keywords: Blockchain technology, Agriculture, Supply chain, Traceability, Food safety.

Failure mode and effect analysis using VIKOR method based on interval-valued linguistic Z-numbers

Failure mode and effect analysis (FMEA) is an effective quality management tool used to improve product quality and reliability. However, with the application of FMEA, its shortcomings are exposed regarding risk assessment, weight determination, and failure mode risk prioritization. This paper proposes a new FMEA model using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method based on the Interval-valued linguistic Z-numbers (IVLZNs). Specifically, IVLZNs and the Interval-valued linguistic Z-numbers weighted arithmetic averaging (IVLZNWAA) operator are used to evaluate and aggregate risk information of failure modes; the maximum deviation method is used to determine the weight of risk factors; the IVLZNs-VIKOR method is used to determine the risk priority of failure modes. Then, a numerical example is given to verify the effectiveness of the proposed model. Finally, a comparative analysis is made to demonstrate the feasibility and rationality of the proposed method.

Application of Multidimensional Quality Management Tools in the Management of Medical Adverse Events.

This study aims to investigate the impact of multidimensional quality management tools in establishing a medical adverse event management system, with the aim of continuously improving medical quality and safety while ensuring patient well-being. This study introduces risk management theories, such as the "Gray Rhino Theory", and employs quality management tools like the Plan-Do-Check-Act (PDCA) cycle, Quality Control Circle (QCC), and Root Cause Analysis (RCA), to provide relevant quality management education and training to employees. This approach facilitates the establishment of a medical adverse event management system that encourages reporting and fosters a blame-free reporting culture, while simultaneously implementing quality management across the entire process. The regular utilization of the QCC facilitates ongoing quality improvement. Furthermore, for sentinel events and patient harm incidents with educational values, the study employs the Incident Decision Tree (IDT) to determine appropriate actions. Additionally, the hospital initiates RCA for system-wide improvements, focusing on areas such as management, institutional processes, and environmental aspects. Moreover, an internal medical quality improvement case competition is organized, with outstanding cases being selected to participate in the multidimensional quality management competition organized by the National Quality Management Alliance. The study reveals a significant improvement in employees' awareness of adverse events, the percentage of employees reporting adverse events increased significantly from 39.15% in 2019 to 49.77% in 2022, P=0.002. Furthermore, the adverse event reporting rate has risen significantly from 2.78% (2019) to 5.96% (2022), P=0.002. Additionally, each department has been able to utilize QCC or RCA tools for quality improvement, thereby further reinforcing the development of a patient safety culture. Multidimensional quality management tools play a crucial role in establishing a hospital's adverse event management system, promoting continuous improvement in medical quality, ensuring patient safety, and effectively implementing a culture of patient safety.

Primena i uticaj principa i alata menadžmenta kvaliteta na uspeh projekta

Project became an adequate solution for achieving different goals of a company, thus its successful realization is of great importance. On the other side, quality is a phenomenon wanted to be acquired in order to achieve customer satisfaction and profitability. Although these two fields of research were developing parallely, they rest on the same principles. The goal of this research was to determine the link between quality management and project management in companies on Serbian market, meaning the influence of quality management principles on project success and level of usage of quality management tools. The results of this empirical research suggest that the quality management principles of continuous process improvement, fact based management, leadership, continuous improvement of employee performance and customer satisfaction have a significant positive impact on project success, while simpler quality management tools are in use more than the more complex ones. Project success should not be measured solely on the old success criteria of time, cost and quality, yet more important criteria are alignment of the project's goals with the company's goals and the fulfillment of user requirements.

Lean manufacturing and quality management: Ways to improve efficiency

The reasons for the growing interest in lean manufacturing and quality management systems in engineering production systems are considered. It is shown that these systems currently work without proper efficiency. The essence of lean manufacturing and quality management concepts is considered. The main errors in their understanding and application are analyzed. It is established that it is expedient to implement these systems on the basis of research of processes of the organization's activity. The hierarchy of organizational and technological systems is considered and the main tasks solved within the lean manufacturing and quality management systems are presented. The tools of lean manufacturing and quality management are offered, which it is expedient to use in providing highly efficient machine-building production. Suggestions for restructuring of engineering departments of machinebuilding enterprises are given.

МЕРОПРИЯТИЯ ПО ОПТИМИЗАЦИИ ИНСТРУМЕНТОВ И МЕТОДОВ УПРАВЛЕНИЯ КАЧЕСТВОМ НА ПРОМЫШЛЕННЫХ ПРЕДПРИЯТИЯХ

After the new century, the world economy gradually became more integrated and developed, which opened up new opportunities and problems for Russian enterprises. In the new economic conditions, our local enterprises must continue to move forward in order to achieve good development, must improve product quality based on the principle of “fair competition, survival of the fittest”, consider quality as the basis of competition and promote the development of enterprises. This article discusses measures to optimize quality management tools and methods in industrial enterprises.

Influence of organization profile on the application of quality management tools and techniques

Effective application of tools and techniques is critical for successful Total Quality Management (TQM) implementation. The influence of organization profile on the application of TQM tools and techniques and outcomes is investigated in Indian organizations. Correlation analysis and ANalysis Of VAriance (ANOVA) are utilized for the research. Tools and techniques are found having significant relation to the outcomes of implementation. Organization’s age, size, and turnover significantly influenced the TQM results. Organization’s size and turnover significantly influenced the application of tools and techniques. The study suggests taking the organization profile into consideration for effective implementation. Further research can be conducted in other countries.

Enhancing Process Stability and Quality Management: A Comprehensive Analysis of Process Capability Indices

Process Capability Indices such as Process Capability Index (Cp) and Corrected Process Capability Index (Cpk), along with Process Performance Index (Pp) and Process Performance Corrected Index (Ppk), are most commonly used tools in quality management within manufacturing processes. They determine whether a process is capable of producing products within established tolerance limits, addressing both short-term and long-term variability. Despite widespread use, the analysis of Process Capability Indices often overlooks special variability within processes, which may lead to misleading interpretations of a process's capability, especially when the determination of tolerance limits is inadequately conducted. This article aims to verify the hypothesis that current analyses of Process Capability Indices fail to consider special variability, which could mislead the interpretation of a process's ability to meet its specifications. Statistical analyses were conducted using the Minitab software, based on dynamic viscosity measurements from the production process of solvent-based paint, to explore the implications of special variability on the interpretation of Process Capability Indices. The study revealed that while Process Capability Indices are useful for identifying quality management opportunities, their effectiveness is limited when special variability is present, often resulting in misinterpretations of a process's true capabilities. The findings highlight the need for methodologies that incorporate considerations of all forms of variability to ensure accurate process capability assessments. This gap in traditional analyses can affect the strategic decision-making in quality management, suggesting a critical area for further research and methodological development. The research confirms the need for a revised approach in analyzing Process Capability Indices, advocating for advanced methods that accurately reflect all forms of variability to improve quality management practices. Future research should focus on developing these methodologies to ensure more reliable and effective use of Process Capability Indices in quality management.

Applicability of Quality Management Tools for Evaluating Processes in Primary Health Care

Objective: The objective of the study was to understand the role of health managers in primary care, using validated quality tools, created and implemented within the Unified Health System (SUS). Method: This is a bibliographical review, of an exploratory and descriptive nature, developed using the qualitative method, through published scientific articles and literary works, without a time frame, in the Portuguese language. The Virtual Health Library (VHL) databases were used, specifically LILACS and MEDLINE, and BDTD, Google Scholar, SciELO and CAPES journals. Results and conclusion: The literature review identified that the quality tools validated in the SUS can be used by health managers to improve primary care, helping them to develop strategies to improve the provision of care to users. Among the tools identified, the PCATool-Brasil instrument was considered the most complete, as it evaluates primary care in several aspects, such as organization, access, care process, effectiveness and user satisfaction. Research implications: The research results indicate that quality tools validated in the SUS can be an important tool for health managers in improving primary care. However, it is important that managers adapt the tools to their local reality, taking into account the most relevant indicators for their population. Originality/value: The study is original because it presents a bibliographical review that addresses the role of health managers in primary care, using quality tools validated in the SUS. The study is also relevant for identifying that the PCATool-Brasil instrument is a complete tool that can be used by health managers to improve primary care.

Separate rooms for patient induction, case set-up and breakdown: Innovative operating room turnover through quality management

Turnover time (TT) is the time it takes to prepare an operating room (OR) between consecutive surgeries. Short TT improves OR efficiency, while maintaining patient and staff safety and satisfaction. Yet the multitude of staff involved and steps required creates process complexity that can hinder this goal. Leveraging a unique case study setting, this study deploys a collection of quality management tools to investigate how the use of separate support rooms for patient induction (the administration of anesthesia), case set-up, and case breakdown can reduce OR TT while maintaining safety and satisfaction. Key results show that separate rooms for patient induction and case breakdown can enable parallel processing, improve patient and staff safety, and patient experience. We use post implementation observations to measure TT reduction when using an induction room, which allows time for an extra case per day. We develop decision-support models practitioners can use to identify the potential benefits of separate support rooms during the OR turnover, based on operating conditions and surgical characteristics. We provide considerations from buffer theory and coordination theory for the separation of physical space, proposing a novel view of the OR turnover process as a set of service modules and interfaces.

Supply Chain Resilience Meets Quality Management

Purpose: This study is aimed at supply chain resilience and quality management, which are critical in today's volatile global economy marked by various disruptions. The focus is on how these traditionally separate disciplines can be combined to enhance organizational performance.
 
 Theoretical framework: The theoretical framework revolves around the concepts of supply chain resilience, which includes elements like proactive risk assessment, adaptability, collaborative networks, strategic redundancies, and quality management principles. The research scrutinizes their interplay, revealing significant overlap and implications for organizational outcomes.
 
 Design/Methodology/Approach: An exploratory case study approach forms the core methodology, primarily utilizing secondary data to gain insights into real-world scenarios. This approach enables a deep understanding of the critical factors influencing supply chain resilience and the impact of quality management concepts on customer satisfaction and product/service quality in supply chain processes. The questionnaire is distributed to get insights into the company's practices.
 
 Findings: The findings indicate that integrating supply chain resilience with quality management leads to more sustained and improved organizational outcomes. A resilient supply chain is quicker to recover from disruptions, minimizing financial impacts. Concurrently, a steadfast focus on quality ensures customer satisfaction and loyalty. This integration offers synergistic benefits, contributing to operational efficiency, market competitiveness, and a more substantial brand reputation.
 
 Research, Practical & Social implications: The study provides insights into critical factors of supply chain resilience and their interconnections with quality management concepts. These insights can inform strategies for enhancing resilience and quality management in supply chains. Practically, the study offers a guide for organizations to adopt a unified approach that optimizes supply chain resilience and quality management. This approach is particularly relevant for organizations seeking to improve operational efficiency and customer satisfaction through better risk management and quality assurance processes. Socially, the study highlights the importance of resilient and high-quality supply chains in today’s business landscape by maintaining Sustainable Developmental Goals.
 
 Originality/Value: This research highlights how resilience, characterized by adaptability and robust recovery in disruptive scenarios, and stringent quality management, crucial for meeting customer expectations, are fundamental in modern supply chains. The study emphasizes the importance of a risk management culture and the role of digital technologies in enhancing supply chain resilience, underscoring their vital contributions during crises like the COVID-19 pandemic. Thus, this work offers valuable insights into achieving more resilient and high-quality supply chains in today's challenging business environment through the integration of Quality Management Tools.