Process Quality Level Research Articles

Improving group problem solving through awareness of members’ problem-solving preferences

PurposeMany groups in organisations are unsuccessful in problem solving. However, the principle of continuous improvement necessitates that organisations refine their employees’ problem-solving skills. In this mixed-method, field-based lab experiment, we explored the impact of a treatment to enhance the quality of group problem-solving processes.Design/methodology/approachWe focused on the structured problem-solving process in Kaizen Events by differentiating six consecutive phases. About 16 Kaizen Event groups (101 members) participated in a field-based lab experiment that used a lean simulation game to establish a group problem-solving context. Data were collected via video, surveys and group interviews. We examined if a high-quality process is strengthened through group members’ elevated awareness of problem-solving preferences. About 11 groups received a treatment of tailor-made individual feedback to increase awareness of their problem-solving preferences. Additionally, we repeated the experiment in five control groups, where member preferences were not shared.FindingsIn the treatment groups, where problem-solving preferences had been shared, we observed a clear improvement in Kaizen Event process quality and higher problem-solving self-efficacy levels. Moreover, their self-reported Kaizen Event behaviour had changed. Within the control groups, the participants also reported that their problem-solving self-efficacy had improved, but this did not have a positive impact on the quality of the objectively measured Kaizen Event process.Originality/valueBy combining insights from operations management and organisational behaviour, we demonstrate that the structured Kaizen Event problem-solving process improves when group members’ individual problem-solving preferences are shared. We thus add an individual-level variable to the extant models of Kaizen Event success factors. Our results provide fresh insights into how to improve the continuous improvement process within organisations. Kaizen Event stakeholders and their facilitators are offered guidance on how to increase one’s awareness of own and others’ problem-solving preferences in group-based problem-solving events.

Certification by the German Society for Neurorehabiliation, DGNR : Performance and quality of the "Centers for weaning from aventilator in early neurological and neurosurgical rehabilitation"

Certification of centers for weaning from aventilator in neurological neurosurgical early rehabilitation (NNER) by the German Society for Neurorehabilitation (DGNR) is possible since 1 October 2021. The results of certification of facilities in the first year after starting the procedure are presented. As part of the certification process 28criteria are assessed including aset of mandatory characteristics of the facility. The criteria are divided into structural criteria (i = 7), diagnostic criteria (i = 6), personnel criteria (i = 3), internal organization criteria (i = 7), and quality management criteria (i = 5). A total of 13 centers were certified in the first year, with acombined total of 283 beds for weaning from aventilator in the NNER and served 2278 persons to be weaned from aventilator in the year before certification, with amedian of 134 per facility (range 44-414). Only rarely was weaning unsuccessful, requiring conversion to home mechanical ventilation before discharge (invasive home mechanical ventilation median per facility 10persons, range 2-25; non-invasive home mechanical ventilation median 0persons, range 0-57). Ahigh level of process and structural quality was documented for the certified centers: across all areas of assessment, the individual certification criteria were met in the vast majority of cases (median degree of complete fulfilment 86%) or met with improvement potentials documented by the auditors (median 11%). Successful weaning in NNER and ahigh level of process and structural quality can be demonstrated by the certification results of centers that follow this integrative approach to weaning from aventilator in aNNER setting.

Construct the Optimum Process Model for Transistor Gaskets with Six-Sigma DMAIC

The improvement process of six-sigma DMAIC refers to the process that the industry performs to enhance process quality via the following five procedures: (1) define, (2) measure, (3) analyze, (4) improve, and (5) control. It is a common tool used in the industry to ameliorate and enhance process quality. In addition, the process capability index is a tool that the industry most frequently uses to measure process quality. Though many studies have discussed six-sigma methods, none of the specific theoretical models could be provided as a reference for the above five DMAIC improvement steps, making it difficult to control the effect. In order to solve this problem, this paper developed the multi-characteristic process capability analysis chart, MPCAC, using the process capability index and applied the method of statistical inference as a tool promoting define, measure, and analyze in the improvement process of six-sigma DMAIC. Additionally, the testing of the orthogonal table in the Taguchi method can efficiently assist process engineers in finding the optimum combination of machining parameters to improve or boost the process quality level of the quality characteristics by reducing the number and the cost of experiments. Therefore, this paper then adopted the testing of the orthogonal table in the Taguchi method as a tool for process improvement in the fourth step. Finally, according to the optimal combination of machining parameters improved in the fourth step, a standard operating procedure for transistor gaskets was established as a tool for process control in the fifth step.

Method of Fuzzy Analysis of Qualitative-Environmental Threat in Improving Products and Processes (Fuzzy QE-FMEA)

Improving products and production processes is necessary to ensure the competitiveness of the organisation. As part of these improvements, the popular approach is to use the FMEA method (Failure Mode and Effect Analysis). In the traditional FMEA approach, only the qualitative aspect is included, i.e., the analysis of the quality level of products or processes, its possible incompatibilities, and then proposing improving actions for them. It seems insufficient in times of expansion of the idea of sustainable development and dynamically changing customer requirements. Hence, the purpose of the research is to develop a fuzzy QE-FMEA method to simultaneously analyze hazards for product quality and the natural environment. This method will be based on a fuzzy decision environment. The main elements of originality of the developed method are: (i) extension of the characteristics of the selection of ratings for indicators with triangular fuzzy numbers and the development of a new characteristics of the selection of ratings for the environmental impact indicator, (ii) development of a selection matrix for the qualitative-environmental indicator (QE) according to the rules of triangular fuzzy numbers, (iii) determination of the method of estimating the value of the threat priority, additionally considering the qualitative-environmental indicator (RQE). The complement of research is developed procedure of the Fuzzy QE-FMEA method. It was shown that it is possible to include the effects of incompatibilities (effects of defects occurring in products or processes), which were simultaneously assessed considering the importance and impact on the natural environment. This method will be useful for any company for analysing defects of any products or processes mainly with significant impact on the natural environment.

Fuzzy Evaluation Models for Accuracy and Precision Indices

The random variable X is used to represent the normal process containing two important parameters—the process average and the process standard deviation. The variable is transformed using Y = (X − T)/d, where T is the target value and d is the tolerance. The average of Y is then called the accuracy index, and the standard deviation is called the precision index. If only the values of the accuracy index and the process precision index are well controlled, the process quality level as well as the process yield are ensured. Based on this concept, this paper constructed a control chart for the accuracy index and the precision index and derived the confidence intervals of the accuracy index and the precision index using in-control data, as the process was stable. This paper aims to control process quality via monitoring the accuracy and precision of the process. At the same time, fuzzy tests are developed for the indicators of process accuracy and precision to evaluate whether the process quality can reach the k-sigma quality level, as well as offer suggestions about directions of improvement when it fails to reach the k-sigma quality level. Obviously, the model in this paper cannot only evaluate whether the process meets the requirements of the quality level; it can also provide a decision regarding whether the process should be improved. It is very helpful for the enhancement of enterprises’ process capabilities.

Process quality recheck for Gamma quality characteristic from supplier products: a case study on radio-frequency power

Quality fraud seriously damages the right-to-known of customers. Therefore, it is necessary for customers to recheck the process quality level declared by suppliers, which is often measured by process capability indices (PCIs). However, there exist two practical problems when rechecking PCIs based on a quality characteristic (QC). First, QC data often do not follow normal distributions. Second, to enhance the market competitiveness, the supplier usually conducts a full inspection and eliminates the non-conforming items before selling them, which causes the QC data to be truncated. To overcome these problems, motivated by the radio-frequency (RF) power output data, this paper proposes a two-phase process capability recheck method, including data-filling and generalised p-value (GE-P) test. The novel data-filling method integrates the quantile-filling (QA) and bias-correction closed-form (BC) estimator for Gamma distributions, and simulation results show it performs better than the traditional methods on unbiasedness and consistency. Based on the filling pseudo-complete data, GE-P is adopted to complete the recheck by testing whether the process capability reaches the supplier declared level. Numerical analysis indicates it performs well on two types of errors. Finally, a real case study on the motivating example is presented to verify the effectiveness of the proposed method.

Fuzzy Quality Evaluation Model Constructed by Process Quality Index

Many studies have pointed out that the-smaller-the-better quality characteristics (QC) can be found in many important components of machine tools, such as roundness, verticality, and surface roughness of axes, bearings, and gears. This paper applied a process quality index that is capable of measuring the level of process quality. Meanwhile, a model of fuzzy quality evaluation was developed by the process quality index as having a one-to-one mathematical relationship with the process yield. In addition to assessing the level of process quality, the model can also be employed as a basis for determining whether to improve the process quality at the same time. This model can cope with the problem of small sample sizes arising from the need for enterprises’ quick response, which means that the accuracy of the evaluation can still be maintained in the case of small sample sizes. Moreover, this fuzzy quality evaluation model is built on the confidence interval, enabling a decline in the probability of misjudgment incurred by sampling errors.

Parameter values for lot size and quality level via CAE simulation, statistical method, and mathematical programming

Because of the stochastic nature of production systems, it is necessary to first build an uncertainty model for subsequent real applications. Moreover, process parameter planning, quality design, and production inventory management are interdependent elements. In this research, a computer simulation model via computer-aided engineering (CAE) was developed to determine the optimal process parameters, lot size, and back order intervals for an integrated process design and inventory management system with simultaneous quality and cost considerations. Based on the estimated process time and costs obtained using CAE, the derived production rate and unit cost were then used for production inventory applications. In consideration of the uncertainty factor, the response surface method (RSM) was employed to analyze the output, namely the total costs incurred in employing the proposed approach, as well as the inputs, which include the cutting parameters, production quantity, and back order intervals. After the RSM was used to obtain the response functions, which represent the output of the collective interests, the mathematical programming (MP) was formulated based on the response functions to determine the optimal process parameters, process quality levels, production order quantities, and back order intervals. The total cost per set time unit was minimized by determining the required quality level, process parameter values, Economic Production Quantity (EPQ), and back order intervals. A cutting example was chosen to demonstrate the proposed approach. Two cases were used for comparison: the Integrated Case (the proposed approach herein) and the Disintegrated Case.

Production data evaluation analysis model: a case study of broaching machine

ABSTRACT To increase machine tool reliability and overall product quality, process quality must be increased for all parts and components. Aside from quality, processing efficiency is also a crucial indicator of machine tools, which has brought about various customized machine tools with high processing efficiency. As broaching machines offer high production efficiency and are simple in structure and operation, we used broaching machines as a case study to construct a model for process capability evaluation, analysis, and improvement. Furthermore, the Six Sigma quality index directly reflects process yield as well as process quality levels. We then derived the relationships between the quality level of the product and those of individual quality characteristics to establish quality assessment standards and created a process quality analysis chart for products with multiple quality characteristics. We used a mathematical programming model to find the corresponding coordinate point of the upper confidence limit for Six Sigma quality indices. Process engineers need only check whether the corresponding coordinate points fall in the acceptable-quality zone or the poor-quality zone to identify critical-to-quality characteristics. Finally, we used a cause-and-effect diagram to determine the causes of poor process quality and formulate suggestions for improvement.

Developing a Novel Fuzzy Evaluation Model by One-Sided Specification Capability Indices

When all of the one-sided specification indices of each quality characteristic reach the requirements of the process quality level, they can ensure that the process capability of the product meets the requirements of the process quality level. This study constructs a fuzzy membership function based on the upper confidence limit of the index, derives the fuzzy critical value, and then labels the fuzzy critical value on the axis of the visualized radar chart as well as connects adjacent critical points to shape a regular polygonal critical region. Next, this study calculates the observed value of the index to estimate and mark it on the axis for forming a visualized fuzzy radar evaluation chart. Obviously, this fuzzy evaluation model not only reduces the testing cost but also makes the quality level quickly meet the requirements of the specifications. Further, the radar chart can reduce the risk of misjudgment attributable to sampling errors and help improve the accuracy of evaluation by a confidence-upper-limit-based fuzzy evaluation model. Therefore, this easy-to-use visualized fuzzy radar evaluation chart is used as an evaluation interface, which has good and convenient management performance to identify and improve critical-to-quality quickly. Improving the quality of the process before the product is completed will also have the advantage of reducing social losses and environmental damage costs.

Experimental approach towards parameter evaluation in laser powder bed fusion of metals

ABSTRACT Additive manufacturing is being increasingly focused on the production of end-use parts. Compared to the prototyping application, the production of end-use parts demands a higher level of repeatability and process quality. To achieve this, increased knowledge is required about the influence of various process parameters on the part characteristics and the parameter interrelations. Design of Experiment methods can be applied to gain knowledge on the process behavior, but the applicability of different DoE methods for AM processes has to be validated. This paper describes the application of a definitive screening design for the identification of influencing parameters in Laser Powder Bed Fusion of CoCrW alloy. The impact of various hatch parameters on the part porosity is analyzed. The experimental setup and results are described. The results are validated in an additional test series, comparing the part quality achieved by parameter-sets obtained by different optimization approaches. Furthermore, the correlation of the porosity towards mechanical properties is investigated. Finally, the opportunities and limitations of the method are discussed.

Level of Implementation Fidelity of Youth-Friendly Services in Northwest Ethiopia

BackgroundYouth-Friendly Services (YFS) is an evidence-based public health intervention aimed to reduce sexual and reproductive health problems among youth. To achieve the desired outcomes, YFS has to be implemented with a high level of fidelity. However, there is an evidence gap in its implementation with fidelity. Hence, the main objective of the study was to describe the level of implementation fidelity of YFS among public health centers in Central Gondar Zone, northwest Ethiopia. Therefore, this study investigated the level of implementation fidelity of YFS among public health centers in Central Gondar Zone, northwest Ethiopia.MethodsAn institutional-based cross-sectional study was conducted from September to December in 2019. A total of 1029 youths, who came to 11 health centers where YFS has been implemented were randomly selected. A validated tool, comprised of 65 Likert scaled items, designed to measure the three dimensions of fidelity was used to collect data. Face to face clients exit interview approach was used to collect the data. Summary statistics were calculated for each fidelity dimension first, and then the overall fidelity of implementation score was computed.ResultsOverall, only 48 (4.7%; 1.6–11.3%) of youth received the YFS with a high level of implementation fidelity. Whereas 770 (74.8%; 57.6–90.5%) and 211 (20.5%; 912.7–31.0%) of youth received the YFS with a medium and low level of implementation fidelity, respectively. Considering each fidelity dimension, 241 (23.4%) youth received the YFS with a high level of adherence, 212 (20.6%) received the YFS with a high level of the structural quality of care, 323 (31.4%) received the YFS with a high level of process quality of care, 508 (49.3%) received the YFS with a high level of outcome quality of care, and 114 (11.1%), received the YFS with a high level of participant responsiveness.ConclusionThe identified low level of implementation fidelity of YFS indicates the urgent need to strengthen the YFS by the government and programmers. Further studies that can address barriers to the implementation fidelity of Youth-Friendly Services are recommended. In addition, further analysis to know the factors that may influence the fidelity of YFS are recommended.

A Study on the Process Quality Level of K5 Gas Mask

A Study on the Process Quality Level of K5 Gas Mask

Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

The quality characteristics with unilateral specifications include the smaller-the-better (STB) and larger-the-better (LTB) quality characteristics. Roundness, verticality, and concentricity are categorized into the STB quality characteristics, while the wire pull and the ball shear of gold wire bonding are categorized into the LTB quality characteristics. In terms of the tolerance, zero and infinity (∞) can be viewed as the target values in line with the STB and LTB quality characteristics, respectively. However, cost and timeliness considerations, or the restrictions of practical technical capabilities in the industry, mean that the process mean is generally far more than 1.5 standard deviations away from the target value. Researchers have accordingly proposed a process quality index conforming to the STB quality characteristics. In this study, we come up with a process quality index conforming to the LTB quality characteristics. We refer to these two types of indices as the unilateral specification process quality indices. These indices and the process yield have a one-to-one mathematical relationship. Besides, the process quality levels can be completely reflected as well. These indices possess unknown parameters. Therefore, sample data are required for calculation. Nevertheless, interval estimates can lower the misjudgment risk resulting from sampling errors more than point estimates can. In addition, considering cost and timeliness in the industry, samples are generally small, which lowers estimation accuracy. In an attempt to increase the accuracy of estimation as well as overcome the uncertainty of measured data, we first derive the confidence interval for unilateral specification process quality indices, and then propose a fuzzy membership function on the basis of the confidence interval to establish the two-tailed fuzzy testing rules for a single indicator. Lastly, we determine whether the process quality has improved.

The Fuzzy Process Quality Evaluation Model for the STB Quality Characteristic of Machining

Whether it is important components of a machine tool itself or various important components processed by the machine tool, many vital quality characteristics mostly belong to the smaller-the-better type. When the process quality levels of these quality characteristics do not attain to the criteria, friction loss may increase during the machine operation, affecting not only the process precision and accuracy but also the lifetime of the product. Therefore, this study applied a smaller-the-better six-sigma quality index simultaneously demonstrating process quality level and process yield. Besides, in coping with statistical process control data, a one-tail confidence-interval-based fuzzy testing method was developed to evaluate process quality. Because this approach is built on the basis of confidence intervals, it can reduce the possibility of misjudgment resulting from sampling errors as well as integrate past experience to enhance the accuracy and precision of the assessment, and then it can grasp the timeliness of improvement.

Supplier Selection by Fuzzy Assessment and Testing for Process Quality under Consideration with Data Imprecision

Supply chain management models integrate upstream and downstream organizations to enable rapid response to consumer needs. For the manufacturing industry, the process quality of suppliers is thus the foundation of sustainable growth for firms and an important indicator of whether a firm can effectively reduce waste and protect the environment. To this end, this paper proposes a model of supplier selection for manufacturers based on process quality assessment. First of all, Six Sigma quality index Qpk is adopted as the assessment tool to conveniently measure the quality level of process. Practical applications require estimates of Qpk from the data collected to analyze the process quality of each supplier. The fact that uncertainty is unavoidable in the collected data means that using the crisp estimate of Qpk can lead to misjudgment of the process quality. To enhance the reliability of evaluation and reduce the risk of misjudgment, the fuzzy number Q^˜pk is proposed to perform the fuzzy testing of two indices Qpk provided by suppliers with the intent of making reliable decisions on supplier selection.

Розробка функціональної моделі виконання технологічних процесів на автосервісних підприємствах

The service quality at car service enterprises is an important characteristic. For researching the influence of various factors on the quality level of technological processes, a functional model (scheme) was developed. The main elements of the scheme are energy, mass and information flows, which form the inputs and outputs in the system processes, feedback, environments and constraints. This model clearly identifies all the important parameters that depend on the quality of technological processes in the enterprise. These parameters include: availability of technological design documentation, availability of manuals for repair and operation of car brands that are relevant in this period of time, the availability of modern technology in the production process, the degree of compliance with technical regulations, the degree of consumer requirements consideration. Also, the main factors of the external environment which affect the level of technological processes quality and the place of their influence in the production process of the car service enterprise were determined. The method of determination of the most up-to-date car brands that require maintenance and repair is offered. The presence of feedbacks in each process ensures the continuous improvement of the technological process and the quality of the services too. Through the use of this functional model, which reflects the implementation of technological processes, it becomes possible for the leadership of the car service enterprises to respond in time to changes in the external environment and to make strategic decisions on optimization of the enterprise functioning.

Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems

Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied “from scratch”; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising.

Pengaruh Kualitas Produk, Kualitas Proses, Kualitas Pelayanan Dengan Peran Mediasi Kepuasan Pelanggan Terhadap Loyalitas Pelanggan

This study is an empirical study in coastal areas or coastal villages in the region of Tabanan. which aims to determine the effect of Product Quality, process quality, service quality with mediation satisfaction to Loyalty of small and medium business customers. Methodelogi used, Statistics method with Moderation or Mediation variables. The number of samples taken from the population of 30 respondents. The results showed the relationship of Product Quality (X1) to the quality of process (X2) 0.971, Quality of service (X3) 0.795 so that the average relationship 0.883, with the mediation 0.816 so as to produce the level of customer satisfaction 0.818. The correlation of process quality (X2) with product quality (X1) 0,971, Service Level (X3) 0,846, average 0,908 mediated correlation 0,850 resulted Customer Loyalty level 0,842, with mediation equal to 0,850 resulted Average Customer Loyalty 0,58 process quality, product quality and service level. Relationship Level of Service Quality (X3) 0,846 with process quality (X1) 0,795, with average 0,820 with mediation 0,971 resulted high customer satisfaction 0,826. So with a mediation of 0.971 mediate the median level of 0.771. Which the average relationship of 0.820 with mediation 0.971 resulted in the level to the average customer Loyalty of -0.151.the relationship that has been established either does not require the level of mediation

Fuzzy Supplier Selection Method Based on Smaller-The-Better Quality Characteristic

Many important parts of tool machines all have the important smaller-the-better (STB) quality characteristics. The important STB quality characteristics will impact on the quality of the end-product. At the same time, supplier quality influences the quality and functionality of the end-product, so suppliers must be selected with caution. The six sigma quality index for the STB quality characteristics can directly reflect process quality levels. Besides, this index possesses a mathematical relationship with process yield. Nevertheless, the point estimation will cause the risk of misjudgment, due to sampling errors. As a result, this study applies the confidence interval of the index to a two-tailed fuzzy testing method, in order to select appropriate suppliers. Now that this method is on the basis of the confidence interval, the possibility of misjudgment caused by sampling errors will be reduced, while the precision of the selection will be enhanced. The method can help companies increase product quality, as well as the competitiveness of the industry chain as a whole. Finally, a numerical example is presented to show how to approach this method and its efficacy.