Safety as a Process Quality Characteristic

2013 ◽  
pp. 1-12 ◽  
Author(s):  
Timo Varkoi
Keyword(s):  
Process Quality ◽  
Quality Characteristic

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

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1076
Author(s):  
Wei Lo ◽  
Chun-Ming Yang ◽  
Kuei-Kuei Lai ◽  
Shao-Yu Li ◽  
Chi-Han Chen
Keyword(s):  
Evaluation Model ◽  
Critical Value ◽  
Process Quality ◽  
Quality Characteristic ◽  
Fuzzy Membership Function ◽  
Environmental Damage ◽  
Quality Level ◽  
Fuzzy Evaluation ◽  
Radar Chart ◽  
Fuzzy Evaluation Model

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.

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

2020 ◽  
Vol 10 (22) ◽  
pp. 8272
Author(s):  
Win-Jet Luo ◽  
Kuen-Suan Chen ◽  
Chun-Min Yu ◽  
Ting-Hsin Hsu
Keyword(s):  
Machine Tool ◽  
Evaluation Model ◽  
Quality Characteristics ◽  
Process Quality ◽  
Quality Characteristic ◽  
Quality Level ◽  
Sampling Errors ◽  
Statistical Process ◽  
Accuracy And Precision ◽  
Fuzzy Process

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.

A fuzzy approach to determine process quality for one-sided specification with imprecise data

2020 ◽  
Vol 234 (9) ◽  
pp. 1198-1206 ◽  
Author(s):  
Kuen-Suan Chen ◽  
Tsang-Chuan Chang
Keyword(s):  
Six Sigma ◽  
Corporate Sustainability ◽  
Measurement Data ◽  
Process Quality ◽  
Quality Characteristic ◽  
Quality Indices ◽  
Process Yield ◽  
Testing Method ◽  
Yield And Quality ◽  
Measurement Tools

Process quality is a crucial determinant of client satisfaction and affects a product’s value; therefore, maintaining quality control is a vital aspect of corporate sustainability and development. Many researchers have developed evaluation models for process quality. The majority of such research assumes that collected measurement data are precise, but fuzziness and stochastic uncertainty are unavoidable features of any collected data. When the measurements of a quality characteristic are insufficiently precise, a crisp-based approach is not suitable for the assessment of process quality. This study endeavored to use one-sided Six Sigma quality indices as measurement tools to accurately reflect process yield and quality levels. Taking Buckley’s approach into consideration, we extend the crisp estimators from the indices into fuzzy estimators. We then develop a fuzzy hypothesis testing method for one-sided Six Sigma quality indices, with the intent of increasing reliability of evaluation for process quality levels. Finally, we present a real-world case to illustrate implementation of the proposed approach, demonstrating its effectiveness and practical applicability.

Quality Characteristic of Glasswort (Salicornia herbacea L.) Fermented by Bacillus subtilis

2009 ◽  
Vol 38 (7) ◽  
pp. 902-908 ◽  
Author(s):  
In-Bae Park ◽  
Jeong-Wook Park ◽  
Young-Jae Lee ◽  
Gung-Won Shin ◽  
Hae-Seop Kim ◽  
...  
Keyword(s):  
Bacillus Subtilis ◽  
Quality Characteristic ◽  
Salicornia Herbacea

Home Health Agencies: Empirical Evidence on the Patient-Driven Groupings Model’s Expected Effects on Agency Reimbursements

2021 ◽  
pp. 108482232199038
Author(s):  
Elizabeth Plummer ◽  
William F. Wempe
Keyword(s):  
Ordinary Least Squares ◽  
Process Quality ◽  
Home Health ◽  
Least Squares Regression ◽  
Residual Variation ◽  
Health Agencies ◽  
Dual Eligible ◽  
Home Health Agencies ◽  
Seriously Ill Patients ◽  
Seriously Ill

Beginning January 1, 2020, Medicare’s Patient-Driven Groupings Model (PDGM) eliminated therapy as a direct determinant of Home Health Agencies’ (HHAs’) reimbursements. Instead, PDGM advances Medicare’s shift toward value-based payment models by directly linking HHAs’ reimbursements to patients’ medical conditions. We use 3 publicly-available datasets and ordered logistic regression to examine the associations between HHAs’ pre-PDGM provision of therapy and their other agency, patient, and quality characteristics. Our study therefore provides evidence on PDGM’s likely effects on HHA reimbursements assuming current patient populations and service levels do not change. We find that PDGM will likely increase payments to rural and facility-based HHAs, as well as HHAs serving greater proportions of non-white, dual-eligible, and seriously ill patients. Payments will also increase for HHAs scoring higher on quality surveys, but decrease for HHAs with higher outcome and process quality scores. We also use ordinary least squares regression to examine residual variation in HHAs’ expected reimbursement changes under PDGM, after accounting for any expected changes related to their pre-PDGM levels of therapy provision. We find that larger and rural HHAs will likely experience residual payment increases under PDGM, as will HHAs with greater numbers of seriously ill, younger, and non-white patients. HHAs with higher process quality, but lower outcome quality, will similarly benefit from PDGM. Understanding how PDGM affects HHAs is crucial as policymakers seek ways to increase equitable access to safe and affordable non-facility-provided healthcare that provides appropriate levels of therapy, nursing, and other care.

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