Development of fuzzy U control chart for monitoring defects

2014 ◽  
Vol 31 (7) ◽  
pp. 811-821 ◽  
Author(s):  
Soroush Avakh Darestani ◽  
Azam Moradi Tadi ◽  
Somayeh Taheri ◽  
Maryam Raeiszadeh
Keyword(s):  
Control Chart ◽  
Fuzzy Number ◽  
Control Charts ◽  
Statistical Process ◽  
Content Type ◽  
Trapezoidal Fuzzy Number ◽  
Defect Sensitivity ◽  
Control Patterns ◽  
Definition Of

Purpose – Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to investigate the attributes of fuzzy U control chart. Design/methodology/approach – If the data were uncertain, they were converted into trapezoidal fuzzy number and the fuzzy upper and lower control limits were trapezoidal fuzzy number calculated using fuzzy mode approach. The result was grouped into four categories (in control, out of control, rather in control, rather out of control). Finally, a case study was presented and the method coding was done in MATLAB software using design U control chart; then, the results were verified. Findings – The definition of fuzzy numbers for each type of defect sensitivity and the unit can be classified into four groups: in-control and out-of-control, rather in-control and rather out-of-control which represent the actual quality of the products. It can be concluded that fuzzy control chart is more sensitive on recognition out of control patterns. Originality/value – This paper studies the use of control charts, specifically the attributes of a fuzzy U control chart, for monitoring defects in the format of a case study.

SURVEILLANCE OF DIABETES PREVALENCE RATE THROUGH THE DEVELOPMENT OF A MARKOV-BASED CONTROL CHART

2012 ◽  
Vol 12 (04) ◽  
pp. 1250083
Author(s):  
PERSHANG DOKOUHAKI ◽  
RASSOUL NOOROSSANA
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
High Performance ◽  
Discrete Data ◽  
Correlated Data ◽  
Statistical Process ◽  
Control Charting ◽  
Attribute Quality ◽  
Related Process

In the field of statistical process control (SPC), usually two issues are addressed; the variables and the attribute quality characteristics control charting. Focusing on discrete data generated from a process to be monitored, attributes control charts would be useful. The discrete data could be classified into two categories; the independent and auto-correlated data. Regarding the independence in the sequence of discrete data, the typical Shewhart-based control charts, such as p-chart and np-chart would be effective enough to monitor the related process. But considering auto-correlation in the sequence of the data, such control charts would not workanymore. In this paper, considering the auto-correlated sequence of X1, X2,…, Xt,… as the sequence of zeros or ones, we have developed a control chart based on a two-state Markov model. This control chart is compared with the previously developed charts in terms of the average number of observations (ANOS) measure. In addition, a case study related to the diabetic people is investigated to demonstrate the applicability and high performance of the developed chart.

scholarly journals ESTUDO COMPARATIVO ENTRE OS MÉTODOS DE CÁLCULO DE PULMÃO UTILIZANDO A LÓGICA FUZZY E MÉTODO DOS CINQUENTA POR CENTO

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Tatiana Gondim do Amaral ◽  
Zhou Guan Chen ◽  
Vitor Hugo Martins E Resende ◽  
Marcos Antônio De Sousa
Keyword(s):  
Real Time ◽  
Fuzzy Number ◽  
Trapezoidal Fuzzy Number ◽  
Physical Control ◽  
Construction Company ◽  
Mixed Use ◽  
Definition Of ◽  
Critical Chain

RESUMO: Levantamentos realizados apontam atrasos em obras gerando insatisfações aos clientes, motivando processos judiciais sobre as construtoras além de custos indiretos adicionais no projeto, levando a prejuízos ao mesmo. Mediante o exposto são necessárias ferramentas mais eficazes para o controle de prazo de obra. Assim, o trabalho visa aplicar e avaliar o método da corrente crítica no planejamento e controle físico de obras, calculando o pulmão do projeto utilizando duas técnicas distintas: Métodos Número Fuzzy trapezoidal (TrFN) e cinquenta por cento. Portanto, realizou-se um estudo de caso de um empreendimento mixed use em Goiânia e um acompanhamento dos serviços estruturais de uma junta, que estão presentes na corrente crítica do projeto. Assim, ao avaliar e comparar os pulmões resultantes, durações calculadas e reais do projeto, se conclui que o Método TrFN é o que mais se aproxima à duração real, pois consulta especialistas; e, mais se integra à equipe, aumentando a aplicabilidade do planejamento, pois os executores do projeto participam na elaboração do mesmo. Já o Método dos Cinquenta por Cento é mais fácil de ser calculado, com a definição mais prática do pulmão resultando um planejamento mais rápido. Baseando-se nesses cálculos, a principal contribuição do trabalho é comparar os resultados de cada técnica quanto à sua agilidade de aplicação, precisão em relação ao real e aplicabilidade em obra. ABSTRACT: Conducted surveys indicate delays in projects that impact clients, allowing lawsuits over Construction Company and overhead costs of the project, enabling the economic impracticability of it. Through the above are needed more effective tools for term control of the project. Therefore, the work objective is to implement and evaluate the method of the critical chain in the planning and physical control of the project by calculating the project buffer using two different techniques: Trapezoidal Fuzzy Number (TrFN) and Fifty percent Method. Therefore, there was a case study of a mixed use building in Goiânia and monitoring of structural services of a joint, which are present in the project critical chain. Thus, to evaluate and compare the resulting buffer, calculated duration and really happened duration, it is concluded that the TrFN method comes closest to real time as it consults experts; and more integrates the team, increasing the applicability of planning, because the project performers are involved in preparing it. Now the fifty percent method is easier to be calculated with faster definition of the buffer and planning. Based on this calculus, the main contribution of this work is to compare the results of each technique in: application agility, application accuracy, applicability during construction.

Factors influencing control charts usage of operational measures

2017 ◽  
Vol 21 (3) ◽  
pp. 225-238
Author(s):  
Anna Ericson Öberg ◽  
Peter Hammersberg ◽  
Anders Fundin
Keyword(s):  
Control Charts ◽  
Empirical Studies ◽  
Content Type ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Motivation For Change ◽  
Definition Of ◽  
The Right ◽  
High Level

Purpose The purpose of this paper is to identify factors influencing implementation of control charts on key performance indicators (KPIs). Design/methodology/approach Factors driving organizational change described in literature are analyzed inspired by the affinity-interrelationship method. A holistic multiple-case design is used to conduct six workshops to affect the usage of control charts on KPIs at a global company in the automotive industry. The theoretical factors are compared with the result from the case study. Findings The important factors for implementation success differ to some extent between the theoretical and empirical studies. High-level commitment and a clear definition of the goal of change could be most important when creating a motivation for change. Thereafter, having a dedicated change agent, choosing an important KPI and being able to describe the gain in financial terms becomes more important. Practical implications By using control charts on KPIs, the organization in the case study has become more proactive, addressing the right issues upstream in the process, in the right way, cross-functionally. Originality/value Factors affecting the implementation of already available solutions in the industry are highlighted. This potentially provides a basis for improved decision making, which has a significant value.

Statistical process control application on service quality using SERVQUAL and QFD with a case study in trains’ services

2016 ◽  
Vol 28 (2) ◽  
pp. 195-215 ◽  
Author(s):  
Hadi Akbarzade Khorshidi ◽  
Sanaz Nikfalazar ◽  
Indra Gunawan
Keyword(s):  
Process Control ◽  
Service Quality ◽  
Statistical Process Control ◽  
Control Charts ◽  
Quality Function Deployment ◽  
Process Capability ◽  
Statistical Process ◽  
Content Type

Purpose – The purpose of this paper is to implement statistical process control (SPC) in service quality using three-level SERVQUAL, quality function deployment (QFD) and internal measure. Design/methodology/approach – The SERVQUAL questionnaire is developed according to internal services of train. Also, it is verified by reliability scale and factor analysis. QFD method is employed for translating SERVQUAL dimensions’ importance weights which are derived from Analytic Hierarchy Process into internal measures. Furthermore, the limits of the Zone of Tolerance are used to determine service quality specification limits based on normal distribution characteristics. Control charts and process capability indices are used to control service processes. Findings – SPC is used for service quality through a structured framework. Also, an adapted SERVQUAL questionnaire is created for measuring quality of train’s internal services. In the case study, it is shown that reliability is the most important dimension in internal services of train for the passengers. Also, the service process is not capable to perform in acceptable level. Research limitations/implications – The proposed algorithm is practically applied to control the quality of a train’s services. Internal measure is improved for continuous data collection and process monitoring. Also, it provides an opportunity to apply SPC on intangible attributes of the services. In the other word, SPC is used to control the qualitative specifications of the service processes which have been measured by SERVQUAL. Originality/value – Since SPC is usually used for manufacturing processes, this paper develops a model to use SPC in services in presence of qualitative criteria. To reach this goal, this model combines SERVQUAL, QFD, normal probability distribution, control charts, and process capability. In addition, it is a novel research on internal services of train with regard to service quality evaluation and process control.

A Conceptual Methodology for Recognition of Constrained Control Chart Patterns

2013 ◽  
Vol 845 ◽  
pp. 696-700
Author(s):  
Razieh Haghighati ◽  
Adnan Hassan
Keyword(s):  
Pattern Recognition ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Fault Tolerant ◽  
Constrained Control ◽  
Statistical Process ◽  
Control Chart Patterns ◽  
Control Chart Pattern ◽  
Chart Patterns

Traditional statistical process control (SPC) charting techniques were developed to monitor process status and helping identify assignable causes. Unnatural patterns in the process are recognized by means of control chart pattern recognition (CCPR) techniques. There are a broad set of studies in CCPR domain, however, given the growing doubts concerning the performance of control charts in presence of constrained data, this area has been overlooked in the literature. This paper, reports a preliminary work to develop a scheme for fault tolerant CCPR that is capable of (i) detecting of constrained data that is sampled in a misaligned uneven fashion and/or be partly lost or unavailable and (ii) accommodating the system in order to improve the reliability of recognition.

Territorial capital as a company intangible

2016 ◽  
Vol 17 (1) ◽  
pp. 148-167 ◽  
Author(s):  
Mariachiara Barzotto ◽  
Giancarlo Corò ◽  
Mario Volpe
Keyword(s):  
Great Difficulty ◽  
Future Research ◽  
Manufacturing Companies ◽  
Policy Makers ◽  
Content Type ◽  
Capital Growth ◽  
Multiple Case ◽  
Definition Of ◽  
A Company

Purpose – The purpose of this paper is twofold. First, to explore to what extent being located in a territory is value-relevant for a company. Second, to understand if a company is aware of, and how it can sustain, the territorial tangible and intangible assets present in the economic area in which it is located. Design/methodology/approach – The study presents an empirical multiple case-study, investigating ten mid-/large-sized Italian companies in manufacturing sectors. Findings – The results indicate that the sampled manufacturing companies are intertwined with the environment in which they are embedded, both in their home country and in host ones. The domestic territorial capital has provided, and still provides, enterprises with workers endowed with the necessary technical skills that they can have great difficulty in finding in other places. In turn, companies support territorial capital generation through their activities. Research limitations/implications – To increase the generalisability of the results, future research should expand the sample and examine firms based in different countries and sectors. Practical implications – Implications for policy makers: developing effective initiatives to support and guide a sustainable territorial capital growth. Implications for managers and investors: improving managerial and investors’ decisions by disclosing a complete picture of the enterprise, also outside the firm boundaries. Originality/value – The study contributes to intangibles/intellectual capital literature by shedding light on the importance of including territorial capital in a company’s report to improve the definition of the firm’s value. Accounting of the territorial capital would increase the awareness of the socio-economic environment value in which companies are located and its use.

Improving Phase I Monitoring of Dirichlet Regression Profiles

2018 ◽  
Vol 25 (06) ◽  
pp. 1850030 ◽  
Author(s):  
Hourieh Foroutan ◽  
Amirhossein Amiri ◽  
Reza Kamranrad
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Control Charts ◽  
Compositional Data ◽  
Ratio Test ◽  
Statistical Process ◽  
Explanatory Variables ◽  
Monitoring Procedure ◽  
Multivariate Control

In most statistical process control (SPC) applications, quality of a process or product is monitored by univariate or multivariate control charts. However, sometimes a functional relationship between a response variable and one or more explanatory variables is established and monitored over time. This relationship is called “profile” in SPC literature. In this paper, we specifically consider processes with compositional data responses, including multivariate positive observations summing to one. The relationship between compositional data responses and explanatory variables is modeled by a Dirichlet regression profile. We develop a monitoring procedure based on likelihood ratio test (lrt) for Phase I monitoring of Dirichlet regression profiles. Then, we compare the performance of the proposed method with the best method in the literature in terms of probability of signal. The results of simulation studies show that the proposed control chart has better performance in Phase I monitoring than the competing control chart. Moreover, the proposed method is able to estimate the real time of a change as well. The performance of this feature is also investigated through simulation runs which show the satisfactory performance. Finally, the application of the proposed method is illustrated based on a real case in comparison with the existing method.

scholarly journals Monitoring of mechanical sugarcane harvesting through control charts

2015 ◽  
Vol 35 (6) ◽  
pp. 1079-1092 ◽  
Author(s):  
Murilo A. Voltarelli ◽  
Rouverson P. da Silva ◽  
Cristiano Zerbato ◽  
Carla S. S. Paixão ◽  
Tiago de O. Tavares
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Grinding Wheel ◽  
Individual Values ◽  
Statistical Process ◽  
Minas Gerais State ◽  
Weighted Moving Average

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

Fuzzy U control chart based on fuzzy rules and evaluating its performance using fuzzy OC curve

2018 ◽  
Vol 30 (3) ◽  
pp. 232-247 ◽  
Author(s):  
Somayeh Fadaei ◽  
Alireza Pooya
Keyword(s):  
Fuzzy Control ◽  
Sample Size ◽  
Control Chart ◽  
Control Charts ◽  
Operating Characteristic ◽  
Fuzzy Rules ◽  
Type Ii ◽  
Type Ii Error ◽  
Content Type ◽  
Variable Sample Size

Purpose The purpose of this paper is to apply fuzzy spectrum in order to collect the vague and imprecise data and to employ the fuzzy U control chart in variable sample size using fuzzy rules. This approach is improved and developed by providing some new rules. Design/methodology/approach The fuzzy operating characteristic (FOC) curve is applied to investigate the performance of the fuzzy U control chart. The application of FOC presents fuzzy bounds of operating characteristic (OC) curve whose width depends on the ambiguity parameter in control charts. Findings To illustrate the efficiency of the proposed approach, a practical example is provided. Comparing performances of control charts indicates that OC curve of the crisp chart has been located between the FOC bounds, near the upper bound; as a result, for the crisp control chart, the probability of the type II error is of significant level. Also, a comparison of the crisp OC curve with OCavg curve and FOCα curve approved that the probability of the type II error for the crisp chart is more than the same amount for the fuzzy chart. Finally, the efficiency of the fuzzy chart is more than the crisp chart, and also it timely gives essential alerts by means of linguistic terms. Consequently, it is more capable of detecting process shifts. Originality/value This research develops the fuzzy U control chart with variable sample size whose output is fuzzy. After creating control charts, performance evaluation in the industry is important. The main contribution of this paper is to employs the FOC curve for evaluating the performance of the fuzzy control chart, while in prior studies in this area, the performance of fuzzy control chart has not been evaluated.

Process Improvement in Surgery

2015 ◽  
Author(s):  
Frederick H Millham
Keyword(s):  
Health Care ◽  
Control Chart ◽  
Process Improvement ◽  
Control Charts ◽  
Multidisciplinary Care ◽  
Payoff Matrix ◽  
Flow Maps ◽  
History Of ◽  
Improvement Programs ◽  
Definition Of

Process improvement is a skill all physicians need to be familiar with. This is particularly true for surgeons, who work in complex systems requiring multidisciplinary care in the health care system’s most expensive location: the operating room. Surgical leaders need to be familiar with the techniques and themes of process improvement. The current literature suggests that formal process improvement programs can be effective in improving clinical, operational, and financial performance of hospitals. This review outlines a general approach to process improvement, in addition to providing evidence for the efficacy of process improvement in health care, a definition of processes, and the history of process improvement. Tables outline forms of waste applied to health care and heuristic approaches to project improvement. Figures include a project charter, control chart, X-bar control chart, Pareto table and chart, Fishbone cause-and-effect diagram, diagrams of the Plan-Do-Study-Act process and cost/payoff matrix, statistical software control charts, and process flow maps. This review contains 10 figures, 2 tables, and 22 references.

Sign in / Sign up

Export Citation Format

Share Document