scholarly journals Construction of Fuzzy Control Chart with Multinomial Quality using Process Capability

2019 ◽  
Vol 9 (1) ◽  
pp. 1460-1465
Keyword(s):  
Fuzzy Control ◽  
Control Charts ◽  
Multinomial Distribution ◽  
Process Capability ◽  
Fuzzy Sets Theory ◽  
Statistical Process ◽  
Quantitative Form ◽  
The Times ◽  
Sets Theory ◽  
Control Limits

Control charts are the effective and quietest form of statistical process control methods. Many of the times, data are obtained in quantitative form; however there are many quality characteristics that cannot be expressed in numerical measure, such as characteristics for appearance, smoothness and colour, etc. Fuzzy sets theory is an impressive mathematical methodology to evaluate the vagueness related uncertainty that can linguistically express data in these situations. In this paper, we construct a fuzzy control limits under fixed and varying sample size with various quality levels for observing a manufacturing process based on the multinomial distribution using degrees of membership and process capability.

scholarly journals Combining Capability Indices and Control Charts in the Process and Analytical Method Control Strategy

2020 ◽  
Author(s):  
Alexis Oliva ◽  
Matías Llabrés
Keyword(s):  
Control Strategy ◽  
Analytical Method ◽  
Control Charts ◽  
Process Capability ◽  
Real Data ◽  
Process Capability Indices ◽  
Specification Limits ◽  
Capability Indices ◽  
And Control ◽  
Control Limits

Different control charts in combination with the process capability indices, Cp, Cpm and Cpk, as part of the control strategy, were evaluated, since both are key elements in determining whether the method or process is reliable for its purpose. All these aspects were analyzed using real data from unitary processes and analytical methods. The traditional x-chart and moving range chart confirmed both analytical method and process are in control and stable and therefore, the process capability indices can be computed. We applied different criteria to establish the specification limits (i.e., analyst/customer requirements) for fixed method or process performance (i.e., process or method requirements). The unitary process does not satisfy the minimum capability requirements for Cp and Cpk indices when the specification limit and control limits are equal in breath. Therefore, the process needs to be revised; especially, a greater control in the process variation is necessary. For the analytical method, the Cpm and Cpk indices were computed. The obtained results were similar in both cases. For example, if the specification limits are set at ±3% of the target value, the method is considered “satisfactory” (1.22<Cpm<1.50) and no further stringent precision control is required.

Attaining competitive positioning through SPC – an experimental investigation from SME

2014 ◽  
Vol 18 (4) ◽  
pp. 86-103 ◽  
Author(s):  
Rajiv Sharma ◽  
Manjeet Kharub
Keyword(s):  
Conceptual Framework ◽  
Control Charts ◽  
Small And Medium Enterprises ◽  
Process Capability ◽  
Theory And Practice ◽  
Management Support ◽  
Statistical Process ◽  
Content Type ◽  
Proper Training ◽  
Automotive Parts

Purpose – The purpose of this paper is to provide a conceptual framework which connects theory with straightforward application of statistical process control (SPC) in discovering and analyzing causes of variation to eliminate quality problems, which not only helps small and medium enterprises (SMEs) to improve their processes but also helps to attain competitive positioning. Design/methodology/approach – Based on theory and methodological framework, an experimental study has been presented. Use of histograms, X (bar) and R control charts and process capability plots and cause-and-effect diagrams have been made to analyse the assignable causes. A case from an SME engaged in machining of automotive parts is investigated. Findings – The results demonstrate the effectiveness of SPC in evaluating and eliminating quality problems. The machine capability (CP) and the process capability (CPk) values are also obtained to know inherent variation in the process. If these quality tools are applied with management support and apt knowledge, attained through proper training and motivation, then in this cut-throat competitive world, SMEs can establish their market position by enhancing the quality and productivity of their products/processes. Practical limitations/implications – From the study, the authors conclude that application of SPC requires thorough preparation, management commitment and human resource management through proper training, teamwork and motivation embedded with a sound measurement and control system. Originality/value – The present study bridges the gap between theory and practice by developing a conceptual framework and providing a practical support by illustrating a case from an SME engaged in machining of automotive parts.

scholarly journals A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 312 ◽  
Author(s):  
Muhammad Zahir Khan ◽  
Muhammad Farid Khan ◽  
Muhammad Aslam ◽  
Seyed Taghi Akhavan Niaki ◽  
Abdur Razzaque Mughal
Keyword(s):  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Real Life ◽  
Fuzzy Data ◽  
Process Mean ◽  
Statistical Process ◽  
Control Charting ◽  
Real Life Data

Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data.

scholarly journals Control Limits for an Adaptive Self-Starting Distribution-Free CUSUM Based on Sequential Ranks

Technologies ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 71
Author(s):  
Michael Lang
Keyword(s):  
Control Charts ◽  
Simulation Experiments ◽  
Statistical Process ◽  
Distribution Free ◽  
Extensive Simulation ◽  
Detection Delay ◽  
Calibration Algorithm ◽  
Cusum Control Charts ◽  
Control Limits ◽  
Large Representative

Since their introduction in 1954, cumulative sum (CUSUM) control charts have seen a widespread use beyond the conventional realm of statistical process control (SPC). While off-the-shelf implementations aimed at practitioners are available, their successful use is often hampered by inherent limitations which make them not easily reconcilable with real-world scenarios. Challenges commonly arise regarding a lack of robustness due to underlying parametric assumptions or requiring the availability of large representative training datasets. We evaluate an adaptive distribution-free CUSUM based on sequential ranks which is self-starting and provide detailed pseudo-code of a simple, yet effective calibration algorithm. The main contribution of this paper is in providing a set of ready-to-use tables of control limits suitable to a wide variety of applications where a departure from the underlying sampling distribution to a stochastically larger distribution is of interest. Performance of the proposed tabularized control limits is assessed and compared to competing approaches through extensive simulation experiments. The proposed control limits are shown to yield significantly increased agility (reduced detection delay) while maintaining good overall robustness.

STATISTICAL PROCESS CONTROL FOR LOW NONCONFORMITY PROCESSES

1995 ◽  
Vol 02 (01) ◽  
pp. 15-22 ◽  
Author(s):  
T.N. GOH ◽  
M. XIE
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Exact Probability ◽  
Statistical Process ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Cumulative Count ◽  
Pattern Recognition Approach ◽  
Control Limits

Statistical process control of high quality products is an important issue in modern quality control applications because of the success of continuous improvement efforts worldwide. The conventional Shewhart control charts based on 3-sigma control limits tend to encounter certain practical and theoretical problems as “zero-defect” is approached. In this paper, we describe some general approaches to solving this problem, focusing on the control charts for nonconformities or defects. We suggest that for a moderate nonconformity process, the exact probability limits should be used. For a lower non-conformity process, a “pattern recognition” approach can be applied. Finally, for a near-zero nonconformity process, a modified approach based on the cumulative count of nonconformities can be used.

scholarly journals Construction of Fuzzy Mean using Standard Deviation ( X  S  ) Control Chart with Process Capability

2019 ◽  
Vol 8 (4) ◽  
pp. 5390-5396
Keyword(s):  
Quality Control ◽  
Standard Deviation ◽  
Set Theory ◽  
Control Chart ◽  
Control Charts ◽  
Total Quality ◽  
Statistical Process ◽  
Research Article ◽  
Fuzzy Mean ◽  
Control Limits

The Quality has established over a number of points such as inspection, quality control, quality assurance, and total quality control and the effects produced by the above phases are used to check and develop the production/service procedure. Statistical process control (SPC) is a powerful collection of problem solving tools valuable in attaining process steadiness and enlightening capability through the decline of variability. Fuzzy set theory is a utilitarian tool to succeed the uncertainty environmental circumstances and the Fuzzy control limits provide a more accurate and flexible rating than the traditional control charts. The purpose of this research article is to construct the fuzzy mean using standard deviation ( X S   ) control chart with the assistance of process capabilityThe Quality has established over a number of points such as inspection, quality control, quality assurance, and total quality control and the effects produced by the above phases are used to check and develop the production/service procedure. Statistical process control (SPC) is a powerful collection of problem solving tools valuable in attaining process steadiness and enlightening capability through the decline of variability. Fuzzy set theory is a utilitarian tool to succeed the uncertainty environmental circumstances and the Fuzzy control limits provide a more accurate and flexible rating than the traditional control charts. The purpose of this research article is to construct the fuzzy mean using standard deviation ( X S   ) control chart with the assistance of process capability

Application of Statistical Process Control Tools to Improve Product Quality in Manufacturing Processes

2011 ◽  
Vol 110-116 ◽  
pp. 4023-4027
Author(s):  
Omar Bataineh ◽  
Abdullah Al-Dwairi
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Process Capability ◽  
Manufacturing Processes ◽  
Statistical Process ◽  
Free Products ◽  
Drug Companies

Quality control and improvement at the process level is a vital activity for the achievement of defect-free products in various manufacturing processes. This study employs statistical process control (SPC) tools such as control charts and process capability ratio for quality control and improvement. The control charts employed are , R and the cumulative-sum (CUSUM). The process capability ratio used is the so called process capability index (PCI). These tools have been implemented with the aid of Minitab® statistical software. In this study, the manufacturing process of gelatin capsules is investigated in terms of quality of the capsules, which are produced and shipped for use by various drug companies. As a result of implementation of SPC tools, an expected reduction in the number of defective capsules by 29% relative to the stage before implementation was achieved.

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.

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