Process capability and stability of analytical systems assessed from proficiency testing data

1994 ◽  
Vol 40 (5) ◽  
pp. 723-728 ◽  
Author(s):  
R W Jenny
Keyword(s):  
Proficiency Testing ◽  
Control Charts ◽  
Total Quality ◽  
Statistical Process ◽  
Laboratory Performance ◽  
Continuous Quality ◽  
Valuable Adjunct ◽  
Capability Analysis ◽  
Performance Specifications ◽  
And Performance

Abstract Participation in a proficiency testing (PT) program is a valuable adjunct to laboratory activities dedicated to the maintenance of reliable analytical methods. The PT program may facilitate continuous quality improvement if laboratory performance is presented in the context of expectations espoused by healthcare professionals for optimal patient care. Statistical process control (SPC) and capability analysis are tools used by industry in a Total Quality Management environment to characterize and monitor the performance of its processes relative to performance specifications. I conceptualized the use of an analytical system by many laboratories as a process that periodically produces results from the analysis of PT specimens. I treated a set of five PT results (theophylline) reported by a laboratory as a process sample and subjected the samples collected from many laboratories to SPC and capability analysis. The control charts--mean-(X-bar) and s-charts--produced by the analysis readily identify significant analytical errors in the context of peer performance and performance specifications provided by the regulatory program and analytical goal setting. The capability index (desirable Cp > 1.0) determined from clinical specification limits for the three analytical systems evaluated suggests an opportunity for improvement of laboratory performance.

Statistical Process and Quality Control

2000 ◽  
pp. 233-244
Keyword(s):  
Quality Control ◽  
Quality Improvement ◽  
Quality Management ◽  
Total Quality Management ◽  
Statistical Process Control ◽  
Control Charts ◽  
Statistical Testing ◽  
Total Quality ◽  
Statistical Process ◽  
Sampling Plans

Abstract This chapter provides an introduction to statistical process control and the concept of total quality management. It begins with a review of quality improvement efforts in the extrusion industry and the considerations involved in developing sampling plans and interpreting control charts. It then lays out the steps that would be followed in order to implement statistical testing for billet casting, die performance, or any other process or variable that impacts extrusion quality. The chapter concludes with an overview of the fundamentals of total quality management.

Inspection in Process Control

1998 ◽  
Vol 42 (16) ◽  
pp. 1170-1174
Author(s):  
Somchart Thepvongs ◽  
Brian M. Kleiner
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Signal Detection ◽  
Control Chart ◽  
Control Charts ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Statistical Quality Control ◽  
Total Quality ◽  
Statistical Process

Consistent with the precepts of total quality control and total quality management, there has been a resource shift from incoming and outgoing inspection processes to statistical quality control of processes. Furthermore, process control operators are responsible for their own quality, necessitating the in-process inspection of components. This study treated the statistical process control task of “searching” control charts for out-of-control conditions as an inspection task and applied the Theory of Signal Detection to better understand this behavior and improve performance. Twelve subjects participated in a research study to examine how the portrayal of control chart information affected signal detection theory measures. The type of display did not have a significant effect on the sensitivity and response criterion of subjects. These results are discussed in terms of the applicability of Signal Detection Theory in control chart decision making as well as implications on display design.

TOTAL QUALITY MANAGEMENT USING PARETO DIAGRAMS AND STATISTICAL PROCESS CONTROL CHARTS (SPC) APPLIED TO PRECAST BEAMS

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Mifta Priyanto
Keyword(s):  
Quality Management ◽  
Process Control ◽  
Total Quality Management ◽  
Statistical Process Control ◽  
Control Charts ◽  
Range Limit ◽  
Total Quality ◽  
Statistical Process ◽  
Process Control Charts ◽  
Statistical Process Control Charts

This paper presents the application of Total Quality Management Method using Pareto diagrams and Statistical Process Control charts (SPC). These tools can be applied to both the manufacturing and construction sectors. A Pareto diagram can figure out some of the dominant problems of the projects, and SPC can determine whether the data variation is within control limits. SPC can measure the quality of performance in learning curve using the upper-range limit and lower-range limit of the control analysis. A case study was conducted on a precast beams installation at a rental multi-story residential project in Jakarta, Indonesia. Based on the measurement, some data are outside of the control limit due to the problems identified in the Pareto diagram. Further analysis by measuring the Process Capability Ratio (Cp) produces a value <1, indicating that project management needs to be careful about process variation.

scholarly journals Application of SPC in Short Run and Small Mixed Batch Production: Case of Bakery Equipment Producer

2018 ◽  
Vol 22 (3) ◽  
pp. 55 ◽  
Author(s):  
Darina Juhászová
Keyword(s):  
Control Charts ◽  
Single Case ◽  
Statistical Process ◽  
Pareto Analysis ◽  
Environmental Consequences ◽  
Starting Point ◽  
Short Run ◽  
And Performance ◽  
And Control ◽  
The Impact

<p><strong>Purpose: </strong>The purpose of this paper is to present preliminary research in statistical process control (SPC) of short run and small mixed batches (SR-SMB) at the organization producing bakery equipment.</p><p><strong>Methodology/Approach:</strong> The starting point of the research is a literary survey of possibilities of using SPC for SR-SMB and analysis of the current state of production in a particular organization. Through Pareto analysis, verifying the normality of the data obtained during eleven months, calculation of process capability and performance it was possible to prepare control charts. Finally, the single-case study shows that the proposed control charts are applicable in a small batch and mixed production in the organization producing bakery equipment.</p><p><strong>Findings: </strong>Through SPC implementation in bakery equipment SR-SMB production it is possible to understand the behaviour of the process and to organize better and control the production of expensive precision components.</p><p><strong>Research Limitation/implication:</strong> Limitation of the research is that data have not been reviewed by individual machines and the impact of individual machines and their settings is not displayed separately.</p><strong>Originality/Value of paper:</strong> Using SPC in the bakery equipment industry is far from common practice. The article presents the first part of the research, which is the starting point for more detailed analysis needed to optimize the use of materials, energy and environmental consequences.

scholarly journals Assessment of process stability and capability in a manufacturing organization: a case study

2021 ◽  
Vol 343 ◽  
pp. 05011
Author(s):  
Carmen Simion
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Statistical Quality Control ◽  
Statistical Tool ◽  
Statistical Process ◽  
Monitoring Process ◽  
Capability Analysis ◽  
Process Output ◽  
Manufacturing Organization

Quality is considered asthe principal factor that determines the long-termsuccess or failure of any organization. Organizations perform quality control by monitoring process output using Statistical Quality Control, performed as part of the production process (Statistical Process Control, SPC) or as a final quality control check (Acceptance Sampling).SPC is a major quality management statistical tool and its instruments (control charts and capability analysis) are applied to virtually any type of organization (manufacturing, services or transactions - for example, those involving data, communications, software, or movement of materials). The aim of this paper is to present a case study, realized in a manufacturing organizationfrom Sibiu, for a new product used in the automotive industry to check its conformance to designed requirements. The output data were analyzed using statistical analysis software Minitab.

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

scholarly journals Improving Process Performance of Cotton Spinning by Using Statistical Process Control Techniques

2020 ◽  
pp. 15-22
Author(s):  
Dereje Girma ◽  
Omprakash Sahu
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Shop Floor ◽  
Statistical Control ◽  
Statistical Process ◽  
Process Capability Analysis ◽  
Capability Analysis ◽  
R Process ◽  
And Control

Identifying the presence and understanding the causes of process variability are key requirements for well controlled and quality manufacturing. This pilot study demonstrates the introduction of Statistical Process Control (SPC) methods to the spinning department of a textile manufacturing company. The methods employed included X Bar and R process control charts as well as process capability analysis. Investigation for 29 machine processes identified that none were in statistical control. Recommendations have been made for a repeat of the study using validated data together with practical application of SPC and control charts on the shop floor and extension to all processes within the factory.

Statistical control charts as a support tool for the management of livestock production

2010 ◽  
Vol 149 (3) ◽  
pp. 369-384 ◽  
Author(s):  
K. MERTENS ◽  
E. DECUYPERE ◽  
J. DE BAERDEMAEKER ◽  
B. DE KETELAERE
Keyword(s):  
Control Charts ◽  
Integrated Approach ◽  
Statistical Characteristics ◽  
Statistical Control ◽  
Process Data ◽  
Livestock Farming ◽  
Statistical Process ◽  
Support Tool ◽  
And Performance ◽  
On Farm

SUMMARYThe concepts of control charts, an important tool in statistical process control, are commonly used for monitoring industrial production processes. In the context of precision livestock farming, their use has been demonstrated by many, although the statistical properties of livestock process data often do not comply with the basic assumptions of such control charts. The focus of the current review is on the most important aspects, recommendations, pitfalls and opportunities for the development and performance of control charts on livestock process data. An important hurdle to tackle is the statistical characteristics of the raw livestock process data which are mostly violating the control charts’ assumptions. An integrated approach, like synergistic control, appears to be promising in handling this issue. The availability of real-time on-farm validation of proposed systems will be crucial for lifting them from the potential level to direct practical relevance.

Total Quality Management and Quality Engineering

2020 ◽  
pp. 451-468
Author(s):  
Shubhajit Das ◽  
Kakoli Roy ◽  
Tage Nampi
Keyword(s):  
Quality Management ◽  
Process Control ◽  
Total Quality Management ◽  
Continuous Improvement ◽  
Failure Modes ◽  
Quality Engineering ◽  
Total Quality ◽  
Statistical Process ◽  
Continuous Quality ◽  
Management Approaches

Total quality management (TQM) is a set of the systematic management approaches for the continuous improvement of quality standards of products, services, and business relations with employees and consumers. This chapter mainly focusses on the eight key principles of TQM, the involvement of workers, leadership, process approach, strategic approach, continuous improvement, together for a factual approach to decision-making and communication. This chapter also discusses a four-part management model that implements continuous quality improvements and process control in different stages of an organization based on the Deming cycle or the Shewhart cycle. Quality engineering encompasses a broad range of methodologies and tools, which include quality management systems, advanced product quality planning (APQP), tools like quality function development (QFD), failure modes, and effects analysis (FMEA), statistical process control (SPC), and are widely accepted methodologies used in industries.

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