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.

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.

scholarly journals Application of Statistical Quality Control in Monitoring the Production, Packaging and Marketing Process of Sachet Water

2020 ◽  
pp. 32-45
Author(s):  
Terna Godfrey Ieren ◽  
Samson Kuje ◽  
Abraham Iorkaa Asongo ◽  
Innocent Boyle Eraikhuemen
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Statistical Quality Control ◽  
Statistical Control ◽  
Statistical Process ◽  
Marketing Process ◽  
Product Sales ◽  
Sachet Water

Statistical process control is a technique employed to enhance the quality and productivity of processes and the distribution or marketing of its products. Sachet water is a product that has become popular and is being used as a replacement for lack of potable water. It is an alternative that is readily available, affordable but with questions about its purity, production and marketing processes. The objective of this study is to apply statistical control charts in monitoring the production, packaging and distribution or marketing processes of sachet water in Nigeria. This paper employed statistical quality control approach to monitor process stability in a Table Water manufacturing company. Quality control tools such as p-chart, u-chart, X-bar and R charts as well as process capability chart were use to observed field data obtained from the sachet water manufacturing company on important processes of sachet water production and marketing for 30 working days. This was done to check if the processes were in control or out of control and to verify the capability of the marketing process of the product meeting preset specifications. With this, the statistical control charts suitable for the processes were constructed using package “qcc” in R software version 3.6.1. The results from p-chart and u-chart showed that the production and packaging process of the product is not in control and hence the need for further investigations and corrective measures to prevent variability in the process and thus allowing improvement in the quality of the product. Also, the results from X-bar and R charts showed that the marking process was in statistical process control in respects of the product sales recorded by the four independent marketers, with no assignable cause of variation. It also revealed that, the product marketing process has low capability of successfully attending the preset specification limits in respect of the product sales and hence generating low profit for the company.

Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues

1998 ◽  
Vol 19 (4) ◽  
pp. 265-283 ◽  
Author(s):  
David Birnbaum ◽  
James C. Benneyan
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Statistical Properties ◽  
Statistical Quality Control ◽  
Statistical Process ◽  
Hospital Epidemiology ◽  
Research Issues ◽  
Statistical Quality ◽  
Chart Design ◽  
Theoretical Foundations

ABSTRACTThis is the second in a two-part series discussing and illustrating the application of statistical process control (SPC) in hospital epidemiology. The basic philosophical and theoretical foundations of statistical quality control and their relation to epidemiology are emphasized in order to expand the mutual understanding and cross-fertilization between these two disciplines. Part I provided an overview of the philosophy and general approach of SPC, illustrated common types of control charts, and provided references for further information or statistical formulae. Part II now discusses alternate possible SPC approaches, statistical properties of control charts, chart-design issues and optimal control limit widths, some common misunderstandings, and more advanced issues. The focus of both articles is mostly nonmathematical, emphasizing important concepts and practical examples rather than academic theory and exhaustive calculations.

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.

Monitoring intensive care unit performance using statistical quality control charts

1993 ◽  
Vol 10 (3) ◽  
pp. 155-161 ◽  
Author(s):  
William H. Chamberlin ◽  
Kevin A. Lane ◽  
James N. Kennedy ◽  
Scott D. Bradley ◽  
Charles L. Rice
Keyword(s):  
Intensive Care Unit ◽  
Quality Control ◽  
Intensive Care ◽  
Control Charts ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Quality Control Charts ◽  
Unit Performance

scholarly journals Process Capability Analysis in a Polypropylene Bag Manufacturing Company

2021 ◽  
Vol 25 (8) ◽  
pp. 1477-1482
Author(s):  
O.F. Odeyinka ◽  
F.O. Ogunwolu ◽  
O.P. Popoola ◽  
T.O. Oyedokun
Keyword(s):  
Control Charts ◽  
Process Capability ◽  
Production Plant ◽  
Specification Limit ◽  
Process Capability Analysis ◽  
Capability Analysis ◽  
Vertically Aligned ◽  
Lower Specification Limit ◽  
And Control

Process capability analysis combines statistical tools and control charts with good engineering judgment to interpret and analyze the data representing a process. This work analyzes the process capability of a polypropylene bag producing company. The case study organization uses two plants for production and data was collected over a period of nine months for this study. Analysis showed that the output spread of plant 1 was greater than the specification interval spread which implies poor capability. There are non-conforming parts below the Lower Specification Limit (LSL: 500,000 metres) and above the Upper Specification Limit (USL: 600,000 metres) and that the output requires improvement. Similarly, the capability analysis of plant 2 shows that the overall output spread is greater than the specification interval spread (poor capability). The output centre in the specification and overall interval are vertically aligned, thus specifying that the output from plant 2 is also process centered and requires improvement. Recommendations were made to improve the outputs from each production plant.

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.

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