Note: Sample below may appear distorted but all corresponding word document files contain proper formattingExcerpt from essay:
c. Statement of the Problem
i. AS9103 requirements
Section 4.9.1 is a part of the AS9100 and AS9103 requirements that states that suppliers shall identify and plan installation, production as well as servicing process that affect the quality production .Under the requirements, suppliers are to achieve these objectives through specified process. Moreover, the AS9103 requirements provide a standard method to enhance a quality performance in the production and maintenance process with the goals of minimizing variations.
In a manufacturing process, variation control is a critical tool that enhances the quality of products delivered to customer. A problem occurs when the variation exceeds the customer's expectation, which may lead to non-conformance to AS9103 and customer's dissatisfaction. If an organization is unable to enhance quality conformance, the quality of the product will be automatically degraded thereby leading to the increase in the cost of production and decrease in profitability. Variation of products leads to a process where products deviate from customer's requirements, and the issue will lead to a customer's dissatisfaction, which may lead to a decline in a firm's profitability.
Process control is a strategic tool to identify variations in order to eliminate wastes. Once an organization identifies the sources of variations, it will be possible to minimize the level of variations in the production process. Statistical process control is an effective tool to manage variation in order to satisfy AS9103 requirements. Despite the importance of statistical process for the management of variation in the Aerospace and Aviation industry, there is still a scanty of scholarly research that focuses on the statistical process control in variation management to satisfy AS9103 requirements.
Gordon, (2007) pointed out that AS9103 is an advanced quality product planning to enhance production process in the Aviation and Aerospace industry. However, the author made no mention how the variation occurs in the production process. Moreover, the author did not identify statistical process control in the management of variation in order to satisfy the AS9103 requirements.
The thesis fills the gap created with the paucity on scholarly research on statistical process control and variation management to satisfy AS9103 requirements. The thesis addresses the challenges that Aerospace and Aviation industry is facing with variations in the production process.
ii. Applying and selecting key characteristics to drawings
According to AS9103, 5.1 & 5.4, the key characteristics applicable to drawing is that the measurable evidence related to variation control is effective. Moreover, an appropriate monitoring methodology should be implemented to ensure continued performances.
Section 5.4 of AS9103 requires variation control method for process such as tooling standard process as well as ensuring process and stability. Moreover, measurements used for the control charts should represent the normal production output. Thus, the control chart for the process must be appropriate for the application. (Society of Automotive Engineers, 2009).
iii. Using SPC to meet the requirements of AS9103
The SPC is very critical to meet the requirements of A39103, and the SPC tool assists in error control, which is appropriate for application. Thus, advanced statistical techniques using SPC is useful in identifying and correcting sources of variation in KC (Key Characteristics). (Society of Automotive Engineers, 2012). The control chart, control line, capability analysis, histogram, range, mean and standard deviation are the SPC tools used in AS9103.
"A control chart (also called process chart or quality control chart) is a graph that shows whether a sample of data falls within the common or normal range of variation. A control chart has upper and lower control limits that separate common from assignable causes of variation. The common range of variation is de-ned by the use of control chart limits. A process is out of control when a plot of data reveals that one or more samples fall outside the control limits." (Wiley 2009, P 176).
d. Purpose of the Study
The purpose of this study is to investigate the statistical process control and variation management to satisfy AS9103 requirements. The SPC is the statistical tool in satisfying the requirements of AS9103. The study is expected to guide the Aviation and Aerospace professionals on the use of statistical techniques to reduce variations in order to enhance continuous improvement in quality and productivity in the Aerospace industry.
The thesis also provides the strategies to reduce variations and enhance process control to maintain high production standards that will lead to zero defects in the manufacturing of aviation and aerospace product.
ii. Applying SPC to Manufacturing
Application of SPC in the manufacturing process assists in producing high quality product as well as enhancing manufacturing lines with zero defects. Typically, SPC assists in quality control in the manufacturing process, which includes inspection that enhances early detection and prevention of problems in the production process. Apart from assisting manufacturing firms to reduce waste, SPC assists in eliminating time waste required for the production process.
iii. Combining SPC and variation management to satisfy the requirements of AS9103
Combination of SPC and variation management will assist in enhancing quality standard in the production process. The SPC will assist in identifying variations in production process and the variation management will assist an organization to ensure that quality product is on the control line.
e. Theoretical Bases and Organization
The project uses the process model to fulfill the requirements of the production process. The model consists of several stages starting from product definition related to KCs variation management. The process model ends with the product monitoring and maintenance. The research will follow the theoretical model using SPC to enhance manufacturing of quality product. The following hypothesis is developed to evaluate the importance SPC tool in the variation management.
HO: SPC tools and variation management are the effective tools to satisfy AS9103 requirements.
H1: SPC tools and variation management are not the effective tools to satisfy AS9103 requirements.
To test the hypothesis, the study is organized in the following format:
Chapter one provides the background of the study, the theoretical framework and the overview of the SPC and the AS9103.
Chapter two of the study provides the literatures review that explores the past studies on the SPC and the AS9013.
The chapter three reveals the research methodology that provides the method of data collection, and data analysis using a quantitative technique.
Chapter four provides the results and discussion.
Chapter five provides summary and conclusion.
ii. AS9103SAE standard
AS9103 SAE is the Society of Automotive Engineering standard that applies to product of Aviation parts, and the standard is applicable in the production process, which influences the variation of Key Characteristics. The AS9103SAE Standard is designed to drive up the improvement in the manufacturing processes using effective management and adequate planning of Key Characteristic.
f. Limitation of the Study
The study will be limited to the statistical process for the variation control in the production of Aviation and Aerospace parts. The thesis will also be limited to the production process that could influence variation of KC (Key characteristics). The application of AS9103 will be limited to the Aerospace industry in enhancing adequate planning and effective management of KC variation.
Crossley, M.L. (2008).The Desk Top reference of Statistical Quality Methods.Milwaukee, WI: ASQ Quality Press
Fontanares, R. (1997). Statistical Process Control Implementation in an Aerospace Manufacturing Machine Shop (Master's Thesis).Available from California State University, Dominguez Hill Library.
Gordon, D.K. (2007). Changes Coming in Aerospace Standards. Quality Progress. 40(1):74.75.
International Organization for Standards, (2008).ISO 9001:2008 Quality management system-requirements.Geneva, Switzerland: ISO/IEC.
SAE Aerospace (2012). Aerospace Series - Quality Management Systems -Variation Management of Key Characteristics. SAE Internatioanal.
Society of Automotive Engineers. (2012)Aerospace Standard AS9103AVariation management of key characteristics.London, United Kingdom: SAE.
Society of Automotive Engineers. (2009) Aerospace Standard AS9100CQuality management systems - requirements for aviation, space and defense organizations. London, United Kingdom: SAE.
"Statistical Process Control And Variation" (2013, June 24) Retrieved December 4, 2016, from http://www.paperdue.com/essay/statistical-process-control-and-variation-92464
"Statistical Process Control And Variation" 24 June 2013. Web.4 December. 2016. <http://www.paperdue.com/essay/statistical-process-control-and-variation-92464>
"Statistical Process Control And Variation", 24 June 2013, Accessed.4 December. 2016, http://www.paperdue.com/essay/statistical-process-control-and-variation-92464
Applying Statistical Process Control Pharmaceutical Manufacturing The use of applied statistics in studying a pharmaceutical manufacturing process is examined in the work of Tiani (2004) reports that health care quality is critically important in society and the quality of health care is important to all individuals. It is important that treatment is given in an accurate manner and this is particularly true of medications given to patients as it is expected
However, there are certain aspects regarding quality that the company can improve. The objective of Costco is to improve quality of products and services while reducing their price. This is a difficult objective to reach, given the fact that high quality is usually attributed to higher prices. Therefore, Costco understands that it must work on technological aspects in order to improve the quality of its process and maintaining its cost
It should be verified why things started off so great and then dipped very close to the lower control limit and stayed there for such a vast portion of the timeline. That being said, the fact that no values are outside of the lower control limit shows that the graph is at least somewhat accurate if not completely accurate, but it is worth of review nonetheless. Conclusion In short, control charts
This analysis was deemed necessary as differences can exist on the basis of an overall group effect and not an individual variable (question) effect and visa versa. Male Mean Per Question Analysis x Department Source of Sum of d.f. Mean Variation Squares between error Required F. value: 4.21 ? < 0.05 Concluding Statement: With a received F. value of 1.04 and a required value of F = 4.21 the conclusion can be drawn that no statistically
One of the best examples of the use of statistical quality control in clerical operations is found in Aldens' Mail Order House in Chicago. Statistical quality control was begun at Aldens' early in 1945 by the installation of sample inspection and the control chart in one of the order-picking departments (Mercer, 2003). Within two months, the error ratio in this department fell from 3% to less than 1% while
ability of plants to respond to environmental factors such as soil temperatures. This paper examines the effects of arti-cially warmed environment using open-top chambers (OTCs). It investigates the effect of temperature changes on the growth of Dryas integrifolia. This is in light of the growing concern of the changing climatic weather condition more so in the cold climatic regions of the world. It hypothesizes the difference in growth of
Quality Management In the contemporary business environment, business control chart is very critical to enhance continuous business process and business improvement. The use of statistical process control charts (SPC) is very critical to enhance improvement and quality of products and service. Process control chart is a statistical tool that allows business to record data regarding the performances of business process on a regular basis. The data may be recorded hourly, weekly