Downloads & Free Reading Options - Results

This module for a 1-semester Total Quality Management (TQM) course for high school or community college students gives a brief introduction to some statistical tools. It includes a section on process variation that is intended to familiarize students with the causes of variation such as common and special causes. Statistical definitions are given for common statistical terms such as "mean" and "standard deviation," with examples to aid students in understanding the meanings of the terms. Run charts and control charts are described. An eight-step process is defined for the use of control charts, and two examples are provided. An example of a run chart also is provided. A bibliography lists six references. Handouts and transparency masters are included. (KC)

A subset of Statistical Process Control (SPC) methodology known as Control Charting is introduced. SPC methodology is a collection of graphical and inferential statistics techniques used to study the progress of phenomena over time. The types of control charts covered are the null X (mean), R (Range), X (individual observations), MR (moving range), p (measure of successes), and U (number of events per unit). Uses of charts in the binomial case and the Poisson case are considered. These charts provide the researcher with a combined graphical and hypothesis testing procedure for assessing the stability of various types of data collected across time. The details of constructing each of these charts and the methods for hypothesis testing are described. Examples drawn from the field of education illustrate chart construction. SPC procedures described are understandable by researchers who are not mathematically oriented; whereas other procedures often recommended in the statistically-oriented literature on single-subject design are not easily understood by these researchers. All of the SPC techniques introduced can be modified to allow the analysis of data from one-way multiple group designs and factorial designs. Although these charts do not represent all the control charting techniques used in process control, they are the ones most applicable to educational research. Five tables present sample data, and seven graphs illustrate SPC charts. (SLD)

Historically, quality inspection systems focused on out of spec material instead of providing control strategies to prevent defects from occurring. New inspection systems include Contractor Inspection Systems and Statistical Process Control. The overall objective of this Short Term Project was the design, development and demonstration of a plan for automated manufacturing process controls and in-process inspection for the manufacture of combat rations. The Statistical Process Control Plan for CRAMTD was approved by DPSC and a final SPC Manual released. Also developed and released were a cookbook for Beef Chunks and Gravy in Tray-Pack and the on-line quality control procedures. Several new concepts were examined as advanced on-line sensors. Recommendations for expanded work were developed and undertaken by the DOD. It was recommended that qualities relevant to food manufacturing control be quantified (Funded as DAAK60-92-C-0087); that key hardware and computer software to implement SPC at the CRAMTD site be acquired (funded as STP #12); and that the ability of a food manufacturing process to produce products be quantified (funded as STP #20).

This report contains the instructor's manual for the course Measurement of Work Processes Using Statistical Process Control. The course was developed for Naval Air Rework Facility personnel by the Performance Management Division, Human Performance Department, Navy Personnel Research and Development Center (NAVPERSRANDCEN) to introduce statistical process control techniques for measuring work processes. Statistical process control (SPC) is a methodology that can be used to help improve any work process from manufacturing to paperwork. It maximizes product quality through control of work processes rather than through postproduction inspection. Statistical process control involves identification and removal of systemic causes of defects and reduces the variation in the critical process components. Once a process has attained a state of statistical control, it is expected as a result of systematic management action to create consistently defect-free products or services. Products and services without defects eliminate waste and rework costs, minimize inspection costs, and lead to increased productivity. The SPC methodology includes the following activities: (1) problem identification, (2) data collection, (3) data summarization and graphic presentation, and (4) recommendations and changes to be implemented based on the data. This approach is cyclical in nature and is often referred to as the Plan-Do-Check-Act (PDCA) cycle by management specialists.

This report contains the instructor's manual for the course Measurement of Work Processes Using Statistical Process Control. The course was developed for Naval Air Rework Facility personnel by the Performance Management Division, Human Performance Department, Navy Personnel Research and Development Center (NAVPERSRANDCEN) to introduce statistical process control techniques for measuring work processes. Statistical process control (SPC) is a methodology that can be used to help improve any work process from manufacturing to paperwork. It maximizes product quality through control of work processes rather than through postproduction inspection. Statistical process control involves identification and removal of systemic causes of defects and reduces the variation in the critical process components. Once a process has attained a state of statistical control, it is expected as a result of systematic management action to create consistently defect-free products or services. Products and services without defects eliminate waste and rework costs, minimize inspection costs, and lead to increased productivity. The SPC methodology includes the following activities: (1) problem identification, (2) data collection, (3) data summarization and graphic presentation, and (4) recommendations and changes to be implemented based on the data. This approach is cyclical in nature and is often referred to as the Plan-Do-Check-Act (PDCA) cycle by management specialists.

This report contains the instructor's manual for the course Measurement of Work Processes Using Statistical Process Control. The course was developed for Naval Air Rework Facility personnel by the Performance Management Division, Human Performance Department, Navy Personnel Research and Development Center (NAVPERSRANDCEN) to introduce statistical process control techniques for measuring work processes. Statistical process control (SPC) is a methodology that can be used to help improve any work process from manufacturing to paperwork. It maximizes product quality through control of work processes rather than through postproduction inspection. Statistical process control involves identification and removal of systemic causes of defects and reduces the variation in the critical process components. Once a process has attained a state of statistical control, it is expected as a result of systematic management action to create consistently defect-free products or services. Products and services without defects eliminate waste and rework costs, minimize inspection costs, and lead to increased productivity. The SPC methodology includes the following activities: (1) problem identification, (2) data collection, (3) data summarization and graphic presentation, and (4) recommendations and changes to be implemented based on the data. This approach is cyclical in nature and is often referred to as the Plan-Do-Check-Act (PDCA) cycle by management specialists.

This report contains the instructor's manual for the course Measurement of Work Processes Using Statistical Process Control. The course was developed for Naval Air Rework Facility personnel by the Performance Management Division, Human Performance Department, Navy Personnel Research and Development Center (NAVPERSRANDCEN) to introduce statistical process control techniques for measuring work processes. Statistical process control (SPC) is a methodology that can be used to help improve any work process from manufacturing to paperwork. It maximizes product quality through control of work processes rather than through postproduction inspection. Statistical process control involves identification and removal of systemic causes of defects and reduces the variation in the critical process components. Once a process has attained a state of statistical control, it is expected as a result of systematic management action to create consistently defect-free products or services. Products and services without defects eliminate waste and rework costs, minimize inspection costs, and lead to increased productivity. The SPC methodology includes the following activities: (1) problem identification, (2) data collection, (3) data summarization and graphic presentation, and (4) recommendations and changes to be implemented based on the data. This approach is cyclical in nature and is often referred to as the Plan-Do-Check-Act (PDCA) cycle by management specialists.

One may receive the most benefit from an evaluation of an educational program or the performance of a teacher if the evaluation process is approached from a Total Quality Management (TQM) point of view. Under the philosophy of TQM, the purpose of any evaluation process is to provide feedback for the continual improvement of the educational process that is being evaluated. In the process of obtaining this feedback, the evaluator must be cognizant of two concepts that are basic to the TQM philosophy. First, an educational program or the work of a teacher can be viewed as a system. Second, since every system is impacted by numerous factors, any outcome variable of the system will experience variation. The implication of these two concepts is that an educational evaluator must be able to separate the impact of a change in an educational system from the noise of that system. This paper discusses how an evaluator can increase the ability of an individual values chart to separate a signal in an outcome variable, a signal caused by a change in the educational program or in the teacher's pedagogical practices, from the normal variation found in that variable when the chart is used in conjunction with the Western Electric Zone tests. In an individual values chart, the outcome variable is plotted on a chart in which the time factor and the outcome variable are placed on the X axis and Y axis respectively. The Western Electric Company has proposed four statistical control detection tests to be used in conjunction with control charts to increase the chances of detecting such a signal. (Contains two tables, three figures, and nine references.) (Author/SLD)

180 pages

Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.

Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.

Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.

This guide, part of a series of workplace-developed materials for retraining factory workers, provides teaching materials for a workplace mathematics course. The course is a review of basic number concepts focused on helping participants to understand the meaning of numbers and arithmetic operations. The introductory materials include a course outline, course objectives, a topical outline, information on course length and continuing education units, and resources. Lesson plans for four sessions are provided on the following topics: adding, subtracting, and multiplying decimals; using the metric system; calculating percents; interpreting control charts; and interpreting histograms. The session materials include objectives, topics, methods, evaluation criteria, a pretest and a posttest, information sheets, and handouts. (KC)

This instructor guide, which was developed for use in a manufacturing firm's advanced technical preparation program, contains the materials required to present a learning module that is designed to prepare trainees for the program's statistical process control module by improving their basic math skills in working with line graphs and teaching them how to plot points on a graph. The guide is divided into five sections. The first section contains the following preliminary information: individual assessment sheet, instructor notes, and primary objectives. The second section consists of lessons and worksheets on the following topics: graph-related vocabulary, types of graphs and their purposes, introduction to control charts, and plotting points. The final three sections contain answers to the worksheets, a posttest, and a posttest answer key. (MN)

Study deals with an analysis of data to the effect that it improves the quality of statistical tools in processes of assembly of automobile seats. Normal distribution of variables is one of inevitable conditions for the analysis, examination, and improvement of the manufacturing processes (f. e.: manufacturing process capability) although, there are constantly more approaches to non-normal data handling. An appropriate probability distribution of measured data is firstly tested by the goodness of fit of empirical distribution with theoretical normal distribution on the basis of hypothesis testing using programme StatGraphics Centurion XV.II. Data are collected from the assembly process of 1st row automobile seats for each characteristic of quality (Safety Regulation -S/R) individually. Study closely processes the measured data of an airbag´s assembly and it aims to accomplish the normal distributed data and apply it the statistical process control. Results of the contribution conclude in a statement of rejection of the null hypothesis (measured variables do not follow the normal distribution) therefore it is necessary to begin to work on data transformation supported by Minitab15. Even this approach does not reach a normal distributed data and so should be proposed a procedure that leads to the quality output of whole statistical control of manufacturing processes. / W artykule dotyczącym metod i technik prognozowania wartości kluczowych wskaźników efektywności obsługi (ang. Key Performance Indicators) zdefiniowano oraz sklasyfikowano te wskaźniki. Przedstawiono wymagania oraz sposób doboru wskaźników KPI. Dokonano przeglądu, a także scharakteryzowano metody i techniki prognozowania stanu organizacji utrzymania ruchu. Przedstawiono koncepcję wykorzystania wybranych metod oraz narzędzi gromadzenia oraz przetwarzania danych dla potrzeb prognozowania tego stanu.

Study deals with an analysis of data to the effect that it improves the quality of statistical tools in processes of assembly of automobile seats. Normal distribution of variables is one of inevitable conditions for the analysis, examination, and improvement of the manufacturing processes (f. e.: manufacturing process capability) although, there are constantly more approaches to non-normal data handling. An appropriate probability distribution of measured data is firstly tested by the goodness of fit of empirical distribution with theoretical normal distribution on the basis of hypothesis testing using programme StatGraphics Centurion XV.II. Data are collected from the assembly process of 1st row automobile seats for each characteristic of quality (Safety Regulation -S/R) individually. Study closely processes the measured data of an airbag´s assembly and it aims to accomplish the normal distributed data and apply it the statistical process control. Results of the contribution conclude in a statement of rejection of the null hypothesis (measured variables do not follow the normal distribution) therefore it is necessary to begin to work on data transformation supported by Minitab15. Even this approach does not reach a normal distributed data and so should be proposed a procedure that leads to the quality output of whole statistical control of manufacturing processes. / W artykule dotyczącym metod i technik prognozowania wartości kluczowych wskaźników efektywności obsługi (ang. Key Performance Indicators) zdefiniowano oraz sklasyfikowano te wskaźniki. Przedstawiono wymagania oraz sposób doboru wskaźników KPI. Dokonano przeglądu, a także scharakteryzowano metody i techniki prognozowania stanu organizacji utrzymania ruchu. Przedstawiono koncepcję wykorzystania wybranych metod oraz narzędzi gromadzenia oraz przetwarzania danych dla potrzeb prognozowania tego stanu.

Study deals with an analysis of data to the effect that it improves the quality of statistical tools in processes of assembly of automobile seats. Normal distribution of variables is one of inevitable conditions for the analysis, examination, and improvement of the manufacturing processes (f. e.: manufacturing process capability) although, there are constantly more approaches to non-normal data handling. An appropriate probability distribution of measured data is firstly tested by the goodness of fit of empirical distribution with theoretical normal distribution on the basis of hypothesis testing using programme StatGraphics Centurion XV.II. Data are collected from the assembly process of 1st row automobile seats for each characteristic of quality (Safety Regulation -S/R) individually. Study closely processes the measured data of an airbag´s assembly and it aims to accomplish the normal distributed data and apply it the statistical process control. Results of the contribution conclude in a statement of rejection of the null hypothesis (measured variables do not follow the normal distribution) therefore it is necessary to begin to work on data transformation supported by Minitab15. Even this approach does not reach a normal distributed data and so should be proposed a procedure that leads to the quality output of whole statistical control of manufacturing processes. / W artykule dotyczącym metod i technik prognozowania wartości kluczowych wskaźników efektywności obsługi (ang. Key Performance Indicators) zdefiniowano oraz sklasyfikowano te wskaźniki. Przedstawiono wymagania oraz sposób doboru wskaźników KPI. Dokonano przeglądu, a także scharakteryzowano metody i techniki prognozowania stanu organizacji utrzymania ruchu. Przedstawiono koncepcję wykorzystania wybranych metod oraz narzędzi gromadzenia oraz przetwarzania danych dla potrzeb prognozowania tego stanu.

The Navy is currently working to increase the size of the Fleet without corresponding increases in the maintenance budget. In light of that situation, it is critical that the Navy adopt management systems such as statistical process control (SPC) to achieve and maintain quality (Taft, 1985). SPC management approaches are intended to improve an organization's productivity and product or service quality by reducing variation in the work process. These approaches have been found to improve performance in a variety of industrial settings. The requirements in order to implement SPC management approaches in naval repair and maintenance organizations, however, still must be determined. The purposes of this research were to: (1) identify the basic characteristics of SPC management approaches, (2) distinguish among the major SPC management approaches, (3) determine the suitability of commercial SPC training programs for use in naval facilities, (4) identify those conditions within naval repair and maintenance organizations necessary for the development of SPC management approaches, and (5) begin to identify policies and practices in naval repair and maintenance facilities that inhibit use of SPC management approaches.

In this study a cumulative-sum (CUSUM) procedure from the theory of Statistical Process Control was modified and applied in the context of person-fit analysis in a computerized adaptive testing (CAT) environment. Six person-fit statistics were proposed using the CUSUM procedure, and three of them could be used to investigate the CAT in online test taking. The usefulness of these statistics was explored in a small simulation study. In most CUSUM procedures standard normally distributed statistics are used. Based on this normality, boundaries can be determined to decide when a process is out of control. The statistics proposed in this study, however, are not standard normally distributed, and therefore, in this study, boundaries were determined using simulated data. The study investigated whether the numerical values of the upper and lower threshold were dependent on the latent trait, theta. Results show that the upper and lower bound were rather stable across theta-levels. However, the height of the boundaries was dependent on the particular statistic chosen. (Contains 12 figures and 14 references.) (Author/SLD)

The purpose of this thesis is to provide personnel, who are undergoing Total Quality Leadership (TQL) implementation at their telecommunications-related commands, an understanding of Statistical Process Controls (SPCs) and their potential application to telecommunication issues. Basic SPC tools common to all total quality programs are discussed. Advanced SPC methods including Analysis of Means (ANOM), Analysis of Variance (ANOVA) Weibull analysis and Taguchi methods are also presented. Selected SPC training programs of both the U.S. Navy and the commercial telecommunication industry are examined. A case study of a telecommunication-related issue is provided to demonstrate an integrated approach to the use of SPCs.

This article is from BMC Medical Research Methodology , volume 13 . Abstract Background: Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. Methods: Monthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. Results: The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag40 and 35% had autocorrelation through to lag40; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. Conclusions: The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.

In order to promote public education and public safety, equal justice for all, a better informed citizenry, the rule of law, world trade and world peace, this legal document is hereby made available on a noncommercial basis, as it is the right of all humans to know and speak the laws that govern them. (For more information: 12 Tables of Code ) Name of Standards Organization: Bureau of Indian Standards (BIS) Division Name: Management and Systems Section Name: Statistical Methods for Quality and Reliability (MSD 3) Designator of Legally Binding Document: IS 15202-2 Title of Legally Binding Document: Guidelines for Implementation of Statistical Process Control (SPC) Part 2 Catalogue of Tools and Techniques Number of Amendments: Equivalence: ISO 11462 -2:2010 Superceding: Superceded by: LEGALLY BINDING DOCUMENT Step Out From the Old to the New --Jawaharlal Nehru Invent a new India using knowledge. --Satyanarayan Gangaram Pitroda

188p

This article is from Journal of Family & Community Medicine , volume 16 . Abstract Objective:: To use statistical control charts in a series of audits to improve the acceptance and consistant use of guidelines, and reduce the variations in prescription processing in primary health care. Methods:: A series of audits were done at the main satellite of King Saud Housing Family and Community Medicine Center, National Guard Health Affairs, Riyadh, where three general practitioners and six pharmacists provide outpatient care to about 3000 residents. Audits were carried out every fortnight to calculate the proportion of prescriptions that did not conform to the given guidelines of prescribing and dispensing. Simple random samples of thirty were chosen from a sampling frame of all prescriptions given in the two previous weeks. Thirty six audits were carried out from September 2004 to February 2006. P-charts were constructed around a parametric specification of non-conformities not exceeding 25%. Results:: Of the 1081 prescriptions, the most frequent non-conformity was failure to write generic names (35.5%), followed by the failure to record patient's weight (16.4%), pharmacist's name (14.3%), duration of therapy (9.1%), and the use of inappropriate abbreviations (6.0%). Initially, 100% of prescriptions did not conform to the guidelines, but within a period of three months, this came down to 40%. Conclusions:: A process of audits in the context of statistical process control is necessary for any improvement in the implementation of guidelines in primary care. Statistical process control charts are an effective means of visual feedback to the care providers.

Public interest in educational outcomes has markedly increased in the most recent decade; however, quality management and statistical process control have not deeply penetrated the management of academic institutions. This paper presents results of an attempt to use Statistical Process Control (SPC) to identify a key impediment to continuous improvement in the academic setting and to facilitate continual improvement of instructional performance. An illustration from an undergraduate degree program in business administration is provided.

Public interest in educational outcomes has markedly increased in the most recent decade; however, quality management and statistical process control have not deeply penetrated the management of academic institutions. This paper presents results of an attempt to use Statistical Process Control (SPC) to identify a key impediment to continuous improvement in the academic setting and to facilitate continual improvement of instructional performance. An illustration from an undergraduate degree program in business administration is provided.

The U.S. Navy is concerned with receiving high quality hybrid microelectronic circuits. The Navy recognizes that in order to obtain high quality circuits a manufacturer must have an effective statistical process control (SPC) program implemented. The implementation of effective SPC programs is an objective of the military hybrid microelectronics industry. Often the smaller sized manufacturers in the industry have little SPC implementation, while the larger manufacturers have practices originally developed for the control of other product lines outside the hybrid technology area. The industry recognizes that SPC programs will result in high quality hybrid microcircuits which the U.S. Navy requires. In the past, the goal of the industry had not been to put in effective process control methods, but to merely meet the government military standards on the quality of the hybrids they produce. This practice, at best, resulted in a 'hit or miss' situation when it comes to hybrid microcircuit assemblies meeting military standards. The U.S. Navy through its MicroCIM program has been challenging and working with the industry in the area of SPC practice methods. The major limitations so far have been a lack of available sensors for the real time collection of effective SPC data on the factory floor. This paper will discuss the Navy's efforts in bringing about effective SPC programs in the military hybrid manufacturing industry.

Maintenance management at the 135th Airlift Wing, Maryland Air National Guard desires a visualization tool for their maintenance performance metrics. Currently they monitor their metrics via an electronic spreadsheet. They desire a tool that presents the performance information in a graphical manner. This thesis effort focuses on the development of a visualization tool utilizing two of the seven tools offered by Statistical Process Control (SPC). This research demonstrates the application of p-charts and Pareto diagrams in the aircraft maintenance arena. P-charts are used for displaying mission capable (MC) rates and flying scheduling effectiveness (FSE) rates. Pareto diagrams are then used to highlight which aircraft subsystems are affecting those two performance indicators.

This instructional guide, one of a series developed by the Technical Education Advancement Modules (TEAM) project, is a 6-hour introductory module on statistical process control (SPC), designed to develop competencies in the following skill areas: (1) identification of the three classes of SPC use; (2) understanding a process and how it works; (3) tracking the process; and (4) analyzing the process. Project TEAM is intended to upgrade basic technical competencies of unemployed, underemployed, and existing industrial employees. The materials in this module serve as a student outline and an instructor guide. The manual includes six sections: (1) introduction--including course objectives and classes of SPC use; (2) what is a process; (3) how a process works; (4) Dr. W. E. Deming's plan to save America--the assignment of responsibilities and the master plan for process improvement; (5) tracking the process--the average and range chart, and detecting lack of control; and (6) analyzing and removing special causes--individual and team problem solving, simple diagnostic tools, and return to Deming's plan. (NLA)

The National Aeronautics and Space Administration has been developing a novel docking system to meet the requirements of future exploration missions to low-Earth orbit and beyond. A dynamic gas pressure seal is located at the main interface between the active and passive mating components of the new docking system. This seal is designed to operate in the harsh space environment, but is also to perform within strict loading requirements while maintaining an acceptable level of leak rate. In this study, a candidate silicone elastomer seal was designed, and multiple subscale test articles were manufactured for evaluation purposes. The force required to fully compress each test article at room temperature was quantified and found to be below the maximum allowable load for the docking system. However, a significant amount of scatter was observed in the test results. Due to the stochastic nature of the mechanical performance of this candidate docking seal, a statistical process control technique was implemented to isolate unusual compression behavior from typical mechanical performance. The results of this statistical analysis indicated a lack of process control, suggesting a variation in the manufacturing phase of the process. Further investigation revealed that changes in the manufacturing molding process had occurred which may have influenced the mechanical performance of the seal. This knowledge improves the chance of this and future space seals to satisfy or exceed design specifications.

The National Aeronautics and Space Administration has been developing a novel docking system to meet the requirements of future exploration missions to low-Earth orbit and beyond. A dynamic gas pressure seal is located at the main interface between the active and passive mating components of the new docking system. This seal is designed to operate in the harsh space environment, but is also to perform within strict loading requirements while maintaining an acceptable level of leak rate. In this study, a candidate silicone elastomer seal was designed, and multiple subscale test articles were manufactured for evaluation purposes. The force required to fully compress each test article at room temperature was quantified and found to be below the maximum allowable load for the docking system. However, a significant amount of scatter was observed in the test results. Due to the stochastic nature of the mechanical performance of this candidate docking seal, a statistical process control technique was implemented to isolate unusual compression behavior from typical mechanical performance. The results of this statistical analysis indicated a lack of process control, suggesting a variation in the manufacturing phase of the process. Further investigation revealed that changes in the manufacturing molding process had occurred which may have influenced the mechanical performance of the seal. This knowledge improves the chance of this and future space seals to satisfy or exceed design specifications.

The National Aeronautics and Space Administration has been developing a novel docking system to meet the requirements of future exploration missions to low-Earth orbit and beyond. A dynamic gas pressure seal is located at the main interface between the active and passive mating components of the new docking system. This seal is designed to operate in the harsh space environment, but is also to perform within strict loading requirements while maintaining an acceptable level of leak rate. In this study, a candidate silicone elastomer seal was designed, and multiple subscale test articles were manufactured for evaluation purposes. The force required to fully compress each test article at room temperature was quantified and found to be below the maximum allowable load for the docking system. However, a significant amount of scatter was observed in the test results. Due to the stochastic nature of the mechanical performance of this candidate docking seal, a statistical process control technique was implemented to isolate unusual compression behavior from typical mechanical performance. The results of this statistical analysis indicated a lack of process control, suggesting a variation in the manufacturing phase of the process. Further investigation revealed that changes in the manufacturing molding process had occurred which may have influenced the mechanical performance of the seal. This knowledge improves the chance of this and future space seals to satisfy or exceed design specifications.

The National Aeronautics and Space Administration has been developing a novel docking system to meet the requirements of future exploration missions to low-Earth orbit and beyond. A dynamic gas pressure seal is located at the main interface between the active and passive mating components of the new docking system. This seal is designed to operate in the harsh space environment, but is also to perform within strict loading requirements while maintaining an acceptable level of leak rate. In this study, a candidate silicone elastomer seal was designed, and multiple subscale test articles were manufactured for evaluation purposes. The force required to fully compress each test article at room temperature was quantified and found to be below the maximum allowable load for the docking system. However, a significant amount of scatter was observed in the test results. Due to the stochastic nature of the mechanical performance of this candidate docking seal, a statistical process control technique was implemented to isolate unusual compression behavior from typical mechanical performance. The results of this statistical analysis indicated a lack of process control, suggesting a variation in the manufacturing phase of the process. Further investigation revealed that changes in the manufacturing molding process had occurred which may have influenced the mechanical performance of the seal. This knowledge improves the chance of this and future space seals to satisfy or exceed design specifications.

The National Aeronautics and Space Administration has been developing a novel docking system to meet the requirements of future exploration missions to low-Earth orbit and beyond. A dynamic gas pressure seal is located at the main interface between the active and passive mating components of the new docking system. This seal is designed to operate in the harsh space environment, but is also to perform within strict loading requirements while maintaining an acceptable level of leak rate. In this study, a candidate silicone elastomer seal was designed, and multiple subscale test articles were manufactured for evaluation purposes. The force required to fully compress each test article at room temperature was quantified and found to be below the maximum allowable load for the docking system. However, a significant amount of scatter was observed in the test results. Due to the stochastic nature of the mechanical performance of this candidate docking seal, a statistical process control technique was implemented to isolate unusual compression behavior from typical mechanical performance. The results of this statistical analysis indicated a lack of process control, suggesting a variation in the manufacturing phase of the process. Further investigation revealed that changes in the manufacturing molding process had occurred which may have influenced the mechanical performance of the seal. This knowledge improves the chance of this and future space seals to satisfy or exceed design specifications.

The National Aeronautics and Space Administration has been developing a novel docking system to meet the requirements of future exploration missions to low-Earth orbit and beyond. A dynamic gas pressure seal is located at the main interface between the active and passive mating components of the new docking system. This seal is designed to operate in the harsh space environment, but is also to perform within strict loading requirements while maintaining an acceptable level of leak rate. In this study, a candidate silicone elastomer seal was designed, and multiple subscale test articles were manufactured for evaluation purposes. The force required to fully compress each test article at room temperature was quantified and found to be below the maximum allowable load for the docking system. However, a significant amount of scatter was observed in the test results. Due to the stochastic nature of the mechanical performance of this candidate docking seal, a statistical process control technique was implemented to isolate unusual compression behavior from typical mechanical performance. The results of this statistical analysis indicated a lack of process control, suggesting a variation in the manufacturing phase of the process. Further investigation revealed that changes in the manufacturing molding process had occurred which may have influenced the mechanical performance of the seal. This knowledge improves the chance of this and future space seals to satisfy or exceed design specifications.

The National Aeronautics and Space Administration has been developing a novel docking system to meet the requirements of future exploration missions to low-Earth orbit and beyond. A dynamic gas pressure seal is located at the main interface between the active and passive mating components of the new docking system. This seal is designed to operate in the harsh space environment, but is also to perform within strict loading requirements while maintaining an acceptable level of leak rate. In this study, a candidate silicone elastomer seal was designed, and multiple subscale test articles were manufactured for evaluation purposes. The force required to fully compress each test article at room temperature was quantified and found to be below the maximum allowable load for the docking system. However, a significant amount of scatter was observed in the test results. Due to the stochastic nature of the mechanical performance of this candidate docking seal, a statistical process control technique was implemented to isolate unusual compression behavior from typical mechanical performance. The results of this statistical analysis indicated a lack of process control, suggesting a variation in the manufacturing phase of the process. Further investigation revealed that changes in the manufacturing molding process had occurred which may have influenced the mechanical performance of the seal. This knowledge improves the chance of this and future space seals to satisfy or exceed design specifications.

Efforts to prevent psychopathology from an early stage are likely most effective when targeted at the right moment in time. Just-in-time adaptive interventions (JITAIs) are personalized smartphone interventions that aim to target these moments by monitoring people in their daily lives. An interesting target for such moments of vulnerability is the transdiagnostic process of delayed recovery of affect following daily life stress, which has been shown to play a role in the development of mental illness. However, the exact point at which the recovery begins to put an individual at risk is not yet established. The current study aims to define thresholds for delayed affective recovery that are associated with increased risk for the development of mental illness. We use the experience sampling method (ESM) to investigate affective recovery following daily life stressors. We use statistical process control (SPC) to establish norms of affective stress recovery duration and slopes using general population data and subsequently use these to create control limits (thresholds) for healthy recoveries. We can then apply this control limit to individual recoveries from various populations, and test whether they can accurately distinguish between healthy and at-risk individuals. Our results will reveal whether control limits derived from SPC can be used to assess if an individual is at risk for psychopathology. Identifying thresholds for delayed affective recovery from daily-life stress may provide a very concrete timing for JITAI interventions for individuals at risk for the development of psychopathology.

The problem of classifying pilot mental workload is important to the United States Air Force. Pilots are more subject to errors and G-induced loss of consciousness during periods of mental overload and task saturation. Often the result is the loss of aircraft. and in extreme cases, the loss of the pilot's life. Current research efforts use different psychophysiological features to classify pilot mental workload. These include cardiac, ocular, respiratory, and brain activity measures. The focus of this effort is to apply statistical process control methodology on different psychophysiological features in an attempt to classify pilot mental workload. The control charts track these features throughout the flight, and classify a segment as high workload if the measurements of these feature, are greater than predefined control limits. We find that using certain control charts prove to be effective workload classifiers and maintain high classification accuracies when applied to other flight data.

This report describes a cooperative effort between the Navy Personnel Research and Development Center and the Naval Supply Center at Oakland (NSCO) to develop a pilot project on quality control of supply operations using statistical process control (SPC). Although SPC is not novel, its application to a military supply center is unusual. An important feature of this research was the use of sociotechnical evaluation procedure to determine variance points where SPC procedures should be applied. The report describes the first three phases of the pilot project: (1) the technical analysis of the supply functions to be controlled, (2) the procedures for data collection analysis, and (3) some applications of SPC methods to supply operations. The results of the project support the practicality of using SPC methods to monitor and control supply operations. The STEP procedure was an effective method to obtain information about processes requiring control and to suggest measures useful for monitoring supply operations. When used in an iterative fashion, STEP was able to identify key variance points to be controlled by SPC methods. Supply Center managers understood SPC methods when applied to supply center data. SPC methods gave imformation to the managers about their activity which they previously did not have. Quality keywords: Statistical quality contrtol; Statistical Process Control (SPC); Sociotechnical analysis; NISTARS.

Statistical process control has been compared with two other approaches (confidence intervals and acceptance testing) to determine the potential for this technology in developing realistic sampling plans that offer equal protection for both consumer and producer. These theories were first assessed from a practical standpoint and then were subjected to experimental manipulation using a computer simulation program. Results showed that the optimal solution is to use a combination of process control (the p-chart method) and acceptance testing (Military Standard 105D) to evaluate service quality. This approach offers realistic output that protects the consumer and producer at a similar level; in addition, process control allows historical data to be used so that a contractor who has performed well in the past can be sampled less stringently. Finally, a major advantage is that overall quality of contract services should improve by implementing this approach because the contractor will receive feedback that identifies inconsistencies in services performed; the faults can then be corrected and, over time, the contractor will learn what needs to be done to provide acceptable performance. A step-by-step implementation plan has been proposed. It is recommended that the Army field- test this approach and develop an automated system for rapid evaluation.

This instructor's manual for workplace trainers contains the materials required to conduct a course in pre-statistical process control. The course consists of six lessons for workers and two lessons for supervisors that discuss the following: concepts taught in the six lessons; workers' progress in the individual lessons; and strategies for helping workers continue to develop the skills taught in the course. The following materials are provided: lesson plan, learning activities based on real-life situations, handouts, assessments (including pretests and posttests), and transparencies. Each lesson plan contains objectives, a list of materials needed, classroom setup information, and detailed instructions for conducting all activities. Lessons cover the following topics: learning and working together; arithmetic review; decimals and percentages; descriptive statistics; basic statistics; and control charts. (MN)

This instructor's manual for workplace trainers contains the materials required to conduct a course in pre-statistical process control. The course consists of six lessons for workers and two lessons for supervisors that discuss the following: concepts taught in the six lessons; workers' progress in the individual lessons; and strategies for helping workers continue to develop the skills taught in the course. The following materials are provided: lesson plan, learning activities based on real-life situations, handouts, assessments (including pretests and posttests), and transparencies. Each lesson plan contains objectives, a list of materials needed, classroom setup information, and detailed instructions for conducting all activities. Lessons cover the following topics: learning and working together; arithmetic review; decimals and percentages; descriptive statistics; basic statistics; and control charts. (MN)

This instructor's manual for workplace trainers contains the materials required to conduct a course in pre-statistical process control. The course consists of six lessons for workers and two lessons for supervisors that discuss the following: concepts taught in the six lessons; workers' progress in the individual lessons; and strategies for helping workers continue to develop the skills taught in the course. The following materials are provided: lesson plan, learning activities based on real-life situations, handouts, assessments (including pretests and posttests), and transparencies. Each lesson plan contains objectives, a list of materials needed, classroom setup information, and detailed instructions for conducting all activities. Lessons cover the following topics: learning and working together; arithmetic review; decimals and percentages; descriptive statistics; basic statistics; and control charts. (MN)

In recent years there has been a push within NASA to use statistical techniques to improve the quality of production. Two areas where statistics are used are in establishing product and process quality control of flight hardware and in evaluating the uncertainty of calibration of instruments. The Flight Systems Quality Engineering branch is responsible for developing and assuring the quality of all flight hardware; the statistical process control methods employed are reviewed and evaluated. The Measurement Standards and Calibration Laboratory performs the calibration of all instruments used on-site at JSC as well as those used by all off-site contractors. These calibrations must be performed in such a way as to be traceable to national standards maintained by the National Institute of Standards and Technology, and they must meet a four-to-one ratio of the instrument specifications to calibrating standard uncertainty. In some instances this ratio is not met, and in these cases it is desirable to compute the exact uncertainty of the calibration and determine ways of reducing it. A particular example where this problem is encountered is with a machine which does automatic calibrations of force. The process of force calibration using the United Force Machine is described in detail. The sources of error are identified and quantified when possible. Suggestions for improvement are made.

In recent years there has been a push within NASA to use statistical techniques to improve the quality of production. Two areas where statistics are used are in establishing product and process quality control of flight hardware and in evaluating the uncertainty of calibration of instruments. The Flight Systems Quality Engineering branch is responsible for developing and assuring the quality of all flight hardware; the statistical process control methods employed are reviewed and evaluated. The Measurement Standards and Calibration Laboratory performs the calibration of all instruments used on-site at JSC as well as those used by all off-site contractors. These calibrations must be performed in such a way as to be traceable to national standards maintained by the National Institute of Standards and Technology, and they must meet a four-to-one ratio of the instrument specifications to calibrating standard uncertainty. In some instances this ratio is not met, and in these cases it is desirable to compute the exact uncertainty of the calibration and determine ways of reducing it. A particular example where this problem is encountered is with a machine which does automatic calibrations of force. The process of force calibration using the United Force Machine is described in detail. The sources of error are identified and quantified when possible. Suggestions for improvement are made.

The application of statistical methods to process control provides a better understanding of the behavior of any operation. This understanding is a necessary piece of management information. It is essential for making good decisions about process adjustments and assessing the effect of process improvement actions. The use of statistical methods also makes this understanding available to the employees who are actually working the processes. Fortunately it is not necessary to understand a great deal about the mathematics of statistical theory in order to make full use of statistical methods. The concepts and techniques of a methodology known as statistical process control (SPC) form a repertoire of powerful statistical tools that are effective in bringing about concurrent improvements in both quality and productivity.

Traditionally, medical surveillance of liver disease generally involves a battery of tests. This research used multivariate analysis techniques to reduce the number of measures required to identify liver dysfunction and found using a Transferase Index (a combination of three tests; ALT, AST, and GGT) provided the most satisfying assessment, but the single best indicator, ALT, may be sufficient. Transferase Index and ALT criterion were both applied to SPC control charts. Through the use of statistical process control (SPC), this research identified work zones possessing signs of adverse effects to an individual's liver as a possible result of their work environment and demonstrated SPC as an excellent way to conduct medical surveillance. Industry has embraced SPC, and control charts, this research extended their scope and demonstrated their effective use in medical surveillance of the liver. This research showed they provide easy, efficient ways to monitor work environments.

This research effort concentrated on applying statistical process control techniques to the results of seven years worth of spirometry exams of workers at Wright-Patterson AFB in helping the occupational health squadron identify potential work hazard areas. Each spirometry exam was classified as abnormal or normal based on a comparison with established normals or a significant loss of function from the previous year's exam for the individual. Each test was classified into the work area of the individual and the number of abnormalities per population of the work area was examined.

The current tools used by squadron supervisors to monitor the bombing performance of aircrews flying F-111E aircraft are monthly reports that have little predictive capability. No real-time methodology exists for monitoring and predicting aircrew bombing performance and preventing bombing problems that might cause an individual to become unqualified. It has been suggested that Statistical process Control (SPC) can be applied to the bombing process to develop tools for managing the process, correcting problems, and improve the bombing performance of a squadron. This study investigates the application of SPC to the bombing process. It examines data taken from an F-111E Fighter Wing during a sixth-month training period. The goal is to develop a control charting scheme that is both useful to squadron supervisors as well as simple to apply by squadron weapons officers. The results indicate that SPC methodologies can have a significant impact on the bombing process. Control carts generated from the data can give insights to the bombing performance and capabilities of individual aircrews. These insights can lead to improvements in the bombing performance of individuals, as well as in the bombing performance of their squadron as a whole. (AN)

This research effort focuses on applying statistical process control (SPC) techniques to the departure reliability improvement process for Air Mobility Command, Mission Reliability Office. Basic SPC methods, including Pareto analysis, cause and effect diagrams, and control charts are determined to be effective tools for analyzing and assessing departure reliability. In addition, two more alternative measures of effectiveness for departure reliability are evaluated and compared using AMC's data. SPC methods are also shown to be useful in assessing in performance and in determining the reporting frequency for on-time performance information. Additional statistical analyses are conducted, including on-way analyses of variance, hypothesis tests, autocorrelation of time series and normal probability plots.