Attribute Control Charts Research Articles

Some Discussions on the Variation by Chance Cause of the Control Charts for Attributes

Some Discussions on the Variation by Chance Cause of the Control Charts for Attributes

Economically optimalc- andnp-control charts

In v. Collani (1981, 1986, 1987a and 1987b) simple procedures are developed to determine the approximately optimal economic design of control charts for measurements. Applying these procedures to the case of control charts for attributes, nomograms are obtained from which the approximately optimal design ofc-charts, i.e. charts for defects, is readily available. Furthermore it is shown that this method also provides good approximately optimalnp-charts, i.e. charts for defectives.

SPC Simplified: Practical Steps to Quality

Written in clear language, this hands-on manual simplifies the essentials for monitoring, analyzing, and improving quality. While the authors employ statistical tools, these are rooted in practical examples, which require only basic math skills. The book explains how to set up and use variable and attribute control charts, as well as analyze freque

Modern Methods for Quality Control and Improvement.

History and Evolution of Quality Control Quality and Quality Systems Review of Fundamental Statistical Concepts Introduction to Control Charts Additional Control Chart Principles Control Charts for Attributes Control Charts for Variables Special Control Charts METHODS FOR QUALITY IMPROVEMENT: Graphical Methods for Quality Process Control Techniques Specification Limits, Tolerances and Related Techniques ACCEPTANCE SAMPLING: Single and Double Attribute Sampling Sequential and Multiple Attribute Sampling Plans Attribute Sampling Special Attribute Sampling Procedures Acceptance Sampling by Variables DESIGN QUALITY ASSURANCE: Industrial Experimentation Reliability TABLES: Author and Subject Indexes.

Introductory Applied Probability.

Chapter 1: Introduction. Law of Unintended Consequences. Enterprise Excellence. Enterprise Excellence Model. Continuous Measurable Improvement. Achieving Enterprise Excellence. Key Points. Chapter 2: Managing and Leading Enterprise Excellence. Lead People - Manage Things. Management Systems. Leading Enterprise Excellence. The Leadership Model . Leading and Managing Teams. Effective Meetings. Understanding and Overcoming Resistance to Change. The 3Cs: Communication, Cooperation, and Coordination. Key Points. Chapter 3: Enterprise Excellence Deployment. Enterprise Excellence Infrastructure . Deployment Measurement, Analysis and Reporting. Enterprise Excellence Deployment Planning. Establishing Enterprise Excellence Policies, Guidelines and Infrastructure. Key Points. Chapter 4: Enterprise Excellence Implementation. Management and Operations Plans. Enterprise Excellence Projects. Enterprise Excellence Project Decision Process . Planning The Enterprise Excellence Project. Tollgate Reviews. Project Notebook. Key Points. Chapter 5: Listening to the Voice of the Customer. Voice of the Customer (VOC). Answering the Voice of the Customer. Technology Development. Development of Products, Services and Processes. Quality Function Deployment. CDOV Process. Key Points. Chapter 6: Define (Knowing and Understanding Your Processes). Understanding Process Variation. Acquire All Process Documentation. Process Mapping. Value Stream Mapping. Value Stream Analysis. Process Walkthrough. Failure Modes and Effects Analysis. Key Points. Chapter 7: Measure. Process Measurement. Statistical Process Control. Statistical Process Control Charts. Control Charts Analysis. Variation. Type of Control Charts & Applications. Attribute Control Charts. Process Capability Analysis. Measurement Systems Evaluation (MSE). Gauge Reproducibility & Repeatability (R&R). Transactional MSE. Key Points. Chapter 8: Analyze and Improve Effectiveness. Analysis of Variance. One-Way ANOVA. Two-Way ANOVA. Linear Contrasts. Design of Experiments. Key Points. Chapter 9: Analyzing and Improving Efficiency. 5S Process. The Seven Forms of Waste. Takt Time. Cycle Time. Routing Analysis. Spaghetti Diagram. Work Content Analysis. Process Availability Analysis. Process Yield Measures. Cycle Time. Just in Time. Kanban. Mixed-Model Production. A B C Material Handling. Workable Work. Workload Balancing. One Piece Flow. Work Cell Design. Kanban Sizing. Key Points. Chapter 10: Control and Continuous Measurable Improvement. Management Systems. Statistical Process Control. Visual Controls. Graphic Work Instructions. Mistake Proofing (Poka-Yoke). Single-Minute Exchange of Die (SMED). Total Productive Maintenance. Rapid Improvement Events. Continuous Measurable Improvement. Key Points. Appendix A: Bibliography. Appendix B: Glossary. Appendix C: Basic Math Symbols. Appendix D: List of Acronyms.

Introductory Applied Probability

Chapter 1: Introduction. Law of Unintended Consequences. Enterprise Excellence. Enterprise Excellence Model. Continuous Measurable Improvement. Achieving Enterprise Excellence. Key Points. Chapter 2: Managing and Leading Enterprise Excellence. Lead People - Manage Things. Management Systems. Leading Enterprise Excellence. The Leadership Model . Leading and Managing Teams. Effective Meetings. Understanding and Overcoming Resistance to Change. The 3Cs: Communication, Cooperation, and Coordination. Key Points. Chapter 3: Enterprise Excellence Deployment. Enterprise Excellence Infrastructure . Deployment Measurement, Analysis and Reporting. Enterprise Excellence Deployment Planning. Establishing Enterprise Excellence Policies, Guidelines and Infrastructure. Key Points. Chapter 4: Enterprise Excellence Implementation. Management and Operations Plans. Enterprise Excellence Projects. Enterprise Excellence Project Decision Process . Planning The Enterprise Excellence Project. Tollgate Reviews. Project Notebook. Key Points. Chapter 5: Listening to the Voice of the Customer. Voice of the Customer (VOC). Answering the Voice of the Customer. Technology Development. Development of Products, Services and Processes. Quality Function Deployment. CDOV Process. Key Points. Chapter 6: Define (Knowing and Understanding Your Processes). Understanding Process Variation. Acquire All Process Documentation. Process Mapping. Value Stream Mapping. Value Stream Analysis. Process Walkthrough. Failure Modes and Effects Analysis. Key Points. Chapter 7: Measure. Process Measurement. Statistical Process Control. Statistical Process Control Charts. Control Charts Analysis. Variation. Type of Control Charts & Applications. Attribute Control Charts. Process Capability Analysis. Measurement Systems Evaluation (MSE). Gauge Reproducibility & Repeatability (R&R). Transactional MSE. Key Points. Chapter 8: Analyze and Improve Effectiveness. Analysis of Variance. One-Way ANOVA. Two-Way ANOVA. Linear Contrasts. Design of Experiments. Key Points. Chapter 9: Analyzing and Improving Efficiency. 5S Process. The Seven Forms of Waste. Takt Time. Cycle Time. Routing Analysis. Spaghetti Diagram. Work Content Analysis. Process Availability Analysis. Process Yield Measures. Cycle Time. Just in Time. Kanban. Mixed-Model Production. A B C Material Handling. Workable Work. Workload Balancing. One Piece Flow. Work Cell Design. Kanban Sizing. Key Points. Chapter 10: Control and Continuous Measurable Improvement. Management Systems. Statistical Process Control. Visual Controls. Graphic Work Instructions. Mistake Proofing (Poka-Yoke). Single-Minute Exchange of Die (SMED). Total Productive Maintenance. Rapid Improvement Events. Continuous Measurable Improvement. Key Points. Appendix A: Bibliography. Appendix B: Glossary. Appendix C: Basic Math Symbols. Appendix D: List of Acronyms.

Design of attribute control charts for multistage production systems-based on cost criteria

This paper develops a method to design attribute control charts, p -charts and c -charts, for multistage production systems based on quality cost criteria. The production systems are structured and formulated as a dynamic programming model. Each productions station and its inspection station is viewed as a combined station and is treated as the stage in the dynamic programming model. The model has quality distribution as the state variable and the stage transformation of this model is also developed. the stage return of this dynamic programming model is the quality cost of implementing an attribute control chart to that stage and the decision variables are the control chart parameters, n -sample size and control limits. Methods to combine quality distributions for finding type I and type II errors are then developed. As the dynamic programming model has quality distribution as state variable, conventional methods of solving dynamic programming problems are not applicable. A method, for solving this model, is thus developed based on branch and bound approach. The method itself may be applied to other dynamic programming problems where conventional methods are inefficient or not applicable. An example of the foundry process of cylinder liners is included to illustrate the developed method.

Designing np control charts

This paper presents the results of a simulation approach to the design of economic attribute control charts. The approach taken differs from the classical studies reported in the literature in two respects. First, the often made assumption that the out-of-control state of a process will remain so until detection is relaxed. This in effect allows the average proportion of defectives to deteriorate further before the out-of-control state is detected. Second, there is no requirement for detailed information on the exact process behaviour in the out-of-control state. It is assumed that a multitude of out-of-control causes exist and that the proportion defective is equally likely to be within a specified range of values. The relaxation of these two assumptions makes the model realistic and gives it wide practical applications in the control of a manufacturing process. The simulation computer programs developed are sufficiently compact to run on the low cost microcomputers.

Economical Quality Control Policies for a Single Cause System

Abstract We consider four types of process control procedures; variables control charts, attributes control charts, a never-inspect policy, and a regular inspection policy consisting of periodic process reset and/or repair. Using a designed experiment, we compare the optimal economic designs associated with these policies for a practical range of costs and other system parameters. We find that the regular inspection policy is economically optimal in many configurations. Moreover, we find that the type of policy that yields minimum cost depends primarily on the expected magnitude of the process shift, the relative cost of variable versus attribute sampling, and the expected search time for an assignable cause. Some general guidelines for the appropriate type of control procedure for use in practice are given.

Optimal narrow-limit control charts

An optimal attribute control chart has been derived in two stages. Initially the conditions for the fictitious specification limit of compressed-limit gauging acceptance sampling plans to obtain a minimum sample size have been adapted to control charts. Using these requirements for minimum sample size, the sample size, the frequency of sampling, and the extent of the control limits from the fictitious process average have been determined, assuring the minimum expected total cost incurred in utilizing narrow-limit gauging fraction-defective control charts for controlling current production.