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Statistical Process Control, Reference Manual

Statistical process control (SPC) is defined as the use of statistical techniques to control a process or production method. SPC tools and procedures can help you monitor process behavior, discover issues in internal systems, and find solutions for production issues. Statistical process control is often used interchangeably with statistical quality control (SQC).

Statistical Process Control, Reference Manual

Statistical quality control (SQC) is defined as the application of the 14 statistical and analytical tools (7-QC and 7-SUPP) to monitor process outputs (dependent variables). Statistical process control (SPC) is the application of the same 14 tools to control process inputs (independent variables). Although both terms are often used interchangeably, SQC includes acceptance sampling where SPC does not.

A LASSO-Based Diagnostic Framework For Multivariate Statistical Process Control (Technometrics) Several statistical process control examples are presented to demonstrate the effectiveness of the adaptive LASSO variable selection method.

Rethinking Statistics For Quality Control (Quality Engineering) As methods used for statistical process control become more sophisticated, it becomes apparent that the required tools have not been included in courses that teach statistics in quality control. A basic description of these tools and their applications is provided, based on the ideas of Box and Jenkins and referenced publications.

SPC: From Chaos to Wiping the Floor (Quality Progress)A history of statistical process control shows how it has gone from taming manufacturing processes to enabling all organizations to maintain their competitive edge.

Statistical Process Control For Monitoring Nonlinear Profiles: A Six Sigma Project On Curing Process (Quality Engineering) This article describes a successful Six Sigma project in the context of statistical engineering for integrating SPC to the existing practice of engineering process control (EPC) according to science.

The new CQI-25 SPC Quick Start Guide is a supplement to the full SPC Manual and provides the building blocks of statistical process control for those who are new to the topic or have only experienced SPC from a limited perspective.

This document is a tool for a customer or supplier organization's internal audit group to assess a statistical process control (SPC) system against the requirements of IPC-9191. This document should be used by customers and suppliers of any size and for any commodity. This tool can be used to perform an assessment of the use of SPC at both organizational and process levels. The questions in this evaluation form are based on the guidelines for SPC implementation given in IPC-9191, which, in turn, was developed to reflect the principals of SPC represented by the International Organization for Standardization (ISO) Statistical Methods Technical Committee's document: ISO 11462-1.

IPC-9191 reflects the principals of statistical process control (SPC) represented by ISO/DIS 11462-1, Guidelines for Implementation of Statistical Process Control (SPC) -- Part 1: Elements of SPC. This document outlines the SPC philosophy, implementation strategies, tools, and techniques used for relating process control and capability to final product requirements. Supersedes IPC-PC-90.

Ensure that statistical process control (SPC) data is accurately collected on time, every time by using the powerful features of the Sepasoft SPC Module for Ignition. SPC will help you reduce or eliminate late or missing sample collection, inaccurate sample data, or other issues leading to quality problems. Deliver your SPC data in real-time to the right people in a comprehensive format using the flexible control charts and analysis tools.

SPC is method of measuring and controlling quality by monitoring the manufacturing process. Quality data is collected in the form of product or process measurements or readings from various machines or instrumentation. The data is collected and used to evaluate, monitor and control a process. SPC is an effective method to drive continuous improvement. By monitoring and controlling a process, we can assure that it operates at its fullest potential. One of the most comprehensive and valuable resources of information regarding SPC is the manual published by the Automotive Industry Action Group (AIAG).

SPC data is collected in the form of measurements of a product dimension / feature or process instrumentation readings. The data is then recorded and tracked on various types of control charts, based on the type of data being collected. It is important that the correct type of chart is used gain value and obtain useful information. The data can be in the form of continuous variable data or attribute data. The data can also be collected and recorded as individual values or an average of a group of readings. Some general guidelines and examples are listed below. This list is not all inclusive and supplied only as a reference.

The data points recorded on a control chart should fall between the control limits, provided that only common causes and no special causes have been identified. Common causes will fall between the control limits whereas special causes are generally outliers or are outside of the control limits. For a process to be deemed in statistical control there should be no special causes in any of the charts. A process in control will have no special causes identified in it and the data should fall between the control limits. Some examples of common cause variation are as follows:

When monitoring a process through SPC charts the inspector will verify that all data points are within control limits and watch for trends or sudden changes in the process. If any special causes of variation are identified, appropriate action should be taken to determine the cause and implement corrective actions to return the process to a state of statistical control.

The concept of statistical process control has been around for a while. In 1924, Bell Laboratories employee, William A Shewart, designed the first control chart and pioneered the idea of statistical control. This quality control process was extensively used during World War II in facilities for ammunition and weapons. Statistical process control monitored the quality of products without compromising on safety.

Following the war, the use of statistical process controls slowed down in America but was picked up by the Japanese--who applied it to their manufacturing sector and continue to use it today. In the 1970s, statistical process control came back into use in America to rival products from Japan.

Inspection continues to be the key form of detecting quality issues for most companies, but its efficacy is debatable. With statistical process control, an organization can shift from being detection-based to prevention-based. With the constant monitoring of process performance, operators can detect changing trends or processes before performance is affected.

As with any new process, the first step to using statistical process control is evaluating where the manufacturing business is facing waste or performance issues. These could be related to reworking products, wasting entire ranges of products, or long inspection times. A company benefits by applying statistical process control systems to these trouble areas first.

During analysis, all data points recorded on the control chart have to be within control limits, as long as there are no special causes. Common causes result in data points falling within control limits, but special causes tend to be outliers. To classify a process as being in statistical control, there should be no outliers on any chart. When a process is in control, it will not have any identified special causes and all the data will fall between control limits.

While monitoring processes with statistical process control charts, all data points are constantly verified to check if they are remaining within control limits. They will watch out for any changes in trends or abrupt changes in processes. If a special cause is identified, necessary action is taken to determine the cause. Next is to remedy it and allow the process to come back to statistical control.

While the emphasis of statistical process control is on early detection, implementing the system in a manufacturing set up can take a long time. Additionally, the process of monitoring and filling out charts is time consuming. Since the system has to be integrated into an existing framework, training of personnel is required which takes time.

As machine learning and artificial intelligence advance, the abilities of statistical process control will increase. This will lead to bigger gains for the manufacturer, increased competitive advantage, and satisfied customers.

One way to improve a process is to implement a statistical process control program. Typically used in mass production, an SPC program enables a company to continually release a product through the use of control charts rather than inspecting individual lots of a product. As long as a device company maintains meticulously reviewed and signed documentation of its process, and the process is within specification, FDA will allow product release using SPC. This will reduce time to market by eliminating interruptions in production. SPC enables a company to detect trends and defects earlier in production, thereby reducing inspection, rework, and scrap costs. SPC is usually represented by a control chart, which is a simple graph of process information.2 The use of graphs to understand processes, improve efficiencies, and reduce costs began when Walter Shewhart introduced the control chart at Bell Labs in 1924.3

Prior to actually using an SPC chart, it is a good idea to update the applicable work instruction to reference the actual usage of the chart to ensure that the operator completes the charting process per the requirements of the work instruction. This step should also include an update to the device master record (DMR) for the product or products represented by the chart. The charting activity may also be referenced in the device history record (DHR). Since most medical manufacturing systems are equipped to track DHR activities and work instructions, this step should not be a burden to implement. 041b061a72

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