Statistical control

Many organizations use statistical process control to bring the organization to Six Sigma levels of quality, in other words, so that the likelihood of an unexpected failure is confined to six standard deviations on the normal distribution. This probability is less than four one-millionths. Items controlled often include clerical tasks such as order-entry as well as conventional manufacturing tasks.

Traditional statistical process controls in manufacturing operations usually proceed by randomly sampling and testing a fraction of the output. Variances of critical tolerances are continuously tracked, and manufacturing processes are corrected before bad parts can be produced.

Statistical Process Control (SPC) is an effective method of monitoring a method through the use of organizes charts. Organize charts allow the use of objective criteria for distinguishing background distinction from actions of impact based on statistical techniques. Much of its control lies in the capability to monitor both process center and its variation about that center. By collecting data from samples at various points within the process, variations in the process that may affect the quality of the end product or service can be detected and corrected, thus reducing waste as well as the likelihood that problems will be passed on to the customer. With its emphasis on early detection and prevention of problems, SPC has a distinct advantage over quality methods, such as inspection, that apply resources to detecting and correcting problems in the end product or service.

In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product or service from end to end. This is partially due to a diminished likelihood that the final product will have to be reworked, but it may also result from using SPC data to identify bottlenecks, wait times, and other sources of delays within the process. Process cycle time reductions coupled with improvements in yield have made SPC a precious tool from both a cost reduction and a customer satisfaction standpoint.

History

Statistical Process Control was pioneered by Walter A. Shewhart in the early 1920s. The concept of quality control in manufacturing was first advanced by Walter Shewhart The first to apply the newly discovered statistical methods to the problem of quality control was Walter A. Shewhart of the Bell Telephone Laboratories. He issued a memorandum on May 16, 1924 that featured a sketch of a modern control chart. W. Edwards Deming later introduced SPC methods in the United States during World War II, thereby successfully improving quality in the manufacture of weapons and other strategically important products. Deming was also instrumental in introducing SPC methods to Japanese industry after the war had ended.

Shewhart formed the basis for the control chart and the concept of a state of statistical control by carefully designed experiments. While Dr. Shewhart drew from pure mathematical statistical theories, he understood that data from physical processes seldom produces a "normal distribution curve". He revealed that observed variation in manufacturing data did not always behave the same way as data in nature. Dr. Shewhart concluded that while every process displays variation, some processes display controlled variation that is natural to the process, while others display uncontrolled variation that is not present in the process causal system at all times .

In 1989, the Software Engineering Institute introduced the idea that SPC can be applied to non-manufacturing processes, such as software engineering processes, in the Capability Maturity Model (CMM). This idea exists today within the Level 4 and Level 5 practices of the Capability Maturity Model Integrated (CMMI).

General

In mass-manufacturing, the worth of the completed article was usually achieved through post-manufacturing inspection of the product; accepting or rejecting each piece based on how well it met its design specifications. In contrast, Statistical Process Control uses statistical tools to observe the performance of the production process in order to predict significant deviations that may later result in rejected product.

Two kinds of variation occur in all manufacturing processes. The first is known as natural or common cause variation and may be variation in temperature, difference in raw materials, voltage. This variation is minute, the observed values generally being quite close to the average value. The second kind of variation is known as special cause variation, and happens less frequently than the first.

How to Use SPC

Initially, one starts with a quantity of data from a manufacturing process with a definite metric, i.e. mass, length, surface energy of a widget. There should be upper threshold and lower threshold. The Upper threshold Limits of the process would be set to average plus three units and the Lower Control Limit would be set to average minus three units. The action taken depends on gauge and where each run lands on the SPC chart in order to control but not tamper with the process.

After some times other process-monitoring tools have been developed, including:

Cumulative Sum (CUSUM) charts: the ordinate of each plotted point represents the algebraic sum of the previous ordinate and the most recent deviations from the target.

Exponentially Weighted Moving Average (EWMA) charts: each chart point represents the weighted average of current and all previous subgroup values, giving more weight to recent process history and decreasing weights for older data.