Statistical Process Control Essay Research Paper Statistical

Statistical Process Control Essay, Research Paper

Statistical Process Control

Consistent high quality has become a requirement in today s competitive market. One vital tool for preventing problems is Statistical Process Control (SPC).

SPC is the use of statistics to analyze a process or output so actions can be taken to achieve and maintain statistical control and to improve the consistency of the process. (Jirka, 1995)

Statistical drawing conclusions using a scientific/mathematical approach of analyzing data

Process the whole combination of people, equipment, materials, methods, and environment working together to produce output; any work area that has identifiable, measurable output

Control making something behave in a predictable consistent manner (Jirka, 1995)

Benefits of SPC

Applications of Statistical Process Control in the manufacturing sector have been refined and tested with considerable success. Obvious reductions in production and warranty costs, and significant improvements in employee morale are the proven rewards for implementing SPC.

Using SPC methods, a company can:

+ Identify critical problem areas

+ Reduce variation and monitor for unusual variation

+ Determine the capability of the process

+ Understand and optimize the process

+ Determine the reliability of the product (”Statistical Process Control”, 1998)

New quality requirements such as ISO 9000 have forced many manufacturers to purchase and implement quality systems. Many companies now demand that suppliers practice statistical process control in meeting contractual specifications.

“Most of the major manufacturers are prodding people into using statistical-process-control techniques,” said Phillip Allen, President of Tricor Systems Inc., a small company that supplies hardware, software, and instrumentation products for large industries such as McDonnell Douglas Corp.

According to Allen, the company s assembly errors have been reduced by 43% since implementing SPC. (Chase, 1997)

Cost of SPC

Benefits obtained can quickly repay any investment to implement SPC. The cost largely depends on the method used to collect and process the information. SPC systems generally fall into three main types: manual, semi-automated, and fully-automated.

In a manual SPC system, a person records a small set of readings at regular intervals on a standard SPC form, along with the time and their name. A quick look at the chart for any trends is usually enough to check for potential problems. Advantages are that a manual system is flexible and requires no special equipment. Disadvantages are that it is less reliable than automated systems, more time consuming, and requires operator training.

In a semi-automated SPC system, information is entered directly into a computer, either by typing or by a gauge linked to the computer. This permits automatic alerting of potential problems, improved reliability, and easy access to the information by other people. Advantages include better analysis, reduced training, and easy access. A disadvantage could be the operator is required to use a computer.

In a fully-automated SPC system, computerized monitoring equipment is directly connected to the machine which records both process parameters and machine status. An advantage is minimal operator intervention. Disadvantages are that the system is expensive and difficult to implement.

SPC tools

The cornerstone of statistical process control is measurement. When a process goes unmonitored, unmeasured and uncorrected, very large and unpredictable swings in both quality and quantity often occur. The first step in measuring process control or stability is the use of a control chart, which is the tool most closely associated with SPC.

Control Charts were invented in 1924 by Walter A. Shewhart, who is known as the father of statistical quality control. The control chart represented an initial step toward what Shewhart called “the formulation of a scientific basis for securing economic control.” (”Walter A. Shewhart”, No date)

Control Charts plot observations of the same characteristics as they change over time. By examining the chart for non-random behavior, it can be seen that something is causing the process to act differently than before. The next step is to discover that cause of variation and find a way to eliminate it.

To employ control charts successfully, two things are necessary: good data collection and documentation of the process.

Points on a control chart represent subgroups. A subgroup consists of one or more samples of a characteristic. The mean or average of the samples is plotted on one chart. The spread (difference between the biggest and the smallest measure on a corresponding chart) is also plotted.

The top chart is called an X-bar chart. In mathematical terms, X-bar is the mean (average). The X-bar chart is used to examine the changes from one subgroup to another. The bottom chart is called an R chart. R is an abbreviation for range (the difference between the largest and smallest measurement in each subgroup). The range chart examines the variation in samples of a subgroup.

Control charts include control limits calculated from collected data. When plotted po

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