Читать книгу Introduction to Statistical Process Control - Muhammad Amir Aslam - Страница 13

Quality improvement is a continuous process adopted in all business activities for two purposes: to compete the market and to maximize the profits. It is a competitive tool used for improving and controlling many organizations, transportation, health care, and government agencies. A goods and services providing agency delighted its customers by improving and controlling its quality, which dominates over its competitors. Shewhart control charts are used for this purpose, but according to experts of quality control, the process should not be disturbed until sound statistical inference evidence is used for indicating that the process is misbehaving. Without statistical evidence the process should not be modified. Consistent quality improvement can be sustained not only by modification of the process but also by redesigning of the process. The process design can only be changed after the full satisfaction of the quality control personnel who is running the process fully in control state (Figure 1.1).

Control charts are constructed using a reasonable size normally of 5 to 25 units of rational subgroups, periodically, from the running process. The statistically calculated values of these subgroups are posted on the limits of thus calculated values from that process. The posting of these subgroups indicates the fluctuations caused by the common or/and special causes of variations of the process under study. When all these subgroups are commingled with, then these values do not provide us the required information from the process as most of the information will be lost.

This book is written with the objective to help the quality control personnel to use statistical tools and techniques for monitoring and improving the quality of the product. Different techniques are available for different situations. The proper choice of the available techniques is the required competency of the quality control personnel.

Figure 1.1 A typical control chart.

Quality improvement is an ever‐present marvel. Originally developed techniques for the manufacturing environment are applied for the ever‐increasing competition of the markets. These techniques not only are meant for the manufacturing processes but also have a wide scope and range in different areas from health care to education to government services. Statistical process control (SPC) is collection of techniques and methods for thinking about the data. The apparent utilization of these techniques may be to monitor the diameter of the bolt being produced on a manufacturing unit in bulk. Therefore, the collection of the sample from the assembly line to declaring the process whether it is in control or out of control is literally performed through SPC. Adopting these techniques will indicate the manufacturing deterioration of the bolt production may be caused by raw material or the fault in the production steps.

The SPC notion instigated during the twentieth century when Walter A. Shewhart float the idea of control chart during 1924. Another important technique of SPC is the acceptance sampling plans, which were introduced by Dr. H. F. Dodge and H. G. Roming in 1928 at Bell Laboratories. The variations in the products are important to designate when they deviate from the affordable/acceptable levels of variations. These variations are based on the principle of random process and monitored through control charts. The common steps used for the construction of a typical control charts can be listed as:

1 Decide a variable continuous or discrete to be monitored for the product. We consider a continuous variable here.

2 Calculate the mean of the targeted variable and the grand mean being used as central line (CL).

3 Calculate the standard deviation of the targeted variable.

4 Calculate the upper control limit (UCL) and the lower control limit (LCL) of the targeted variable with the deviation of three‐sigma from the grand mean.

5 Plot the means collected from the targeted variable on the chart with the mean − 3sd and mean + 3sd sigma limits defined in Step 4.

6 On plotting the means there may be some points falling outside the UCL or LCL. If this exists, then probe the matter for the reasons behind these out‐of‐control values.

7 Revise the control chart for the new CL, UCL, and LCL after discarding the disturbing means.

8 Plot the means of the next collected data on the constructed limits and decide to declare the process as within control or out of control.

The SPC techniques are commonly used in the health care sector. For example, different blood parameters of the collected samples can be monitored for possible unusual changes occurred in the targeted variables. The quick detection technique of exponentially weighted moving average (EWMA) has been frequently used for efficient monitoring of the data consisting of various parameters of blood test in cats and dogs. It has been shown that the application of EWMA technique on the blood parameters is an effective method for such data.

The SPC techniques are also very commonly used in the human resource management sector. For example, the monitoring of misconduct of the workers, unrest among the employees of an organization, racial discrimination in a community, women harassment incidence in an office are being monitored by the application of the SPC techniques. Such parameter allows the managers for observing the prompt changes in the current status quo and any deviation of the prevailing policies of the organization.

The important concern of the SPC is based on the use of sampling techniques. The already developed methodologies are the single, double, with plenty of literature available in the books of SPC, but the repetitive sampling and multiple dependent state sampling schemes, which are the most commonly used in quite near past, are explained in this book. Many other sampling schemes, which are also tenderly accepted in SPC, are the probability to ratio sampling scheme, the ranked set sampling, and fast initial response set sampling scheme.