# What is Statistical Process Control (SPC)? Objectives, Methods

## What is Statistical Process Control?

Statistical Process Control (SPC) is a quality control technique used to monitor and improve processes in manufacturing, service delivery, or other industries. It involves the use of statistical methods to measure and analyze the variability of a process over time, with the goal of identifying and correcting any sources of variation that might lead to defects or other quality issues.

The term ‘statistical’ is used to define the mathematical models and theories for collecting, analyzing, and deducing data. A ‘process’ is a set of steps that are performed to convert inputs into a desired output. For example, you want to quickly make a pizza at home. You have basic ingredients, such as:

• 1 unbaked pizza crust
• 1 jar of pizza sauce
• Favorite pizza toppings
• 2 cups of grated mozzarella cheese

This is an example of a process that has inputs (the pizza ingredients) and an output (the cooked pizza).

The term ‘process control’ means a method to oversee a process to ensure that all its operations are performing according to the predefined specifications to produce the required output. For example, when you drive a car on road, you need to control its speed to avoid any accidents. In this case, you are controlling the process of driving a car.

Similarly, organizations need to control all the processes to get the desired output. To control a process, an organization applies statistical tools and techniques. This procedure is called Statistical Process Control (SPC). SPC is a statistical decision-making tool that enables you to verify whether a process is working at its full potential to produce the desired product.

## Objectives of SPC

Variation in products is inherent in a process. The role of SPC is to control the number of defective products. For example, you are working in an electronics manufacturing company. Here, the role of SPC is to control the number of defective products, resulting from the assembly line of products.

• Evaluate a process
• Enable methods to change the process
• Identify the results of improvement made in a process
• Identify and reduce the variations in a process
• Determine trends in the data of a process
• Apply the process data for forecasting
• Reduce the need for inspection
• Check the quality of the process

## Methods of SPC

The purpose of SPC is to ensure that processes work in tandem to conform to product requirements. SPC helps in formulating, implementing, and controlling business processes effectively.

### Control Charts

These charts represent variations in processes in a graphical manner. They also show the causes of these variations.

In Figure, a straight line depicts a job mix process. The crooked line depicts the normal variations in the process. The dotted line depicts the upper and lower specification limits. At point 6.2 on the y-axis, an abnormal variation has occurred, as the normal variation has crossed the upper limit of the control chart.

### Continuous Improvement of Processes

An organization continuously tries to improve its processes, services, and products. This continuous improvement in processes helps the organization to maintain and improve its efficiency. The figure shows the methodology used to implement continuous improvement in organizations:

### Design of Experiments (DOE)

It is a common methodology used in SPC that collects activities in a controlled environment, where variation is present. For example, a medical researcher wants to study the effect of a particular drug to treat cancer.

So he/she will apply the drug to the laboratory rats in a controlled environment for the treatment of cancer and examine the results for analysis. This process of performing experiments and collecting, analyzing, and interpreting the data is called DOE.

ARTICLE SOURCES
• Beckford, J. (1998). Quality. London: Routledge.

• Charantimath, P. (2011). Total quality management. New Delhi, India: Dorling KIndersley (India).

• Evans, J., & Lindsay, W. (2002). The management and control of quality. Australia: South-Western.

• Gitlow, H. (2001). Quality management systems. Boca Raton, Fla.: St. Lucie Press.

• Hellard, R. (1993). Total quality in construction projects. London: T. Telford.

• Kanji, G. (1995). Total quality management. London: Chapman & Hall.

• Pollitt, C., & Bouckaert, G. (1995). Quality improvement in European public services. London: Sage.

• Thorpe, B., Sumner, P., & Thorpe, B. (2004). Quality management in construction. Aldershot, England: Gower