Quality is a term that is very commonly used in everyday life for judging the standard of something. In other words, the rank that you give to a certain product in the business world based on the assessment of its worth and excellence, is its quality. Take a basic example; the quality of a book is judged by how well the author has presented it, the story-line, the writing style, the standard of English used, and the organization of text etc. If the standards are respectable, the book can be called qualitative. (Beckford, 2002)
Every single day, world wide, innumerable products are manufactured in factories or are irrigated on agricultural lands. Production is so outsized that even keeping track becomes tough. What we need to understand is that production is not the difficult task here; producing quality goods is what we need to concentrate on. Without quality and class in products, an industry, or an economy as a whole can never succeed.
Most importantly, meeting the criterion of good quality products is a subjective matter. That is, evaluation of the quality of a certain product is all a matter of perception. Everybody has a different criterion for assessing things – this is where consumer expectations come in. If a consumer is expecting brilliance and is presented with moderation, he will definitely be disappointed and consider it to be a low quality good.
In the competitive world of today with consumer economies all over the world, where suppliers are producing high quality goods, even moderate goods cannot compete. There are many brands for the same product, but only the one with the best quality thrives.
Every organization needs to have a quality management system for accomplishment. This system is necessary to guarantee that all the activities that involve the planning, proposing, cultivating and executing of the product and services are efficient and effective. There are three basic constituents of quality management – quality control, quality assurance and quality improvement. Thus, the quality manager should be well aware of the quality standards and policies set by the organization, government and also the international standards.
As the term suggests, quality control is process to ensure that the products are of best quality and fulfill consumer expectations. A product undergoes certain verifications of the product for certain characteristics. The main aim is to guarantee a product that is reliable, satisfactory and lucrative. Different producers have distinct methods of quality checks to meet their standards. The quality controller is responsible for reporting to the company heads if a problem is identified and even ceasing production if necessary. Quality control might only be for products and services, but also the employees. If a certain workers deem to be useless or harmful for the firm, they need to be discharged. However, there is a tendency of confusing quality control and quality assurance. Quality assurance is related to the processes that take place in a factory, while quality control is basically focused on the product. (Dhillon, 1985)
Statistical Process Control
Statistical process control is to use statistical methods to discover and manage the particular cause of variation in a process. Statistical process control is mainly used in manufacturing businesses. Its chief aim is to keep a check on the product quality and to maintain the activities and processes regarding production to a certain standard. There are a lot of other methods used for this purpose apart from statistical process control such as; variation reduction, sampling plans, process capability analysis, experimental designs and process improvement plans. Statistical process control is not responsible for devising a benchmark of quality; it simply ensures that the product and production process meets the standard required. Therefore, it is obtuse to expect this control process to improve a low quality good. It simply helps you in measuring and controlling the variables that affect your business processes. (Wheeler & Chambers)
Statistical process control involves complex mathematical calculations for which mostly computers are used. Computers are efficient and prompt in collecting, organizing, storing of information and calculating answers. Computers then present the conclusion in simple and comprehendible graphs called control charts. Computers are the preferred tool as they can detect the slightest change in a process and notify you immediately.
An example of SPC is its usage in the management of software development efforts. To every action there is an output. The attributes of these outputs are measured, which are either natural or assignable (special). If the variability of the attributes measured is within the range of the variability of natural causes, the statistical process is said to be under control.
Shrinking conflicts and discrepancies in internal processes help a business in saving many resources such as time and money. A greater profit is yielded this way because consumer satisfaction increases and thus, sales rise up too. To guarantee business security, steps such as the statistical process control are extremely crucial in today’s competitive world.
Digressing away from the target specification is the biggest flaw of production. In order to measure how much the production has digressed, that is, how much the variability around the target specification is, we use charts and graphs. In order to form these charts, we first take a sample size from the total production and draw line charts to measure the variability of the actual production from the target specification. These line graphs are called control charts. Two other names for these graphs are ‘Shewhart chart’ or ‘process-behavior chart’. These control charts are a prime tool of statistical process control in order to measure the variability. When we say variability, it actually means the variability of the quality of the actual good produced from that of the target quality.
Control Charts for the Mean
A mean control chart is one that uses the mean to measure the variability of the quality of the product from the target requirement. The mean of the variations of all processes taking place in manufacturing and production is calculated to compute the upper and lower limits. When drawing the control charts, we use an upper control limit, and a lower control limit. These upper and lower limits are actually the range of our target. We have to make sure that our mean lies within it. The formulae used to calculate these limits are:
UCL (Upper control limit)
LCL (Lower control limit)
Where; x-double bar is the Grand Average and x is Process Sigma.
Control Charts for Attributes
In control charts for attributes, we actually use the average of the process that is taking place. For example, if we are keeping a check on proportion or the rate, we will use average proportion or average rate. That is, the approximation of the process is a function of the average of the process.
For control charts for attributes, only standardized short run charts are available. A short run standardized graph means that the variability of quality of different parts needs to be identical. That is, there should be no skewness. There are various examples of attribute control charts such as; C, U, Np or P charts, which are different types of control charts.
If one attribute of quality is being measured, it is called a univariate control chart and if more than one attribute of quality is being measured, it is called a multivariate control chart.
There are different functions of the different types of attributes charts. C chart is used to measure the number of defectives in the entire batch of production. The U chart is used to plot the rate of defectives, that is, the number of defectives divided by the number of units inspected. The P chart is used to plot the percentage of defectives per batch. However, unlike the C and U charts, the P chart uses the binomial distribution. Even the Np chart is based on a binomial distribution and plots the number of defectives per batch, similar to the C chart.
Control Charts for Process Variability
The quality of a product in the industrial world involves numerous correlated variables. For example, the chemical mechanical polishing process is a very critical process of wafer manufacturing. Two correlated variables are responsible for the quality of a polished wafer – the thickness of polished wafer’s remnants, and the consistency of the thickness contained by the polished wafer. When we have two processes running simultaneously, it is favorable for us to use a multivariate chart. With new technical expertise and research along with multivariate tasks, industries are guaranteed to progress in quality and statistical control. (Yeh, 2005)
All industrial processes involving measurement and manufacturing demonstrate variation. For example, when we extract part of the output process as the sample for testing the variability, such as accurate and important proportions, or resistance, we discover that there is always a discrepancy in the values obtained. The stretch of values around and about the target value is what is known as the spread or variability. We represent variability as either histograms or control charts and have termed the mathematical value as variance.
Total Quality Management
Total quality management is an array of principles practiced throughout an organization, implemented to ensure that the production is of high quality and that it meets or even surpasses consumer expectations. Total quality management focuses on the tracking of process taking place in the organization and controlling it towards perfection. If consumer needs are satisfied by eradicating all practices that compromise quality, an organization can reach new heights. (Evans and Lindsay, 2007)
Implementation of total quality management is a difficult task and surveys reveal that only 20 to 36 percent companies have benefited greatly by it. However, all the prestigious and triumphant companies have had enhancement in productivity, competitiveness and profits and of course quality by this management implementation.
This is a statistical process that deals with either approving or rejecting the production batch after verifying it for quality. For example, it is impractical to taste or check every box of apple juice produced by the company for best quality, as that would leave the company with no products for sale. Therefore, acceptance sampling is a process by which a sample product or two are selected at random from the stock available. The entire batch is approved or rejected on the conclusion reach by assessing that sample product. That sample is taken to represent the entire production. It is also known as Lot Acceptance Sampling.
In order for an organization to flourish, it needs to make sure there is quality production taking place. Checks need to be kept and processes such as the statistical process control need to be implemented along with total quality management. Usage of control charts is a very proficient way of keeping a check on the quality. In the cutthroat competition of today’s world, quality is extremely crucial for a flourishing economy and ever increasing profits.
- Yeh, Arthur B (2005), A multivariate EWMA control chart for monitoring process variability with individual…)
- B. S. Dhillon, 1985, Quality Control, Reliability, and Engineering Design: Industrial engineering
- Donald J. Wheeler, David S. Chambers, Understanding Statistical Process Control
- John Beckford, 2002, Quality
- Dale H. Besterfield, 2008, Quality Control, 8th Edition
- James R. Evans and William M. Lindsay, 2007, Managing for Quality and Performance Excellence
- Levin/Rubin, 1997, Statistics for Management, 7th Edition