2 Methods of Quality Control in An Organisation

This article throws light upon the top two methods of quality control. The methods are: 1. Inspection 2. Statistical Quality Control (SQC).

Method # 1. Inspection:

Inspection means checking the product through visual or testing examination, at the input stage, transformation stage or output stage, against standards. The task of carrying out inspection is inspection function and the people who perform inspection are called inspectors. Inspectors measure quality of goods against standards and separate acceptable units from the non-acceptable ones.

This helps them investigate into matters like ‘why an unacceptable product has been produced’ and take corrective action. The quality standards can be more objectively measured in manufacturing systems than service systems where measures of quality are comparatively less objective.

When and how often inspection should be done depends upon the cost of inspection and cost of no-inspection. While inspection of every product involves huge cost, no-inspection results in defective output. A trade-off is, therefore, maintained to carry out the inspection activity.

Usually, every product is not inspected. A sample of product is inspected and size of the sample depends upon the degree of accuracy required for the product. If high degree of precision is required, the product sample has to be large and vice-versa.

Types of Inspection:

Inspection can be:

(1) 100 per cent inspection, or

(2) Sample inspection.

(i) 100 per cent inspection:

It involves inspection of each and every item of products. It nullifies the chances of passing the defective items and is more accurate.

100 per cent inspection is done where:

(a) Products are highly valuable, for example, bullion.

(b) There is tendency for the products (at any production stage) to have large number of rejections.

However, it suffers from the following limitations:

(a) It is neither possible to inspect each and every item when the production is in process nor is it possible to have access to all the data relevant to the production process.

(b) Even if relevant data are available, it will be costly in terms of time and money to inspect every product.

(c) 100 per cent inspection of some items like bakery and confectionery may lead to destruction of all the units.

(ii) Sample inspection:

In order to avoid limitations of 100 per cent inspection, sample inspection of raw materials and control processes is done. Under this method, a sample of the work is inspected which forms the basis for quality control.

A sample of products is drawn and inspected for acceptance or rejection of the entire lot of goods. If the sample rejects the lot, the lot may be rejected or put to 100 per cent inspection where all good parts are retained and bad parts are rejected. This method of inspection is also known as partial inspection. It saves time and money and checks the work any time during the process. It facilitates on-the-spot checking of goods at various stages of production process.

Benefits of Inspection:

Inspection has the following benefits:

(i) It smoothens the production process by inspecting the raw material and ensures effective quality of goods.

(ii) It ensures right quality of the products.

(iii) It justifies the costly production processes as they produce quality goods.

(iv) It locates the sources and causes of defects and takes preventive action to avoid their occurrence.

(v) It improves relationships with customers by providing them quality goods.

Method # 2. Statistical Quality Control (SQC):

SQC is a statistical technique used to monitor quality of the products (goods or services). “Statistical Quality Control is a method to measure the degree of conformance of the various parameters involved in the processing of products to previously laid down specifications.”

It is based on statistical theories and methods of probability to control the:

(i) Incoming materials

(ii) Processes during production, and

(iii) Final products.

It can be done in the following ways:

(a) Acceptance Sampling:

Acceptance sampling determines whether the products conform to design specifications or not by picking a sample of finished goods and testing against the standard quality measurements. It defines the tolerance limit or variation in quality against standards that can be acceptable.

Though some percentage of defective items (say 2% or 3%) may be acceptable depending on the degree of precision required, the method ensures that, on an average, not more than specified percentage of defective items are passed and sold in the market. Acceptance sampling is effective when the percentage of products (sample) is representative of the whole lot.

If the sample is defective, it means rejection of the whole lot or 100 per cent inspection. To avoid costs related to rejection of outputs, and ensure that quality is as specified, random samples are chosen rationally and scientifically. As a measure of control, when output does not conform to identified specifications, production manager may search for a new supplier or a new machine or train the incompetent workers.

Increase in the Utility of Acceptance Sampling:

The utility of acceptance sampling can be enhanced though the following measures:

(i) Production managers should check a number of samples rather than one sample, that is, sample numbers should be increased.

(ii) They can increase the sample size. The number of products tested should increase from say, 100 to 200 or even more in a lot of 1,000 units.

(iii) The sample plans can be tightened by decreasing the acceptance numbers.

These measures help in increasing the quality of outputs but at increased inspection costs. Increasing inspection efforts will reduce passing defective goods but at increasing cost. Therefore, 100 per cent sample or increasing inspection is justified if costs or losses due to passing of defective goods to consumers (loss of contracts and consumers) are more than inspection costs.

(b) Process-Control Methods /Process Sampling:

Process sampling evaluates products during the production process so that problems can be detected before producing the product, if defects are beyond the tolerance limit, production should be stopped to avoid further losses. Changes can be made while the product is being produced or the service is being rendered.

Variations in production processes fall into two categories:

(i) Variations due to chance:

These variations arise due to minor causes which do not form significant part of total variation. They occur randomly and managers can do very little about correcting them in the production process.

(ii) Variations due to assignable causes:

These variations can be traced and have assigned causes.

The reasons for variations can be:

(a) Differences amongst workers,

(b) Differences amongst machines,

(c) Differences amongst materials,

(d) Differences in interaction amongst workers, machines and materials.

Such variations are eliminated through statistical techniques. Variations due to chance with respect to number of defects, chemical composition, weight of the product etc. represent that process is in a state of statistical control but when variations occur due to assignable causes, they indicate problem in the production process which requires remedial action.

Process sampling is facilitated with the help of charts known as control charts. The control charts are graphic representations where managers set standards for expected normal variation due to chance causes which are acceptable within the range of upper quality level and lower quality level.

Variation outside the acceptable limits is investigated for its causes and managers correct those deviations, whether they are with respect to wear and tear of the machine or poor quality of materials or obsolescence of machines or disharmony in superior — subordinate interaction etc.

A sample control chart is represented as follows:

Any variation from a standard unit of 6 up to 8 (upper limit) and 4 (lower limit) is acceptable but for variation beyond that, the fault is found in the operation process.