Quality Control/Assurance is a system for evaluating the performance, service, and quality of the merchandise against system, standard, or specified requirements for purchasers. Planned activity or systematic approach provides adequate confidence of product and service. It is a way of preventing mistakes or defects in manufactured products and avoiding problems when delivering solutions or services to customers.
ISO-9000 defines Quality Assurance as, "A part of quality management focused on providing confidence that quality requirements are going to be fulfilled".
Below are the 7 quality control tools which are used by industries to ensure quality in their processes and products. These processes also help to determine drawbacks and defects in the operation.
1. Tally Sheet
It is a listing within which stripes should be filled and hence is called a tally. A tally sheet about compiling data and analysis for decision making. It is used for the easy registration of data at the workstation. The test data or defects are filled in the related column in a preprinted form. The observed or measured values need not be written down but can be written with a strip or a line.
2. Histogram
Histogram plots data in a statistical distribution table. When employed in process capability studies, histograms can display specification limits to indicate what portion of the data does not meet the specifications. After the information is collected, they are grouped in value and frequency and plotted in graphical form. A histogram's shape shows the nature of the distribution of the data, as well as central tendency and variability. Specification limits are often accustomed to display the capability of the process.
3. Pareto Diagram
The Pareto diagram is named after Vilfredo Pareto, a 19th-century Italian economist who postulated that a large portion of wealth is owned by a small percentage of the population. The basic principle translates well into quality problems-most quality problems result from a small number of causes. Quality experts often refer to the principle as the 80/20 rule, i.e. 80% of the problems are caused by 20% of the potential sources.
4. Cause and Effect diagram
A cause and effect diagram describes a relationship between variables. The undesirable outcome is shown as the effect, & related causes are shown as leading to or potentially leading to said effect. A fishbone diagram displays all contributing factors and their relationships to the outcome to identify areas where data should be collected and analyzed.
Steps:
1. Determine the quality characteristic.
2. Write the chosen quality characteristic on the right-hand side of a sheet of paper & enclose the characteristic in a square.
3. Draw the backbone line from left to right.
4. Primary causes which directly affect the quality characteristic be drawn as big bones.
5. Secondary causes are linked by medium-sized bones.
6. Tertiary causes come from small bones.
7. Assign importance to each factor and mark particularly important factors that seem to have a significant effect on the quality characteristic.
The major areas of potential causes are shown in the main bones, e.g., Materials, Methods, Men, Measurements, Machines. Later, the sub-areas are depicted. A thorough analysis of each cause can eliminate causes one by one, and the most probable root cause can be selected for corrective action.
5. Scatter Diagram
A scatter diagram shows how two variables are related and are thus used to test for cause and effect relationships. It cannot prove that one variable causes the change in the other, only that a relationship exists and how strong it is. In a scatter diagram, the horizontal (x) axis represents the measurement values of one variable, and the vertical (y) axis represents the measurements of the second variable.
6. Stratification
Stratification is the bifurcation of data to ascertain differences. How do we classify?
1. By material: Manufacturer, buyer, brand, place of production, purchase date, lot received, production lot, components, purity, size, parts, time stored place, etc.
2. By machine, equipment, or tool: Machine type, number, model, performance, and age: by factory, line, tool, and die.
3. By operator: Individual, team, group, age, experiences, gender, etc.
4. By operating procedure and by operating conditions: Temperature, pressure, speed, rational frequency, line speed, location of operation, illumination, air temperature, humidity, weather, operating procedure, etc.
7. Control charts
A control chart displays statistically determined upper and lower limits drawn on either side of a process average. This chart shows if the collected data are within upper and lower limits previously determined through statistical calculations of raw data from earlier trials. The construction of the control chart is based on statistical principles and statistical distributions, namely normal Distribution. When used in conjunction with a manufacturing process, such charts can indicate trends and signals when a process is out of control.
The centerline of the chart represents the mean of the process; the upper and lower critical limits are also shown. The process results are examined over time and should remain within the control limits; if they do not, an investigation is held for the causes and appropriate action is taken. A control chart helps measure variability so it can be reduced as much as is economically justifiable.
