Basic Tools of TQM Basic Tools of TQM

Basic Tools of TQM

Basic Tools of TQM There are Seven Basic Tools of Quality control which can be used to analyze and subsequently intervene to eliminate the problems from the production system. Seven tools should be included in the list which varies a little from expert to expert. Thus alternative eight commonly found tools are listed below: 1. Check sheet 2. Stratification analysis 3. Histogram 4. Pareto analysis 5. Process flow chart 6. Cause-effect diagram 7. Scatter diagram 8. Control chart

Check sheet The check sheet, also called a “Defect Concentration Diagram” is basically a data collection sheet. It is a simple tool used to record data for further processing. The data collection sheet should be pre-printed and highly systematic and structured, such that identification of problem becomes easier.

Check sheet Buyer: ___________ Batch No. _________ Roll no. : ______________ Inspectors name __________ Approved by __________ Shift in charge Yarn Lot no. ________ Date: _______ Type of defect Checks Hole ###II 17 Horizontal stripe ##I 11 Needle line #II 7 Sinker mark IIII 4 Soil mark II 2 Total defect ####I 41 Frequency

Stratification analysis Stratification is a process of breaking down or sorting a large database so that meaningful subsets, classification or groups can be developed. For example, data can be collected analyzed by supplier, failure category, time of the day, machine, production line, customer etc. The main reason to do that is to clearly identify ‘what, when, where, how, who and why’ of any problem. Stratification, also known as run chart, is a technique used in combination with other data analysis tools. When data from a variety of sources or categories have been lumped together, the meaning of the data can be impossible to see. This technique separates the data so that patterns can be seen.

When to use: - Ø When data come from several sources or conditions, such as shifts, days of the week, suppliers or population groups. Ø When data analysis may require separating different sources or conditions.

3. Histograms: - A frequency distribution shows how often each different value in a set of data occurs. A histogram is the most commonly used graph to show frequency distribution and its pattern or shape. The shape determines its statistical nature of the collected data sets. It looks very much like a bar chart, but there are important differences between them.

3. Histograms: -

When to use When data are numerical. When the organization wants to see the shape of the data’s distribution, especially when determining whether the output of a process is distributed approximately normally. When analyzing whether a process can meet the customer’s requirements. When analyzing what the output from a supplier’s process looks like. When seeing whether the outputs of two or more processes are different. When the organization wishes to communicate the distribution of data quickly and easily to others.

Procedure to construct Collect at least 50 consecutive data points from a process. Determine the range of data (i. e. largest and smallest values) in observations. Determine cell numbers and division categories or class range desired. (e. g. 5 – 10, 11 – 16 etc. ) Calculate the midpoints of each cell or class. Usually, all cells are of equal width. Place each observation value in only one cell, and no two cell can overlap. Display the frequency of each cell in vertical (or occasionally horizontal) bars.

Pareto diagram This is a simple statistical chart, also known as Pareto diagram or Pareto analysis, but very useful in quality control. Description: A Pareto chart looks like a cumulative bar graph. The lengths of the bars represent frequency or cost (time or money) and are arranged with longest bars on the left and the shortest on the right. The longest bar represents the most vital cause. This is graphical tool for ranking causes from most significant to least significant. It depicts a series of vertical bars lined up in a descending order – from high to low – to reflect frequency, importance, or priority.

Pareto diagram Areas of complaints Wrong dimension Surface scars Broken Wrong material Others Total Frequency 49 20 15 10 6 100 observations percent 49 20 15 10 6 100

Pareto diagram

When to use When analyzing data about frequency of problems or causes in a process. When there are many problems or causes and the quality analyst wants to focus on the most significant. When analyzing broad causes by looking at their specific components. When analyzing the characteristics of the shop, production process.

Scatter diagram The scatter diagram graphs pairs of numerical data, with one variable on each axis, to look for a relationship between them. If the variables are correlated, the points will fall along a line or curve, or very close to the line. The better the correlation, the tighter the points will hug the line. The more scattered the points are, the weaker the relationship is. If the points are equally scattered on both x and y axes, then there exist no relationship between the two variables.

When to use When paired numerical data need to be compared. When trying to determine whether the two variables are related, such as when trying to identify potential root causes of problems. When depending variable may have multiple values for each value of independent variable. After brainstorming causes and effects using a fishbone diagram, to determine objectively whether a particular cause and effect are related. When determining whether two effects those appear to be related both occur with the same cause. When identification of trend for a variable is necessary. Etc

Control chart, a tool of TQM, constitute a major part of popularly known Statistical Quality Control, or Statistical Process Control (SQC/ SPC). The chart is the outgrowth of the original work of Walter A. Shewhart in the bell laboratories in 1920 s. That is why the chart is also known as ‘Schewart control chart’. This chart helps the shop in controlling the process, by analyzing whether it goes ‘outof-control’ or is ‘in-control’. An out of control situation indicates necessity of immediate intervention, in order to take it back to normal ‘in-control’ situation.

When to use Ø When controlling ongoing processes by finding and correcting problems as they occur. Ø When predicting the expected range of outcomes from a process. Ø When determining whether a process is stable (in statistical control) Ø Ø When analyzing patterns of process variation from special causes (non-routine events) or common causes (built in to the process). Ø Ø When determining whether quality improvement project should aim to prevent specific problems or to make fundamental changes to the process.