3 Pareto DiagramPareto charts are a type of bar chart in which the horizontal axis represents categories of interest, rather than a continuous scale. The categories are often “defects.”This tool is based on the idea that the majority of defects are caused by few defective item, which classifies the quality problem into the “vital few” and “trivial many” (80-20 rule).A cumulative percentage line helps you judge theadded contribution of each category.Pareto charts can help to focus improvement efforts onareas where the largest gains can be made.
4 Figure 1 : Example of Pareto diagram 80-20 ruleFigure 1 : Example of Pareto diagram
5 Procedure1. Decide on the problem to be addressed or items to study and collect data.2. Decide also the period for which the data is to be collected.3. Arrange the data in order of decreasing size.4. Calculate the cumulative number and percentage.5. Draw horizontal and vertical axes on graph paper.6. Draw the bar graph.7. Draw the vertical axis on the right edge and scale it.8. Draw the cumulative curve.Complete the diagram with titles and units of reference.( Figure 1)
6 Cause and Effect Diagram The cause and effect diagram analysis was first developed by Professor Kaoru Ishikawa of the University of Tokyo in the 1940s’, is also known as the ‘Fishbone Diagram’ or the ‘Ishikawa Diagram’.His first application of this technique was in the Fulsai iron work Due to its’ final form, some people called it the “Fishbone Diagram”.This tool is a picture of lines and symbols designed to represent the relationship between the effects as problems and the causes influencing them.There is no “correct” way to construct a fishbone diagram, some types lend themselves well to many different situations.
7 Figure 2 : Example of Cause and Effect Diagram BackboneFactorMiddle boneSmall boneEffectFigure 2 : Example of Cause and Effect Diagram
8 Uses of Ishikawa Diagram 1. To recognize important causes2. To understand all effects and causes3. To compare operational procedures4. To find major solutions5. To figure out, what to do?6. To improve the process
9 Procedure1. State the problem as precisely as possible and draw the back bone.2. Draw the large bone.3. Get all members involved by participating in the brainstorming session to obtain as many ideas as possible.4. The ideas collected are then critically examined to classify them into the main grouping and subsequent grouping (middle bone, small bone and fine bone). (Figure 2)5. Dram the middle bones, small bones and fine bones.6. Check to see whether any causes have been left.7. Identify the important causes by members vote, proper analysis of data and Pareto diagram.8. Fill in all related information such title, product, process, etc..
10 GraphGraph refer to the results of statistical analysis of data (numbers) which are shown in diagrammatic form to communicate information.There are numerous types of graphs as listed are commonly use;a. Bar graphb. Line graphc. Radar graphf. Pie graphEach of above graphs is applicable based on analysis requirement.
12 Check SheetCheck sheets are sheets that are design in advance to collect the necessary data easily and systematically, which allow the efficient checking of all items for inspection and verification.
13 Procedure 1. Specify the aim of collecting data 2. Decide on the item to be check3. Decide on the method for stratification4. Format the check sheet5. Analyze the data6. Make clear the causes7. Implementation of counter measure8. Grasp the effect9. Standardization of operations to practice the new and improves method properly.
14 Scatter DiagramScatter diagram is a diagram where the relationship between two characteristic value are plotted and analyze as to whether a correlation exists between the two set of data.Several types of correlation could be found from scatter diagram are;1. Positive strong correlation2. Negative strong correlation3. Positive moderate correlation4. Negative moderate correlation5. Absence of correlation
15 Figure 3 : Example of Scatter Diagram (Positive strong correlation)
16 Procedure 1. Collect and count the number of data 2. Determine the largest (L) and smallest (S) value of data3. Select number of classes (bars)Number of Data Number of Classes (K)4. Find class interval (H)H = L - S / K5. Determine starting point of classes6. Calculate mid value of each class (half of the measurement unit)7. Count frequency of data8. Prepare the histogram
17 HistogramA histogram is a vertical bar chart that depicts the distribution of a set of data.It is a useful tool to study the dispersion of data and analyze certain quality characteristic of the product or service to which the data in histogram refers.A histogram does not reflect the process behavior over time
19 Procedure 1. Collect and count the number of data 2. Determine the largest (L) and smallest (S) value of data3. Select number of classes (bars)Number of Data Number of Classes (K)4. Find class interval (H)H = L - S / K5. Determine starting point of classes6. Calculate mid value of each class (half of the measurement unit)7. Count frequency of data8. Prepare the histogram
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