I 1. Cause Analysis Tools Tips and tools for the first step to improvement: identifying the cause of a problem or situation. 2. Evaluation and Decision-Making Tools Making informed decisions and choosing the best options with a simple, objective rating system, and determining the success of a project. 3. Process Analysis Tools How to identify and eliminate unnecessary process steps to increase efficiency, reduce timelines and cut costs. 4. Seven Basic Quality Tools These seven tools get to the heart of implementing quality principles.
5.Data Collection and Analysis Tools How can you collect the data you need, and what should you do with them once they’re collected? 6. Idea Creation Tools Ways to stimulate group creativity and organize the ideas that come from it. 7. Project Planning and Implementing Tools How to track a project’s status and look for improvement opportunities.
8.Seven New Management and Planning Tools Ways to promote innovation, communicate information and successfully plan major projects.
Use evaluation and decision-making tools when you want to narrow a group of choices to the best one, or when you want to evaluate how well you’ve done something. This includes evaluating project results.
Decision matrix: Evaluates and prioritizes a list of options, using pre-determined weighted criteria.
Multivoting: Narrows a large list of possibilities to a smaller list of the top priorities or to a final selection; allows an item that is favored by all, but not the top choice of any, to rise to the top.
When you want to understand a work process or some part of a process, these tools can help:
Flowchart: A picture of the separate steps of a process in sequential order, including materials or services entering or leaving the process (inputs and outputs), decisions that must be made, people who become involved, time involved at each step and/or process measurements.
Failure modes and effects analysis (FMEA): A step-by-step approach for identifying all possible failures in a design, a manufacturing or assembly process, or a product or service; studying the consequences, or effects, of those failures; and eliminating or reducing failures, starting with the highest-priority ones.
Mistake-proofing: The use of any automatic device or method that either makes it impossible for an error to occur or makes the error immediately obvious once it has occurred.
4. Histogram: The most commonly used graph for showing frequency distributions, or how often each different value in a set of data occurs.
5.Pareto chart: Shows on a bar graph which factors are more significant.
6.Scatter diagram: Graphs pairs of numerical data, one variable on each axis, to look for a relationship.
7.Stratification: A technique that separates data gathered from a variety of sources so that patterns can be seen (some lists replace "stratification" with "flowchart" or "run chart").
Use the following tools to collect or analyze data:
A generic tool that can be adapted for a wide variety of purposes, the check sheet is a structured, prepared form for collecting and analyzing data.
2. Control chart:
A graph used to study how a process changes over time. Comparing current data to historical control limits leads to conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).
3. Design of experiments:
A method for carrying out carefully planned experiments on a process. Usually, design of experiments involves a series of experiments that start by looking broadly at a great many variables and then focus on the few critical ones.
Use tools like these when you want to come up with new ideas or organize many ideas:
1. Affinity diagram:
Organizes a large number of ideas into their natural relationships.
A structured process for comparing your organization’s work practices to the best similar practices you can identify in other organizations, and then incorporating the best ideas into your own processes.
A method for generating a large number of creative ideas in a short period of time.
4. Nominal group technique:
A structured method for group brainstorming that encourages contributions from everyone.
In 1976, the Union of Japanese Scientists and Engineers (JUSE) saw the need for tools to promote innovation, communicate information and successfully plan major projects. A team researched and developed the seven new quality control tools, often called the seven management and planning (MP) tools, or simply the seven management tools. Not all the tools were new, but their collection and promotion were.
The seven MP tools, listed in an order that moves from abstract analysis to detailed planning, are:
shows the relationship between two, three or four groups of information and can give information about the relationship, such as its strength, the roles played by various individuals, or measurements.
5. Matrix data analysis :
a complex mathematical technique for analyzing matrices, often replaced in this list by the similar prioritization matrix. One of the most rigorous, careful and time-consuming of decision-making tools, a prioritization matrix is an L-shaped matrix that uses pairwise comparisons of a list of options to a set of criteria in order to choose the best option(s).
6. Arrow diagram:
shows the required order of tasks in a project or process, the best schedule for the entire project, and potential scheduling and resource problems and their solutions.
7. Process decision program chart (PDPC):
systematically identifies what might go wrong in a plan under development.
Other symbols can be used for different specific types of processes; for example, a description of a software program might contain specific symbols for input/output, storage, and so forth. The user of flow charts should be able to utilize any symbols that are needed to represent the processes that are being dealt with.
this tool is referred to by several different names: Ishikawa diagram, Cause and Effect diagram, Fishbone and Root Cause Analysis. These names all refer to the same tool.
The first name is after the inventor of the tool - K. Ishikawa (1969) who first used the technique in the 1960s.
Cause and Effect also aptly describes the tool, since the tool is used to capture the causes of a particular effect and the relationships between cause and effect. The term fishbone is used to describe the look of the diagram on paper. The basic use of the tool is to find root causes of problems; hence, this last name.
The Ishikawa diagram, like most quality tools, is a visualization and knowledge organization tool. Simply collecting the ideas of a group in a systematic way facilitates the understanding and ultimate diagnosis of the problem. Several computer tools have been created for assisting in creating Ishikawa diagrams. A tool created by the Japanese Union of Scientists and Engineers (JUSE) provides a rather rigid tool with a limited number of bones. Other similar tools can be created using various commercial tools.
Only one tool has been created that adds computer analysis to the fishbone.
Bourne et al. (1991) reported using Dempster-Shafer theory (Shafer and Logan, 1987) to systematically organize the beliefs about the various causes that contribute to the main problem.
Based on the idea that the main problem has a total belief of one, each remaining bone has a belief assigned to it based on several factors; these include the history of problems of a given bone, events and their causal relationship to the bone, and the belief of the user of the tool about the likelihood that any particular bone is the cause of the problem
The control chart is probably the best known and best understood quality tool.
Control charts are statistically based.
In brief, the concept is that processes have statistical variation. One must assess this variation to determine if a process is operating between expected boundaries or if something has happened that has caused the process to go 'out-of-control.'
The concept of the control chart is to measure a variation, taking repeated samples, and calculate control limits (upper and lower). If any point exceeds these limits, there may be cause to consider making an adjustment (or at least watching the process more closely).
Histograms are built to examine characteristics of variation and provide an excellent visualization tool for stochastically varying data. Consider tabulating a set of data secured from a manufacturing process in which the width of a particular component is repeatedly measured.
Another visualization that is similar to the histogram is the bar chart .
Bar charts look like histograms; however, the abscissa is not a range of cells, but different labels which can represent almost anything.
For example, consider counting the numbers of yellow, blue, and red items passing by on a conveyor belt. The count of each item could be displayed as vertical bars with the height of each bar corresponding to the count of each item.
histograms and bar charts are distinctly different entities. I
if one creates cells, for counting occurrences of items, one can effectively simulate a histogram by use of a bar chart. This property makes it easy to generate histograms using standard plotting packages.
Scatter plots are representations of two or more variables plotted against each other.
The utility of the scatter plot for quality assessment is the determination by measuring variables in a process to see if any two or more variables are correlated or uncorrelated.
This information can be useful in the 'relations' diagram discussed below. The specific utility of finding correlations is to infer causal relationships among variables and ultimately find root causes of problems. The scatter plot is simply one of the tools which can contribute additional small amounts of information to a quality assessment.
The basic idea in stratification is that data that are examined may be secured from sources with different statistical characteristics.
For example, consider that a measurement of the width of a particular part in a manufacturing assembly may be influenced by two different machines, for example a cutting machine and a polishing machine. Each machine will contribute to variations in width of the final product, but with potentially a different statistical variation.
Consider Figure in which two distributions are displayed, labeled Dist 1 and Dist 2, which could be distributions of variations in a part measurement due to each individual machine. Note that Dist 1 has a mean (highest peak) at a lower value than Dist 2. The third set of bars shows the sum of the two distributions, in our example, the measurement of the final product. This final distribution is a smeared histogram which yields little information about differences in variation that are contributed by the two machines in our example. The information that we have collected should be stratified, that is, separated so that these important individual characteristics can be observed.
A check sheet is a simple means of data collection. The most straightforward check sheet is simply to make a list of items that you expect will appear in a process and to mark a check beside each item when it does appear. This type of data collection can be used for almost anything from checking off the occurrence of particular types of defects or the counting of expected items (e.g., the number of times the telephone rings before being answered). Check sheets can be directly coupled to histograms to provide a direct visualization of the information collected. Various innovations in check sheets are possible;
for example, the figure above , in which a map of the U.S. is shown.
The idea in this check sheet is for the user to simply mark on the map the location of each sale that is made.
The check sheet, if computer-based, simply adds the number of checks on the map and produces a tally in bins at the bottom of the sheet that provides bar-chart-ready information about total sales in different regions of the country.
This concept is frequently used in check sheets for determining defects in automobiles. For example, an image of the exterior of the body of an automobile is presented on a page, and the user can simply check the location at which a defect is located.
Which of these tools are most important? That is difficult to say; however, it is possible to comment on which tools are used most often in practice.
The control chart is probably most widely used of any of these tools, simply for historical reasons. Rooted in the practice of statistical process control, the use of control charts has become widely used in North American industry.
The fishbone appears to be the most popular tool for use among workers that are not statistically inclined. The check sheet can be used for tabulating information, but is otherwise not a very sophisticated tool.
Use of histograms, stratification, and scatter plots appears to be less prevalent, simply because the average worker using the tools does not have the necessary statistical background to interpret the results of collecting and analyzing data in this way.
This is particularly true of the stratification chart, which requires making analytical judgments and decisions about how to segment data being collected, a task that is not easy for the average worker on an industrial team.