Cause-and-effect diagrams

Fig. 4. Cause and effect diagram of off-standard polymer production.

The seventh tool is the scatter or correlation diagram also known as an XY plot (50). This plot of one variable vs another is most useful in confirming interrelationships. Thus, scatter diagrams can verify the relationships shown in the cause and effect diagram. 

Q7 PROCESS CHART. PARETO ANALYSIS, CAUSE AND EFFECT DIAGRAM, HISTOGRAM, CORRELATION DIAGRAMS, PROCESS CONTROL CHARTS, CHECK SHEETS... 

The so-called Q7 tools and techniques, Cause and Effect Diagrams, Pareto Analysis, etc. (Bicheno, 1994 Dale and McQuater, 1998 Straker, 1995), are applicable to any stage of the product development process. Indeed they support the working of some of the techniques mentioned, for example using a Pareto chart for prioritizing the potential risks in terms of the RPN index for a design as determined in FMEA (see Appendix III). 

The marketing information primarily identifies either problems or opportunities. Problems will relate to your existing products and services and should indicate why there has been a decline in sales or an increase in returns. In order to solve these problems a search for possible causes should be conducted and one valid method for doing this is to use the Cause and Effect Diagram. Opportunities will relate to future products and services and should indicate unsatisfied wants. There are three ways of collecting such data by observation, survey, and experiment. 

Cause and effect diagrams - used to analyze the characteristics of a process or situation... 

Statistical techniques can be used for a variety of reasons, from sampling product on receipt to market analysis. Any technique that uses statistical theory to reveal information is a statistical technique, but not all applications of statistics are governed by the requirements of this part of the standard. Techniques such as Pareto Analysis and cause and effect diagrams are regarded as statistical techniques in ISO 9000-2 and although numerical data is used, there is no probability theory involved. These techniques are used for problem solving, not for making product acceptance decisions. 

Fishbone Diagrams are cause-and-effect diagrams used in quality management to help describe all the activities that can influence the management process and its outcome. These diagrams show the relationship between different activities and how they are grouped around specific types of activity. 

A cause and effect diagram (sometimes known as the Ishikawa"" or the fishbone diagram"") represents the relationships between a given effect and its potential causes. The cause and effect analysis relates the interactions among the factors affecting a process. 

The cause and effect diagram is widely used when identifying the effects on a result, including a chemical analysis result. It is used for example in measurement uncertainty to analyse the uncertainty sources. A cause and effect diagram describes a relationship between variables. The undesirable outcome is shown as an effect, and related causes are shown as leading to, or potentially leading to, this effect. 

Cause-and-Effect Diagram A cause-and-effect diagram relates potential causes of a problem to their effects. This is a tool that could be very useful in diagnosing a process. It focuses on the possible causes of a specific problem in a structured and systematic way. The following steps are suggested for constructing a cause-and-effect diagram ... 

Figure 1 shows a cause-and-effect diagram which is used to identify causes to yield a problem in a biopharmaceutical manufacturing process. Possible main causes and subcauses are identified. Once the causes are identified, other tools are employed to determine the contribution of various causes to the effect. Actions are taken to eliminate or minimize the impact of these causes. 

The use of cause-and-effect diagrams is highly recommended as a tool for structured thinking about a problem. A chemical example is given by Meinrath and Lis (2002). 

Figure 4.7. Cause-and-effect diagram for a problem with a liquid chromatographic analysis.

To construct a cause-and-effect diagram of uncertainty sources from the information contained in the procedures and equations of an analytical method, follow these steps. First, draw a horizontal right-facing arrow in the middle of a sheet of paper. Label the arrow end with the symbol for the measurand. Starting from the sources identified by the equation for the value of the measurand, draw arrows to this line at about 45°, one for each of the quantities in your equation plus any other sources identified that are not already counted, plus one for repeatability. Label the start of each arrow with a symbol for the quantity. Figure 6.3 shows a draft cause-and-effect diagram for the purity of the acid. 

Figure 6.3. First draft of a cause-and-effect diagram for the measurement of the purity of a sample of potassium hydrogen phthalate. See text for description of symbols.

Figure 6.4. Second round of a cause-and-effect diagram for the measurement of the purity of a sample of potassium hydrogen phthalate. T is temperature, and Cal is the calibration of the volume. The molar mass of potassium hydrogen phthalate and the mass of potassium hydrogen phthalate dissolved for analysis are excluded from the diagram because they have negligible uncertainties.

I have already suggested that the analyst might have some idea about the magnitude of the uncertainties as the cause-and-effect diagram evolves. Obviously, minor components such as the uncertainties of molar masses can usually be assessed and omitted at this early stage. The analyst should have a feel for typical uncertainties of masses, volumes, and temperatures, some of which are discussed below. 

Once the uncertainty components have been identified and quantified as standard uncertainties, the remainder of the procedure to estimate uncertainty is a somewhat complicated but mostly straightforward. Most software products on the market will perform this task. Otherwise, some spreadsheet manipulation or mathematics must be done to reach the uncertainty. The combined standard uncertainty of a result is obtained by mathematical manipulation of the standard uncertainties as part of the uncertainty budget. These standard uncertainties may also be combinations of other uncertainties, and so on, as the branches and sub-branches of the cause-and-effect diagram are worked through. A combined standard uncertainty of a quantity is written uc(y). 

Key documents for the technical definition of the process are the flow diagram, the cause-and-effect diagram, and the influence matrix. The details of the cause-and-effect diagram and the influence matrix will be discussed under experimental approach in a later section. 

An efficient representation of complex relationships between many process and formulation variables (causes), and a single response (effect) can be shown by using a cause-and-effect diagram [1], Figure 4 is a simple example. 

A central arrow in Figure 4 points to a particular single effect. Branches off the central arrow lead to boxes representing specific process steps. Next, principle factors of each process step that can cause or influence the effect are drawn as subbranches of each branch, until a complete cause-and-effect diagram is developed. This should be as detailed a summary as possible. An example of a more complex cause-and-effect diagram is illustrated in Figure 5. A separate summary for each critical product characteristic (e.g., weight variation, dissolution, friability) should be made. 

Figure 5 Cause-and-effect diagram (granulated product). 

QC tools will be used, such as check sheets, histograms, Pareto diagrams, and cause-and-effect diagrams. Scatter diagrams and control charts will be provided, where appropriate, for in-process attributes and finished-product data as an attachment. 

Figure B19. Cause and Effect Diagram (Ishikawa, Fishbone)...

Cause and effect diagram. This is also known as the Ishikawa diagram. This was introduced as a formal way of representing a specific effect and the possible causes that influence this effect. They are used to display all the possible causes of a specific problem and are usually constructed around two sets of four basic causes. 

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