tcb_roadmap_to__qualitiy_vol1

Section managers appraise the results of implemented

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Unformatted text preview: xamine the actual spot where the problem occurred. b. Decide on the quality characteristics that the evaluation of quality will be based on. (When these are converted into quantitative data they are called characteristic values.) c. Formulate clearly the objectives for which the data is being collected. d. Gather accurate data. e. Analyse this data using statistical techniques. f. Obtain accurate information from this analysis. Discussion The following questions ask you to think about how the ideas in the text could be applied in your company. Some of the ideas may not be relevant to you. Concentrate on what is relevant. Keep notes of your conclusions – you will need them to prepare your action plan afterwards. Where appropriate ask yourself the RADAR questions. Note: Always include in your discussion any figures referred to in the text, if you feel these are relevant to your company. a. Parag. 1: To what extent would you say that the approach to problem solving in your company is based on experience, and to what extent on quantified facts? Would you like to see the balance changed? b. Parag. 2 suggests six steps for establishing the facts. Apply the RADAR questions to these. Action plan Take the ideas you have found useful in the text, and in your discussion, and present them in a well-structured action plan for introducing improvements in your company. You might like to follow the 6-Point Structure. Alternatively you may choose to prepare one action plan when you have discussed several texts. A Roadmap to Quality 19 Unit 9 - Problem solving 05-87581_unit 9.qxd 09/09/2005 11:49 Page 20 9.7 Managing dispersion 1. Once data has been collected, it has to be interpreted. Averages are the most common way of interpreting data, but they often fail to give a true picture of what the data means. Measuring how the data is dispersed gives a more complete picture. Dispersion refers to how the different items of data are spread out or scattered in relation to how they are supposed to be, i.e. in relation to the standard or target values. For example, a residential street with 10 houses with an average price of $240,000 and where each price differs only a little from the average, would be very different from a street with the same average house price, but with 2 houses valued at $1 million and the other 8 each costing around $50,000. 2. The first thing to do when the data seems to indicate a problem is to clarify whether it is the average or the dispersion that indicates that there is a problem. Otherwise it is impossible to solve the problem. Problems that are indicated by averages of the data can be solved relatively easily. Just review the processing conditions and any other factors that affect the results. When problems are indicated by dispersion, base your countermeasures on whether: a. The range of dispersion (the distance of the maximum and minimum data points) from the standard or target values (also referred to as the technical standard) is acceptable, but the average is skewed (distorted or biased). b. The range of dispersion is too wide. c. There are outliers (An outlier is an item of data, or a value, that falls well outside the dispersion range of the rest of the data). 3. Dispersion may be due to chance or to abnormalities. There will always be some dispersion even when the materials and work methods are those prescribed by the standards. It cannot be avoided. This type of chance dispersion stays within a certain range. The values tend to form a bell curve, with the average in the centre. This pattern is known as normal distribution. 4. Dispersion caused by abnormalities may result from the following factors: a. Employees do not follow the operational standards. b. There are changes in materials. c. Inexperienced employees replace experienced employees. These factors skew the average and cause outliers. 5. Dispersion in the quality of a product results from the dispersion of something in the manufacturing process that is strongly related to quality. This dispersion provides a good opportunity to find out the causes of such problems. It indicates that the cause of the problem is strongly related to the results. Such a cause can be identified by searching for any divergent factors (factors that are different from what they should be) and examining their correlation to the dispersed results. Unit 9 - Problem solving 20 A Roadmap to Quality 05-87581_unit 9.qxd 09/09/2005 11:54 Page 21 6. The three charts in Figure 9.7a, Quality Dispersion, give examples of problems indicated by the average of the data, by the dispersion of the data, and by outliers. Figure 9.7a Quality dispersion Causal relationship Technical standards Technical standards Problem concerning average values Problem solution through the movement of average values Technical standards Observation Dispersive characteristics Confirmation of dispersive characteristics Occurs to factors showing high contribution ratios Identification of causes through the analysis of dispersive factors that correspond to dispersive characteristi...
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