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Quality management system courses In this file, you can ref useful information about quality q uality management system courses such as quality management system coursesforms, tools for quality management system courses, quality management system coursesstrategies … If you need more assistant for quality management system courses, please leave your comment at the end of file. Other useful material for quality management system courses:
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I. Contents of quality management system courses ////////////////// I&' %((1 principles underpin all our Quality 0anagement &ystem raining ourses. 'ur courses ill help you to interpret and apply app ly the requirements of I&' %((1 quality manage management ment systems to build and maintain an effective and robust quality management system. Now that the ISO/DIS 9001:2014 has een release! we ha"e sche!ule! our first wa"e of #ulic training courses from $anuary 201%& 'ou can now oo( on the following courses: ISO 9001:201% )hat*s it all aout then+ ,re#aring for ISO 9001:201% ISO 9001:201% for Internal -u!itors
“Overall a well presented course. Delivered by an experienced and Knowledgeable tutor.” Kevin Camplin, BA !ystems he I&' %((1 ppreciation and Interpretation ourse provides an overvie of I&' %((1. It4s suitable starting point for ne management systems professionals and accessible information for
managers and staff from.ourse other teams an effective uditor ith the Q0& Internal , thentoo. ta"e5earn your ho s"illstoupbecome to the ne6t level ith our I7 -u!itor
registered -u!itor/ea! -u!itor .ourse. If you4re already an e6perienced auditor in another discipline, the I7 registered S -u!itor/ea! -u!itor .on"ersion .oursecuts don your study time from $ days to 3 days by ta"ing into account your e6isting s"ills and e6perience. hoose the New uality Systems anager course if you4re in charge of the hole management system and audit programmes.
S#ecialist uality anagement Systems raining
ic"I plus is the ne standard for softare developers and related I processes. 'ur 1 !ay quality approach, a##reciation course provides an overvie of ho to adopt the ic"I plus plus quality then ta"e the 3-S accre!ite! ic(I plus 8oundation plus 8oundation ourse for the s"ills to qualify and implement. 8or the automotive industry e provide ppreciation and Interpretation courses for an accessible overvie of ISO/S 1949 and an Internal uditor ourse to competently audit against the automotive quality standard. 9se our range of ISO ISO 1546% training courses to learn ho to apply quality management principles to the comple6 needs of the 0edical :evices industry. //////////////////
III. Quality management tools
1. Check sheet he chec" sheet is a form ;document< used to collect data in real time at the location here the data is generated. he data it captures can be b e quantitative or qualitative. =hen the information is quantitative, the chec" sheet is sometimes called a tally sheet. he defining characteristic of a chec" sheet is that data are recorded by b y ma"ing mar"s ;>chec"s>< on it. typical chec" sheet is divided into regions, and mar"s made in different regions have different significance. :ata are a re read by observing the location and number of mar"s on the sheet. hec" sheets typically employ a heading that ansers the 8ive =s? •
=ho filled out the chec" sheet
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=hat as collected ;hat each chec" represents,
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an identifying batch or lot number< =here the collection too" place ;facility, room,
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apparatus< =hen the collection too" place ;hour, shift, day of the ee"<
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=hy the data ere collected
2. Control chart
ontrol charts, also "non as &hehart charts ;after =alter =alter . &hehart< or process!behavior charts, in statistical process control are tools used to determine if a manufacturing or business process is in a state of statistical statistical control. If analysis of the control chart indicates that the process is currently under control ;i.e., is stable, stable, ith variation only coming from sources common to the process<, then no corrections or changes ch anges to process control parameters are needed or desired. In addition, data from the process can be used to predict the future performance of the process. If the chart indicates that the monitored process is not in control, analysis of the chart can help determine the sources of variation, as this ill result in degraded process p rocess performance.@1A process that is stable but operating outside of desired ;specification< limits ;e.g., scrap rates may be in statistical control but above desired limits< needs to be improved through a deliberate effort to understand the causes of current performance and fundamentally improve the process. he control chart is one of the seven basic tools of quality control.@3A ypically control charts are used for time!series data, though they can be b e used
for data that have logical comparability ;i.e. you ant to compare samples that ere ta"en all at the same time, or the performance of different individuals<, hoever the type of chart used to do this requires consideration.
5& ,areto chart
+areto chart, named after Bilfredo +areto, is a type of chart that contains both bars and a line graph, here individual values are represented in descending order by bars, and the cumulative total is represented by the line.
he left vertical a6is is the frequency of occurrence, but it can alternatively represent cost or another important unit of measure. he right vertical a6is is the cumulative percentage of the total number of occurrences, total cost, or total of the particular unit of measure. Cecause the reasons are in decreasing order, the cumulative function is a concave function. o ta"e the e6ample above, in order to loer the amount of late arrivals by D#E, it is sufficient to solve the first three issues.
he purpose of the +areto chart is to highlight the most important among a ;typically large< set of factors. In quality control, it often represents the most common sources of defects, the highest occurring type t ype of defect, or the most frequent reasons for customer complaints, and so on. =il"inson ;2((-< devised an algorithm for producing statistically based acceptance limits ;similar to confidence intervals< for each bar in the +areto chart.
). Scatter plot Method
scatter plot, scatterplot, or scattergraph is a type of mathematical diagram using artesian coordinates to display values for to variables for a set of data. da ta. he data is displayed as a collection of points, each having the value of one variable determining the position on the horiFontal a6is and the value of the other variable determining the position on the vertical a6is.@2A his "ind of plot is also called a scatter chart, scattergram, scatter diagram,@3A or scatter graph. scatter plot is used hen a variable e6ists that is under the control of the e6perimenter. If a parameter e6ists that is systematically incremented andor decremented by the other, it is called the control parameter or independent variable and is customarily plotted along the horiFontal ho riFontal a6is. he measured or dependent variable is customarily plotted along the vertical a6is. If no dependent variable e6ists, either type of variable can be plotted on either a6is and a scatter plot ill illustrate only the degree of correlation ;not causation< beteen to variables. scatter plot can suggest various "inds of correlations beteen variables ith a certain confidence interval. 8or e6ample, eight and height, eight ould be on 6 a6is and height ould be on the y a6is. orrelations may be positive ;rising<, negative ;falling<, ;falling<, or null ;uncorrelated<. If the pattern of dots slopes from loer left to upper right, it suggests a positive correlation beteen the variables being studied. If the pattern of dots slopes from upper left to loer right, it suggests a negative correlation. co rrelation. line of best fit ;alternatively called 4trendline4< can be dran in order to study the correlation beteen the variables. n equation for the correlation beteen the variables can be determined by established best!fit procedures. 8or a linear correlation, the best!fit procedure is "non as linear regression and is guaranteed to generate a correct solution in a finite time. Go universal best!fit procedure is
guaranteed to generate a correct solution for arbitrary relationships. scatter plot is also very ver y useful hen e ish to see ho to comparable data d ata sets agree ith each other. In this case, an identity line, i.e., a y/6 line, or an 1?1 line, is often dran as a reference. he more the to data sets agree, the more the scatters tend to concentrate in the vicinity of the identity lineH if the to data da ta sets are numerically identical, the scatters fall on the identity line e6actly.
%&Ishi(awa !iagram
Ishi"aa diagrams ;also called fishbone diagrams, herringbone diagrams, cause!and!effect diagrams, or 8ishi"aa< are causal diagrams created by *aoru Ishi"aa ;1%-#< that sho the causes of a specific event. @1A@2A ommon uses of the Ishi"aa diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. ach cause or reason for imperfection is a source of variation. auses are usually grouped into maor categories to identify these sources of variation. he categories typically include • •
+eople? nyone involved ith the process 0ethods? Jo the process is performed and the specific requirements for doing it, such as policies, procedures, rules, regulations and las
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0achines? ny equipment, computers, tools, etc. required to accomplish the ob
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0aterials? 7a materials, parts, pens, paper, etc. used to produce the final product
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0easurements? :ata generated from the process that are used to evaluate its quality
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nvironment? he conditions, such as location, time, temperature, and culture in hich the process operates
-. Jistogram method histogram is a graphical representation of the distribution of data. It is an estimate of the probability distribution of a continuous variable ;quantitative variable< and as first introduced by *arl +earson.@1A +ea rson.@1A o construct a histogram, the first step is to >bin> the range of values !! that is, divide the entire range of values into a series of small intervals !! and then count ho many man y values fall into each interval. rectangle is dran ith height proportional to the count and idth equal to the bin siFe, so that rectangles abut each other. histogram histogram may also be normaliFed displaying relative frequencies. It then shos the proportion of cases that fall into each of several categories, ith the sum of the heights equaling equa ling 1. he bins are usually specified as consecutive, non!overlapping intervals of a variable. he bins ;intervals< must be adacent, and usually equal siFe.@2A he rectangles of a histogram are dran so that they touch each other to indicate that the original variable is continuous.@3A
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