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I. Contents of quality management system course ho should ta!e this course 0hose requiring an introduction to the I%& $''1 family of standards, especially those involved in the design, implementation and management of a %. 0he successful completion of this course is a requirement for certification as an internal auditor on the% scheme. 0opic areas 0he process approach 0he eight quality management principles 0he requirements of I%& $''1 ourse duration &ne day or 2' hours by distance learning
Internal % auditor course ho should ta!e this course 0hose -ho audit an organi4ation5s processes as part of the internal audit programme.
0he successful completion of this course is a requirement for certification as an internal auditor on the% scheme. 0opic areas 6o- to plan an audit 6o- to audit a process 6o- to report an audit ourse duration 0-o days
% auditor / lead auditor course ho should ta!e this course 0hose intending to acquire the competence to audit an organi4ation5s I%& $''1based management system, either as third or second party audit. 0he successful completion of this course is a requirement for certification as an auditor on the % scheme. 0opic areas 0he process approach to quality management manag ement systems and auditing 0he eight quality management principles 0he requirements of I%& $''1 7and guidance in the accompanying document $''(8 6o- to plan, complete and report the audit of an entire quality management man agement system ourse duration 9ive days plus a t-ohour -ritten e:amination
III. Quality management tools
1. Check sheet
0he chec! sheet is a form 7document8 used to collect data in real time at the location -here the data is generated. 0he data it captures can be b e quantitative or qualitative. hen the information is quantitative, the chec! sheet is sometimes called a tally sheet. 0he defining characteristic of a chec! sheet is that data are recorded by b y ma!ing mar!s 7;chec!s;8 on it. < typical chec! sheet is divided into regions, and mar!s made in different regions have different significance. =ata a re read by observing the location and number of mar!s on the sheet. hec! sheets typically employ a heading that ans-ers the 9ive s>
•
ho filled out the chec! sheet hat -as collected 7-hat each chec! represents,
•
an identifying batch or lot number8 here the collection too! place 7facility, room,
•
apparatus8 hen the collection too! place 7hour, shift, day of
•
the -ee!8 hy the data -ere collected
•
2. Control chart
ontrol charts, also !no-n as %he-hart charts 7after alter alter <. %he-hart8 or processbehavior 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 7i.e., is stable, stable, -ith variation only coming from sources common to the process8, 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.?1@ < process that is stable but operating outside of desired 7specification8 limits 7e.g., scrap rates may be in statistical control but above desired limits8 needs to be improved through a deliberate effort to understand the causes of current performance and fundamentally improve the process. 0he control chart is one of the seven basic tools of quality control.?3@ 0ypically control charts are used for timeseries data, though they can be b e used for data that have logical comparability 7i.e. you -ant to compare samples that -ere ta!en all at the same time, or the performance of different individuals8, ho-ever the type of chart used to do this requires consideration.
3. Pareto chart
< *areto chart, named after Ailfredo *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. 0he left vertical a:is is the frequency of occurrence, but it can alternatively represent cost or another important unit of measure. 0he right vertical a:is is the cumulative percentage of the total number of occurrences, total cost, or total of the particular unit of measure. Because the reasons are in decreasing order, the cumulative function is a concave function. 0o ta!e the e:ample above, in order to lo-er the amount of late arrivals by C"D, it is sufficient to solve the first three issues. 0he purpose of the *areto chart is to highlight the most important among a 7typically large8 set of factors. In quality control, it often represents the most common sources of defects, the highest occurring t ype of defect, or the most frequent reasons for customer complaints, and so on. il!inson 72''8 devised an algorithm for producing statistically based acceptance limits 7similar to confidence intervals8 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 t-o variables for a set of da ta. 0he data is displayed as a collection of points, each having the value of one variable determining the position on the hori4ontal a:is and the value of the other variable determining the position on the vertical a:is.?2@ 0his !ind of plot is also called a scatter chart, scattergram, scatter diagram,?3@ or scatter graph. < scatter plot is used -hen a variable e:ists that is under the control of the e:perimenter. If a parameter e:ists that is systematically incremented and/or decremented by the other, it is called the control parameter or independent variable and is customarily plotted along the ho ri4ontal a:is. 0he measured or dependent variable is customarily plotted along the vertical a:is. If no dependent variable e:ists, either type of variable can be plotted on either a:is and a scatter plot -ill illustrate only the degree of correlation 7not causation8 bet-een t-o variables. < scatter plot can suggest various !inds of correlations bet-een variables -ith a certain confidence interval. 9or e:ample, -eight and height, -eight -ould be on : a:is and height -ould be on the y a:is. orrelations may be positive 7rising8, negative 7falling8, 7falling8, or null 7uncorrelated8. If the pattern of dots slopes from lo-er left to upper right, it suggests a positive correlation bet-een the variables being studied. If the pattern of dots slopes from upper left to lo-er right, it suggests a negative co rrelation. < line of best fit 7alternatively called 5trendline58 can be dra-n in order to study the correlation bet-een the variables. <n equation for the correlation bet-een the variables can be determined by established bestfit procedures. 9or a linear correlation, the bestfit procedure is !no-n as linear regression and is guaranteed to generate a correct solution in a finite time. Eo universal bestfit procedure is guaranteed to generate a correct solution for arbitrary relationships. < scatter plot is also ver y useful -hen -e -ish to see ho- t-o comparable data d ata sets agree -ith each
other. In this case, an identity line, i.e., a y: line, or an 1>1 line, is often dra-n as a reference. 0he more the t-o data sets agree, the more the scatters tend to concentrate in the vicinity of the identity lineF if the t-o da ta sets are numerically identical, the scatters fall on the identity line e:actly.
5.Ishikawa diagram
Ishi!a-a diagrams 7also called fishbone diagrams, herringbone diagrams, causeandeffect diagrams, or 9ishi!a-a8 are causal diagrams created by )aoru Ishi!a-a 71$"8 that sho- the causes of a specific event. ?1@?2@ ommon uses of the Ishi!a-a diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. Gach cause or reason for imperfection is a source of variation. auses are usually grouped into ma+or categories to identify these sources of variation. 0he categories typically include • •
*eople> <nyone involved -ith the process ethods> 6o- the process is performed and the specific requirements for doing it, such as policies, procedures, rules, regulations and la-s
•
achines> <ny equipment, computers, tools, etc. required to accomplish the +ob
•
aterials> Ha- materials, parts, pens, paper, etc. used to produce the final product
•
easurements> =ata generated from the process that are used to evaluate its quality
•
Gnvironment> 0he conditions, such as location, time, temperature, and culture in -hich the process operates
. 6istogram method
< histogram is a graphical representation of the distribution of data. It is an estimate of the probability distribution of a continuous variable 7quantitative variable8 and -as first introduced by )arl *earson.?1@ *ea rson.?1@ 0o 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- man y values fall into each interval. < rectangle is dra-n -ith height proportional to the count and -idth equal to the bin si4e, so that rectangles abut each other. < histogram histogram may also be normali4ed displaying relative frequencies. It then sho-s the proportion of cases that fall into each of several categories, -ith the sum of the heights equa ling 1. 0he bins are usually specified as consecutive, nonoverlapping intervals of a variable. 0he bins 7intervals8 must be ad+acent, and usually equal si4e.?2@ 0he rectangles of a histogram are dra-n so that they touch each other to indicate that the original variable is continuous.?3@
III. Other topics related to quality management system course (pdf download) quality management systems quality management courses quality management tools iso $''1 quality management system quality management process quality management system e:ample quality system management quality management techniques quality management standards quality management policy quality management strategy quality management boo!s
• qualitymanagement123.com/23freeeboo!sforqualitymanagement • qualitymanagement123.com/1"#freequalitymanagementforms • qualitymanagement123.com/free$"I%&$''1templatesandform qualitymanage ment123.com/free$"I%&$''1templatesandformss • qualitymanagement123.com/top"(qualitymanagement)*Is • qualitymanagement123.com/top1"qualitymanagement+obdescriptions • qualitymanagement123.com/"qualitymanagementintervie-questionsandans-er qualitymanage ment123.com/"qualitymanagementintervie-questionsandans-erss
I. Contents of quality management system course ho should ta!e this course 0hose requiring an introduction to the I%& $''1 family of standards, especially those involved in the design, implementation and management of a %. 0he successful completion of this course is a requirement for certification as an internal auditor on the% scheme. 0opic areas 0he process approach 0he eight quality management principles 0he requirements of I%& $''1 ourse duration &ne day or 2' hours by distance learning
Internal % auditor course ho should ta!e this course 0hose -ho audit an organi4ation5s processes as part of the internal audit programme.
0he successful completion of this course is a requirement for certification as an internal auditor on the% scheme. 0opic areas 6o- to plan an audit 6o- to audit a process 6o- to report an audit ourse duration 0-o days
% auditor / lead auditor course ho should ta!e this course 0hose intending to acquire the competence to audit an organi4ation5s I%& $''1based management system, either as third or second party audit. 0he successful completion of this course is a requirement for certification as an auditor on the % scheme. 0opic areas 0he process approach to quality management manag ement systems and auditing 0he eight quality management principles 0he requirements of I%& $''1 7and guidance in the accompanying document $''(8 6o- to plan, complete and report the audit of an entire quality management man agement system ourse duration 9ive days plus a t-ohour -ritten e:amination
III. Quality management tools
1. Check sheet
0he chec! sheet is a form 7document8 used to collect data in real time at the location -here the data is generated. 0he data it captures can be b e quantitative or qualitative. hen the information is quantitative, the chec! sheet is sometimes called a tally sheet. 0he defining characteristic of a chec! sheet is that data are recorded by b y ma!ing mar!s 7;chec!s;8 on it. < typical chec! sheet is divided into regions, and mar!s made in different regions have different significance. =ata a re read by observing the location and number of mar!s on the sheet. hec! sheets typically employ a heading that ans-ers the 9ive s>
•
ho filled out the chec! sheet hat -as collected 7-hat each chec! represents,
•
an identifying batch or lot number8 here the collection too! place 7facility, room,
•
apparatus8 hen the collection too! place 7hour, shift, day of
•
the -ee!8 hy the data -ere collected
•
2. Control chart
ontrol charts, also !no-n as %he-hart charts 7after alter alter <. %he-hart8 or processbehavior 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 7i.e., is stable, stable, -ith variation only coming from sources common to the process8, 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.?1@ < process that is stable but operating outside of desired 7specification8 limits 7e.g., scrap rates may be in statistical control but above desired limits8 needs to be improved through a deliberate effort to understand the causes of current performance and fundamentally improve the process. 0he control chart is one of the seven basic tools of quality control.?3@ 0ypically control charts are used for timeseries data, though they can be b e used for data that have logical comparability 7i.e. you -ant to compare samples that -ere ta!en all at the same time, or the performance of different individuals8, ho-ever the type of chart used to do this requires consideration.
3. Pareto chart
< *areto chart, named after Ailfredo *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. 0he left vertical a:is is the frequency of occurrence, but it can alternatively represent cost or another important unit of measure. 0he right vertical a:is is the cumulative percentage of the total number of occurrences, total cost, or total of the particular unit of measure. Because the reasons are in decreasing order, the cumulative function is a concave function. 0o ta!e the e:ample above, in order to lo-er the amount of late arrivals by C"D, it is sufficient to solve the first three issues. 0he purpose of the *areto chart is to highlight the most important among a 7typically large8 set of factors. In quality control, it often represents the most common sources of defects, the highest occurring t ype of defect, or the most frequent reasons for customer complaints, and so on. il!inson 72''8 devised an algorithm for producing statistically based acceptance limits 7similar to confidence intervals8 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 t-o variables for a set of da ta. 0he data is displayed as a collection of points, each having the value of one variable determining the position on the hori4ontal a:is and the value of the other variable determining the position on the vertical a:is.?2@ 0his !ind of plot is also called a scatter chart, scattergram, scatter diagram,?3@ or scatter graph. < scatter plot is used -hen a variable e:ists that is under the control of the e:perimenter. If a parameter e:ists that is systematically incremented and/or decremented by the other, it is called the control parameter or independent variable and is customarily plotted along the ho ri4ontal a:is. 0he measured or dependent variable is customarily plotted along the vertical a:is. If no dependent variable e:ists, either type of variable can be plotted on either a:is and a scatter plot -ill illustrate only the degree of correlation 7not causation8 bet-een t-o variables. < scatter plot can suggest various !inds of correlations bet-een variables -ith a certain confidence interval. 9or e:ample, -eight and height, -eight -ould be on : a:is and height -ould be on the y a:is. orrelations may be positive 7rising8, negative 7falling8, 7falling8, or null 7uncorrelated8. If the pattern of dots slopes from lo-er left to upper right, it suggests a positive correlation bet-een the variables being studied. If the pattern of dots slopes from upper left to lo-er right, it suggests a negative co rrelation. < line of best fit 7alternatively called 5trendline58 can be dra-n in order to study the correlation bet-een the variables. <n equation for the correlation bet-een the variables can be determined by established bestfit procedures. 9or a linear correlation, the bestfit procedure is !no-n as linear regression and is guaranteed to generate a correct solution in a finite time. Eo universal bestfit procedure is guaranteed to generate a correct solution for arbitrary relationships. < scatter plot is also ver y useful -hen -e -ish to see ho- t-o comparable data d ata sets agree -ith each
other. In this case, an identity line, i.e., a y: line, or an 1>1 line, is often dra-n as a reference. 0he more the t-o data sets agree, the more the scatters tend to concentrate in the vicinity of the identity lineF if the t-o da ta sets are numerically identical, the scatters fall on the identity line e:actly.
5.Ishikawa diagram
Ishi!a-a diagrams 7also called fishbone diagrams, herringbone diagrams, causeandeffect diagrams, or 9ishi!a-a8 are causal diagrams created by )aoru Ishi!a-a 71$"8 that sho- the causes of a specific event. ?1@?2@ ommon uses of the Ishi!a-a diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. Gach cause or reason for imperfection is a source of variation. auses are usually grouped into ma+or categories to identify these sources of variation. 0he categories typically include • •
*eople> <nyone involved -ith the process ethods> 6o- the process is performed and the specific requirements for doing it, such as policies, procedures, rules, regulations and la-s
•
achines> <ny equipment, computers, tools, etc. required to accomplish the +ob
•
aterials> Ha- materials, parts, pens, paper, etc. used to produce the final product
•
easurements> =ata generated from the process that are used to evaluate its quality
•
Gnvironment> 0he conditions, such as location, time, temperature, and culture in -hich the process operates
. 6istogram method
< histogram is a graphical representation of the distribution of data. It is an estimate of the probability distribution of a continuous variable 7quantitative variable8 and -as first introduced by )arl *earson.?1@ *ea rson.?1@ 0o 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- man y values fall into each interval. < rectangle is dra-n -ith height proportional to the count and -idth equal to the bin si4e, so that rectangles abut each other. < histogram histogram may also be normali4ed displaying relative frequencies. It then sho-s the proportion of cases that fall into each of several categories, -ith the sum of the heights equa ling 1. 0he bins are usually specified as consecutive, nonoverlapping intervals of a variable. 0he bins 7intervals8 must be ad+acent, and usually equal si4e.?2@ 0he rectangles of a histogram are dra-n so that they touch each other to indicate that the original variable is continuous.?3@
III. Other topics related to quality management system course (pdf download) quality management systems quality management courses quality management tools iso $''1 quality management system quality management process quality management system e:ample quality system management quality management techniques quality management standards quality management policy quality management strategy quality management boo!s
