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Quality management system consultants In this file, you can ref useful information about quality q uality management system consultants such as quality management system consultantsforms, tools for quality management system consultants, quality management system consultantsstrategies … If you need more assistant for quality management system consultants, please leave your comment at the end of file. Other useful material for quality management system consultants:

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I. Contents of quality management system consultants ////////////////// 0 quality system is not ust a badge ! its about bringing real and ongoing benefits to your organisation. 0t 0ntaris 0ntaris e help our clients to implement quality qu ality management systems that not only meet their  needs and certification requirements but also bring significant business benefits. hese include • • • • •

Increased client confidence and satisfaction Improved efficiency and increased productivity 4educed costs due to reduced errors and reor"  +ositive impact on company profile and reputation Improved staff motivation

We're Here to Guide You

0t 0ntaris 0ntaris e understand the difficulties and benefits of implementing a quality management system. 'ur company is registered to I&' %((1, and our high calibre personnel hav havee assisted a ide range of Irish and multinational companies to establish effective quality management systems and achieve certification to standards such as I&' %((1.

 

0t 0ntaris e have a 1((5 success rate ith our clients ho have applied for certification of their quality management systems to I&' %((1. What we Deliver We steer you through the entire process, and our quality management services include:

• • • • • •

6esign and implementation of formal quality management system 7ap analysis analysis of quality management management systems systems to the international international standards standards requirements requirements &etting quality obectives and targets +rocess mapping +re!assessment and internal audits 8ustomer satisfaction measurement

9e provide provide professional and e:pert e: pert quality management consultancy to businesses across all sectors, and can tailor these services to suit your particular needs. 9e also provide consultancy on quality systems for specific sectors, including •

I&'&1-%)%, Quality &ystem requirements for the automobile industry

• • •

I&' 13)#$, ;edical 6evices Q;& I&' 1<(2$, =aboratory Q;& I&'0&>? %1((, 0erospace Q;&

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III. Quality management tools

1. Check sheet he chec" sheet is a form @documentA 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. 9hen 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 @Bchec"sBA on it. 0 typical chec" sheet is divided into regions, and mar"s made in different regions have different significance. 6ata are a re read by observing the location and number of mar"s on the sheet.

 

8hec" sheets typically employ a heading that ansers the Cive 9s • •

9ho filled out the chec" sheet 9hat as collected @hat each chec" represents, an identifying batch or lot numberA





9here the collection too" place @facility, room, apparatusA 9hen the collection too" place @hour, shift, day of 



the ee"A 9hy the data ere collected

2. Control chart

8ontrol charts, also "non as &hehart charts @after 9alter 9alter 0. &hehartA 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 processA, 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.D1E 0  process that is stable but operating outside of desired @specificationA limits @e.g., scrap rates may be in statistical control but above desired limitsA 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.D3E 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 individualsA, hoever the type of chart used to do this requires consideration.

! "areto chart

0 +areto chart, named after Filfredo +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 a:is is the frequency of occurrence,  but it can alternatively represent cost or another important unit of measure. he right vertical a:is is the cumulative percentage of the total number of occurrences, total cost, or total of the particular unit of  measure. Gecause the reasons are in decreasing order, the cumulative function is a concave function. o ta"e the e:ample above, in order to loer the amount of late arrivals by <#5, it is sufficient to solve the first three issues. he purpose of the +areto chart is to highlight the most important among a @typically largeA 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. 9il"inson @2((-A devised an algorithm for producing statistically based acceptance limits @similar to confidence intervalsA for each bar in the +areto chart.

 

). Scatter plot Method

0 scatter plot, scatterplot, or scattergraph is a type of mathematical diagram using 8artesian coordinates to display values for to 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 horiHontal a:is and the value of the other variable determining the position on the vertical a:is.D2E his "ind of plot is also called a scatter chart, scattergram, scatter diagram,D3E or scatter graph. 0 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 horiHontal ho riHontal a:is. he 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 @not causationA beteen to variables. 0 scatter plot can suggest various "inds of correlations  beteen variables ith a certain confidence interval. Cor e:ample, eight and height, eight ould be on : a:is and height ould be on the y a:is. 8orrelations may be  positive @risingA, negative @fallingA, @fallingA, or null @uncorrelatedA. 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 correlation. co rrelation. 0 line of  best fit @alternatively called trendlineA can be dran in order to study the correlation beteen the variables. 0n equation for the correlation beteen the variables can be

 

determined by established best!fit procedures. Cor a linear  correlation, the best!fit procedure is "non as linear regression and is guaranteed to generate a correct solution in a finite time. ?o universal best!fit procedure is guaranteed to generate a correct solution for arbitrary relationships. 0 scatter plot is also very 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. he more the to data sets agree, the more the scatters tend to concentrate in the vicinity of the identity line if the to data da ta sets are numerically identical, the scatters fall on the identity line e:actly.

#!$shi%awa diagram

Ishi"aa diagrams @also called fishbone diagrams, herringbone diagrams, cause!and!effect diagrams, or Cishi"aaA are causal diagrams created by *aoru Ishi"aa @1%-#A that sho the causes of a specific event. D1ED2E 8ommon uses of the Ishi"aa 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. 8auses are usually grouped into maor categories to identify these sources of variation. he categories typically include • •

+eople 0nyone involved ith the process ;ethods Jo the process is performed and the specific requirements for doing it, such as policies,  procedures, rules, regulations and las



;achines 0ny equipment, computers, tools, etc. required to accomplish the ob



;aterials 4a materials, parts, pens, paper, etc. used to produce the final product



;easurements 6ata generated from the process that are used to evaluate its quality

 



>nvironment he conditions, such as location, time, temperature, and culture in hich the process operates

-. Jistogram method 0 histogram is a graphical representation of the distribution of data. It is an estimate of the probability distribution of a continuous variable @quantitative variableA and as first introduced by *arl +earson.D1E +ea rson.D1E o construct a histogram, the first step is to BbinB 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. 0 rectangle is dran ith height proportional to the count and idth equal to the bin siHe, so that rectangles abut each other. 0 histogram histogram may also be normaliHed displaying relative frequencies. It then shos 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 @intervalsA must be adacent, and usually equal siHe.D2E he rectangles of a histogram are dran so that they touch each other to indicate that the original variable is continuous.D3E

III. Other topics related to Quality management system consultants (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

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