Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)

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Amold Bruce J. Chalmer and David G. Whitmore Applied Regression Analysis and Experimental Design, Whitmore and 6NCe J. Chalmer Design and Analysis of Experiments, Roger G. Petersen Bergmanand John C. Gittins Goodness-of-Fit Techniques, edited by Ralph6. D'Agostino and Michael A. Stephens Statistical Methods in Discrimination Litigation,edited by D.

Kaye and Mike1 Aickin Robust Inference, M. Tiku, W. Tan, and N. Balaknshnan Wegman and Douglas J. DePnest Econometrics and Structural Change, Lyle D. Broemeling and Hiroki Tsurumi Hams Kleinen Edgingion Fowlkes Giles dra K. Snvastava and David Response Surfaces: Designs and Analyses, Andre 1. Khun and John A. Come11 Nash and Mary Walker-Smith Cancer Modeling, edited by James R. Thompson and Bany W. Bmwn McLachlan and Kaye E.

Basford Peace Odeh andD. Crow and Kunio Shimizu Properties of Estimators for the Gamma Distribution,K. Bowman and L. Shenton Eubank Linear Least Squares Computations, R. Farebrother Exploring Statistics, Damaraju Raghavarao Nazem Spall Chhikara and J. Leroy Folks Clifford Cohen and Betty Jones Whitten Dielman Consul Money Borowiak Doran Continued Fractions in Statistical Applications,K.

Statistical Methodologyin the Pharmaceutical Sciences. Donald A. Peace Handbook of Nonlinear Regression Models.

Frontiers in Statistical Quality Control 11

Lawrence and Jeffrey L. Arthur U-Statistics: Theory and Practice,A. Lee Liepins and V. Uppulun' Barker Hams and Adelin Albert Bain and Max Engelhanlt Federer Handbook of Sequential Analysis,6. Ghosh and P. Sen A. Clifford Cohen Truncated and Censored Samples: Theory and Applications, Survey Sampling Principles,E. Foreman M. Bethea and R. RussellRhinehart Applied Engineering Statistics, Robert Handbook of the Logistic Distribution, edited by N.

Balakn'shnan Chap T. Le Fundamentals of Biostatistical Inference, Correspondence Analysis Handbook,J. Benzbcn' Mafhai and Serge 8. Provost and FranklinA. Confidence Intervals on Variance Components. Richard K. Burdick Graybill edited by Karl E. Biopharmaceutical Sequential Statistical Applications. Baker Ratkowsky, Marc A. Evans,and J. Richard Alldredge Gin' edited by Lynne K. Edwards Applied Analysis of Variance in Behavioral Science, Gilbert LOn3nZen and Virgil L.

Ralph Buncherand Jia-Yeong Tsay Solanky Eethea, BenjaminS. Eoullion Growth Curves, Anant M. Kshirsagarand William Boyce Smith Ham's and James C. Boyd Edgington K. Multivariate Statistical Analysis, Narayan C. Giri Patel and CampbellB. Bayesian Biostatistics, edited by DonaldA. Berry and Dalene K. Stangl Khuri and JohnA. Schucany,and William E. Smith F. Chinchilli Giles Gmber L. Nonparametrics, and Time Series, edited by Subir Ghosh Park and G. Geoffrey Vining Rao and Gabor J. Geoffrey Vining p. ISBN: alk. Process control-Statistical methods.

Quality control-Statistical methods. Optimal designs Statistics I. Park, Sung H. Vining, G. S Headquarters Marcel Dekker. All Rights Reserved. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage and retrieval system, without permission in writing from the publisher. The book coversrecent advanced topics in statistical reasoning in qualitymanagement,controlcharts,multivariateprocessmonitoring, processcapabilityindex,designofexperiments DOE andanalysisfor process control, and empirical model building for process optimization.

It will also be of interest to managers, quality improvement specialists, graduate students, and other professionals with an interest in statistical process control SPC and its related areas. Ayearlateraftertheconference,the editors agreed to edit this book and invited some key conference participants and some other major contributors in the field who did not attend the conference.

Authors from 15 nations have joinedin this project, making this truly a multinational book. The book provides useful information for those who are eager to learn about recent developments in statistical process monitoring and optimization. We would like tothankElizabethCurione,productioneditor of Marcel Dekker, lnc.

We very much appreciate the valuable contributions and cooperationof the authorswhich made the book a reality. We sincerely hopethat it is a usefulsourceofvaluable informationfor statistical process monitoring and optimization. Sung H. Park G. Reynolds, Jr. Montgonwy mlcl Christinrr M. Schnuh, c 1 n d C d Modigl? Park and Je H. Nefland Raymond H. InstituteforTechnologyManagement, of St.

Gallen, St. Choi, Ph. Gnanadesikan, Ph. Hayter, Ph. Kanji, B. Kettenring, Ph. Khuri, Ph. Lin, Ph. Mastrangelo, Ph. Montgomery, Ph. Myers, Ph. Neff, Ph. Nelder, DSc. College, London. Park, Ph. Ramalhoto, Ph. Spiring, Ph. Valeroso, Ph. Geoffrey Vining, Ph. Young, Ph. However, it is important to not merely have the product qualitymeet specifications but to also endeavor to bring quality as close as possible to the ideal value.

The comparison was madeonthebasis of thecolor distribution, which is related to the color balance. Although both factories usedthe same design, the TVs fromtheSanDiegofactoryhadabad reputation,andAmericanspreferredtheproductsfromJapan. Based on this fact, Mr. Yamada, the vice presidentofSonyUnitedStatesatthat time, described the difference in the article.

The difference in the quality characteristic distributions is shown in Figure 1. TVs shown by the solid curve have approximately a normal distribution withthetargetvalue at thecenter;itsstandarddeviation is about onesixth of the tolerance or 10 in certain units. In qualitycontrol,the index of tolerancedivided bysix standard deviations is called the process capability index, denoted by C,,: tolerance c'' -- 6 x standard deviation The process capability of the Japanese-made TVs is therefore 1, and the average quality level coincides with the target value. The quality distribution of the sets produced in San Diego, shown by the dash-dot curve, on the other hand, has less out-of-specification product than that of the Japanese-made sets and is quitesimilartotheuniform distribution for those products that are within the tolerance.

A productwithout-of-tolerancequality is abadproduct. It is an unpassedproduct, so it shouldnot be shippedout. Fromtheopposite On-LineQualityControl 3 point of view, a product within tolerance should be considered good and should be shipped. In a school examination, a score above 60 with as the full mark is considered to be a passing grade. A product quality that coincides withitstargetvalue shouldhavea full mark. Qualitygradually becomesworsewhen it deviatesfromthetarget value, and fails when it exceeds the specification limits, or f 5 in this example.

In a school examination, a score of 59 or below 59is failing, 60 or above 60 is passing. To reduce the Japan-United States difference, Mr. Yamada dictated a narrower tolerance for the San Diego factory, specifying B as the lowest allowable quality limit. This is wrong, since specifying a more severe tolerance because of inferior process capability is similar to raising the passing scorefrom 60 to 70 becauseof theincapabilityofstudents. Inschools, teachers do not raise the limit for such students.

Instead, teachers used to lower the passing limit. As stated above, loss is caused when the quality characteristic denoted by y deviates from the target value denoted by m regardless of how small the deviation is. Let the loss be denoted by LO,. LO, is the minimum wheny coincides with the target value m , and we may put the loss to be 0. If the third-order term and the following terms can be omitted, the loss function is then Let the allowance or the deviation of y from the middle value by A.

The more y deviates from m , the middle value, the more loss is caused. A product whose deviation is less than its allowance A should pass inspection; otherwisethe company will losemore. Whenthedeviationexceedsthe allowance, the product should not be passed. Let A yen signify the loss caused by disposing of a failed product. Putting A and allowance A in Eq. There is no second machinery of the same type producted. Since the variation of a single product is zero, standard deviation in statistics is not applicable in our case. Therefore, it can be obtained from Eq.

When there is more than one product, the averageof Eq. Variance a2 ,theaverageofthe square ofdifferences between y and the target value, is used for this purpose. I 1 as shown in Table 1. Table 1 shows that although the fraction defective of theJapanese Sony factory is larger, its loss is one-third that of the U. Sony factory. Inotherwords,theJapanesequality levelis threetimeshigher. I f such an improvement were attained by repairing or adjusting failed products whose quality level exceeds m f but lies within m f 5, holding The correct solution to this problem is to apply both on-line and off-line quality control techniques.

Inspectors tend to consider production quality as perfect if the fraction defective is zero. Instead,thecompaniesalwaysattempt to reducethe quality distribution within the tolerance range. Nippon Denso Company, for example, demands that its production lines and vendors improve their process capability indexes above 1. To determine the process capability index, data yl , y2,. The standard deviation is obtained from the following equation, where m is the target value. Manufacturers contribute to society and grow through a series of activities including product planning, product development, design, production, and sales.

Within these steps, routine quality control activity on productionlines is called on-line quality control. It includes the following threeactivities: I. Diagnosisandadjustmentof processes. This is called process control. A manufacturing process is diagnosed at constant intervals. Whentheprocess is judgedto be normal,productionis continued;otherwisethecause of abnormality is investigated, the abnormal condition is corrected, and production is restarted.

On-LineQualityControl 7 Preventive activities suchasadjustingamanufacturing process when it appears to become abnormal are also included in this case. Prediction and modification. In order to control a variable quality characteristic in a production line, measurements are made at constantintervals. Fromthemeasurementresults,theaverage quality of the products to be produced is predicted. If the predictedvaluedeviatesfromthetargetvalue,correctiveaction is taken by moving the level of a variable called a signal factor in on-line quality control to bring the deviation back to the target value.

This is called feedback control. This is also called inspection. Every product from a production lineis measured, and its disposition, such as scrapping or repair, is decided on when the result shows the product to be out of specification. Case 3 is different from cases 1 and 2 in that a manufacturing process is the major objectof treatment for cases 1 and 2, while products are thesole object of disposition in case 3.

The abovecases are explainedby an example of controlling the sensors or measuring systems used in robots or in automatic control. Measurement and disposition, case3, concern products, classifying them into pass and fail categories and disposing of them. In a measuring system, it is important to inspectthemeasuringequipmentandtodeterminewhetherthesystem should be passed or failed.

This is different from the calibration of equipment. Calibration is meant to correct the deviationof parameters of a piece of measuring equipment after a certain period of use that corresponds to the concept implied in case 2, prediction and modification. When measuring equipmentfdls out of calibration, either graduallyor suddenly, it is replaced or repaired, which corresponds to the conceptin case 1, diagnosis and adjustment.

I t is difficult in many cases to decide if the equipment should be repaired or scrapped. Generally, the decision to repair or replace is made when the error of the measuring equipment exceeds the allowance of the product quality characteristic. When measuring equipment cannot be adjusted by calibration and has to be repaired or scrapped called adjustment in on-line quality control , and whenthere is ajudgingprocedure called diagnosis in on-linequality control for these actions, it is more important to design a diagnosis and adjustment system than to design a calibrating system.

Radical countermeasures such as determining the cause of the variation, followed by taking action to prevent a relapse which are described in control chart methods and called off-line quality control countermeasures are not discussed in this chapter. I am confident that a thorough on-line 8 Taguchi quality control system design is the way to keep production lines from falling out of control. It is the objective of this chapter to briefly describe online quality control methods and give their theoretical background. Qualitycontrolactivity is necessary to ensure normal production at each step.

One of the steps, called boring by reamers, is explained asan example, whichis also describedin detail in Ref. Approximately 10 holes are bored at a time in each cylinder block by reamers.

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A cylinder block is scrapped as defective if there is any hole bore that is misaligned by more than I O pm, causing an yen loss, which is denoted by A. The diagnosis cost to know whether holes are being bored straight, designated by B, is yen, and the diagnosing interval, denotedby 11, is 30 units.

In the past half-year, 18, units were produced, and there were seven quality control problems. The total cost, including the cost of stopping the production line, tool replacement, and labor, is called the adjustment cost; it is denoted by C and is equal to 20, yen in this example. Insuchaprocessadjustmentforon-linequalitycontrol,theparameterscharacterizingthethreesystemelement-theprocess,diagnosing method,andadjusting method-include A , B , C, U, and C the time lag caused by diagnosis. The improvement in quality control is needed to reduce the quality control costgiven by Eq.

For this purpose, there are two methods: one from the pertinent techniques and one from managerial techniques. The former countermeasures include simplification of the diagnosis method or reduction of adjusting the cost, which must be specifically researched case by case. For this, see Chapters of Ref. There are methods to reduce quality control cost while keeping current process, current diagnosis, and adjustment methods unchanged. These managerial techniques are soft techniques applicable to all kinds of production processes.

Two of these techniques are introduced in this chapter. One is the determination of the diagnosis interval, and the other is the introduction of preventive maintenance such a periodic replacement. Next, the introduction of preventive a maintenance system is explained. Inpreventivemaintenance activities, there are periodic checks andperiodicreplacement. Inperiodicreplacement,acomponentpart which could be the cause of the trouble is replaced with a new one at a certain interval. For example. Periodic checking is done to inspect products at a certain interval and replace tools if product quality is within specification at the time inspected but there is the possibility that it might become out-of-specification before the next inspection.

In this chapter, periodic replacement is described. In the case of reamer boring, a majority of the problems are caused by tools. Therefore, the probability of the process causing trouble becomes very small. Assume approximately the same as the adjustment cost that the probability of the process causing trouble is 0.

This probability includes the instance of a reamer being bent by the pinholes existing in the cylinder block, or some other cause. If there were similar improvements in each of the 27 cylinder block production steps, it would be an improvement of 42 million yen per annum. Such a quality control improvement is equivalent to the savings that might be obtained from extending the average interval been problems 6.

In other words, this preventive maintenance method has a merit parallel to thatof an engineering technology that is so fantastic that it could extend the problem-causing interval by 6. For details, see Chapters of Ref. Equations 18 and 20 may be approximately applied with satisfaction regardless of the distribution of the production quantity before the problem and despite variations in the fraction defective during the problem period. These statements are proved in Sections 5 and 6. In the loss function L, the average number of defectives in the second term is Since the first, third, and fourth terms of Eq.

The average problem-causing interval is ii. When a process is under normal conditions, it may be deemed that there are no defectives. Assume that the fraction defective under abnormal conditions is p and the loss when a defective units is not disposed of but is 14 Taguchi sent on to thefollowing steps is D yen. After the process causes trouble, the probability of detecting the trouble at the diagnosis is p and the probability of failing to detect the trouble is 1 - p.

Thus we obtain Table 2. The amount of loss in Eq. When the fraction defective during the trouble periodis not Y0,it is normal to trace back and find defectives when a trouble is found. Although the fraction defective can have any value,it would be good enough to consider about 0. In that case, 1. For 16 Taguchi example, pressure of a pressis a signal factor to control the thickness of steel sheets, and flow of fuel is a signal factor to control temperature.

For such a control, the following three steps must be taken: 1. Forecast the average quality of products produced before the next measurement. Determine the optimum modifying quantity against the deviation of the forecasted value from the target value. After the above parameters are determined, 4.

Modify the quality characteristic made signal factor. The simplestprediction method is to consider the measured value itself as the average quality of all productsto be producedbeforethenextmeasurement.

Titles in this series

Therearemany methods for this purpose. The optimum modifying quantity in step 3 is determined by forecasting the average in step 2, which is denoted by! For such systems, the center of quality control is the calibration of sensors measuring devices employed by automatic machinery or robots and the diagnosis of hunting phenomena.

Table of contents

Steps 1 4 are therefore required. A simple example is illustrated in the following. The specification of the thickness of a metal sheet is f 5 pm. The loss caused by defects is yen per meter. The daily production is20,00Om, and the production line operates 5 days a week or 40hr a week. Currently, measurement is made once every 2 hr,costing yen forthemeasurement andadjustment correction or calibration. Thereis a tendency for the averageand variation of thickeness to increaseduringthecourseofproduction. Theaverage thickness increases 3 pm every 2 hr, and the error variance increases 8 pm2 in 2 hr.

There is additional see improvementdue to thereduction of thepredictionerror. Forthis, Chapter 9 of Ref. Taguchi, G. The ultimate judge of quality is the customer, which meansthata systemofqualitymeasurement shouldfocusontheentire process that leads to customer satisfaction in the company, from the supplier to the end user. Total quality management TQM argues that a basic factor in the creation of customer satisfaction is leadership, and it is generally accepted that a basic aspect of leadership is the ability to deal with the future. This has been demonstrated very nicely by, amongothers,Mr.

Jan Leschly, president of Smith Kline, who in a recent speech in Denmark compared his actual way of leading with the ideal as he saw it. His points are demonstrated in Figure 1. I t appears that Mr. The situation described by Mr. Leschly holds true of many leaders in the Western world. There is a clear tendency for leaders i n general to be much more focused on short-term profits than on the process that creates profit.

This again may lead to firefighting and to the possible disturbance of processes that may be in statistical control. Courtesy of Jan Leschly, Smith Kline. This does not, of course, mean that the results are uninteresting per se, but rather that when the results are there you can do nothing about them. They are the results of actions taken a long time ago. All this is much easier said than done. In the modem business environment leaders may not be able to do anything but act on the short-term basis because they do not have the necessary information to do otherwise.

Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)

To act on a long-term basis requires that you have an information system that provides early warning and that makes it possible for youto make the TotalQualityManagement 21 necessary adjustments to the processes and gives you time to make them before they turn into unwanted business results. This is what modern measurement of total quality is all about.

This idea isinvery goodaccordance with the official thoughts in Europe. In a recent workingdocumentfromtheEuropeanCommission, DGIII, the following is said aboutqualityandqualitymanagement European Commission, : The use of the new methodologies of total quality management is for the leaders of the European companies a leading means to help themin the currenteconomicscenario, whichinvolves not only dealing with changes. Thus,totheEuropeanCommission,quality is primarilyaquestion of changes and early warning.

To create an interrelated system of quality measurement it has been I , where decided to definethemeasurementsystem accordingtoTable measurements are classified according to two criteria: the interested party the stakeholder and whether we are talking about processes or results. Other typesofmeasurementsystemsaregiven in KaplanandNorton As Table 1 illustrates, we distinguish between measurements related to the process and measurements related to the results. The reason for this is obvious in the light of what has been said above and in the light of the definition of TQM.

Traditional measurements have focused on the lower left-hand corner of this table, Le. However, as mentioned above, this type of information is pointing backwards in time, and at this stageit is too late to do anything about the results. What we need is something that can tell us about what is going to happen with business results in the future.

This type of information we find in the rest of the table, and we especially believe and also have documentation to illustrate that the top set of entries in the table are related in a closed loop that may be called the improvement circle. This loop is demonstrated in Figure 2.

The improvementis particularly due to an increase in customer loyalty stemmingfrom an increase in customersatisfaction. Therelationship between customer satisfaction and customer loyalty has been documented empirically several times. One example is Rank Xerox. Figure 2 Theimprovementcircle. TotalQualityManagement 23 Anotherexample is a largeDanish real estatecompanywho in a customer satisfaction survey asked approximately customers to evaluate the company on 20 different parameters. From this evaluation an averwas calculated.

I n addition to the questions on parameters, a series of questions concerning loyalty were asked, and from this a loyalty index was computed and related to the customer satisfaction index. This analysis revealed some very interesting results, which are summarized in Figure 3. It appears that there is a very close relationship between customer satisfaction and customer loyalty.

The relationship is beautifully described by a logistic model. When the customer satisfaction increases to 4, a dramatic increase in loyalty is observed. Thus the area between 3 and 4 is very important, and it appears that even very small changes in customer satisfaction in this area may lead to large changes in the probability of loyalty. The observed relationship between business results and customer loyalty on the one hand and customer satisfaction on the other is very impor- Probability of loyalty 12 q I I 1 1 1.

This information provides an early warning about future business results and thus provides management with an instrument to correct failures before they affect business results. The next logical step will be to take the analysis one step further back and find internal indicators of quality that are closely related to customer satisfaction. Inthiscasethewarningsystem willbe even better. These indicators, which in Table 1 arenamedcontrolpointsandcheckpoints, will, of course,be company-specificeven if somegenericmeasures are defined.

Moving even further back, we come to employee satisfaction and other measuresoftheprocess in thecompany. We expectthese tobe closely related to the internally defined quality. This is actually one of the basic assumptions of TQM. The moresatisfied and more motivated your employees, the higher the quality in the company [see Kristensen ]. In order to verify the hypothesis of theimprovement circle in Figure 2, employeesatisfactionandcustomer satisfaction were measured for 19 different districts in the cleaning division of the company in The results were measured on a traditional fivepoint scale, and the employee satisfaction and customer satisfaction indices were both computed as weighted averages of the individual parameters.

The results are shown in Figure 4. Theseinteresting figures showaclearlinearrelationshipbetween employee satisfaction and customer satisfaction. The higher the employee satisfaction, the higher the customer satisfaction. Thus the standard deviationoftheconstantterm is 0. Furthermore, we cannot reject a hypothesis that the slope is equal to 1. It appears from this that a unit change in employee satisfaction gives more or less the same change in customer satisfaction. We cannot, from these figures alone, claim that this is a causal relationship, but we believe that combined with other information this is strong evidence for the existence of an improvement circle like the one described in Figure 2.

To us, therefore, the creation of a measurement system along the lines given in Table 1 is necessary. It will be seen that the system follows the methodology given in the Process section of Table 1. A Model of Satisfaction and Loyalty Since exactlythesamemodelapplies to bothcustomersatisfactionand employee satisfaction we can without loss of generality base the entire discussion on a model for customer satisfaction. II', is the importance of 21 given parameter, and c, is the evaluation.

I t was assumed that the profit of the company could be described as where cp is an increasing function linking customer satisfaction to conlpany on the right-hand side is a quadratic cost earnings and the second factor function with IC, as a cost parameter. TotalQualityManagement By maximizing 2 with respect to the individual satisfactions can be shown that for identical cost parameters, i.

This is based on the fact that the first-order condition for maximization of Eq. This is the reason we later on elaborate a little on the quality map. But even if this is the case we intend to take model 2 a step furtherin order to incorporate customer loyalty. The reason for this is that customer loyalty has gained a lot of interest among quality management researchers recently because it seems so obvious that loyalty and quality are related.

The likelihood of buying is, of course, the loyalty function. We assume that this function can be described as follows: 28 Kristensen where where c; is the satisfaction of parameter i for the main competitor. Thus the elements of the loyalty function are related to the competitive position of a given parametercombined withthe importance of theparameter. We assume that the quantity bought given loyalty is a function of the customer satisfaction index. This means that we will model the income or revenue of the company as This tells us that you may be very satisfied and still not buy very much, because competition is very tough and hence loyalty is low.

On the other hand, when competition is very low, you may be dissatisfied and still buy from the company even though you try to limit your buying as much as possible. Combining IO with the original model in 2 , we come to the following model for the company profit: Hence the optimum allocation of resourceswill be found by maximizing this function with respect to e;, which is the only parameter that the company can affect in the short run.

Long-run optimizationwill, of course, be different, but this is not part of the situation we consider here. The first-order condition for the optimization of Eq. This seems to be a very logical conclusion that will improve the interpretation of the results of customer satisfaction studies. Practical use of results 4 , 6 , and 15 will be easy, because in their presentformyouonlyneedmarketinformationtousethem. Onceyou collect information about c,, c;, vi, and the customers' buying intentions, the models can be estimated.

In the case of a loyalty model you will most likely use a logit specification for L and then L; will be easy to calculate. Statistical Monitoring of the Satisfaction Process Let. Assume that. Then we may substitute the average. If, on the other hand, a parameter falls outside the limits, the process needs adjustment.

An Example An actual data set from a Danish company is presented in Table 2. Seven parameters were measured on a seven-point rating scale. Now we are ready to set up the control chart for customer satisfaction. The measurementof quality will no longer be limited totheproductionprocess. A European Quality Promotion Policy. Bruxelles, Feb. The Balanced Scorecard. Kristensen K. Relating employee performance to customer satisfaction. World Class Europe-Striving for Excellence. EFQM, Edinburgh, , pp.

The European Way to Excellence. CaseStudy Series. DirectorateGeneral , European Commission, Bruxelles. Kristcnscn K, Martcnsen A. Linkingcustomersatisfaction to loyalty and performance. On measurement of customer satisfaction. Total Quality Managemcnt 3 2 : What differentiates total quality management from other managementprocesses is the emphasis on continuous improvement. Total quality is not a quick fix; it is about changing the way things are done-forever.

Seen in thisway, totalqualitymanagement is aboutcontinuous performanceimprovement. To improveperformance,people need to do andhowtodoit,havetherighttoolsto do it, be knowwhatto abletomeasureperformance,and receive feedbackoncurrent levelsof achievement. Total quality management Kanji and Asher, provides this by adhering to a set of general governing principles. They are: 1. Delightthecustomer 2. Management by fact 3. People-basedmanagement 4.

Total Quality Management 5: To achieve this, each principle is translated into practice by using two core concepts, which show how to make the principle happen. These concepts are: Customer satisfaction Internal customers are real All work is a process Measurement Teamwork People make quality Continuous improvement cycle Prevention Further details of the four principles with the core concepts follow. The pyramid principles of TQM are shown in Figure 1.

Delighting the customer means being bestat what matters most to customers, and this changes over time. Being in touch with these changes and delighting the customer now and in the future form an integral part of total quality management. If we knowwhere we arestartingfrom, we canmeasure our improvement. Having the facts necessary to manage the business at all levels is the second principle of total quality.

From Kanji and Asher, People-basedManagement Knowing what to do and how to do it and getting feedback on performance form one part of encouraging people to take responsibility for the quality of their own work. Involvement and commitment to customer satisfaction are ways togeneratethis. Thethirdprinciple of totalqualitymanagement recognizes that systems, standards, and technology in themselves do not mean quality. The role of people is vital. ContinuousImprovement Total quality cannot be a quick fix or a short-term goal thatwill be reached when a target has been met.

Total quality is not a program or a project. It is a management process that recognizes that however much we may improve, our competitors will continue to improve and our customers will expect morefrom us. The link between customerandsupplier withprocess improvement can be seen in Kanji Here, continuous improvement-incremental change, not major breakthroughs-must be the aim of all who wish to move toward-total quality. Many writers refer to the customer-supplier chain and the need to get the internal relationships working in order to satisfy the external customer. Whether you are supplying information, products, or a service, the people you supply internally depend on their internal suppliers for quality work.

Their requirements are as real as those of external customers; they may be speed, accuracy, or measurement. Making the mostof this idea can be very time-consuming, and manystructuredapproachestakealong time andcan be complicated. Dahlgaard et al. AllWorkIs a Process The previous section looked at internal customers and how to that they are real as a focus for improvement.

By "process" we mean any relationship such a s billing customers or issuing credit notesanything that has an input. A process is a combination of methods,materials,manpower,machinery,etc. All processes contain inherent variability, and one approach to quality improvement is progressively to reduce variation, first by removing variacause tiondue to special causes and second by drivingdowncommon variation.

Various statistical methods, e. Measurement The third core conceptof total quality management is measurement. Having a measure of how we are doing is the first stage in being able to improve. Measurescan focusinternally,i. Prevention The core conceptof prevention is central to total quality management andis one way to move toward continuous improvement.

Prevention means not letting problems happen. The continual process of driving possible failure out of the system can, over time, breed a culture of continuous improvement. There are two distinct ways to approach this. The first is to concena trateonthe designofthe product itself whether a hardproductor 40 Kanji service ; thesecond is to work ontheproductionprocess.

However,the most important aspect of prevention is quality by design using statistical reasoning. There are several frequently used tools, and failure mode and effect analysis FMEA is one of the better known ones. Other frequently used methods are failure prevention analysis, which waspioneered by KepnerTregoe,andfoolproofing orPokaoki. The advantage of all of these methods is that they provide a structure or thought process for carrying the work through. CustomerSatisfaction Many companies, when they begin quality improvement processes, become very introspective and concentrate on their own internal problems almost at the expense of their external customers.

Other companies, particularly in the service sector, have deliberately gone out to their customers, first to survey what is important to the customer and then to measure their own performance against customer targets Kristensen et al. One example is Federal Express, who surveyed their customer base to identify the top I O causes of aggravation.

The pointswere weighted according to customer views of how important they were. A complete check was made of all occurrences, and a weekly satisfaction index was compiled. This allowed the company to keep a weekly monitor of customer satisfaction as measured by the customer. An understanding of survey and statistical methods is therefore needed for the measurement of customer satisfaction.

Teamwork Teamwork can provide an opportunity for people to work together in their pursuit of total quality in ways in whichtheyhave not worked together before. People who work on their own or in small, discrete work groups often have a picture of their organization and the work that it does that is very compartmentalized. They are often unaware of the work that is done even by people who work very close to them.

Under these circumstances they are usually unaware of theconsequencesofpoorquality in theworkthey themselves do. By bringingpeopletogether in terms with a common goal, quality improvement becomes easier to communicate over departmental or func- QualityImprovementandStatisticalReasoning 41 tional walls. In this way the slow breaking down of barriers acts as a platform for change. A benchmarkingapproachcanalsohelptochangethe waythey do things. Teamwork can be improved by benchmarking, a method that is similar to the statistical understanding of outliers. PeopleMakeQuality Deming has stated that the majority of quality-related problems within an organization are not within the control of the individual employee.

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Examples where the system gets in the way of people trying to d o a good job are easy to find, and in all cases simply telling employees to do better will not solve the problem. It is important that the organization develop its quality management system, and it should customize the system to suit its own requirements. Each element will likely encompass several programs. As a matter of fact, this is where the role of statistics is most evident. TheContinuousImprovementCycle Thecontinuous cycle of establishingcustomerrequirements,meeting those requirements, measuring success, and continuing to improve can be used bothexternallyandinternallyto fuel the engine of continuous improvement.

By continually checking with customer requirements, a company can keepfindingareas in whichimprovementscan be made. Thiscontinual supply of opportunity can be used to keep quality improvement plans upto-date and to reinforcetheidea thatthetotalqualityjourney is neverending. In order to practice a continuous improvementcycle it is necessary to obtain continuous information about customer requirements, i. However, we know that market research requires a deep statistical understanding for the proper analysis of the market situation.

For example, Florence Nightingale, the 19th century statisticianandfamousnurse, wasknownas the mother of continuous health care quality improvement. Later, in , WalterShewhart. The fundamental aspect of statistical understanding is the variation that exists in every process, and the decisions are made on that basis. If the variation in a process is not known, then the required outputof that process will be difficult to manage.

I t is also very important to understandthat every processhas an inherentcapabilityandthattheprocess will be doing well if it operates within that capability. However, sometimes one can observe that resources are being wasted in solvingaproblem, and simplynot realize thatthe process is working at its maximum capability. In order to understand variability and the control of variation, it is necessary to understand basic statistical concepts.

These concepts are simple to understand and learn and provide powerful management tools for higher productivity and excellent service. Heij, J. Schumacher, B. Hanzon, K. Praagman Eds , pp. Benveniste, A. Willsky, Multiscale statistical signal processing. Benveniste, K. Chou, A. Willsky, Multiscale statistical signal processing: stochastic processes indexed by trees. Kaasshoek, J. Van Schuppen, A. Ran Eds , pp. Basseville, Detection of abrupt changes in signal processing. Combes, A.

Grossmann, and P. Tchamitchian Eds , pp. Basseville, G. Le Vey, Analyse et surveillance vibratoire d'une machine en rotation. Bensoussan, J. Lions, M. Thoma, A. Wyner Eds , pp. Benveniste, G. Moustakides, A. In Probability Theory and Mathematical Statistics , pp. Byrnes and A. Lindquist Eds , pp. Zhang, M. Basseville, Statistical detection and isolation of additive faults in linear time-varying systems.

Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs) Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)
Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs) Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)
Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs) Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)
Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs) Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)
Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs) Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)
Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs) Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)
Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs) Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)
Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs) Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)
Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)

Related Statistical Process Monitoring and Optimization (Statistics: a Series of Textbooks and Monographs)

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