an application of tqm tools at a maintenance division of a large aerospace company

8/10/2019 An Application of TQM Tools at a Maintenance Division of a Large Aerospace Company

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Journal of Quality in Maintenance EngineeringAn application of TQM tools at a maintenance division of a large aerospace company

E. Vassilakis G. BesserisArticle in format ion:To cite this document:E. Vassilakis G. Besseris, (2009),"An application of TQM tools at a maintenance division of a largeaerospace company", Journal of Quality in Maintenance Engineering, Vol. 15 Iss 1 pp. 31 - 46Permanent link to this document:http://dx.doi.org/10.1108/13552510910943877

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METHODOLOGY AND THEORY

An application of TQM tools at amaintenance division of a large

aerospace companyE. Vassilakis

Technological Educational Institute of Piraeus, Athens, Greece, and

G. BesserisTechnological Educational Institute of Piraeus, Athens, Greece, and

Kingston University, London, UK

AbstractPurpose Devoted to a description and evaluation of a selected maintenance process (assembly) atthe aero-engines maintenance unit of a large aerospace company by implementation of TQM tools, thispaper attempts to identify the causes behind the defect observed and form the scientic platform forinitiatives in a TQM-governed enterprise and to broaden the principles of TQM for the selectedprocess, prior to moving to a more structured plan that will include the entire unit.Design/methodology/approach Process monitoring and evaluation are organised by anapplication of control charts in order to provide vital information regarding the level of control inthe selected process. Quality control data are contrasted with component specications by employingcontrol charts to provide a metric for the level of the process capability index. As a result a Fishbonediagram is constructed to identify existing interrelations between the causes responsible for the defect

observed.Findings The maintenance process selected was the assembly process of an aero-engine module(exhaust nozzle unit) at the aero-engines maintenance unit of a large aerospace company. Processevaluation by means of multivariate control charts and tolerance analysis exhibited poor results. Itwas observed that certain measurement stations were out of control, whilst low actual capability indexvalues were exhibited in others..Research limitations/implications Process monitoring and evaluation carried out for thepurposes of the present study had the form of an off-line tool. The paper shows that the aero-enginesmaintenance unit had no infrastructure for an online process control and monitoring system.Consequently, performed analysis indicated that the implied assembly process was inadequatelyimplemented. As a result, the maintained assembly units were out of stated specications limits.Originality/value The study contributes to the literature on TQM in the aerospace maintenancebusiness.

Keywords Aerospace engineering, Statistical process control, Aerospace engineering,Total quality management

Paper type Case study

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1355-2511.htm

The authors would like to thank the Editor-in-Chief, Professor S.O. Duffuaa and the reviewers fortheir constructive comments. They are especially thankful to Dr Leo Kounis for his criticalcomments.

An application of TQM tools

31

Journal of Quality in MaintenanceEngineering

Vol. 15 No. 1, 2009pp. 31-46

q Emerald Group Publishing Limited1355-2511

DOI 10.1108/13552510910943877


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IntroductionFor more than a century, aerospace industry leads process developments in shaping-upcomplex products to high safety standards (Kumar, 1999). Many tools utilised by modernbusiness initiatives such as Six Sigma have been tested and adopted primarily in thisindustry (Bhuiyan et al., 2006). There are landmark case studies on the application of TQM principles on aerospace manufacturing companies. Cheng (1994) has provided afundamental problem arising in ABC Aerospace in attaining high-level consistencylevels of a heat-treating process. Goh and Lim (1996) focused their efforts to applyingTQM principles to an aerospace maintenance company. Only recently there was a reportstating that the authors had managed to unify the predictive maintainability offered byTPM to the product-trait forecasting espoused by QFD (Pramod et al., 2006). Failuremode and effect analysis (FMEA) has its origin in aircraft prevention documentedcontrol charts. It remains until today a basic tool in TQM implementation projects (Raviand Prabhu, 2001). A modern exposition of statistical tools for continuous improvementmay be found in the treatise of Montgomery (2005). The connection of modellingoptimised maintenance schemes has been well described in Ben-Daya et al. (2000). Beingcapable to direct basic SPC tools and other mainstream quality methodologies for use inthe maintenance function proved to be an insurmountable task for those practitionersthat should have available this knowledge in their workplace). This was identied for therst time by Ben-Daya and Duffuaa (1995). The same team advanced an informativestudy on the application of SPC in maintenance operations (Duffuaa and Ben-Daya,1995). While there are several studies dedicated to hardcore reliability analysis problemsin aircraft components, the number of published case studies is not commensurate of thecriticality of quality improvement in maintainability (Al-Garni et al., 1999; Sohn et al.,2006; Wong et al., 2006; Leung et al., 2007).

Maintenance has become an engineering discipline in its own right, shifting fromthe rather simplistic approach of setting up and adjusting production machinery to a

discipline that works in parallel to production and as a matter of fact aiding productionto keep up with the newly adopted philosophies like exible/lean manufacturing, justin time, etc. (Arajou et al., 1996). Since maintenance no longer serves as a secondaryfunction within a manufacturing organisation, the demands from maintenance unitshave been increased (Crocker, 1999). It is worth mentioning at this point that thecapacity of production heavily depends on the quality of maintenance activities(Knotts, 1999). Despite the tangible benets manifesting from the implementation of TQM in aerospace maintenance, there are still companies who are ineffective inintroducing TQM-related tools in their respective maintenance units. This is attributeddue to lack of management support, trained personnel and absence of a focusedbusiness plan (Rungasamy et al., 2002). This resistance to change is active even when aproblem in a maintenance process is identied. It is the authors view that a keyingredient in encouraging more companies to devote their maintenance operations toquality oriented techniques is to supply current technical literature with as many casestudies as possible. This in turn will unequivocally induce more aerospacemaintenance companies to rely on modern TQM methods for performing theirquality assessment, monitoring and improvement schemes. This may be attained bydirectly mimicking their problem solving approaches by those offered in the technicalliterature as success stories (Krumwiede and Sheu, 1996). Overall, industrial andmanufacturing operations have greatly beneted by this tactic (Booker, 2003).

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It is anticipated that due to the nature of the works undertaken, aerospacemaintenance (either focused on aero-engines or the planes themselves), requires highlevels of professionalism, quality standards and zero-tolerance in delays (Luxhj,1999). Low quality of services will inadvertently lead to safety compromises that mayhave a detrimental impact on air-travel safety. This act may equally diminish theperformance and consequently alter the ight characteristics of an aeroplane (Endsleyand Robertson, 2000). Since maintenance has a rather large share in the operating costsof an airline or an air force, there is an increasing demand for cost-effective and reliablemaintenance units with the ability to provide efcient services. An interestingexposition on SAAB Gripen may aid to envision this (Sandberg and Stromberg, 1999).In addition, aero-engines maintenance has a distinctive characteristic since it can workas an independent unit within an aerospace industry, yet being an integral part of it(Hunt and Hebden, 2001). Modern aero-engines can be separated from the airplanesmain body and receive all necessary services without forcing the plane to be grounded(Solodilova-Whiteley and Johnson, 2006). Modular aero-engine design philosophyfacilitates the ability of machinery to utilise other modules (engines, engine parts,aero-structure parts etc.) whilst the equivalent parts are being serviced (Sachon andPate -Cornell, 2000). The latter is based on the fact that the customer has the necessarystock in aero-engines to keep its eet airborne, while the services take place. However,although there is a practice to maintain stock of critical components, the decliningprotability faced by a large number of airliners, drives them to reduce their inventory.Depending on their strategic plans, a minimum or large stock of working aero-enginesis kept by air forces around the world to ensure that in case of an emergency theirplanes will have the means to achieve the turnaround times needed for completing theirmissions. Overall, a modern airspace maintenance organisation ought to be created bythe same philosophy that has necessitated its existence. This is the core area of TQMand maintainability (Van de Water, 2000). Nonetheless, for maintainability to be

effectively applied an amount of information needs to be processed either with modernexpert systems (Cheung et al., 2005) or by applying selectively simple but powerfuldata mining tools (Komorowski, 2003). Informed maintenance is particularly importantfor organizations supporting ageing aircraft eets around the globe. This is especiallytrue now that costs for acquiring new planes have become a scal burden for eitherprivate or national carriers (Fox and Gormley, 2001; Horst and Trey, 1999).

MethodologyWhile the emphasis in quality improvement methodology varies greatly amongcompanies, focusing on the product key performances rates always very high in thecustomers list (Oakland, 1999, Wisner, 1999). Installing a continuous improvementmentality in an aircraft maintenance company, a comprehensive multi-functionalapproach is required. This approach necessitates encompassing all informationtechnical and operational regarding specic product utilisation (Murthy et al., 2002).Therefore, the methodology should incorporate cost-effective models for gatheringrelevant data to fuel problem solving techniques (Basim and Kans, 2006). Even thoughthe implementation strategy and the tools selection has been standardised throughthe application of modern business initiatives such as Six Sigma, maintenance may bebeneted by more explicit methodology proposals always assorted with an


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appropriate case study (Pyzdek, 2000). A methodology suitable for maintenanceoperations is outlined at this point:

Step 1: Collect and organize dataAircraft maintenance units are usually required to keep extensive records frommaintenance sessions as specied by any particular aircraft manufacturer they service.This is a good source of data that should be stored in a dedicated information systemfor convenient referral and retrieval capabilities leading to extensive presentation andprinting options. Customarily, these records are also expected to be created, maintainedand reviewed by certied aerospace maintenance companies to international standardmanagement systems that either constitutes a generic quality or to more sector-specicmanagement systems. However, in due course each maintenance unit should beversatile to adding extra data collection stations as required per situation. This isparticularly true when identication of a problematic component or system orsub-system functioning require data at a lower level than that collected during routineinspections. The reproducibility and repeatability of the data gathered should beexamined before statistical inference methods are utilised to summarise anycomponent, system and sub-system performance. Measurement system analysis,also known as Gauge R & R analysis, will conrm the usability of the data at hand.When direct data ow received by instituted inspection and control points are notsufcient in providing a performance standing for a component, system or subsystem,while existing organisational experience or an intensive literature search produces noresult in capturing the investigated phenomenon, then data collection should follow aDesign of Experiments (DOE) protocol.

Step 2: Identify problematic components or maintenance processesBeing able to decipher a root cause relies on teamwork that involves maintenance

technicians and foremen as well as middle level managers. But before addressing andquantifying the occurrence and detection of the problem, it is adamant that the teamunderstands thoroughly the maintenance process and the practical signicance of theappointed inspection points in conjunction with the ultimate aircraft functionality.Only then the team will be able to prioritize the mechanisms that may induce the faultoccurrence. This step is assisted by the seven old and the seven new qualitymanagement tools well espoused in TQM philosophy (Pyzdek, 2000; Montgomery,2005). For example, depictions of trends obtained from existing StatisticalProcess/Quality Control (SPC/SQC) monitoring, in combination with process failuremode and effect analysis (FMEA) documentation should be assisted by intensivebrainstorming sessions in examining the impact the current system at hand has on theoverall performance of the aircraft. The team organised to resolve such complicatedissues would detect prioritisation of faults. This act would be enabled through theimplementation of Pareto analysis aided by the corresponding afnity diagram, whichis registered by aircraft logs as an efcient method to zero-in quickly in the root cause.

Step 3: Analyse data after maintenance process monitoring Maintenance personnel should be trained in basic statistical methods coveringreliability and robust inference techniques. The presence of a quality practitionerdedicating sufcient time to problem-solving projects, which arise in the maintenance

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operations is imperative in solidifying data handling and manipulation. The use of inference statistics tabulated as either descriptive statistics or correlation analysis maybe useful when the eet is not comprised by a sufciently large count to discriminatethe inherent distribution behind the investigated phenomenon. In this part,non-parametrics provide agile statistical inference, if applicable, to more technicalminded engineers that nd in-depth statistical analysis an overall difcult process. It isimportant that the techniques employed retain a trivial nature such that the resultsgained provide unequivocally insight to problem resolution.

Step 4: Action planAfter the analysis of maintenance operations data, the emphasis ought to be placed ondeveloping feasibility studies for overcoming the problem. Therefore, an action planought to be initiated to facilitate the implementation of the solution followed by anevaluation of the installed solution through a nal process review.

Case studyThe main objective of the present study is to implement the basic principles of TQM bymeans of statistical process control (SPC) quality tools and Cause and Effect (shbone)diagram into the environment of a maintenance unit of large aerospace company. Theidea to perform such an analysis is not innovative monitoring of quality processeshas been in the business for more than 50 years, yet it is well known that it is difcultto implement TQM principles in a maintenance unit on the assumption that the latterdoes not share the virtues of a production line; managerial and engineering staff working in maintenance units believe that products dismissed (rather thenproduced) from a maintenance unit are either working or not working,acceptable or not acceptable. For example, an aero-engine assembled after periodicservice or overhaul, is either working within the manufacturers specied limits, or not.

The problem behind this misconception has its roots in the way people viewmaintenance activities. Although practice in modern industries has proven theopposite, maintenance activities are considered as secondary or supportive to themain activities. Maintenance is a process and it has to be dealt in that manner. In factit is a much-organised process that in no means defers from a production line. In viewof the previously mentioned, maintenance:

. requires understanding;

. possesses variation;

. must be controlled;

. has a capability indication; and

.

needs continuing improvement.These reasons enhance the presence of modern tools for assessing quality such as SPC,which should be viewed not merely addressing topics about statistics or mathematicsbut they are valuable tools for reinforcing competitiveness. In a sensitive area such asthe aero-engines maintenance business unit of a large aerospace company, one must bein a position to satisfactorily answer the question, if the performed job has been donecorrectly. Additionally, one must be able to support his answer with concrete evidence.This evidence in the engineering profession is usually provided with numerical data


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and charts. This is the area where SPC and other quality tools nd their place into themaintenance process. SPC transforms the continuous quest for monitoring if a jobhas been done correctly to a strategy of prevention and detection of problems in theearly stages of any process that seeks its way to achieving excellence.

The aerospace company housing the aero-engines maintenance plant is one of Europes largest and most experienced aeronautical companies, with a work force of 2,900 skilled and experienced technical and administrative employees. Qualityassurance procedures within the company are covered in full detail in the qualityassurance manual. Since 1999, a plant dedicated on delivering quality assuranceservices has been developed specically for the aero-engines division to be used as apilot by all personnel involved with the works undertaken in the unit. The quality plancontains in brief the processes, the work ow and the engines manufacturer andcustomers requirements during the periodic inspections, major or minor repairs,modications and overhaul procedures. In addition, the quality assurance plan coversthe calibration of instruments or other measuring devices used for the inspection ormaintenance of engines and engines accessories. All production is fully monitored by aquality management system, which has been developed in strict compliance with theInternational Quality Standards and Aviation Regulations applied in the aeronauticalcompany and has been approved by nearly every major aircraft manufacturer in theworld, regulatory authorities, certifying agencies and accreditation councils.

Process description and analysisThe process chosen for the implementation of SPC tools was the assembly of anengines exhaust nozzle. The quality characteristic was the diameter of the nozzlemeasured in sixteen different places in two nozzle positions: fully open and fully closed.Since the engines design is modular, the nozzle assembly process is performedindependently of other engines parts (see Figure 1).

The nozzles main body is attached on a rotating assembly xture and measuredfor correct attachment using an analogue adjustment gauge. The latter is a xedpart of the circular ring that can rotate at 360 degrees. Once this process iscompleted, the nozzle is fully assembled to permit the beginning of inner aps

Figure 1.The nozzle assemblymaintenance schematic

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measurement-adjustment and hydraulic pressure testing. The nozzle is set to thefully open position by supplying a pressure of 8,000kPa (80 bars) using the nozzlehydraulic test rig. The hydraulic rig pumps fuel through high pressure elastic tubesattached to the nozzle in order to allow the operator to control the aps movementbetween the open and close positions. The same device is used for performing thehydraulic test: This is done to ensure that no leakages occur after the completion of the nozzle assembly process is completed. A nozzle-position checking box is used toensure that the aps are in the correct position. The checking box is an electronicdevice attached to two transducers on the nozzle. Each transducer (one after theother) is supplied with a 5v voltage and a 1,000 Hz RMS frequency. The outputvoltage shown on the checking box has to be in the range of 2,990 and 2,980v tovalidate the correct aps position. Should this does not happen an adjustment isperformed by ne-tuning the respective links on the nozzle. Once set in the fullyopen position, the analogue adjustment gauge is used to measure the nozzles innerdiameter in 16 different places (that is, on each inner ap). The readings have to bebetween 424.25 and 424.75 mm (424.25 0.5 mm). The same procedure is repeatedwith the nozzle in the fully closed position. This time the readings have to bebetween 309.00 and 311.00 mm (310 ^ 1 mm).

Data gathered from the assembly of the exhaust nozzle span over a period of threeyears and correspond to the nozzle modules assembled and distributed. Themultivariate T 2 -generalized variance chart (T 2 GVC) method (subgroup size 1) wasemployed for assessing the status of the exhaust nozzle assembly process based on thedata collected from previous works. The selection of the T 2 GVC method wasadditionally supported by the observation that the time entered to maintenance and therespective delivery rate of assembled nozzles were slow and random; the number of nozzles assembled per month was variable and unknown.

Using the data collected, the number of samples for the analysis were m 37assembled exhaust nozzles, which roughly corresponded to a time period of threeyears. Charts using the T 2 GVC method were plotted for all measurement stations inboth the fully open and the fully closed positions. This resulted in two control charts,one for monitoring T 2 and one for the generalized variance. The procedure requiredthat 16 inspection points be monitored symmetrically around the ap. These inspectionpoints form the multi-response simultaneous tracking for the T 2 GVC method. Uponplotting the control charts, the process control status was examined. The statisticalanalysis was carried out in MINITAB (version 15).

The mathematical relations describing the control limits for the T 2 GVC controlchart are given in equation (1) (Montgomery, 2005):

T 2 X 2 X 0S 2 1 X 2 X

X j 1n Xn

i 1

x

S i 12m2 1Xm2 1

i 1

V i V i

V i X i 1 2 X i

1


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Where n and m are the numbers of the responses and observations respectively(n 16, m 37 in the case studied), is the individual data point xij

X j and is the samplemean vector, S i is the sample covariance matrix.

The results obtained from the application of multivariate SPC revealed an assemblyprocess that was not in control (Figure 2) in the closed nozzle position. Measurementsin 22 per cent of the samples (samples 1, 2, 4, 6, 7, 13, 32 and 37) were out of control(Table I). However, only 8 per cent were due to instabilities of the generalized variancebehaviour alone (samples 2, 6 and 37). On the contrary, in the open ap position,generalised variance chart demonstrates that the joint variability of the ap gaugeswere in control, while samples 13 and 15 reveal special-cause instability to the values of T 2 . Stations that were responsible for inducing statistically signicant discrepancies inthe estimation of T 2 are shown in Table I for either of the two monitored ap positions.

Figure 2.Multivariate charts(T2 -generalized variance)of gauge measurements in16 inspection pointsaround the nozzle apsfor: a) closed, and b) openposition (data based on 37jet-ghter nozzle gaugereadings)

Samplepoint

Process statusfully closedposition Process T 2

Generalisedvariance Reason for non-conformance

1 Out of control Special Stations: 5, 9, 10, 14 ( p , 0:05)Present technical inability for ne tuning

2 Out of control Special Measurement inconsistency4 Out of control Special Stations: 2, 8, 15, 16 ( p , 0:05)

Present technical inability for ne tuning6 Out of control Special Special Stations: 9, 10, 16 ( p , 0:05)

Measurement inconsistency7 Out of control Special Station: 2, 3, 4, 9 ( p , 0:05)

13 Out of control Special Station: 2, 4, 8, 10, 12, 14, 16 ( p , 0:05)

Present technical inability for ne tuning32 Out of control Special Station: 1, 7, 8, 14 ( p , 0:05)Present technical inability for ne tuning

37 Out of control Special Special Station: 2, 3, 5, 6, 7, 8, 9, 11, 12, 16 ( p , 0:05)Measurement inconsistency

13 Out of control Special Stations: 1, 3, 6, 14, 15 ( p , 0:05)Present technical inability for ne tuning

15 Out of control Special Stations: 1, 3, 5, 8, 9, 11, 12, 14, 15 ( p , 0:05)Present technical inability for ne tuning

Table I.Flap monitoringperformance in 16sampling positions takenfor 37 exhaust nozzles

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The sample points where the special causes were identied was also included inTable I.

Analysis of causes of poor nozzle assembly processReviewing the nozzle assembly process for more than six months at the aero-enginesmaintenance unit revealed a number of inefciency causes in the assembly process,which are summarised in the cause and effect diagram (see Figure 3). Usually twotechnicians are charged with a nozzles assembly under the supervision of anappointed inspector. Supervision is not continuous since the inspector has to bepresent in other assembly points as well, yet the inspector is present during thevalidation of the measurements and validates them prior the dismissal of thecompleted assembly. The working environment is typical for an industrial area: harsh,noisy and unclean, adding to the adverse working conditions that are typical for anindustrial maintenance unit. Most technical teams working in the aero-enginesmaintenance unit consist of highly experienced personnel usually qualied to work on

at least two or three different engines types. In a number of occasions deviation fromthe technical instructions was observed due to a feeling of over-condence and aconsolidate idea that in some cases experience gained from practice overcomestechnical orders. Measurements on the designated stations are taken using an analogueadjustment gauge. In all assembly processes that were completed during a six-monthperiod a feeling of reluctance was present by the person who was taking theinstruments readings.

In Table I, it is tabulated the nature of the special causes which essential areconned to two kinds:

(1) Measurement inconsistency.(2) Technical inability to ne-tune nozzle assembly stations.

The former cause, was thought to be investigated through a measurement systemsanalysis. Gage repeatability and reproducibility (R&R) analysis on the present studywould reveal the acceptability of the measuring process. It would furthermoreenable to investigate possible sources of measurement error. Therefore, a gage R&Rcomplete study was carried out to assess the measuring capability of the threeindicated operators on the 16 inspection assembly points. The study was duplicatedto strengthen reproducibility concerns, thus in total 96 measurements wererendered. The corresponding gage R&R run-chart is depicted on Figure 4. It is seenimmediately that operator number 2 contributes the most in blurring the measuringcapability of the system while inconsistencies are allowed sparingly by operatornumber 1 which are mostly directed towards reproducibility concerns. Thisbehaviour is conrmed by the Gage R&R (Xbar and R) analysis in Figure 4. Thepart-to-part variation relating to the 16 inspection points has been well contained ata contribution of 19.2 per cent. However, while the measuring system is notacceptable at large, it is the measuring devices that cause the largest percentage invariation and it is estimated at 56.3 per cent whilst all three operators affectmeasurement variation by 24.5 per cent for a total R&R of 80.8 per cent. Thesignicant difference in executing efcient readings by the third operator is welldiscerned in the two subplots of Xbar and R for operators in Figure 4. Clearly, thesecond operator needs additional instructions and experience in carrying out such


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Figure 3.Cause and effect diagramfor a poor nozzle assemblyprocess

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Figure 4.Gage R&R study for

nozzle assemblydimension: a) run-chart, b)

Xbar/R


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important measurements. Regarding the rst operator, it may be necessitated thatthere would be an improvement in obtaining readings via specialised training andmore practicing sessions. Overall, it is advisable to comment on the performanceachieved by the repeatability and reproducibility indications with respect to thetolerance assigned which was set at 0.5 mm. It is observed that each of these twocomponents is greater than 100 per cent while in combination reaching 200 per cent.This signies the enormous effort required to bring the measurement system withinacceptable performance levels.

The replacement of the analogue adjustment gauge with a digital one wouldincrease the inherent precision of the instrument itself, making initial settings andreadings easier. In addition, the provision to use optical signals i.e. green and redlights to indicate whether the measurement taken is within the specied tolerancelimits or not, would further increase the measurements easiness and add to theprecision of the whole assembly process. The latter is considered a cost effectiveand acceptable solution towards the improvement of the nozzle assembly process.Finally the adjustment of the nozzle radius in all measurement stations is achievedby moving each actuators piston rod in the vertical direction using a special toolcalled adjusting wrench. The pistons rod movement is achieved by turning its endtting clockwise or counter clockwise with the wrench. The minimum turn availableis 1/6th of a turn. However, even with minimum attainable adjustments, thecorresponding radius change is 50 to 60 tens of a mm, when in most cases the naladjustment requires 10 to 20 tens of a millimetre. Thus the existing design isinadequate for ne radius tuning and needs improvement; a possible improvementis to reduce the minimum turn from 1/6th to 1/8th of a turn, thus allowing for nerradial adjustments.

ConclusionThe aerospace company studied was one of the rst companies in its host country thathas implemented a quality assurance system. This resulted in a gained experience of over 30 years in documentation, implementation and continuous assessment of thequality system both from internal and external auditors. Despite the accumulatedexperience, the nature of the products and the sound quality system documentation,the aero-engines maintenance unit is far from the implementation of a working totalquality management system. The quality assurance system implemented in theaerospace company includes a large number of process oworks for all divisions,indicating an effort to create and implement a potentially viable and dependablemethodology for product quality.

Relevant studies on the subject of quality and reliability management reveal thatthe largest proportion of faults (75 per cent typically) originates in the development andplanning stage and approximately 80 per cent of problems remain undetected until thenal tests, or when the product is already in use. SPC can provide the tools to avoidsuch defects at the early production phase and save both money and time. However,SPC and the respective control engineering divisions are absent from the majority of the companys divisions that are not considered production lines including theaero-engines maintenance unit. The term used for the latter is maintenance zones,hence SPC-free zones. This practice does not allow even the sensitive by-naturemaintenance work to be preventive and problem solving during its early stages. The

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brief period of SPC tools implementation at the aero-engines maintenance unitrevealed:

. the necessity for implementing and propagating such tools in all maintenancephases;

. an assembly process that was out of control and presented poor capabilityindexes; a problem that could have been solved, had an SPC process control andmonitor been implemented; and

. maintenance is a process in its own right, thus it requires understanding, controland needs improvement.

The process selected at the aero-engines maintenance unit for implementation of theSPC principles was the assembly of an engines exhaust nozzle. Data were collectedfrom existing measurements over a period of three years and the respective controlcharts using the moving average method were plotted. Results showed that 50 percent of the measurement stations (eight out of 16) in the open position were out of

control and 25 per cent of the measurement stations (four out 16) in the closeposition exhibited the same behaviour. The statistical control analysis that followedprovided graphical means for detecting special causes by utilising multi-responsemonitoring considering the 16 inspection points as independent qualitycharacteristics. Repetition of the analysis for increased sensitivity showed nobetter results. In view of the previously mentioned, the results from the presentstudy indicated and showed that by using widely accepted statistical tools theaero-engines maintenance unit required a change in its TQM policy andquality-related strategies. Financial considerations have to be set aside from thecompanys management and the deployment of a statistical quality control programtogether with:

. a group of well trained engineers; and

. state-of-the-art facilities that will allow for an on-line process control.

Based on the outcomes observed it is the authors view that on-line process controlought to be initiated as soon as possible. Modern quality management ideas emphasiseon continuous improvement in all aspects of the business in order to improve itsservices to the customer. These equally include both the production lines and themaintenance units.

The deployment of the new quality scheme at the aero-engines maintenancedivision has to be done in well dened and planned stages. It is suggested that aninitial phase may commence with the re-organisation of material ow and eliminationof the large queues created within the plant. This could be followed by an in-housetraining course aimed at providing the engineering team and the inspection units withthe necessary knowledge for working within a quality assurance environment. Thelatter would require the use of SPC tools. Training would include data collection, toolimplementation and results interpretation. The creation of a data network withelectronic measuring devices for on-line data acquisition and manipulation is deemedas comprising of the next successive step. Finally, a pilot program of processmonitoring using SPC tools will provide the know-how and the means for thepropagation of SQC in the building. The management can further aid this effort byperiodically publishing the obtained results (control charts, capability indices) of


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various processes. Management may imply a rewarding scheme to reward the group of people that participate in processes that either show improvement or remain at highquality levels over a long period of time. The pilot program mentioned in the previousparagraph may commence from the out of control exhaust nozzle assembly processthat was discussed and evaluated in this study. Results obtained can form the basis fora mini 6 s project that will expose the possible defects in the assembly process anddemonstrate ideas for further improvement. Once this is done and improvementsolutions have been implemented, monitoring of the assembly line by means of controlcharts and capability analysis will indicate whether the assembly process wasimproved or not. The combination of the companys intention to apply the SQC as partof a 6s scheme in conjunction with an aim for continuous quality improvements willempower the plans for implementation and continuous presence of a TQM culture inthe company studied.

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Wisner, J.D. (1999), A study of successful quality improvement programs in the transportationindustry, Benchmarking: An International Journal , Vol. 6 No. 2, pp. 147-63.

Wong, W.K., Ng, S.H. and Xu, K. (2006), A statistical investigation and optimization of anindustrial radiography inspection process for aero-engine components, Quality and

Reliability Engineering International , Vol. 22, pp. 321-34.

Further readingThornton, J. (2001), Maintainability drives Fort Worths joint strike ghter design, Assembly

Automation , Vol. 21 No. 3, pp. 204-9.

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an application of tqm tools at a maintenance division of a large aerospace company

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