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Modern approaches to product reliability improvement.

The business impact of product reliability

Reliability assurance development is a key ingredient for improving the reliability performance of manufactured products, but there are a wide range of factors which are also incentive to this performance such as:

* overall management of the reliability function;

* the intensity of reliability assessment and evaluation during the design and development phase;

* utilization of field failure data and the depth of subsequent technical and statistical analysis;

* controlling the consistency of manufacture and assembly;

* effective management and control of engineering changes.

Improving the reliability of a product is an important part of the larger picture of improving product quality. A momentous quality characteristic, reliability is concerned with the performance of the product's function over a stated period of time. Therefore, whereas quality is defined as conformance to requirements, reliability on the other hand is defined as a failure or fault-free performance in all products provided to the customer. Compared with quality which embraces the whole organization, reliability has a more product specific definition and therefore should be the main impetus to continuously improving the performance of the product's functionality.

BS 4778[1] defines reliability as the ability of an item to perform a required function under stated conditions for a stated period of time. The item can refer to a manufactured product or its constituent units and components, manufacturing and assembly systems (e.g. transfer lines), mechanical and electrical process systems and general mechanical, electrical and electronic systems. An expansion of the above formal definition of reliability leads to qualitative and quantitative expressions:

* qualitatively - absence of functional failure during use or service;

* quantitatively - the probability that an item will give failure-free performance of its intended functions for the specified duration of time.

The advantages of reliability assurance development encompassing all the relevant business functions are both qualitative and quantitative. The main benefit is that company becomes more proactive rather than reactive to reliability-related problems. Other benefits include:

* Measurement of the effects of design and process changes on the reliability of the product.

* Potential reliability problems can be objectively and readily identified.

* Development of reliability objectives, setting of goals and means of achieving them.

* Overall increase in customer satisfaction, particularly for suppliers of industrial products and plants.

In achieving these benefits, the effect would be to reduce reliability costs encountered primarily during field operation and burn-in of a product, and command a strong ground in market share.

Reliability: an increasingly important role

Recent research has shown that product reliability is positively correlated with customer confidence and profit margins[2]. This is supported by the fact that ethnographic and subjective measures being carried out by the author in a leading machine tool manufacture, is showing similar relationships. Global customer surveys carried out by the parent company and the recent introduction of customer satisfaction surveys by the UK firm are showing that reliability is perceived as a significant factor both in customer satisfaction and in the decision to purchase another machine tool in the next capital investment planned by the customers.

Preliminary research findings and anecdotal evidence collected by the author has shown a clear correlation between the cost of warranty and reliability of the product during use. This can be further elaborated through a simple example. The methodology used is a simplified version of the one described by Myrick[3]. Table I shows the trend in the number of new CNC machine tools (lathes, [TABULAR DATA FOR TABLE I OMITTED] turning centres, machining centres and milling machines) purchased in the UK on a national scale, within a four-year period[4] and the predicted warranty costs per year for a mean time between failure (MTBF) of six months and 12 months. The MTBF figures are only used as an example and do not refer to the actual reliability of machine tools.

As indicated by the data in the table, a substantial decrease in warranty costs can occur if the MTBF is increased by a factor of 2. More importantly, depending on the validity of the MTBF prediction, it can be used for predicting the number of failures likely to occur per period. A period can refer to a month, week, year etc. In this context, it will aid in planning or forecasting future spares requirements. It is clear from this analysis that after the lapse of warranty, the user will have to suffer the cost of failures.

The exponential distribution was seen to be appropriate for this warranty analysis. The expected number of machines likely to fail every year is given by the following:

R(t) = exp(- t/[Theta]); where t is time and [Theta] is the MTBF in the same units as t.

Previous reliability analysis carried out by the author showed clearly that machine tools exhibit exponential failure rate during the warranty period. For a given MTBF of six months, the expected number of machines likely to survive to start the next year will be 13.53 per cent. Since 13.53 per cent survive to the next year, 86.46 per cent fail each year. Table II summarizes the warranty decisions and assumptions in this example. Again the warranty cost figures is only used as an example.

[TABULAR DATA FOR TABLE II OMITTED]

However, an improvement in product reliability will not only bring about a reduction in warranty parts and labour cost, the impacts of this improvement will also cascade down to support groups, being very substantial in areas such as spares inventory both in physical and monetary terms, product engineering changes and rework both during use, manufacture and development, cost of reworking expensive parts to be used as spare buffer stocks. These benefits are translated diagramatically, as shown in Figure 1.

Two facets of product reliability

An extensive review of the reliability literature suggests trends towards projecting the more mathematical modelling and predictive aspects of the subject. This is unfortunate and a general concern, since it drastically limits the awareness, application and diffusion of the more rational reliability tools in the manufacturing industry. Although proliferation on the more rational reliability techniques developed through sensible engineering and management values are furthered, it is indicative that many journals and conference proceedings sponsored by most of the important engineering associations and institutions devote a large proportion of the literature to mathematical modelling of reliability[e.g. 5,6].

Reliability is also considered as a downstream quality characteristic, particularly measurable during the useful life of a product. Yet the reliability of the product is strongly influenced by the approach taken during the design and development stage. Further to this, in contrast to consuming 15 per cent of the total life-cycle costs (LCC) during the design and development phase, research has shown that as much as 95 per cent of the remaining LCC is determined by decisions made during this stage[7]. A breakdown of the total life-cycle costs for a typical machine tool is shown in Figure 2.

There is then two facets of reliability which the author has identified and relevant literature clearly supports this case, namely O'Connor[8]. On one plane, intense effort is given on the mathematical modelling and predictive aspects, reflected by the endless flow of reliability literature in this area. The overall objective is to yield numerous behavioural characteristics of reliability which have no industrial significance or meaning. These are usually based on Monte-Carlo simulation techniques, Markov methods[9] and the over usage of quantitative methods[10,11]. Proponents within this field of reliability have this general perception that reliability-related data (e.g. utilization of field-failure data) that would have to be inserted into this complex equation are known with some exactness, but otherwise the models have interest only from a theoretical point of view. It is very difficult to find evidence that shows practitioners within the manufacturing industry making use of this material. On the other plane of reliability literature, proliferation of rational reliability methods and techniques is prominent[e.g. 12]. The overall objective of the deployment of such tools is to achieve reliability to a favourable level during the design, development and manufacturing stage.

It is not the intention to further the implications of reliability literature with respect to over utilization of mathematical modelling and prediction. O'Connor[8] who also discusses the implications of adopting a purely "systems" approach to product reliability achievement and improvement, gives an excellent and convincing account of this view. He points out that such an approach taught in many textbooks and other literature is fundamentally misleading and damaging.

Tools and techniques in support of reliability improvement

Despite literature emphasizing the more mathematical modelling and predictive aspects of this theme subject, there are numerous methods and tools in existence to cope with product reliability issues. Approximately 80 methods relating to both product quality and reliability has been identified by Juran in his quality control handbook[13] and in a recent survey of the literature[14]. The working mechanisms of these methods has been described by many proponents in different manipulative ways; for example, as essential components of the concurrent or simultaneous engineering philosophy[e.g. 15], as methods for quality-driven product development[e.g. 16], essential techniques in support for Design for X (DFX), where X can stand for quality, manufacture, reliability, assembly etc.[e.g. 12, 17], methods forming the important elements of the modern integrated approach to quality engineering[e.g. 18].

The author has distinguished numerous methods and tools which have a direct impact on the achievement and improvement of product reliability. These methods can be divided into what can be termed on-line and off-line techniques [ILLUSTRATION FOR FIGURE 3 OMITTED].

Off-line techniques: assuring reliability during product design and manufacture

As shown in Figure 3, off-line techniques can be further categorized into the following areas:

* Methods purely used as a design activity during the introduction of new products and product uplifts and as a result of customer needs and technological advancement. Such methods allow one to focus on the underlying functional requirements of the product and therefore enables the setting of process parameters.

* Methods purely used for analysing the effects of proposed engineering changes to the reliability of the product, acquiring and storing knowledge about the product and the process during the design/redesign process, analysing the effects of purchased material to the reliability of the product, assessing the impacts of potential component failure on the operation of the product etc.

As highlighted in Figure 3, there are a number of off-line techniques that are relevant to product reliability achievement and improvement. Some of the pragmatic techniques within the modern concurrent engineering era are:

* Taguchi methods[19].

* Quality function deployment (QFD)[20].

* Failure modes effects analysis (FMEA)[21].

Taguchi's basic concepts prescribe that products should be designed robust enough to maximize quality and reliability despite oscillations during the manufacture and use of the product, and improve the manufacturing process through improved process design rather than through expensive process control technologies. This is achieved through giving allowances to controllable variations which Taguchi terms control factors and uncontrollable variations termed noise factors. Recently, the Taguchi framework has received much attention in the reliability field[e.g. 22,23!. This is not surprising when one considers that the Taguchi philosophy is an increasingly important design paradigm aimed at reducing the overall variability of the product to achieve maximum reliability at low cost.

Whereas Taguchi's end goal is to optimize simultaneously the design of the product and the associated process, QFD is engaged in cascading down customer requirements through the product introduction process, so that no characteristic given by the customer is lost during this process. QFD, therefore, is concerned with capturing the voice of the customer through horizontal and vertical communications termed the House of Quality (HoQ). Figure 4 illustrates this matrix.

In this context, QFD is used as a planning tool that permits the performance features identified by market research to be translated into the required engineering parameters and values. As well as ensuring that what the customer wants is actually manufactured the first time, leading to a reduction of engineering changes and a significant reduction of lead time, the technique also allows a methodical way of emphasizing design and process activities and control necessary to achieve reliability.

One of the best known and established techniques, however, is FMEA. FMEA or sometimes referred to as FMECA (failure modes, effects and criticality analysis) is viewed as a structured analysis tool which examines a design to determine potential failure modes associated with the design of a product and the effect of each failure mode on operation of the product or system. Therefore, the purpose of conducting a design FMEA is to seek out potential causes of failure before they become a reality during use or service musing unnecessary increments to warranty costs. An FMEA worksheet, similar to the one illustrated in Figure 5, is used to document the process for later use.

The technique deploys inductive logic (bottom-up approach) as opposed to fault-tree analysis (FTA) which employs the top-down approach. This enables the effect of part failure on subassembly operation, subassembly failure on unit operation and unit failure on system or product operation to be analysed.

By identifying a list of potential failure modes for a particular part or part function, an assessment of the seriousness of the effect of each failure mode to the performance of the part, the next assembly or system operation is evaluated through a severity ranking index. Conceivable failure causes to each failure modes is then listed and the probability (likelihood of occurrence) that a specific cause will result in the failure mode is evaluated through a probability ranking index. Through listing planned preventions and detections, the ability of the proposed prevention and detection programme to identify a potential design weakness is evaluated through a detection ranking index. An example of the mechanism for the assessment of the severity, probability and detection is shown in Table III. It should be noted that the terminology and the ranking may vary.

[TABULAR DATA FOR TABLE III OMITTED]

The product of the severity, probability and detection formulates a risk priority number (RPN) for each failure mode or for each part. From Table III, it can be seen that RPN will be between 1 and 125 and corrective action should be directed towards the RPN parts with the highest magnitude. Although FMEA provides a structured approach to highlighting potential problem areas, it does not ensure the attainment of the inherent reliability of the product.

Online techniques: measuring, monitoring and assessing reliability

Online techniques, however, are used purely for measuring, monitoring and assessing the progress of product reliability improvement, identifying inhibitors to poor reliability performance so that design engineers can act on this information. In this context, online techniques is an integrative part of the overall reliability assurance development. On the main, such data will be of a quantitative nature. It will not necessarily effect a reliability improvement in the product, but will aid in bringing about this improvement. Examples of online techniques are also illustrated in Figure 3.

Most noticeable reliability parameters that are used for measuring the reliability of the product are:

* Mean time between failures (MTBF); purely used for products that are repairable.

* Mean time to failure (MTTF); purely used for one shot items which can form parts of a product or a product in its own right.

* Mean time to repair (MTTR) which gives an indication of the maintainability of the product.

* Failure rate or failure intensity; these measures are the inverse of MTTF and MTBF.

* Availability; purely used to evaluate the proportion of total production time that the product will be available for use. This is excluding any logistics delay.

The above reliability parameters can be derived through using appropriate continuous distribution functions. Most noticeable examples are the exponential and the Weibull distribution function. Earlier in this paper, the usefulness of the exponential function as a mechanism for predicting MTBF of machine tools was shown, together with predicting future failures and warranty costs. Continuously measuring, monitoring and assessing reliability using the above parameters as quantitative measures is an important aspect of the overall reliability assurance programme. Implemented effectively, it acts as a mechanism for:

(1) Objectively identifying inhibitors to poor product reliability performance.

(2) Monitoring continuously the reliability-performance of a total population of products in field with reference to a pre-defined reliability performance set by the product design engineers.

(3) Assessing the reliability of a family of products early in its use. This will enable both supplier and customer to discuss whether the product is meeting its specified inherent reliability.

(4) Aiding towards an objective evaluation of reliability during the introduction of new products, major product uplifts and engineering changes.

(5) Feedback of reliability performance information to relevant functional groups.

Recent reliability analysis conducted on a sample of machine tool products substantiated some of the above cited benefits, particularly in relation to items 1, 2 and 5. Using field-failure data, this reliability assessment exercise was carried out as an industrial exercise. It formed the initial base for testing the feasibility of such a task as a continuous reliability improvement tool.

Proliferation of reliability methods in the manufacturing industry

It is widely recognized that statistical quality or process control techniques, being the main focus of attention during the late 1970s and 1980s time horizon, are now well known, valued and applied by many companies as a means of:

* monitoring the output characteristic of a product;

* monitoring the capability of the process in relation to a given tolerance; or

* verifying the correctness to specification of purchased materials or sub-contracted work.

The above three items therefore signify that the purpose of SPC/SQC, otherwise known as on-line quality control, is to reduce output deviations from a target by tracking and eliminating noise factors that contribute to the process variability. Such methods ensured that every manufactured part was passed on to the next customer defect-free (the customer being any physical manufacturing operation in the production process, beginning with the next operation and ending with the end user). From a product reliability viewpoint, early life failures (infant mortality) which is the cause of latent or abnormal defects escaping the final testing of the product, is reduced. Latent defects is said to be directly proportional to the total defects per unit in the entire production process. As total defects per unit is minimized through deploying on-line quality control, this also leads to a reduction in latent defects.

Although recent research findings suggest that online quality control techniques are relatively appreciated in many sectors of the manufacturing industry, recent single industry investigations[e.g. 26] show that most of the online quality control techniques which should be useful in assessing and solving quality during the manufacture of the product remain insufficiently used. Nevertheless, taking the whole manufacturing industry into deliberation, literature suggests that online quality control, on average, has reached maturity through overcoming many barriers to its acceptance. Past research studies[e.g. 27,28] has concluded that the major barriers preventing the introduction of these techniques is lack of knowledge and little industry awareness. From the surveys carried out it was clear that many companies are introduced to online quality control by customers who specify its use.

As a comparison to online quality control, proliferation of the reliability techniques and tools (online and off-line approaches) as discussed above is relatively low. The few empirical studies carried out is indicating that although diffusion of some of the techniques have occurred, the rate of application seem to be with the more larger sized companies. For example, the cited benefits reported from the application of the Taguchi methodology and QFD in numerous product design problems relate primarily to larger sized companies[19]. An explanation may be that larger sized companies have the necessary extra resources to adopt these methods on a regular basis. In many cases, they are able to create functional areas and groups which are primarily responsible for product reliability improvement and who provide the necessary impetus for continuous usage of these methods.

However, application and diffusion of the more contemporary reliability techniques within the small- and medium-sized companies are evident. For example, research on the diffusion of FMEA within the automotive industry[29] indicated moderate and continuous application of the technique. The revealing factor, however, is that most suppliers have been driven or motivated by mandatory requirements laid down by sophisticated customers. The authors preliminary research findings also indicated periodic application of this technique and continuous practice of value engineering[30] within a major machine tool manufacturer (a medium- to large-sized company). Further to this, a recent quality survey[31] indicated that one-third of all smaller organizations (1 to 50 employees) being surveyed looked for cost savings by always undertaking value engineering.

Despite the moderate application of these contemporary and modern reliability techniques in the manufacturing industry, problems or barriers are encountered during the implementation of these techniques. It is conclusive that the general trend experienced in the barriers to acceptance of online quality control seems to exist for the adoption of reliability-related tools and techniques. Review of recent research studies[e.g. 29,32,33] concluded that the following barriers were most frequently encountered during the implementation of these techniques:

* Application of these techniques requires the use of dedicated, cross-functional teams. In this context, a significant drain on resources occurs. This drain on resources cannot be met for long even by a comparatively large company so the secondary solution of a part-time team is often used.

* Lack of training or awareness of these techniques given to relevant personnel before implementation.

* Lack of understanding and application of these techniques is very low, leading to the general perception that it is ineffective.

* Lack of management commitment, with the overall belief that such techniques employ lengthy procedures and distract away from the main engineering activity.

Although it is evident that barriers to the acceptance of such techniques will always occur, consideration must be given to the inherent problem associated with many reliability techniques in the modern concurrent engineering era which past studies failed to address. It is indicative that many tools and techniques employ lengthy procedures for successful application to product reliability improvement. This, the author strongly believes, may downplay its significance and practice in the manufacturing industry. Those who are able to flood the needed resources, namely in terms of manpower and creation of dedicated teams, are able to overcome these initial stumbling blocks quite quickly. If this is not the case, then time constraints become a major issue, as, through the introduction of these techniques, extra activities are added to the engineering and manufacturing groups of a company making it difficult to cope simultaneously with the daily routine tasks.

There is also a general indication from the review of the literature that new reliability tools and techniques evolve and are promoted separately as the tool for solving problems. This is representative both from an industrial and academic research perspective. In the industrial context when heavily promoted, tools and techniques come and go and distraction spreads among the people involved in product design and development improvement. On the other hand, academic researchers and most conferences and journal articles seem to concentrate on the latest tools and techniques with little emphasis given on the more contemporary ones.

Conclusion

The objective of this article was to give a comprehensive account of product reliability improvement and achievement across a number of different dimensions and within the wider context of the "product quality paradigm".

Although application and diffusion of the formal reliability tools and techniques is relatively low, it is conclusive from the literature review that the importance of product reliability is certainly recognized and taken on board by the manufacturing industry. There is also an overall belief that higher reliability will result in lower product life-cycle costs which may give allowances for price reduction, shorter manufacturing lead time leading to shorter delivery time, and more importantly lower field failures, once the product has reached its rightful destination. To summarize, it is also conclusive that achievement and improvement of product reliability is primarily dependent on the following:

* The perspective taken during the design and manufacturing phase within an organization. Fully considering and assessing reliability through the deployment of formal reliability techniques and other mechanisms is the critical success factor in improving reliability during this stage. Further to this, conducting nominal engineering tasks from a reliability engineering viewpoint is also significant to this improvement.

* Improvement of all aspects of the concurrent engineering philosophy within an integrated business operation. As concurrent engineering implies, reliability alongside other product characteristics must be considered and assessed up-front in the design and development cycle.

* The depth at which retrospective reliability analysis is conducted, through the utilization of historical failure data. With respect to this approach, it is particularly beneficial when a proposed redesign of a product is going to contain as many design features as the previous model. As shown here, retrospective reliability analysis not only will identify poor inhibitors to poor reliability performance, but also can be deployed in predicting future failures and warranty costs. In this respect such analysed information can provide a mechanism for future spares requirement planning.

* The level (i.e. the status and structure of the reliability function) at which reliability is considered within the hierarchical organization domain. This will give an indication of the company's commitment to product reliability and the level of understanding of both the impacts of reliability and the basic concepts of reliability engineering, assurance and management.

* The intensity of the application of formal reliability tools and techniques. As reliability can be regarded as an engineering uncertainty, the overall objective is to characterize this uncertainty so that it leads to an improvement in product reliability. The overall objective of these methods is to aid in the understanding and characterization of this uncertainty.

In this context, reliability, like quality, requires a sound management approach and both rely on improvement through a structured method which considers the dynamics of business interaction. However, reliability goes one step further by its dependence on engineering details as a primary requirement. Therefore, optimum quality cannot exist without optimum reliability, even though optimum reliability can exist without optimum quality.

References

1. BS 4778, Glossary of Terms Used in Quality Assurance, British Standard Institution, London, 1987.

2. "Design time: new product design and corporate success", internal report, Bradford University Management Centre, 1994.

3. Myrick, A., "Analysis of warranty costs based on reliability", Proceedings of the Annual Reliability & Maintainability Symposium, 1990, pp. 228-32.

4. "The 1993 CNC machine tool market survey in the UK survey report", Benchmark Research Ltd, MTTA, DTI, Machinery, UK, January 1994.

5. ASQC and IEEE, Proceedings of the US Reliability & Maintainability Symposia, ASQC and IEEE, published annually.

6. IEEE, Transactions on Reliability, IEEE, published annually.

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8. O'Connor, P.D.T., "Quality and reliability: illusions and realities", Quality & Reliability Engineering International, Vol. 9, 1993, pp. 163-8.

9. Choi, H. and Trivedi, K.S., "Conditional MTTF an its computation in Markov reliability models", Proceedings of the Annual Reliability & Maintainability Symposium, 1993, pp. 5663.

10. Schneeweiss, W.G., "Calculating MTBF for modularised fault trees", Proceedings of the Annual Reliability and Maintainability Symposium, 1993, pp. 206-13.

11. Love, C.E. and Guo, R., "An application of a bathtub failure model to imperfectly repaired systems data", Quality & Reliability Engineering International, Vol. 9, 1993, pp. 127-35.

12. Swift, K.G. and Allen, A.J., "Techniques in design for quality and manufacture", Journal of Engineering Design, Vol. 3 No. 1, 1992, pp. 81-91.

13. Juran, J.M., Juran's Quality Control Handbook, McGraw-Hill, New York, NY, 1988.

14. Zaidi, A., SPC Concepts, Methodologies et Outils, Lavoisier, Paris, 1989.

15. Soleniius, G., "Concurrent engineering", Annals of the CIRP, Vol. 41, 1992, pp. 645-57.

16. Krause, F.L., Ulbrich, A. and Woll, R., "Methods for quality driven product development", Annals of the CIRP, Vol. 42, 1993, pp. 151-4.

17. Corbett, J., Dooner, M., Meleka, J. and Pym, C. (Eds), Design for Manufacture: Strategies Principles and Techniques, Addison-Wesley, Reading, MA and Wokingham, 1991.

18. Brown, A.D., Hale, P.R. and Parnaby, J., "An integrated approach to quality engineering in support for design for manufacture", Proceedings of the IMechE, Vol. 203, 1989, pp. 56-63.

19. Hamid, N., "The Taguchi methods: achieving design and output quality", Quality Progress, pp. 72-6.

20. Sullivan, L.P., "Quality function deployment", Quality Progress, Vol. 7, 1986, pp. 29-50.

21. "Failure modes and effects analysis (Design FMEA)", Cincinnati Milacron UK Ltd, 1995.

22. Hamada, M., "Reliability improvement via Taguchi's robust design", Quality & Reliability Engineering International, Vol. 9, 1993, pp. 7-13.

23. Byrne, D. and Quinlan, J., "Robust function for attaining high reliability at low cost", Proceedings of the Annual Reliability & Maintainability Symposium, 1993, pp. 183-91.

24. Bossert, J.L. and Clausing, D., Quality Function Deployment - A Practioner's Approach, ASQC Quality Press, Milwaukee, WI, 1991.

25. Tools and Techniques for TQM. Society of Motor Manufacturers & Traders Ltd, 1991.

26. Dale, B.J. and Shaw, P., "The application of SPC in UK automotive manufacture: some research findings", Quality & Reliability Engineering International, Vol. 5, 1989, pp. 5-15.

27. Keith, G.L, John, S.O. et al., "The barriers to acceptance of statistical methods of quality control in UK manufacturing industry", International Journal of Production Research, Vol. 22 No. 4, 1984, pp. 647-60.

28. Chui, W.K. and Wetherhill, G.B., "Quality control practices", International Journal of Production Research, Vol. 13, 1973, p. 175.

29. Dale, B.G. and Shaw, P., "Failure mode and effects analysis in the UK motor industry. A state-of-the-art study", Quality & Reliability Engineering International, Vol. 6 No. 3, 1990, pp. 179-88.

30. Value Engineering and Analysis, Cincinnati Milacron UK Ltd, 1995.

31. The 1994 Quality in UK Manufacturing Survey, Benchmark Research Ltd, July 1994.

32. Barber, P.R. and Attewell, B., "Barriers to implementing concurrent engineering in SMEs", Proceedings of the International Conference on Advanced Manufacturing, 11-13 September 1995, Section D2.

33. Betts, J. and Tookey, J.E., "Concurrent engineering in the aerospace industry", Proceedings of the International Conference on Advanced Manufacturing, 11-13 September 1995, Section AS.

Further reading

Coppola, A., "What's new in reliability engineering", Quality & Reliability Engineering International, Vol. 10 No. 3, 1994, p. 173.

Knight, C.R., "Four decades of reliability progress", Proceedings of the 1991 Award Reliability & Maintainability Symposium, pp. 317-21.
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Author:Ahmed, Josim U.
Publication:International Journal of Quality & Reliability Management
Date:Mar 1, 1996
Words:5062
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