Reliability factors of production process.

Pribytkova, Marina ; Karaulova, Tatyana

1. INTRODUCTION

Often production processes are mainly oriented on manufacturing volumes and speed and questions of reliable manufacturing are questions of minor importance. Probably the reason is that engineering education is traditionally concerned with teaching how manufactured products work. The way in which products fail, the effects of failures are not usually taugh. Perfectly, the engineer's tasks are to design and maintaine the product so that the failed state is deferred. In these tasks he faces the problems inherent in the variability of engineering materials, processes and applications. Unfortunately, engineering education is basically deterministic and does not usually pay sufficient attention to variability. Yet variability plays a vital role in determining the reliability of most products therefore understanding the causes and effects of variability is necessary for the creation of reliable products and for the solution of problems of unreliability (O'Connor et al., 2002).

The reliabilities of the components grouped together in a series configuration must first be calculated. Quantitative reliability calculations for such a group of components are based on two important considerations (Stapelberg, 2008):

* Measurement of the reliability of the components must be precise as possible

* The way in which the reliability of the series system is calculated.

Fig. 1 is a graphical portrayal of how the reliability of groups of series components changes for different values of individual component reliabilities, where the reliability of each component is identical. This graph illustrates how close to the reliability value of 1 (almost 0 failures) a components reliability would have to be in order to achieve high group reliability, when there are increasingly more components in group.

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The main cause of production-induced unreliability is the variability inherent in production process. Variability exists in all production systems and can have an enormous impact on performance. For this reason, the ability to measure, understand, and manage variability is critical to effective manufacturing management (Hoop & Spearman, 2000).

The present study considers an effect of variability's influence only upon such a part of a production process like machine resources (equipment). This constituent of a process has been chosen after an analysis of one typical Process FMEA was carried out and a following regularity was revealed (see Fig. 2):

* Main components of a production process who do failures are machines and operators

* In spite of knowing that operators do faults more often than machines, according to RPN number, it can be said that machines do more fundamental failures for process in whole

RPN index = machine failures/operator failures = 1,14/1 (1)

The main cause of machine-induced variability in production process is machine breakdowns. Numerical characteristic of machine downtime is availability.

3. AVAILABILITY

The concept of availability was originally developed for repairable systems that are required to operate continuously and are at any random point in time either operating or "down" because of failure and are being worked upon so as to restore their operation in minimum time. In this original concept a system is considered to be in only two possible states operating or in repair--and availability is defined as the probability that a system is operating satisfactorily at any random point in time t, when subject to a sequence of "up" and "down" cycles which constitute an alternating renewal process. In other words, availability is a combination of reliability and maintainability parameters.

For study an availability of complex systems is more suitable mathematical model Markov process. The basic concepts of the Markov process are those of "state" of the system (e.g., operating, nonoperating) and state "transition" (from operating to nonoperating due to failure, or from nonoperating to operating due to repair) (MIL-HDBK-338B, 1998).

Using the consepts of reliability theory, availability can be expressed in follow formula

A = MTTF/MTTF + MTTR (2)

where MTTF--mean time to failure

MTTR--mean time to repaire

Analyzing this formula it can be said that we want MTTF to be big and MTTR to be small or in other words, an effective way to increase a system's availability is to improve its maintainability by minimising the downtime (Stapelberg, 2008). In addition, decreasing the mean time to repair can significantly reduce the effective variability of the machine (Hoop & Spearman, 2000).

Maintainability can be defined as the probability that a failed item will be restored to an operational effective condition wihin a given period of time.

4. OVERALL EQUIPMENT EFFICIENCY (OEE)

The objective of modern assembly processes is to produce high quality customized products at low cost. There are number of different methods to evaluate an efficiency of the process, however, one of the best is OEE.

OEE is a key performance indicator of how machines, production lines or processes are performing in terms of equipment availability: reliability (MTBF), maintainability (MTTR), utilization, throughput performance or speed and quality produced. It identifies losses due to equipment failure, setups and adjustments, idling and minor stops, reduced speed, process defects and start up. All the above factors are grouped under the following three submetrics of equipment efficiency (see Fig. 3):

1. Availability

2. Performance efficiency

3. Rate of quality

The three sub-metrics and OEE are mathematically related as follows:

OEE = Availability x Performance x Quality (3)

Each of the components of the OEE calculation (availability, efficiency, and quality) have sub-component data that must be collected by equipment operators in order to calculate the OEE value. (Heilala et al., 2000)

4.1 Calculating Overall Equipment Effectiveness

Availability losses

Planned shutdown losses: No production scheduled, Planned maintenance;

Downtime losses: Breakdowns & failures, Changeover (product, size), Tooling or part changes, Startup or adjustment.

Performance efficiency losses

Minor stops (jams, circuit breaker trips, etc.);

Reduced speed, cycle time, or capacity.

Quality losses

Defects/rework; Scrap; Yield/transition (from changeover, startup/adjustment).

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OEE analysis can show the time losses and helps in identifying the actual time the system is producing good units (see Fig.4). This can be used for evaluating different production work time and shift arrangements.

The goal of measuring OEE is to improve the effectiveness of equipment. Since equipment effectiveness affects shopfloor employees more than any other group, it is appropriate for them to be involved in tracking OEE and in planning and implementing equipment improvements to reduce lost effectiveness.

4.2 Common Root Causes of Poor Equipment Reliability

Root Cause Analysis (RCA) is a structured and orderly approach for discovering the systemic sources of operations problems and their solutions.

In order to truly prevent future unreliability, is necessary go to the true source of failures known as latent causes. Some of the more common latent roots (Mobley, 1999) that one can cite for premature equipment failure are:

* Operating practices

* Maintenance practices

* Equipment age

* Management systems

OEE data collection, analysis, reporting, and trending provide the fundamental underlying basis for improving equipment effectiveness by eliminating the major equipment-related losses.

5. CONCLUSIONS

It has been established that by improving equipment reliability, the availability measure and the quality measure may also improve. There will then be a dual effect on OEE due to improved reliability. Another metric that would be useful to track is the MTBF and scrap rate on specific pieces of equipment that have been targeted for improvement. The direct correlation between the two measures can be examined and further analyzed.

Acknowledgements: Hereby we would like to thank the Estonian Ministry of Education and Research for targeted financing scheme SF0140113Bs08 that enabled us to carry out this work.

6. REFERENCES

Heilala, J., Helin, K., Montonen, J. & Vaatainen, O. (2006). Life cycle and cost analysis for modular re-configurable final assembly systems, IFIP International Federation for Information Processing,, 1-286, Springer Boston, 978-0387-31276-7, USA

Hoop, W. & Spearman (2000). Factory Phisics, McGraw+Hill Higher Education, ISBN 0-256-24795-1

Mobley, R. (1999). Root Cause Failure Analysis, Elsevier Butterworth-Heinemann

O'Connor, P., Newton, D. & Bromley, R. (2002). Practical reliability engineering

Stapelberg, R. (2008). Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design ISBN-10 Publisher: Springer

***MIL-HDBK-338B (1998) Military Handbook, Electronic Reliability Design Handbook

PRIBYTKOVA, M[arina] & KARAULOVA, T[atyana]*