Influence of variability on a reliable production process.
Pribytkova, Marina ; Polyantchikov, Igor ; Karaulova, Tatyana 等
1. INTRODUCTION
In today's competitive environment, markets are becoming more
international, dynamic, and customer-driven. Customers are demanding
better reliability and faster delivery. There are a number of reasons
why reliability is an important product attribute, including:
reputation, customer satisfaction, warranty costs, competitive advantage
and so on. Classical definition of reliability is: reliability is the
probability that an item can perform its intended function for a
specified interval under stated conditions (Military Handbook, 1998).
Even though a product has a reliable design, when the product is used in
the field, its reliability may be unsatisfactory. The reason for this
low reliability may be an unreliable manufacturing process. If we look
back at the definition of reliability it can be said that in terms of a
production process the definition means that a reliable production
process must fulfill its function or in other words produce qualified
products in required time. In case one of these two characteristics does
not fulfil the requirements the production process becomes unreliable.
Unreliable production processes waste money. Few companies know or
measure the reliability of their processes. All unmeasured processes are
verbalized as reliable. This fantasy continues until the process is
measured. Most processes are unreliable and thus need improvements
(http://www.barringer1.com). There are number of methods to evaluate and
improve process reliability, for instance, PFMEA, FTA, RBD, however in
this paper the question of the process reliability will be evaluated
from the aspect of production process variability.
2. VARIABILITY AND PERFORMANCE
In manufacturing systems, almost all attributes are of interest to
variability. To effectively analyze variability, it must be estimated.
It is achieved by using standard measures.
A relative measure of the variability of a random variable is the
standard deviation divided by the mean, which is called the coefficient
of variation. If we let t denote the mean and a denote the variance, the
coefficient of variation C can be written
C = [sigma] / t (1)
According to this coefficient the variability is divided into three
categories:
--C < 0,75 low variability,
--0,75 < C < 1,33 - average variability,
--C > 1,33 high variability (Koszkul et al.,2006)
To identify strategies for managing production systems in the face
of variability, it is important to understand the causes of variability.
The most prevalent sources of variability are:
--"Natural" variability
--Random outages
--Recycle
The definition of "natural" variability shows itself that
this is a normal part of a process and thus it cannot be eliminated at
all. Random outages or breakdowns are the causes of the largest
variability in many systems.
Variability from recycle happens when a workstation performs a task
and then checks to see whether the task was done correctly. If it was
not, the task is repeated (Hopp & Spearman, 2001).
As it can be seen failures of both characteristics of reliable
production process, quality and time, are direct causes of variability
in the process. Quality is already named like one of the causes, as for
time--all breakdowns in the process lead to decreasing of process
performance.
As process times and quality measures are the characteristics which
are affected by variability, thus it can be said that performance of the
production process is affected by the variability as these
characteristics are components of performance. Certainly, there are also
others components like utilization, work in process, cycle time and so
on, however, in the frame of this article they are not taken into
consideration. Main characteristic of process time is throughput. The
throughput is defined like the average quality of good parts produced
per unit time.
Talking about good part is always meant quality of this part.
Inspection of quality, as usually, refers to a finished part. However
the best way to think about quality is in process control. If the
process is under control, inspection is not necessary (Hopp &
Spearman, 2001).
Quality control is a single link in the total reliability process.
No product can perform reliably without the inputs of quality control
because quality parts and components are needed to go into the product
so that its reliability is assured (http://www.weibull.com).
There are several problems with inspection under traditional
quality control:
1. The inspection process does not add any "value"
2. Inspection is costly
3. It is sometimes done too late in the production process. This
often results in defective or non-acceptable goods actually being
received by the customer
Taking into consideration all these factors it can be said that
such a method like SPC is very suitable for the process tacking.
[FIGURE 1 OMITTED]
Statistical process control (SPC) is the application of statistical
methods to the monitoring and control of a process to ensure that it
operates at its full potential to produce conforming product. Under SPC,
a process behaves predictably to produce as much conforming product as
possible with the least possible waste.
Much of the power of SPC lies in the ability to examine a process
and the sources of variation in that process using tools that give
weight to objective analysis over subjective opinions. Variations in the
process can be detected and corrected, thus reducing waste as well as
the likelihood that problems will be passed on to the customer. With its
emphasis on early detection and prevention of problems, SPC has a
distinct advantage over other quality methods, such as inspection, that
apply resources to detecting and correcting problems after they have
occurred. SPC may be broadly broken down into three sets of activities:
understanding the process; understanding the causes of variation; and
elimination of the sources of special cause variation.
In understanding a process, the process is typically mapped out and
the process is monitored using control charts (figure 1). When, through
the control charts, variation that is due to special causes is
identified, additional effort is exerted to determine causes of that
variance and eliminate it (Oakland, 2008).
4. CASE STYDY
A case study was carried out for a screw driver. Initially there
was a pneumatic screwdriver installed. The process of screws tightening
was unreliable (figure 2). After the SPC analysis was done and the
results were presented graphically (Laaneots & Mathiesen, 2006), it
was decided that a root ca use of the process variability is the
pneumatic screwdriver itself. Thus it was decided to change the
screwdriver for an electric. The analogous SPC analysis was carried out
for the new screwdriver which showed that the process became stable and
its variability was very low (figure 3).
[FIGURE 2 OMITTED]
The coefficient of variation was calculated according to equation
(2):
C = 0,034 / 1,74 = 0,02 (2)
[FIGURE 3 OMITTED]
Which is according to Hopp and Spearman is low variability. The
result of the analysis shows that at the moment the process of
tightening the screws is very stable and reliable. However the
variability may increase in case of, for instance, defected components
or screwdriver's failure.
Thus, it is recommended to track the process further using the same
SPC analysis. More over the analysis can be improved by adding the
reaction limits to the graph. The reaction limits help to detect a
problem in the process at the earlier stages. If the graph of the
process crosses the reaction limit, it is a sign for management to pay
attention to the process and implement the reaction actions before a
failure in the process occurs.
5. CONCLUSION
In the article the problem of variability's influence on a
production process is discussed. The production process becomes unstable
and unreliable in case of high variability in it. The SPC analysis is
offered like one of the best methods for tracking of the process
variability. This method allows not only tracking of variability level
in the process, but also to prevent problems with quality, which lead to
failures in production process. In addition the using of the SPC method
allows to reduce or eliminate additional control of the final product
what considerably saves time and resources of a firm.
6. REFERENCES
Hopp, W.J.; Spearman, M.L. (2001). Factory Physics: Foundations of
Manufacturing Management, Irwin McGraw-Hill, London, 2nd edition.
Koszkul J., Pietrzak M., Gzielo A., Postawa P. (2006). Influence of
variability of polymer processing on the manufacturing system. Journal
of Achievements in Materials and Manufacturing Engineering, Vol.17,
issue 12 July-August.
Laaneots, R.; Mathiesen, O. (2006). An Introduction to metrology,
TUT press, ISBN 9985-59-609-9, Estonia
Oakland, J. (2008). Statistical Process Control,
Butterworth-Heinemann, ISBN-13: 978-0-7506-6962-7, Great Britain
(1998) Military Handbook, Electronic Reliability Design Handbook,
MIL-HDBK-338B.
*** http://www.barringer1.com/pr.htm, Accessed on: 2010-0504
***http://www.weibull.com/LifeDataWeb/reliability_and_quality_control.htm, Accessed on:2010-05-04
