INCREASING PRODUCTION LINE UTILIZATION: RELIABILITY CENTERED MAINTENANCE
Gonca Altuger
Stevens Institute of Technology
INTRODUCTION
In response to today's highly competitive global
marketplace and growing economy, the priorities of the
companies are shifting from being only profit focused to being
customer, technology and cost focused, forcing companies to
improve their existing product designs, diversify and expand
their product lines and use their existing manufacturing and
production lines more efficiently. The formation of product
families, as well as the newly formed relationship between
product families and production lines gained more
importance, and brought several challenges in scheduling,
maintenance and production line utilization area.
Product design process requires the involvement of
design, development, manufacturing, marketing and all other
related fields. Singh [1] defined basic steps for product design
as: problem identification, development of preliminary ideas,
refinement of these ideas, engineering evaluation, selection of
a compromise design, and finally implementation. Li and
Azarm [2] highlighted that two desirable aspects in a product
are its design superiority and success in the marketplace.
Taylor, English and Graves [3] pointed out what has not
heretofore appeared in DFX strategies is a system for product
design analysis relative to its fit in an existing manufacturing
environment. The concept of product family gained more
importance as the companies are faced with the challenge of
providing as much variety as possible for the marketplace.
Jiao and Tseng [4] pointed out that developing a product
family as a means to achieve economy of scale and
standardization of production has been well recognized in
industry. An applicable product family definition can be based
on the one provided by Messac, Martines, and Simpson [5]
where a product family is a group of related products that
share common features, components, and subsystems; and
satisfy market niches. As newer members are added to the
product family, the commonality level in product family is
being measured as well as capability of the production line.
Capability of the production line is the ability of the
production line to handle and adjust the changes in products in
terms of adjusting the production schedule, preventive
maintenance schedules as well as modifying the line
properties. This challenge creates a low utilized production
line for the new and existing members of the product family,
which in today’s competitive market is not desirable.
The challenge of providing a custom tailored
production schedule for each product in the product family
calls for a dynamic monitoring system. In order to be able to
capture the production line behavior at its’ fullest and increase
Prof. Constantin Chassapis
Stevens Institute of Technology
the utilization of the production line independent of which
product is in process, it is crucial to follow the reliability
levels of each station as well as the system, such that an
optimum reliability centered maintenance schedule will be
created.
This paper highlights the importance of dynamic line
monitoring for production lines that are responsible of
manufacturing product families. This paper proposes a
reliability centered optimization approach to obtain an
optimum preventive maintenance schedule that will maximize
the production line output. An example will be demonstrated
to show the implementation of the proposed method.
METHODOLOGY and RESULTS
Setting up a preventive maintenance schedule is tied
to the utilization and scheduling of the production line.
Preventive maintenance scheduling becomes more challenging
if the schedule is built based on reliability. In such a case a
minimum reliability level is set either for the stations
individually or for the system, and based on which member of
the product family is being manufactured the reliability levels
of individual stations follow a unique distribution pattern. In
order to generate the outputs of production layouts, a
simulation model has been created using ARENA software,
where stations, buffers, assembly machines, failure rates, cost,
operator behaviors and skills, time between failures and repair
time distributions can be defined.
Figure 1.Production Line Layout for Arena Simulation
Once the production line simulation model is built
and all parameters are defined, the change in the machine and
system reliabilities can be monitored for either preset time
intervals or over the continuing production life dynamically.
Without proper preventive maintenance schedule, machine
reliabilities are most likely to decrease based on their
reliability and lifetime distributions.
For the created
production line layout, if the behavior of three stations’
reliability levels are examined, along with the overall
production line reliability, it can be seen from the chart that
for a case of equal life times, and reliability levels, stations
behave similarly if not identical, obviously if the time and part
processing rates are identical along with the operator settings,
as well as lifetime and failure distributions, the reliability
decrease over the time for any three stations will be identical.
reliability may very well fall under 60% reliability until the
station or stations get maintained, which may not be desirable.
Also in order to maximize the utilization of the production
line, production engineers need to consider the order of
maintenance.
With proper selection of order of maintenance, it is possible to
perform maintenance less frequently than if all machines were
maintained at the same time, as it can be seen from the results
increasing the time between maintenance to almost 400
minutes, which is higher tan 330 minutes.
Figure 4. RCM -Different PM Schedules
Figure 2. Machine-System Reliability Dist. (No PM)
CONCLUSIONS AND FUTURE WORK
The minimum allowable reliability levels are set by the
product and production line development team; mostly based
on available resources, time and customer expectations.
Keeping the minimum allowable reliability level high will
result in scheduling preventive maintenance very frequently,
which in the long run will cause excessive down time of the
production line, high cost of maintenance and poor use of
resources, whereas setting the minimum allowable reliability
level too low may result in machine or system failures prior to
the scheduled maintenance, which will have higher repairing
costs than budgeted maintenance costs, and will result in
unplanned stop in the production line.
Figure 3. Machine - System Reliability Dist.
For the production line demonstrated in the example, if the
minimum allowable reliability levels are set to 80% for each
three stations that are being considered, depending on the
connection and station characteristics, the overall system
The results also back up the importance of
dynamically monitoring the reliability level change in a
production line, as well as creating an optimum preventive
maintenance schedule for the production line, which in the
long run will bring benefits including a highly utilized line
management, a low failure and repair rate, and low down time
for the production line, and higher efficiency in terms of part
output which also will lead to higher profits.
REFERENCES
[1]N. Singh (1996). “Systems Approach to Computer - Integrated
Design and Manufacturing”, John Wiley & Sons, Inc., USA
[2]H. Li and S. Azarm (2000). “Product Design Selection under
Uncertainty and With Competitive Advantage”, ASME Journal of
Mechanical Design, Vol.122, pp: 411-418
[3]G. D. Taylor, J. R. English, R. J. Graves (1994). “Designing
New Products: Compatibility with Existing Production Facilities and
Anticipated Product Mix”, Journal of Integrated Manufacturing
Systems, Vol.5, No.4/5, pp: 13-21
[4]J. Jiao, M. M. Tseng (2000). “Understanding Product Family
for Mass Customization by Developing Commonality Indices”,
Taylor & Francis Journal of Engineering Design, Vol. 11, No.3, pp:
225-243
[5]A. Messac, M. P. Martinez, and T. W. Simpson (2002).
"Effective Product Family Design Using Physical Programming",
Taylor & Francis Journal of Engineering Optimization, Vol.34, pp:
245-261 Design, Vol.122, pp: 403-410