Ardiyantoą*, Leny latiffah˛, Ari
Suwondoł, Nanang Sulaksono⁴, Gatot Murti Wibowo⁵, Dartini⁶
Poltekkes Kemenkes Semarang, Indonesia1,2,3,4,5,6
Email: ardiyanto1880@gmail.com
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KEYWORDS |
ABSTRACT |
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TPM,
Autonomous maintenance, OEE |
CT scan is a
medical tool used by hospitals to provide radiology patient services. The
working principle of a CT scan tool is to utilize ionizing radiation in the
form of X-rays and a combination of a computer system. The sophistication of
existing equipment does not guarantee that it can be free from disasters such
as sudden breakdowns. According to global medical equipment failure
statistics, 80% of all failures are caused by preventable factors.
Implementation of the Total Productive Maintenance (TPM) model, which is
tailored to top management in service provider organizations such as
hospitals, is considered a powerful tool for maintenance systems and can
minimize the occurrence of failures. The effectiveness of TPM on CT-scan
equipment can be measured using the Overall Equipment Effectiveness (OEE)
method, this method is able to describe equipment performance in theory and
is an accurate calculation of how effectively the machine is used. This type
of research is Research and Development (R&D) with an experiment
quasi-design and a pretest post-test with a control group design. The
researcher used the research stages of the Borg & Gall development model
which consists of 10 steps, then the researcher modified it into 6 steps. The
research results explain that the TPM model that has been prepared uses the
concept of autonomous maintenance with modification and replication of
William N. Dunn's policy analysis; The OEE value of the experimental group
after the model intervention (36%) was lower than before the model
intervention (53%), this was because there were other factors that influenced
it. |
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DOI: 10.58860/ijsh.v3i8.224 |
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Corresponding Author: Ardiyanto
*
Email: ardiyanto1880@gmail.com
INTRODUCTION
Medical equipment is one of the important
factors in the implementation of health services in a Health Service Facility
(Fasyankes) such as a hospital, the equipment must function properly according
to standards (service, quality, security, benefits, safety, and usability).
CT-scan is a medical device used by hospitals to provide services to radiology
patients, the working principle of the device uses ionizing radiation in the
form of X-rays and in combination with a computer system
The sophistication of existing equipment
does not guarantee that it can be free from disasters such as component
failures, sudden breakdowns, and human error
Total Productive Maintenance (TPM) is an
integrated and continuous maintenance method on production machines that
involves the active role of all employees, from top management to operators
Autonomous maintenance is a simple machine
maintenance concept that involves production operators as users to perform
basic maintenance such as cleaning, lubrication, and inspection
Success in the implementation of TPM can be
an indicator that the performance of the equipment has reached its optimal
performance
Previous study on "TPM Analysis of
Linear Accelerator (Linac) Equipment" reported an Overall Equipment
Effectiveness (OEE) value of 58.07%, with each OEE component being Availability
(71.60%), Performance (81.1%), and Quality (100%). These findings indicate that
equipment performance and TPM program implementation are not yet optimal. While
this study provides valuable insights into the current status of Linac
equipment performance, it also highlights the need for further research to
explore strategies to improve OEE and enhance the effectiveness of TPM
programs, particularly in addressing identified deficiencies. This study aims
to fill this gap by investigating (specific aspects to be discussed).
The purpose of this study is to compile a
TPM model and analyze its effect on improving the performance of CT-scan
equipment using the OEE method.
METHOD
This researcher used a type of Research and
Development (R&D) research with a quasi experiment
design and a pretest post-test with control group design
Figure 1. Research and
Development (R&D) research stages
RESULT AND DISCUSSION
1. The rise of the TPM
model
Researchers have collected data at 2 hospitals
(XYZ1 and XYZ2) located in the city of Mataram, West Nusa Tenggara Province.
The two hospitals use CT-scan equipment with the same specifications. The first
hospital was used for the collection of experimental data, while the second
hospital was used for the collection of control data
a. Information collection
Information
collection was carried out through observation, interviews and literature
studies about the maintenance of CT-scan equipment. From the results of the
information collection, it was concluded that hospital XYZ1 had not implemented
TPM, then the researcher conducted a literacy study as a reference in the
preparation of the model design.
b. Design and build
models
The
researcher uses William N. Dunn's policy analysis reference in making model
design. The policy analysis includes problem structuring, forecasting,
prescription, monitoring, and evaluation.
c. Expert validation and
revision
The
researcher used expert validators totaling 3 people, including the first senior
radiographers as equipment operators, the second as hospital management, and
the third Full Service Equivalent Employees (FSE) from one of the medical
device manufacturers in Indonesia. The results of the validity of Aiken's v
related to the expert assessment items have an average score of 0.92, so it can
be said that the instrument used has high validity (>0.6) and is suitable
for evaluating the performance of CT-scan equipment.
d. Model trials
The
researcher involved 30 respondents in conducting the model test stage. The
respondents are CT-scan equipment operators with inclusion criteria including
willingness to be a respondent, working period ≥ 2 years, having a
minimum of DIII radiology education, and being 20-40 years old. The data was
then processed using the Wilcoxon signed ranks test and obtained a significance
value of p-value 0.000 (p-value < 0.05) so that the conclusion was that
there was a significant difference in radiographer's knowledge of the use of
the TPM model on improving the performance of CT-scan equipment between pretest
and post-test.
e. Model revision
This stage
is actually the transformation of the demo product into the final product. This
process involves additional development to ensure that the model can and is
ready to function optimally.
f. Result
The
resulting product is in the form of an innovation in the development of a TPM
model with the concept of autonomous maintenance on CT-scan equipment. The
results of the model development are shown in Figure 2.
Figure 2. Flowchart
of TPM model development with the concept of autonomous maintenance,
modification, and replication of William N. Dunn policy analysis.
2.
Performance analysis of
CT-scan equipment using the OEE method
In this section, we will analyze the OEE
values of the CT-scan device of the experimental group before and after the
subsequent TPM model intervention compared to the control group
OEE: Availability x performance efficiency x rate of
quality
As
explained at the beginning, CT-scan device data collection is carried out in 2
hospitals that use the same device specifications
Table 1.
Comparison
of OEE values
|
Sample |
Initial OEE |
OEE end |
|
CT-scan device of the
experimental group |
Availability: 80% Performance
efficiency: 66% Rate of quality: 100% OEE: 53% Explanation: The OEE
score has not reached the OEE word class (85%); based on the benchmark
standard from JPIM, the score is in the low score category. |
Availability: 72% Performance
efficiency: 50% Rate of quality: 100% OEE: 36% Explanation: The OEE
score has not reached the OEE word class (85%). Based on the benchmark
standard from JPIM, the score is in the low score category. |
|
Control group CT-scan device |
Availability: 94% Performance
efficiency: 40% Rate of quality: 100% OEE: 53% Explanation: The OEE score has
not reached the OEE word class (85%). Based on the benchmark standard from
JPIM, the score is in the low score category. |
Availability: 93.6% Performance
efficiency: 34,7% Rate of quality: 100% OEE: 32% Explanation: The OEE score has
not reached the OEE word class (85%). Based on the benchmark standard from
JPIM, the score is in the low score category. |
The results
of the comparison of OEE values in Table 1 show that the OEE value after the
implementation of the TPM model is 36%, and the OEE value before the
implementation of the model is 53%. Based on the benchmark set by JPIM, the
value has not reached or is still below the world-class OEE standard, which is
85%. This condition is included in the low score category and requires hard
efforts to improve it. This means that the application of the TPM model with
the concept of autonomous maintenance is not effective, causing losses
The
researcher then calculates six big losses to find out the main factors that
cause losses. Six big losses are part of OEE and can be interpreted as a
deductible factor from the total OEE value. The results of the calculation of
six big losses can be seen in Figure 3.
Figure
3. Graph of six big losses CT-scan tool
In Figure
3, the six big losses
graph shows that the main factor causing the losses of the
CT-scan tool is breakdown. Furthermore, to find out the cumulative percentage
of equipment damage from the factors causing losses, analysis was carried out using a
pareto diagram
Figure
4. Pareto six big losses diagram of CT-scan tool
In Figure 4, the pareto six big losses diagram above shows that with the
problem of breakdown loss, the accumulated damage to the tool can reach 60%.
This can be interpreted that breakdown loss is a critical area that causes the
most downtime so that it requires more attention and is the top priority for
action.
The researcher then conducted a brainstorming Focus Group discussion
(FGD) between the operator, the equipment maintenance unit, and the management
to analyze the root cause of the problem (breakdown loss)
Figure 5. Fishbone analysis diagram causes
breakdown problems
The fishbone diagram analysis in Figure 5 revealed several critical
factors affecting downtime:
a.
Man: Technicians are not
adequately prepared, lack necessary daily tools, and tools are not consistently
monitored.
b.
Material: Unavailability of
tool parts, delays in replacing damaged components, and tools not returning to
normal function.
c.
Machine: Aging machinery
with worn components leading to operational issues.
d.
Method: Service value
contract policies restrict maintenance services to specific actions, preventing
immediate replacement of damaged parts by technicians.
Further analysis of the dominant factors identified in the fishbone
diagram indicated that breakdown losses primarily stemmed from aging machinery,
leading to operational disruptions. Additionally, the service value contract
policy prevented timely replacement of spare parts. These findings align with
previous studies indicating that poorly managed maintenance processes and aging
equipment significantly contribute to reduced Overall Equipment Effectiveness
(OEE). This research supports the conclusion that factors not adequately
managed can severely impact OEE performance, causing interventions like the
Total Productive Maintenance (TPM) model with autonomous maintenance concepts
to fall short in improving the performance of CT-scan equipment. These results
are consistent with previous literature that highlights the critical role of
maintenance practices in achieving world-class OEE standards, such as the 85%
benchmark set by JPIM.
CONCLUSION
The TPM model developed in this study
incorporates the concept of autonomous maintenance, with modifications and
replication based on William N. Dunn's policy analysis. Following the intervention,
the OEE value of the CT-scan tool dropped from 53% to 36%. This outcome
indicates that the OEE value remains significantly below the world-class
standard of 85% set by JPIM. This shortfall can be attributed to several
critical factors, including the advanced age of the equipment, which leads to
operational disruptions, and the policy adopted by top management to engage in
service value contracts. This policy restricts technicians from promptly
replacing spare parts when malfunctions occur.
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