Customer demand and planning efficiency in overall equipment effectiveness (OEE).
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
Faculty of Manufacturing Engineering
CUSTOMER DEMAND AND PLANNING EFFICIENCY
IN OVERALL EQUIPMENT EFFECTIVENESS
(OEE)
Teoh Yong Siang
Master of Science in Manufacturing Engineering
2013
© Universiti Teknikal Malaysia Melaka
CUSTOMER DEMAND AND PLANNING EFFICIENCY IN OVERALL EQUIPMENT
EFFECTIVENESS (OEE)
TEOH YONG SIANG
A thesis submitted
in fulfillment of the requirements for the degree of Master of Science
in Manufacturing Engineering
Faculty of Manufacturing Engineering
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
2013
DEDICATION
To my beloved mother and father
ABSTRACT
Overall equipment effectiveness (OEE) is the multiplication of availability, performance
and quality which are looking into the losses such as downtime losses, speed losses and
quality losses respectively. The study is carried out in an aerospace parts manufacturing
company to acquire the time data of Autoclave section via the computerized recording
system. In this study, planning factor is introduced in OEE and defined as the panel
number loaded over the maximum number of panel affordable by autoclaves. This is to
promote the optimization of Autoclave usage or the concept of On Time In Full (OTIF). In
addition to that, customer demand is also incorporated in OEE through Takt Time. This
evaluates Autoclaves with constant and fixed cycle time in varying performance ratio from
time to time according to its real performance. This also provides a balance between the
over-production issue and high utilization of equipment. The results of planning factor and
Takt time incorporation show that curing of Autoclave was not planned up to its maximum
capability; and the demand accepted is actually higher than its maximum capacity. After
that, the OEE data once obtained is then used for versatile usages such as scheduling or
planning of preventive maintenance frequency, examination of customer demand accepted
with respect to Autoclave capacity, comparison of performance over the time horizon as
well as the evaluation of material losses due to planning of equipment process. Among
them the estimation ofbreakdown time (via MTBF) and priority of preventive maintenance
on Autoclave is done by analyzing the availability data and the percentage of downtime
composition. Another possible usage of OEE data is the calculation of material amount
based on the Bill of Material (BOM) and the average of historical quality ratio data. As a
conclusion, objectives are achieved since the customer demand is incorporated into OEE,
efficiency of planning is examined and versatile usage of OEE value and data are
demonstrated. Further study could be suggested is the possibility of implementing Takt
time and planning factor in the equipment with varying cycle time
ABSTRAK
OEE adalah hasil pendarapan daripada kebolehan, keupayaan dan kualiti yang
masing-masing bertujuan untuk menorakan pembaziran seperti ketidak-bolehan mesin,
pembaziran kelajuan, dan pembaziran kualiti. Kajian ini telah dijalankan dalam sebuah
syarikat pembuatan bahagian kapal terbang dan data Autoclave telah diambilkan dari
system rekod. Dalam kajian ini, factor perancangan telah diperkenalkan dan didefinasikan
sebagai pembahagiaan bilangan produck diprocesskan daripada bilangan maximum yang
boleh diprocesskan oleh Autoclave. Ini adalah untuk mempromosikan konsep sempat pada
masa dengan penggunaan sepenuhnya (On Time in Full, OTIF). Tambahan pula,
permintaan pelanggan juga dipertimbangkan dalam OEE dengan adanya Masa Takt. Jni
mengukurkan Autoclave yang mempunyai masa prosess tetap dengan nilai keupayaan
yang berlainan bergantung dengan keupayaannay yang sebenarnya. Masa Takt juga
menggelakkan isu yang wujud dalam penggunaan mesin dan penghasilan produk yang
terlampau banyak. Akibatnya, Autoclave adalah dibuktikan tidak dirancang dengan
sebaiknya ataupun sepenuhnya dan permintaan pelanggan adalah terlalu banyak jikalau
berbanding dengan keupayaan Autoclave itu sendiri. Selapas itu, OEE data dan nilai telah
digunakan dalam pelbagai cara yang termasuk perancangan preventive maintenance
(PM), perbandingan antara permintaan pelanggan dengan keupayaan mesin,
perbandingan keupayaan dari masa ke masa dan juga pemeriksaan pembaziran bahanbahan yang disebabkan oleh perancangan yang tidak memuaskan. Antaranya, pengiraan
masa mesin rosak dan keutamaan Autoclave untuk perancangan PM adalah berdasarkan
analisasi peratusan kebolehan Autoclave dan data kerosakan Autoclave. Penggunaan OEE
data yang lain adalah pengiraan bilangan bahan yang diperlukan ataupun dibazirkan
berdasarkan Bill of Material (BOM) dan purata peratusan kualiti. Sebagai kesimpulannya,
kesemuaannya objektif telah dicapaikan kerana permintaan pelanggan telah
dipertimbangkan dalam OEE, keberkesanan perancangan Autoclave telah diperiksakan
dan akhirnya pelbagai penggunaan OEE data telah ditunjukkan. Kajian yang sama boleh
dipertimbangkan dalam masa depan untuk mengkaji mesin yang mempunyai process masa
yang sentiasa berubah.
11
LIST OFT ABLES
PAGE
TITLE
TABLE
2.1
Summary of OEE improvement timeline by different authors
39
4.1
Standard established to classify uptime and downtime
68
4.2
The summary of time study on 6 Autoclaves in monthly basis
69
4.3
The customer demand, actual production amount and cycle time of
some panel types
72
4.4
The operating time of some panel types in weekly basis
74
4.5
Total monthly operating time of some panel types
75
4.6
Summary of average curing time in monthly basis
76
4.7
The classification of panels cured in each machine based on their
77
purpose.
4.8
Maximum number of panel could be loaded per curing slot in each
Autoclave used.
78
4.9
Availability of Autoclave 1-6 in January and February.
80
4.10
Average time and Takt time in aggregate unit in January and February 82
4.11
The calculation of quality ratio
84
4.12
Planning Factor by Autoclave number and by month.
85
4.13
Average OEE values for 6 Autoclaves
86
4.14
OEE values of 6 Autoclaves as one section
86
5.1
The scheduled preventive maintenance of Autoclave section
90
5.2
Comparison of availability in both approaches
91
5.3
Planning factor of Autoclave incorporating planned maintenance
95
5.4
A vail ability of loading -time approach incorporating planning factor
96
5.5
Demand rate (MPS) and actual production amount of some panel types 103
5.6
Takt time without incorporation of availability and its resulting
104
performance ratio
lV
LIST OF TABLES
TABLE
5.7
5.8
TITLE
PAGE
Performance ratio with incorporation of planning factor in average
cycle time
107
Performance ratio with incorporation of planning factor in Takt time
108
5.9
The summary ofOEE percentage and OEE data
113
5.10
Comparison of theoretical rate and MPS rate scheduled
116
5.11
Historical data of availability and average cycle time are used to
examine gap between production planned and capacity of Autoclave. 119
5.12
Percentage of each downtime composition for each Autoclave
5.13
The summary of Mean Time Between Failures (MTBF) for all
124
Autoclaves
126
5.14
Number of preventive maintenance suggested after every MTBF
127
5.15
The limit of machine capacity allowable versus actual utilization
130
v
LIST OF FIGURES
FIGURES
TITLE
PAGE
2.1
Six big losses for computing OEE. (Alok Mathur et. al., 2011)
15
2.2
OEE computation with ten big losses. (Jeong and Phillips, 2001)
23
2.3
The losses ignored by OEE (Andrew Starr, Farhad Anvari and
Rodger Edwards, 201 0)
30
3.1
Methodology of study
43
3.2
The example of planning factor in OEE (Francis Wauters and
Jean Mauthot, 2007)
3.3
46
Use of availability to examine non-value-added activities of a
production line.
3.4
50
Use ofTakt time to evaluate instantaneous performance of a
production line
56
3.5
Procedure to examine the ERP data in getting quality ratio.
60
4.1 (a)
The downtime composition for each Autoclave in hourly unit
(January)
4.1 (b)
70
The downtime composition for each Autoclave in hourly unit
(February).
4.2
71
The portion of uptime and downtime over the total calendar time
and their corresponding availability.
81
5.1 (a) & (b) The OEE value calculated in: (a) calendar-time approach with
planning factor, (b) loading-time approach without planning factor 95
5.2
Illustration of difference between Takt time and average cycle time
as a portion of performance loss.
5.3
101
The comparison between average cycle time and weekly Takt time. 131
Vl
LIST OF ABBREVIATIONS
AC
-Autoclave
BOM
- Bill of materials
DIRFT
- Do It Right First Time
ERP
- Enterprise Resource Planning
MPS
- Master Production Schedule
MTBF
-Mean Time between Failures
MTTR
- Mean Time to Repair
NVA
- Non value-added
OEE
- Overall Equipment Effectiveness
OLE
- Overall Line Effectiveness
OTIF
- On Time In Full
PM
- Preventive Maintenance
R&D
- Research and Development
TPM
- Total Productive/Preventive Maintenance
VA
- Value- added
WIP
- Work in progress
Vll
TABLE OF CONTENT
PAGE
ABSTRACT
ABSTRAK
ii
ACKNOWLEDGEMENT
iii
LIST OF TABLES
iv
LIST OF FIGURES
vi
LIST OF ABBREVIATIONS
vii
CHAPTER
1.
INTRODUCTION
1.1
2.
I
Background
1.1.1 Background of concepts applied
2
1.1.2 Background of subject of study
3
1.2
Problem statement
5
1.3
Objectives
7
1.4
Significance of the study
9
1.5
Scope of the study
12
LITERATURE REVIEW
14
2.1
Introduction
14
2.2
Application and indication
18
2.3
Advantages
19
2.4
Method of implementation
21
2.5
Difficulty of implementation
26
2.6
Limitations
29
2.7
Modifications
34
2.8
Hypothesis ofthe study
38
Vlll
3.
4.
METHODOLOGY
42
3.1
Overall Procedure of OEE Calculation
42
3.1.1
Planning Factor
46
3.1.2
Availability
48
3.1.3
Performance ratio
53
3.1.4
Quality Ratio
59
3.2
Data Collection
63
3.3
Summary
64
RESULTS
4.1
66
Primary data
67
4.1.1
Cycle time
67
4.1.2
Customer demand and production amount
72
4.1.3
Number of good panel produced per purpose
77
4.1.4
Theoretical maximum and actual number of panel cured
per each curing slot
4.2
5.
78
Secondary data
79
4.2.1
Availability
79
4.2.2
Performance ratio
82
4.2.3
Quality ratio
83
4.2.4
Planning Factor
84
4.2.5
Overall Equipment Effectiveness
85
DISCUSSION
88
5.1
Examination of calculation
88
5 .1.1
A vail ability using total calendar-time approach
89
5.1.2
Planning factor based on capability
93
5.1.3
Performance ratio using Takt time
99
5.1.3.1 Incorporation of planning factor in performance ratio
5.1.4
5.2
for single measurement
106
Quality ratio
110
Versatile utilization OEE value and OEE data
111
5 .2.1
OEE percentage as comparison metric of performance
112
5.2.2
Examination of possibility to achieve production rate
IX
6.
scheduled
115
5.2.3
Material loss evaluation
120
5.2.4
Priorities on machine (preventive) maintenance
123
5.2.5
Demand accepted with respect to capacity of equipment
128
CONCLUSION AND SUGGESTIONS
133
6.1
Conclusion
133
6.2
Suggestions
134
REFERENCE
139
APPENDIX
152
X
CHAPTER 1
INTRODUCTION
This chapter is basically introducing the causes and the existing problems which initiate this
study. It is followed by the background of the study and the narrowed-down objectives. By the
end of the chapter is the clarification of project scope to set the boundary of the study where it
should lie within.
1.1
Background
The section consists of two subsections. The first subsection is the background
concerning the lean philosophy and the concept of OEE to be applied in this study. Brief
introduction and explanation are included in this subsection. The second part of this section is
the background of the subject of study. The introduction of autoclaves and curing process are
included in the second subsection. Introduction here covers the nature and characteristic of
curing process, the product types to be cured and the data required to analyze the OEE
performance of Autoclaves.
1.1.1
Background of concept applied
Lean manufacturing is most frequently associated with the elimination of seven
important wastes to ameliorate the effects of variability in supply, processing time or demand
(Shah and Ward, 2007). Briefly, it is called lean as it uses less, or the minimum, of everything
required to produce a product or perform a service (Hayes and Pisano, 1994 ). As Paul Dean
Ceng (2006) stated in his study, "OEE is an essential metric and methodology for
manufacturers pursuing a lean manufacturing strategy to emphasize zero waste in the value
streams". The OEE is one of the approaches in quantifying if the utilization of equipment or
machine is in lean or wasteful way. It identifies the scraps related to equipment in different
sections, to take corrective measures in order to eliminate them and exploit the potential
capacity of the equipment (Motavallian, 2007).
The overall equipment effectiveness index machine stands in comparison with an ideal
machine which always runs at full speed and capacity at the same time produces good quality
products (Rouhani, 2009). Although the data of overall effectiveness of equipment seem very
simple, extracting useful information from a series of calculations is a very important and
difficult task (Aliahmadi, 2003). The reasons include too much of performance measurements
have been used in an organization and its resulting confusion of data definition. Besides that,
various approaches which are used in the measurement of manufacturing performance could
lead to large number of measurement in different hierarchical levels and most of them are
contradictory (Nakajima, 1989; Kaplan and Norton, 1992; Jones, 1994).
Unavailability or infeasibility of data to be collected in the form required for each
formula is another difficulty of OEE implementation. For instance, often companies struggle
2
to define an ideal cycle time, particularly in non-machinery and less well-automated
manufacture (Bamber et al, 2003 ). The ambiguity of the OEE implementation could further
contribute to deviation in evaluating the utilization and performance of a particular equipment.
'·If you cannot measure it you cannot manage it", as stated by Paul Dean Ceng (2006).
Nevertheless, there is no formula or textbook which addresses the issue of over-production on
highly utilized equipment and the 'ideal cycle time' that an equipment with constant and fixed
process time should have. There is no guidance in clarifYing the trade-off relationship between
high machine utilization and low customer demand as well as the corresponding solution.
Faced with these conditions, there is a necessity of innovative and simple measurement to
evaluate the equipment performance incorporating the customer demand.
1.1.2
Background of subject of study
The study is carried out in an aerospace part-manufacturing company. Within the
company, a manufacturing section comprises of several Autoclaves is chosen as the subject of
study and all the time data is gathered from that. The time data here refers to the beginning
time and ending time of all curing processes which are recorded by computerized data system.
The time data is then interpreted and analyzed in this study to achieve the objectives and
obtain the results. The autoclave is used to harden the ply materials from the layup process.
Layup process as the supplier process of autoclave curing process is actually to stack up one
ply onto another in the amount and at the orientation as per design of aerospace parts. The ply
materials after being cured by autoclave will become harden aero plane parts for assembly of
different area in different aero plane model. These different aero plane parts for various aero
plane models are referred as product type in this study.
3
The manufacturing section comprises of 6 Autoclaves and their dimensions are all
different from each other. Due to the matter of dimension, their capacity or maximum curing
capacitance is then different as well. This means that the maximum number of a particular
product type could be loaded into an Autoclave is different from others. In this study, the
maximum number of panel could be loaded into autoclave is dependent on the dimension of
autoclave and panel itself. The data of maximum number could be loaded for each panel type
is obtained from the system which records the beginning and end of curing time, number of
panels loaded and types of packages cured.
Besides that, there are several products types with different customer specification,
designs and process requirement are to be cured by the Autoclave section. Consequence of
that, the curing recipe of all product types may be different and has to be cured according to
their curing recipe. This means that only those products with same curing recipe can be cured
together.
Curing recipe controls the length of curing process required by a particular product
type. Those product types with same curing recipe means that the requirement of temperature
and pressure are same and this allow them to be cured together. Since the curing recipe for a
particular product type is always same, this contributes to the constant curing time of a product
type from time to time.
In addition to that, not all product types can be cured by all Autoclaves available.
Thermal survey of a particular product type should be carried out in advance on the Autoclave
before the product is being cured. This is to investigate the thermal profile such as the
distribution of temperature and pressure within the Autoclave during the curing process. A
4
product type is allowed to be cured by the Autoclave once the thermal profile is qualified and
the product panel is in good condition after the curing process. In this company, one particular
product type is dedicated to be cured by not more than 3 Autoclaves due to the constraint of
thermal survey.
At last, since the curing recipe is fixed and constant from time to time, there is no
improvement can be done to shorten the curing time. The only variable can do to improve the
productivity of the curing process is to increase the amount of product loaded per each curing
slot. Another option can be considered is to choose the Autoclave according to their dimension.
In other words, small amount of products are recommended to be cured in relatively small
Autoclave to prevent the waste of non-utilized capacity.
1.2
Problem statement
One of the problems in lean implementation is the lack of direction of planning and
adequate project sequencing (Bhasin and Burcher, 2006). From the same perspective in OEE
implementation, it can be observed that the importance of planning is usually being neglected.
In other words, a high value of OEE does not mean that the machine is fully utilized since the
production rate is initially determined by planning. This effectiveness of the planning is
neglected or not evaluated in OEE implementation. Regarding to this problem, there are some
studies performed evaluating the planning factor as the percentage of production time planned
for or realized over the total theoretical production time (Wauters and Mathot, 2007; Icso,
n.d.). In those studies, the planning factor was used to evaluate the efficiency of production
planning in determining the maximum utilization of the equipment. Even that, Icso (n.d.) had
5
suggested retrofitting the existing equipment for better performance rate which is beneficial
from a higher production speed. The similarity of these studies performed is that the machine
utilization is not planned over the maximum capability of equipment. Over the similar issue,
there is a need to formulate an effective lean process, as suggested by Puvanasvaran et. al.
(2009), on the employee ·s deYelopment aspect to unlock the infinite potential of their
workforce. This could be implemented in machines or equipments in this case by planning the
production rate of existing machine up to the maximum capacity.
On the other hand, there is no specific value for so-called '"world-class OEE"
regardless of 85 percent OEE which has been cited frequently. This means the action of
maximizing and pursuing for high OEE value may not be justifiable. According to Williamson,
optimum level of OEE depends on the capability or capacity of the asset, the business
demands, and whether or not it is a constraint in the process flow. The idea is that high
production rate implied in high OEE (availability) especially in the period with low business
demand will contribute to expensive inventory handling cost. Besides that, the ideal cycle time
is always difficult to be defined whereas the speed loss, minor stoppage, and idling are hardly
to be differentiated from the waiting time (Bamber et al, 2003; Kenis, 2006). On the other
hand, the setup and adjustment time which are increasing accordingly with the product mix in
manufacturing company will adversely affect the OEE value (Mileham et al., 1997). Frequent
setup or loadings are required in the environment of mass manufacturing and this will
adversely affect the availability of the equipment. This means the compromise between mass
manufacturing and OEE emphasis and this is actually very illogical from the perspective of
business. Therefore, there is a need to improve the productivity of a manufacturing
organization with respect to different product mixes at the same time considering the customer
6
demand, and defming performance ratio of machines with fixed and constant process time in
pursuing high OEE value (Hilmola, 2005).
Besides that, the OEE value once being evaluated is just a displayed value and
indication of current machine utilization only. Most companies are focusing on the
comparison of particular asset (equipment) with itself over the time horizon via the usage of
historical data (Williamson, 2006). The common usages of the OEE metrics are the
identification of major losses, problem investigation for capital expenditures and enhancement
of capacity (Abidian inc., n.d.; Fuss & O'Neill Manufacturing solution, n.d.). The story behind
the value is seldom tracked out and another possible usage of OEE value is not optimized. So,
there is a need for internal flexibility within manufacturing system which requires changes of
traditional organization methods to manage, measure, and mindset of management and
employees on the role (Sweeney, 1990). This can be done by quantifying the instantaneous
production rate at manufacturing site, as well as comparing the utilization of a particular
machine regarding to the customer demand and examining the corresponding planning of
production using OEE data.
1.3
Objectives
Customer demand nowadays consists of multiple products with different process
parameters requirement. This makes the number of product being loaded during each
production slot with respect to maximum capacity is very important. As a consequence, an
overall measurement covering all the product mix does make sense rather than multiple
measurements for every of single product produced by the particular equipment. In addition to
7
that, balance between equipment utilization with the customer demands becomes more
important to prevent over-production. In order to facilitate the optimization of equipment
utilization, all of the issues stated have to be overcome via the objectives stated below:
To quantify the customer demand into OEE value and to quantify the performance
1.
ratio for an equipment with constant cycle time.
To measure the efficiency of production planning in affecting the machine utilization.
11.
m
To demonstrate three (3) usages of new-defmed OEE measurement to facilitate the
planning and engineering of a particular production section.
The concept applied and assumptions formulated throughout the study are:
1.
Williamson (2006) suggested that the optimum level of OEE depends on several
factors such as capacity or capability of equipments, business demand and condition if
it is a constraint of process flow. Additionally, the pursuit of high OEE will lead to
overproduction because it is measured based on the cycle time but not based on the
Takt time (Harada, n.d.). In view of that, it is a necessity to incorporate the customer
demand in OEE implementation to avoid overproduction during pursuing high
equipment utilization.
11.
The ideal cycle time in the computation of performance is commonly confused or does
not exist for those equipments with constant cycle time. This is because the cycle time
for the equipment is fixed as per process requirement and literally same as the ideal
cycle time. It can be concluded that the performance ratio of those equipments are
always 100% in traditional OEE approach, and this indicates that there is no
8
improvement can be done on equipment to increase the productivity in traditional
approach.
111.
A good management metric should cover all products produced or processed in
equipment instead of measuring them individually. This is to consider the impact of
product mixes on the machine utilization in term of the changeover time (availability)
and the overall performance ratio. This is because whenever the OEE is calculated
individually, the waiting time or setup time between two processes is neglected and
this will contribute to overrated availability. Therefore, a metric which covers all
product types processed in equipment and visualizes the overview of the actual
utilization. This is to ensure that an overall improvement decision can be made to
improve the total performance of the equipment in whole.
1.4
Significance of the study
Manufacturing environment nowadays is characterized with mass production and
demand comprises of numerous product mixes. This has necessitated the concept of On Time
In full (OTIF) to be more important. OTIF is a concept which emphasizes maximum loading
in each production loading and hence reduces the necessity of additional production. In that
any under utilization of machine causes more changeovers in production, which will further
contribute to lower equipment availability and effectiveness. Often times, however, traditional
approach ofOEE neglected the importance of utilizing equipment up to its maximum capacity.
As a result of that, equipment with high OEE value does not mean high utilization but could
probably because that particular machine runs at most of the time, at or close to ideal speed,
9
and produces least defect. There is no concern on number of input loaded per each production
slot. Even though there were some studies including the concept of planning factor in
computation ofOEE (Wauters and Mathot, 2007; Icso, n.d.), but it is just evaluated in term of
time (gross operating time over total calendar time). This defmition could be simplified in
total calendar time approach. In order to overcome the limitation like this, this study will adapt
and evaluate the planning factor in terms of the number of product loaded in each production
slot. With the significance in this study, the efficiency of planner is examined from time to
time to seek for optimization of machine usage.
In addition to that, a good management metric is badly desired and appreciated to be
implemented in a complex manufacturing environment. A simple and comprehensive metric
(de Ron and Rooda, 2005) which incorporates all product types is important. This is to provide
a brief measurement of current condition and to enable on-time decision making based on
actual data. Besides that, a good management metric should be characterized with ease of data
collection (Harada, n.d.). In view of that, flexibility is a must for a good management metric
so that it could be adapted and measured from time to time. As such, creativity could be
promoted in achieving the flexibility and competitive advantage (Oke, 2003; Yung and Chan,
2003; Sanchez and Perez, 2001; Gerwin, 1993; Jordan and Graves, 1995; Upton, 1995a cited
in Ooi, 2005). The flexibility stated can be obtained by defming the measurement in aggregate
unit which covers all product mixes of the equipment. In this study, the aggregate unit is
calculated accordingly with the schedule of the production based on their corresponding
importance factor named weighted averaging factor.
In most compames which are implementing OEE as their equipment performance
evaluation, the customer demand and production rate are in the trade-off relationship and
10
hardly to be balanced. Inappropriate management between these two parameters could lead to
over-production or idling machine, and sometimes the shortage of production. However, the
machine utilization and overproduction should be not always in opposite standpoint and could
be working together. It is possible to avoid or minimize the side-effect of over-production in
the path pursuing high equipment utilization. Significance in this study shows that the
performance ratio is possibly incorporated with customer demand via the introduction of Takt
time. The concept of Takt time in OEE computation has not only balanced the issue of
overproduction and machine utilization, but it quantifies various performance ratio on the
equipment with constant cycle time according to fluctuating customer demand.
As a short summary, this study is exammmg the validity of OEE universal
implementation in various types of manufacturing environments, and some of them were
known to be improperly used. Various elements are introduced in this study to enable OEE
computation. This study is providing new angle in looking at the possibility and flexibility of
modifying the traditional OEE approach to adapt the OEE measurement. The modifications
made are according to different manufacturing nature, which is determined by the types of
equipment used. This study seeks to create an impact on the OEE computation which covers
the considerations stated above via the following significances and novelties in OEE:
1.
11.
Customer demands to avoid over-production issue.
Aggregate unit to cover all product mixes and types produced.
m
Planning factor to promote On Time In Full (OTIF).
IV.
Total calendar approach to track out total downtime wasted.
11
1.5
Scope of the study
This study is focusing on the computation of single OEE measurement over s1x
Autoclaves to cover all the product mixes. The products are cured with fixed curing time and
are all constant as per curing recipe. A single measurement is desired to be simple and
comprehensive to quantify the condition of all product types in utilizing the equipment.
The scope of study also attempts to introduce and implement the concept of Takt time
in pursuit of high OEE level. This is to promote the balance between production rate and the
customer demand. In order to ascertain the generic and applicable characters of the new OEE,
a make-to-{)fder type of industry with various product types, Autoclave with different sizes
and capabilities are selected as the subject of study to represent the sample of manufacturing
process with constant cycle time.
This study does not attempt to demonstrate the implementation of OEE on those
equipments with varying cycle time from time to time within a product type. Such equipment
as this will have its ideal cycle time in defming the performance ratio. Besides that, the
integration of OEE metric with other lean tools is not demonstrated in this study since the
usage ofOEE as a management metric is the only concern and analysis tool in this study
In addition to that, this study is focusing on the Autoclave section only but not other
manufacturing cell or the effectiveness of equipment over the company wide is not within the
scope of the study. According to Williamson (2006), OEE is not intended for use as a
corporate or plant level. It is just a measure of a selected equipment effectiveness only. This is
12
Faculty of Manufacturing Engineering
CUSTOMER DEMAND AND PLANNING EFFICIENCY
IN OVERALL EQUIPMENT EFFECTIVENESS
(OEE)
Teoh Yong Siang
Master of Science in Manufacturing Engineering
2013
© Universiti Teknikal Malaysia Melaka
CUSTOMER DEMAND AND PLANNING EFFICIENCY IN OVERALL EQUIPMENT
EFFECTIVENESS (OEE)
TEOH YONG SIANG
A thesis submitted
in fulfillment of the requirements for the degree of Master of Science
in Manufacturing Engineering
Faculty of Manufacturing Engineering
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
2013
DEDICATION
To my beloved mother and father
ABSTRACT
Overall equipment effectiveness (OEE) is the multiplication of availability, performance
and quality which are looking into the losses such as downtime losses, speed losses and
quality losses respectively. The study is carried out in an aerospace parts manufacturing
company to acquire the time data of Autoclave section via the computerized recording
system. In this study, planning factor is introduced in OEE and defined as the panel
number loaded over the maximum number of panel affordable by autoclaves. This is to
promote the optimization of Autoclave usage or the concept of On Time In Full (OTIF). In
addition to that, customer demand is also incorporated in OEE through Takt Time. This
evaluates Autoclaves with constant and fixed cycle time in varying performance ratio from
time to time according to its real performance. This also provides a balance between the
over-production issue and high utilization of equipment. The results of planning factor and
Takt time incorporation show that curing of Autoclave was not planned up to its maximum
capability; and the demand accepted is actually higher than its maximum capacity. After
that, the OEE data once obtained is then used for versatile usages such as scheduling or
planning of preventive maintenance frequency, examination of customer demand accepted
with respect to Autoclave capacity, comparison of performance over the time horizon as
well as the evaluation of material losses due to planning of equipment process. Among
them the estimation ofbreakdown time (via MTBF) and priority of preventive maintenance
on Autoclave is done by analyzing the availability data and the percentage of downtime
composition. Another possible usage of OEE data is the calculation of material amount
based on the Bill of Material (BOM) and the average of historical quality ratio data. As a
conclusion, objectives are achieved since the customer demand is incorporated into OEE,
efficiency of planning is examined and versatile usage of OEE value and data are
demonstrated. Further study could be suggested is the possibility of implementing Takt
time and planning factor in the equipment with varying cycle time
ABSTRAK
OEE adalah hasil pendarapan daripada kebolehan, keupayaan dan kualiti yang
masing-masing bertujuan untuk menorakan pembaziran seperti ketidak-bolehan mesin,
pembaziran kelajuan, dan pembaziran kualiti. Kajian ini telah dijalankan dalam sebuah
syarikat pembuatan bahagian kapal terbang dan data Autoclave telah diambilkan dari
system rekod. Dalam kajian ini, factor perancangan telah diperkenalkan dan didefinasikan
sebagai pembahagiaan bilangan produck diprocesskan daripada bilangan maximum yang
boleh diprocesskan oleh Autoclave. Ini adalah untuk mempromosikan konsep sempat pada
masa dengan penggunaan sepenuhnya (On Time in Full, OTIF). Tambahan pula,
permintaan pelanggan juga dipertimbangkan dalam OEE dengan adanya Masa Takt. Jni
mengukurkan Autoclave yang mempunyai masa prosess tetap dengan nilai keupayaan
yang berlainan bergantung dengan keupayaannay yang sebenarnya. Masa Takt juga
menggelakkan isu yang wujud dalam penggunaan mesin dan penghasilan produk yang
terlampau banyak. Akibatnya, Autoclave adalah dibuktikan tidak dirancang dengan
sebaiknya ataupun sepenuhnya dan permintaan pelanggan adalah terlalu banyak jikalau
berbanding dengan keupayaan Autoclave itu sendiri. Selapas itu, OEE data dan nilai telah
digunakan dalam pelbagai cara yang termasuk perancangan preventive maintenance
(PM), perbandingan antara permintaan pelanggan dengan keupayaan mesin,
perbandingan keupayaan dari masa ke masa dan juga pemeriksaan pembaziran bahanbahan yang disebabkan oleh perancangan yang tidak memuaskan. Antaranya, pengiraan
masa mesin rosak dan keutamaan Autoclave untuk perancangan PM adalah berdasarkan
analisasi peratusan kebolehan Autoclave dan data kerosakan Autoclave. Penggunaan OEE
data yang lain adalah pengiraan bilangan bahan yang diperlukan ataupun dibazirkan
berdasarkan Bill of Material (BOM) dan purata peratusan kualiti. Sebagai kesimpulannya,
kesemuaannya objektif telah dicapaikan kerana permintaan pelanggan telah
dipertimbangkan dalam OEE, keberkesanan perancangan Autoclave telah diperiksakan
dan akhirnya pelbagai penggunaan OEE data telah ditunjukkan. Kajian yang sama boleh
dipertimbangkan dalam masa depan untuk mengkaji mesin yang mempunyai process masa
yang sentiasa berubah.
11
LIST OFT ABLES
PAGE
TITLE
TABLE
2.1
Summary of OEE improvement timeline by different authors
39
4.1
Standard established to classify uptime and downtime
68
4.2
The summary of time study on 6 Autoclaves in monthly basis
69
4.3
The customer demand, actual production amount and cycle time of
some panel types
72
4.4
The operating time of some panel types in weekly basis
74
4.5
Total monthly operating time of some panel types
75
4.6
Summary of average curing time in monthly basis
76
4.7
The classification of panels cured in each machine based on their
77
purpose.
4.8
Maximum number of panel could be loaded per curing slot in each
Autoclave used.
78
4.9
Availability of Autoclave 1-6 in January and February.
80
4.10
Average time and Takt time in aggregate unit in January and February 82
4.11
The calculation of quality ratio
84
4.12
Planning Factor by Autoclave number and by month.
85
4.13
Average OEE values for 6 Autoclaves
86
4.14
OEE values of 6 Autoclaves as one section
86
5.1
The scheduled preventive maintenance of Autoclave section
90
5.2
Comparison of availability in both approaches
91
5.3
Planning factor of Autoclave incorporating planned maintenance
95
5.4
A vail ability of loading -time approach incorporating planning factor
96
5.5
Demand rate (MPS) and actual production amount of some panel types 103
5.6
Takt time without incorporation of availability and its resulting
104
performance ratio
lV
LIST OF TABLES
TABLE
5.7
5.8
TITLE
PAGE
Performance ratio with incorporation of planning factor in average
cycle time
107
Performance ratio with incorporation of planning factor in Takt time
108
5.9
The summary ofOEE percentage and OEE data
113
5.10
Comparison of theoretical rate and MPS rate scheduled
116
5.11
Historical data of availability and average cycle time are used to
examine gap between production planned and capacity of Autoclave. 119
5.12
Percentage of each downtime composition for each Autoclave
5.13
The summary of Mean Time Between Failures (MTBF) for all
124
Autoclaves
126
5.14
Number of preventive maintenance suggested after every MTBF
127
5.15
The limit of machine capacity allowable versus actual utilization
130
v
LIST OF FIGURES
FIGURES
TITLE
PAGE
2.1
Six big losses for computing OEE. (Alok Mathur et. al., 2011)
15
2.2
OEE computation with ten big losses. (Jeong and Phillips, 2001)
23
2.3
The losses ignored by OEE (Andrew Starr, Farhad Anvari and
Rodger Edwards, 201 0)
30
3.1
Methodology of study
43
3.2
The example of planning factor in OEE (Francis Wauters and
Jean Mauthot, 2007)
3.3
46
Use of availability to examine non-value-added activities of a
production line.
3.4
50
Use ofTakt time to evaluate instantaneous performance of a
production line
56
3.5
Procedure to examine the ERP data in getting quality ratio.
60
4.1 (a)
The downtime composition for each Autoclave in hourly unit
(January)
4.1 (b)
70
The downtime composition for each Autoclave in hourly unit
(February).
4.2
71
The portion of uptime and downtime over the total calendar time
and their corresponding availability.
81
5.1 (a) & (b) The OEE value calculated in: (a) calendar-time approach with
planning factor, (b) loading-time approach without planning factor 95
5.2
Illustration of difference between Takt time and average cycle time
as a portion of performance loss.
5.3
101
The comparison between average cycle time and weekly Takt time. 131
Vl
LIST OF ABBREVIATIONS
AC
-Autoclave
BOM
- Bill of materials
DIRFT
- Do It Right First Time
ERP
- Enterprise Resource Planning
MPS
- Master Production Schedule
MTBF
-Mean Time between Failures
MTTR
- Mean Time to Repair
NVA
- Non value-added
OEE
- Overall Equipment Effectiveness
OLE
- Overall Line Effectiveness
OTIF
- On Time In Full
PM
- Preventive Maintenance
R&D
- Research and Development
TPM
- Total Productive/Preventive Maintenance
VA
- Value- added
WIP
- Work in progress
Vll
TABLE OF CONTENT
PAGE
ABSTRACT
ABSTRAK
ii
ACKNOWLEDGEMENT
iii
LIST OF TABLES
iv
LIST OF FIGURES
vi
LIST OF ABBREVIATIONS
vii
CHAPTER
1.
INTRODUCTION
1.1
2.
I
Background
1.1.1 Background of concepts applied
2
1.1.2 Background of subject of study
3
1.2
Problem statement
5
1.3
Objectives
7
1.4
Significance of the study
9
1.5
Scope of the study
12
LITERATURE REVIEW
14
2.1
Introduction
14
2.2
Application and indication
18
2.3
Advantages
19
2.4
Method of implementation
21
2.5
Difficulty of implementation
26
2.6
Limitations
29
2.7
Modifications
34
2.8
Hypothesis ofthe study
38
Vlll
3.
4.
METHODOLOGY
42
3.1
Overall Procedure of OEE Calculation
42
3.1.1
Planning Factor
46
3.1.2
Availability
48
3.1.3
Performance ratio
53
3.1.4
Quality Ratio
59
3.2
Data Collection
63
3.3
Summary
64
RESULTS
4.1
66
Primary data
67
4.1.1
Cycle time
67
4.1.2
Customer demand and production amount
72
4.1.3
Number of good panel produced per purpose
77
4.1.4
Theoretical maximum and actual number of panel cured
per each curing slot
4.2
5.
78
Secondary data
79
4.2.1
Availability
79
4.2.2
Performance ratio
82
4.2.3
Quality ratio
83
4.2.4
Planning Factor
84
4.2.5
Overall Equipment Effectiveness
85
DISCUSSION
88
5.1
Examination of calculation
88
5 .1.1
A vail ability using total calendar-time approach
89
5.1.2
Planning factor based on capability
93
5.1.3
Performance ratio using Takt time
99
5.1.3.1 Incorporation of planning factor in performance ratio
5.1.4
5.2
for single measurement
106
Quality ratio
110
Versatile utilization OEE value and OEE data
111
5 .2.1
OEE percentage as comparison metric of performance
112
5.2.2
Examination of possibility to achieve production rate
IX
6.
scheduled
115
5.2.3
Material loss evaluation
120
5.2.4
Priorities on machine (preventive) maintenance
123
5.2.5
Demand accepted with respect to capacity of equipment
128
CONCLUSION AND SUGGESTIONS
133
6.1
Conclusion
133
6.2
Suggestions
134
REFERENCE
139
APPENDIX
152
X
CHAPTER 1
INTRODUCTION
This chapter is basically introducing the causes and the existing problems which initiate this
study. It is followed by the background of the study and the narrowed-down objectives. By the
end of the chapter is the clarification of project scope to set the boundary of the study where it
should lie within.
1.1
Background
The section consists of two subsections. The first subsection is the background
concerning the lean philosophy and the concept of OEE to be applied in this study. Brief
introduction and explanation are included in this subsection. The second part of this section is
the background of the subject of study. The introduction of autoclaves and curing process are
included in the second subsection. Introduction here covers the nature and characteristic of
curing process, the product types to be cured and the data required to analyze the OEE
performance of Autoclaves.
1.1.1
Background of concept applied
Lean manufacturing is most frequently associated with the elimination of seven
important wastes to ameliorate the effects of variability in supply, processing time or demand
(Shah and Ward, 2007). Briefly, it is called lean as it uses less, or the minimum, of everything
required to produce a product or perform a service (Hayes and Pisano, 1994 ). As Paul Dean
Ceng (2006) stated in his study, "OEE is an essential metric and methodology for
manufacturers pursuing a lean manufacturing strategy to emphasize zero waste in the value
streams". The OEE is one of the approaches in quantifying if the utilization of equipment or
machine is in lean or wasteful way. It identifies the scraps related to equipment in different
sections, to take corrective measures in order to eliminate them and exploit the potential
capacity of the equipment (Motavallian, 2007).
The overall equipment effectiveness index machine stands in comparison with an ideal
machine which always runs at full speed and capacity at the same time produces good quality
products (Rouhani, 2009). Although the data of overall effectiveness of equipment seem very
simple, extracting useful information from a series of calculations is a very important and
difficult task (Aliahmadi, 2003). The reasons include too much of performance measurements
have been used in an organization and its resulting confusion of data definition. Besides that,
various approaches which are used in the measurement of manufacturing performance could
lead to large number of measurement in different hierarchical levels and most of them are
contradictory (Nakajima, 1989; Kaplan and Norton, 1992; Jones, 1994).
Unavailability or infeasibility of data to be collected in the form required for each
formula is another difficulty of OEE implementation. For instance, often companies struggle
2
to define an ideal cycle time, particularly in non-machinery and less well-automated
manufacture (Bamber et al, 2003 ). The ambiguity of the OEE implementation could further
contribute to deviation in evaluating the utilization and performance of a particular equipment.
'·If you cannot measure it you cannot manage it", as stated by Paul Dean Ceng (2006).
Nevertheless, there is no formula or textbook which addresses the issue of over-production on
highly utilized equipment and the 'ideal cycle time' that an equipment with constant and fixed
process time should have. There is no guidance in clarifYing the trade-off relationship between
high machine utilization and low customer demand as well as the corresponding solution.
Faced with these conditions, there is a necessity of innovative and simple measurement to
evaluate the equipment performance incorporating the customer demand.
1.1.2
Background of subject of study
The study is carried out in an aerospace part-manufacturing company. Within the
company, a manufacturing section comprises of several Autoclaves is chosen as the subject of
study and all the time data is gathered from that. The time data here refers to the beginning
time and ending time of all curing processes which are recorded by computerized data system.
The time data is then interpreted and analyzed in this study to achieve the objectives and
obtain the results. The autoclave is used to harden the ply materials from the layup process.
Layup process as the supplier process of autoclave curing process is actually to stack up one
ply onto another in the amount and at the orientation as per design of aerospace parts. The ply
materials after being cured by autoclave will become harden aero plane parts for assembly of
different area in different aero plane model. These different aero plane parts for various aero
plane models are referred as product type in this study.
3
The manufacturing section comprises of 6 Autoclaves and their dimensions are all
different from each other. Due to the matter of dimension, their capacity or maximum curing
capacitance is then different as well. This means that the maximum number of a particular
product type could be loaded into an Autoclave is different from others. In this study, the
maximum number of panel could be loaded into autoclave is dependent on the dimension of
autoclave and panel itself. The data of maximum number could be loaded for each panel type
is obtained from the system which records the beginning and end of curing time, number of
panels loaded and types of packages cured.
Besides that, there are several products types with different customer specification,
designs and process requirement are to be cured by the Autoclave section. Consequence of
that, the curing recipe of all product types may be different and has to be cured according to
their curing recipe. This means that only those products with same curing recipe can be cured
together.
Curing recipe controls the length of curing process required by a particular product
type. Those product types with same curing recipe means that the requirement of temperature
and pressure are same and this allow them to be cured together. Since the curing recipe for a
particular product type is always same, this contributes to the constant curing time of a product
type from time to time.
In addition to that, not all product types can be cured by all Autoclaves available.
Thermal survey of a particular product type should be carried out in advance on the Autoclave
before the product is being cured. This is to investigate the thermal profile such as the
distribution of temperature and pressure within the Autoclave during the curing process. A
4
product type is allowed to be cured by the Autoclave once the thermal profile is qualified and
the product panel is in good condition after the curing process. In this company, one particular
product type is dedicated to be cured by not more than 3 Autoclaves due to the constraint of
thermal survey.
At last, since the curing recipe is fixed and constant from time to time, there is no
improvement can be done to shorten the curing time. The only variable can do to improve the
productivity of the curing process is to increase the amount of product loaded per each curing
slot. Another option can be considered is to choose the Autoclave according to their dimension.
In other words, small amount of products are recommended to be cured in relatively small
Autoclave to prevent the waste of non-utilized capacity.
1.2
Problem statement
One of the problems in lean implementation is the lack of direction of planning and
adequate project sequencing (Bhasin and Burcher, 2006). From the same perspective in OEE
implementation, it can be observed that the importance of planning is usually being neglected.
In other words, a high value of OEE does not mean that the machine is fully utilized since the
production rate is initially determined by planning. This effectiveness of the planning is
neglected or not evaluated in OEE implementation. Regarding to this problem, there are some
studies performed evaluating the planning factor as the percentage of production time planned
for or realized over the total theoretical production time (Wauters and Mathot, 2007; Icso,
n.d.). In those studies, the planning factor was used to evaluate the efficiency of production
planning in determining the maximum utilization of the equipment. Even that, Icso (n.d.) had
5
suggested retrofitting the existing equipment for better performance rate which is beneficial
from a higher production speed. The similarity of these studies performed is that the machine
utilization is not planned over the maximum capability of equipment. Over the similar issue,
there is a need to formulate an effective lean process, as suggested by Puvanasvaran et. al.
(2009), on the employee ·s deYelopment aspect to unlock the infinite potential of their
workforce. This could be implemented in machines or equipments in this case by planning the
production rate of existing machine up to the maximum capacity.
On the other hand, there is no specific value for so-called '"world-class OEE"
regardless of 85 percent OEE which has been cited frequently. This means the action of
maximizing and pursuing for high OEE value may not be justifiable. According to Williamson,
optimum level of OEE depends on the capability or capacity of the asset, the business
demands, and whether or not it is a constraint in the process flow. The idea is that high
production rate implied in high OEE (availability) especially in the period with low business
demand will contribute to expensive inventory handling cost. Besides that, the ideal cycle time
is always difficult to be defined whereas the speed loss, minor stoppage, and idling are hardly
to be differentiated from the waiting time (Bamber et al, 2003; Kenis, 2006). On the other
hand, the setup and adjustment time which are increasing accordingly with the product mix in
manufacturing company will adversely affect the OEE value (Mileham et al., 1997). Frequent
setup or loadings are required in the environment of mass manufacturing and this will
adversely affect the availability of the equipment. This means the compromise between mass
manufacturing and OEE emphasis and this is actually very illogical from the perspective of
business. Therefore, there is a need to improve the productivity of a manufacturing
organization with respect to different product mixes at the same time considering the customer
6
demand, and defming performance ratio of machines with fixed and constant process time in
pursuing high OEE value (Hilmola, 2005).
Besides that, the OEE value once being evaluated is just a displayed value and
indication of current machine utilization only. Most companies are focusing on the
comparison of particular asset (equipment) with itself over the time horizon via the usage of
historical data (Williamson, 2006). The common usages of the OEE metrics are the
identification of major losses, problem investigation for capital expenditures and enhancement
of capacity (Abidian inc., n.d.; Fuss & O'Neill Manufacturing solution, n.d.). The story behind
the value is seldom tracked out and another possible usage of OEE value is not optimized. So,
there is a need for internal flexibility within manufacturing system which requires changes of
traditional organization methods to manage, measure, and mindset of management and
employees on the role (Sweeney, 1990). This can be done by quantifying the instantaneous
production rate at manufacturing site, as well as comparing the utilization of a particular
machine regarding to the customer demand and examining the corresponding planning of
production using OEE data.
1.3
Objectives
Customer demand nowadays consists of multiple products with different process
parameters requirement. This makes the number of product being loaded during each
production slot with respect to maximum capacity is very important. As a consequence, an
overall measurement covering all the product mix does make sense rather than multiple
measurements for every of single product produced by the particular equipment. In addition to
7
that, balance between equipment utilization with the customer demands becomes more
important to prevent over-production. In order to facilitate the optimization of equipment
utilization, all of the issues stated have to be overcome via the objectives stated below:
To quantify the customer demand into OEE value and to quantify the performance
1.
ratio for an equipment with constant cycle time.
To measure the efficiency of production planning in affecting the machine utilization.
11.
m
To demonstrate three (3) usages of new-defmed OEE measurement to facilitate the
planning and engineering of a particular production section.
The concept applied and assumptions formulated throughout the study are:
1.
Williamson (2006) suggested that the optimum level of OEE depends on several
factors such as capacity or capability of equipments, business demand and condition if
it is a constraint of process flow. Additionally, the pursuit of high OEE will lead to
overproduction because it is measured based on the cycle time but not based on the
Takt time (Harada, n.d.). In view of that, it is a necessity to incorporate the customer
demand in OEE implementation to avoid overproduction during pursuing high
equipment utilization.
11.
The ideal cycle time in the computation of performance is commonly confused or does
not exist for those equipments with constant cycle time. This is because the cycle time
for the equipment is fixed as per process requirement and literally same as the ideal
cycle time. It can be concluded that the performance ratio of those equipments are
always 100% in traditional OEE approach, and this indicates that there is no
8
improvement can be done on equipment to increase the productivity in traditional
approach.
111.
A good management metric should cover all products produced or processed in
equipment instead of measuring them individually. This is to consider the impact of
product mixes on the machine utilization in term of the changeover time (availability)
and the overall performance ratio. This is because whenever the OEE is calculated
individually, the waiting time or setup time between two processes is neglected and
this will contribute to overrated availability. Therefore, a metric which covers all
product types processed in equipment and visualizes the overview of the actual
utilization. This is to ensure that an overall improvement decision can be made to
improve the total performance of the equipment in whole.
1.4
Significance of the study
Manufacturing environment nowadays is characterized with mass production and
demand comprises of numerous product mixes. This has necessitated the concept of On Time
In full (OTIF) to be more important. OTIF is a concept which emphasizes maximum loading
in each production loading and hence reduces the necessity of additional production. In that
any under utilization of machine causes more changeovers in production, which will further
contribute to lower equipment availability and effectiveness. Often times, however, traditional
approach ofOEE neglected the importance of utilizing equipment up to its maximum capacity.
As a result of that, equipment with high OEE value does not mean high utilization but could
probably because that particular machine runs at most of the time, at or close to ideal speed,
9
and produces least defect. There is no concern on number of input loaded per each production
slot. Even though there were some studies including the concept of planning factor in
computation ofOEE (Wauters and Mathot, 2007; Icso, n.d.), but it is just evaluated in term of
time (gross operating time over total calendar time). This defmition could be simplified in
total calendar time approach. In order to overcome the limitation like this, this study will adapt
and evaluate the planning factor in terms of the number of product loaded in each production
slot. With the significance in this study, the efficiency of planner is examined from time to
time to seek for optimization of machine usage.
In addition to that, a good management metric is badly desired and appreciated to be
implemented in a complex manufacturing environment. A simple and comprehensive metric
(de Ron and Rooda, 2005) which incorporates all product types is important. This is to provide
a brief measurement of current condition and to enable on-time decision making based on
actual data. Besides that, a good management metric should be characterized with ease of data
collection (Harada, n.d.). In view of that, flexibility is a must for a good management metric
so that it could be adapted and measured from time to time. As such, creativity could be
promoted in achieving the flexibility and competitive advantage (Oke, 2003; Yung and Chan,
2003; Sanchez and Perez, 2001; Gerwin, 1993; Jordan and Graves, 1995; Upton, 1995a cited
in Ooi, 2005). The flexibility stated can be obtained by defming the measurement in aggregate
unit which covers all product mixes of the equipment. In this study, the aggregate unit is
calculated accordingly with the schedule of the production based on their corresponding
importance factor named weighted averaging factor.
In most compames which are implementing OEE as their equipment performance
evaluation, the customer demand and production rate are in the trade-off relationship and
10
hardly to be balanced. Inappropriate management between these two parameters could lead to
over-production or idling machine, and sometimes the shortage of production. However, the
machine utilization and overproduction should be not always in opposite standpoint and could
be working together. It is possible to avoid or minimize the side-effect of over-production in
the path pursuing high equipment utilization. Significance in this study shows that the
performance ratio is possibly incorporated with customer demand via the introduction of Takt
time. The concept of Takt time in OEE computation has not only balanced the issue of
overproduction and machine utilization, but it quantifies various performance ratio on the
equipment with constant cycle time according to fluctuating customer demand.
As a short summary, this study is exammmg the validity of OEE universal
implementation in various types of manufacturing environments, and some of them were
known to be improperly used. Various elements are introduced in this study to enable OEE
computation. This study is providing new angle in looking at the possibility and flexibility of
modifying the traditional OEE approach to adapt the OEE measurement. The modifications
made are according to different manufacturing nature, which is determined by the types of
equipment used. This study seeks to create an impact on the OEE computation which covers
the considerations stated above via the following significances and novelties in OEE:
1.
11.
Customer demands to avoid over-production issue.
Aggregate unit to cover all product mixes and types produced.
m
Planning factor to promote On Time In Full (OTIF).
IV.
Total calendar approach to track out total downtime wasted.
11
1.5
Scope of the study
This study is focusing on the computation of single OEE measurement over s1x
Autoclaves to cover all the product mixes. The products are cured with fixed curing time and
are all constant as per curing recipe. A single measurement is desired to be simple and
comprehensive to quantify the condition of all product types in utilizing the equipment.
The scope of study also attempts to introduce and implement the concept of Takt time
in pursuit of high OEE level. This is to promote the balance between production rate and the
customer demand. In order to ascertain the generic and applicable characters of the new OEE,
a make-to-{)fder type of industry with various product types, Autoclave with different sizes
and capabilities are selected as the subject of study to represent the sample of manufacturing
process with constant cycle time.
This study does not attempt to demonstrate the implementation of OEE on those
equipments with varying cycle time from time to time within a product type. Such equipment
as this will have its ideal cycle time in defming the performance ratio. Besides that, the
integration of OEE metric with other lean tools is not demonstrated in this study since the
usage ofOEE as a management metric is the only concern and analysis tool in this study
In addition to that, this study is focusing on the Autoclave section only but not other
manufacturing cell or the effectiveness of equipment over the company wide is not within the
scope of the study. According to Williamson (2006), OEE is not intended for use as a
corporate or plant level. It is just a measure of a selected equipment effectiveness only. This is
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