G Model

CIRP-1393; No. of Pages 4

CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

CIRP Annals - Manufacturing Technology

jou rnal homep age : ht t p: // ees .e lse vi er . com /ci r p/ def a ult . asp

Improving the overall equipment effectiveness in high-mix-low-volume

manufacturing environments
Juan M. Jauregui Becker a,*, Jesper Borst a, Abele van der Veen b
a
Faculty of Engineering Technology, University of Twente, The Netherlands
b
VDL Enabling Technologies Group Almelo, The Netherlands

A R T I C L E I N F O A B S T R A C T

Submitted by E.h. Ichiro Inasaki (1), Chubu Manufacturing industries in high-wage countries pursue improving the effectiveness of their production
University, Japan equipment as a means for increasing production throughput while maintaining high-quality standards.
Overall equipment effectiveness (OEE) is a well-known method for enabling this in low-mix-high-volume
Keywords: (LMHV) environments. However, implementing OEE in high-mix-low-volume (HMLV) factories remains a
Manufacturing challenge, as both part and process properties are continuously changing. Responding to the
Quality semiconductor industry’s need for solving this challenge, a new effectiveness method for HMLV was
Overall equipment effectiveness
developed and evaluated, covering both organizational and technological aspects. This paper reports the
results of this research, including the results of an industrial implementation.
ß 2015 CIRP.

1. Introduction Fig. 1), which represents an overall measure of the effectiveness of

an equipment in a particular production system. In OEE, these
As markets are getting more competitive and manufacturing has factors are a function of the time the equipment is producing,
gone global, every production facility has to improve its processes in available for production, or just unavailable. The different times
order to remain competitive. Many methods have been developed to considered in OEE are: factory closed (I), planned downtime (II),
support the improvement of production processes, especially in unplanned downtime (III), speed losses and minor stops (IV), and
Japan during its industrialization quest. A well-known method quality losses (V), as indicated in Fig. 1.
developed by S. Nakajima in 1971 [1] is total productive mainte-
OEE 
¼APQ  (1)
nance (TPM). TPM has the goal of increasing the effectiveness of t operating
A¼  100% (2)
production equipment based on the idea that six types of losses can  t planned production

t net operating
be identified and reduced: failure of equipment, set-up and P¼  100% (3)
adjustment times, idling and minor stoppages, reduced speed of  t operating 
t valuable operating
equipment, defects in process, and reduced yield. TPM also Q¼  100% (4)
t net operating
introduced a systematic method to measure equipment effective-
ness as backbone for eliminating these losses: overall equipment Advancements of OEE started in the 1990s and early 2000 when it
effectiveness (OEE). By continuously measuring the effectiveness of started being implemented in several companies, making the topic
equipment, a monitoring signal is provided to production managers become popular in academia. The first big addition to the OEE was
and operators that allows them to react quickly to eventual done by Raouf in 1994. Raouf states that the factors availability,
production disturbances and that serves them to set-up medium performance and quality might not be equally important and adds
and long-term continuous improvement programmes. weights to the each one of them [3]. Raouf also makes a distinction
OEE considers that effective production requires: (1) the between discrete types of operations and continuous types of
equipment to be running during the planned production time, operations. As both are feasible targets for effectiveness measure-
(2) the equipment to produce the parts in the optimal (expected) ment, his method was regarded as production equipment effective-
speed and (3) the parts to be produced according to specifications.
In OEE each one of these aspects is respectively captured by the
availability (A), performance (P) and quality (Q) factor, which are
assigned a percentage value to indicate its performance. Further-
more, these factors are used to calculate the OEE (as indicated in

* Corresponding author.
E-mail address: j.m.jaureguibecker@utwente.nl (J.M. Jauregui Becker). Fig. 1. Times considered in OEE. Roman numbers correspond to losses.

http://dx.doi.org/10.1016/j.cirp.2015.04.126
0007-8506/ß 2015 CIRP.

Please cite this article in press as: Jauregui Becker JM, et al. Improving the overall equipment effectiveness in high-mix-low-volume
manufacturing environments. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.04.126
G Model
CIRP-1393; No. of Pages 4

2 J.M. Jauregui Becker et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

ness (PEE). De Groote [4] builds on this method by considering the production, where a part’s quality can either be good or defect, this
planned production output rather than operating time in the factor is defined as the percentage of defect products in the total
Performance factor. This was especially useful in mass production output produced during a predefined time period. As HMLV
and manufacturing lines producing one single product. accepts rework of parts that have defects, and given the fact that
As planned downtime is also considered to be a loss, Ivancic [5] this type of manufacturing systems deal with a large variety of
suggests a new method called total equipment effectiveness parts, neither of the former methods can be applied to determine
production (TEEP). Ivancic argues that it is also important to the quality factor in a systematic and normalized fashion. As HMLV
consider the planned downtime since this should also be allows multiple categories of defect products, a possible way of
minimized to fully utilize the potential of equipment. As planned calculating the factor is by considering the costs of the defects. This
downtime cannot be influenced at the shop floor level, TEEP is a variable can be used to standardize the quality, as parts that have
signal that is better used by production managers for deriving not been produced correctly are reworked at the expenses of extra
improvement strategies and not by operators [6]. costs which are directly related to the quality deviation of the
Several authors [6–8] point out that OEE works for single pieces produced part.
of equipment while most equipment is part of some kind of flow or
stream. In general the products have passed upstream equipment 3. Machining equipment effectiveness (MEE)
and will continue to be processed downstream. Therefore the
effectiveness at the factory level, or at least for cells of machining MEE consider the three factors proposed by Nakajima [1] for
groups, should be considered instead of looking at one single measuring the effectiveness of manufacturing equipment.
machine individually. To tackle this, Huang et al. [9] introduced a
method called Overall Throughput Effectiveness (OTE), where the 3.1. Availability factor in MEE
performance measure is determined by dividing the number of
good parts leaving the factory by the theoretical number of parts The goal of this measure is to provide a key performance
leaving the factory [10]. Two similar approaches are those indicator (KPI) for the shop floor such that only downtime reasons
developed by Muchiri et al. [6] and by Lanza [7], namely, overall – which can be influenced by people on the shop floor – are
asset effectiveness (OAE) and overall plant effectiveness (OPE), included in the downtime. Disturbances can vary from changing
respectively. The two most extensive methods for considering pallets to inform planners of possible issues. Any downtime
preliminary processes are overall line effectiveness (OLE) [11] and reasons outside the influence of the shop floor personnel should be
overall equipment effectiveness of a manufacturing line (OEEML) allocated to the planned downtime category or facility closed
[12]. Both methods were developed for manufacturing lines category. In this case an extra effectiveness measure for (depart-
dealing with constant flow. ment) management is needed to show the real effectiveness of the
All methods presented describe the calculation of the different equipment. The A factor can be calculated as indicated in Eq. (2).
factors and percentages but do not indicate how to identify the loss
reasons. Furthermore, no literature reporting OEE methods and 3.2. Performance factor in MEE
implementations in HMLV environments were found. This
coincides with the research of Bulent et al. [2] stating that there The Performance factor represents how well a machine is
is very limited evidence that effectiveness methods are applied in performing when operating during a time interval. The Perfor-
HMLV manufacturing environments. Given the current trends of mance factor should therefore be seen as a trigger to indicate a
bringing manufacturing back to high-wage countries and enabling problem rather than an absolute measure. Only orders that are
flexible manufacturing systems to cope with current market finished within the prescribed time interval are considered. The
demand for highly customized products, the need to count with Performance factor is calculated by dividing the summation of the
OEE methods for HMLV facilities is growing. absolute difference of the optimal (ptoptimal) and actual (pttactual)
This paper documents the results of investigating a new production time of an order by the summation of optimal orders
equipment effectiveness method for HMLV. The new method is within a certain time period, as shown in Eq. (5). The value of n in
coined machining equipment effectiveness (MEE). MEE has been this equation corresponds to the last order within a predefined
implemented and tested at VDL ETG, a Dutch company operating time interval.
in the business of system integration of mechatronic (sub) systems Pi¼1 !
and modules for Original Equipment Manufacturers (OEM) in the n j ptactual  pt o ptimal ji
P MEE ¼ 1 Pi¼1  100% (5)
high-tech capital equipment industry. Results demonstrate that the
n ð pt o ptimal Þi
method is successful and feasible for industrial implementation.
The challenge of HMLV environments producing complex parts is
2. Implications of OEE factors for HMLV how to determine the optimal production time, as equipment is not
dedicated to producing only one product in large series. Taking the
In order to create a new effectiveness method, each factor has time computed and simulated by CAM software will give a wrong
been analyzed in the context of an HMLV environment. This interpretation, as the production process heavily depends on
analysis provides the main rationales behind the new MEE method experience and operator ‘know-how’. Also, small stops are included
described in Section 4. The availability factor of OEE can be directly in the performance factor, such as in-process measuring and tool
used without any redefinition, as its definition in OEE considers up- inspection, which cannot be simulated. Therefore, the optimal
and downtime of the equipment, and these two concepts are equal production time should be modified by taking shop-floor factors into
for HMLV and LMHV. account. The planned and actual time should continuously be
As described in Section 1, the Performance factor in the original compared to make sure that differences are kept at a minimum.
OEE is calculated by dividing the net operating time by the
operating time. In mass production, this factor is commonly 3.3. Quality factor in MEE
calculated by dividing the theoretical output by the operating time.
When dealing with HMLV the problem of using this approach is In MEE, the quality factor considers four categories: (1) good,
that there is a variety of different products being processed within (2) concession, (3) rework and (4) reject. Good products are
a given time interval. As the optimal operating time differs for each according to specification and accepted by the customer without
product, in HMLV the performance should be calculated for every any problems. Concession products are not according to specifica-
product and then summed up. tion, but are still accepted by the customer after consultation and
In the original OEE, the quality factor is calculated by dividing agreement on the deviation. Rework products are those products
the valuable operating time by the net operating time. In mass not produced according to specification, but accepted by the

Please cite this article in press as: Jauregui Becker JM, et al. Improving the overall equipment effectiveness in high-mix-low-volume
manufacturing environments. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.04.126
G Model
CIRP-1393; No. of Pages 4

J.M. Jauregui Becker et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx 3

customers after reparation. The fourth category is rejected

products and contains products that cannot be repaired and are
considered to be scrap.
In order to have one parameter that is useful in these four cases,
the economic value of the production time (valueto perating ) and the
costs of concessions, rework or scrap is used, as indicated in Eq. (6).

Q MEE
Pi¼1 !
Valueto perating  n ðCostsconcession;i þ Costsrework;i þ Costsscra p;i Þ
¼
Valueto perating
100
(6) Fig. 2. Measured MEE (yellow) and its availability (blue), performance (orange) and
quality (grey) factors for the (a) Matec 50HV-1 (BAZ1-1) and (b) the Matec 50HV-2
(BAZ2-1).
The economic value of the actual production time is calculated
by multiplying the actual production time with the hourly rate of
that particular machine. The costs for concessions and rework
depend on the amount and type of work that is required to get the
product accepted by the customer. The seriousness of the damage
is also considered since an increase in rework time or a particular
expensive rework task will lead to higher costs, resulting in a lower
quality level. In particular for the manufacturing of complex parts,
small rework is less important than extra machining hours. By
using the costs as an indicator this difference is represented in the
quality factor. Costs of rejected products are determined by
calculating all the value added to the product until the moment it
turned scrap. For example the added value will include material
costs, working hours and machining hours on the product.
It is possible that the quality level becomes negative. If the parts
that are rejected are at its final stage and a lot of effort and costs are
invested, the costs of the rejected products can exceed the value of
the actual production time, resulting in a negative percentage
value for Q. Although this can be interpreted as a problem, Q still
fulfils its signalling function, as it indicates that the machine
producing the scrap part is not making any profit at all. Costs of
concessions and rework are not expected to exceed the value, since Fig. 3. Measured availability for Matec 50HV-1 (BAZ1-1).
rework or concession is only beneficial if the costs are not
extremely high.

4. Industrial implementation at VDL ETG

VDL ETG Almelo operates in the business of system integration

of mechatronic (sub)systems and modules for Original Equipment
Manufacturers (OEM) in the high-tech capital equipment industry.
As a system supplier, they cover the value chain from (co-
)engineering through parts production to assembly and testing. In-
house production technologies include; machining, high-speed
milling, precision grinding, sheet metal production, laser cutting,
mechanical and electrical (clean room) assembly, testing, product
certification and onsite installation.
The machining department of VDL ETG Almelo has nine
strategic machining centres and several smaller manual machining
centres. The implementation of MEE was carried out at the Matec
50 machining centre, which contains two Matec 50 machines, 9-
pallet system and production control software called Soflex.

4.1. Pilot project: implementation details

The implementation and test of MEE was carried out in a pilot

project measuring data during a time span of 4 weeks. The
calculated A, P and Q factors are shown in Fig. 2. Downtime tables
Fig. 4. Downtime reasons for the 4 weeks measured data.
indicating the reasons for both planned and unplanned downtime
were constructed by interviewing the operators, shop floor
managers and technical support staff. Figs. 3 and 4 show the To further analyse the data and evaluate the potential of MEE
identified causes. Finally, a schedule of meetings for data analysis for improving the effectiveness of equipment, one team was
was elaborated and flowcharts were made to streamline the order formed consisting of a highly involved operator, a production
in which the MEE data was to be analysed for proposing feasible manager, a factory/lean engineer and a planner. This team
improvements. Each flowchart describes a protocol relating the analysed the data in a meeting involving 4 phases. First, a
cause for time loss with possible plant issues. statement on the goal of MEE was described and MEE was

Please cite this article in press as: Jauregui Becker JM, et al. Improving the overall equipment effectiveness in high-mix-low-volume
manufacturing environments. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.04.126
G Model
CIRP-1393; No. of Pages 4

4 J.M. Jauregui Becker et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

presented to the attendees. Second, a quiz with ten basic questions 5. Conclusions
was done to assess the level of understanding of the method.
Incorrect answers where once more discussed to clarify the Many effectiveness methods have been developed in the past
method. Third, the method was contrasted with OEE. Finally, two decades. The first and most used effectiveness method is the
groups were formed and received the MEE charts containing, Overall Equipment Effectiveness (OEE) method developed by
among others, the A, P and Q factors (shown in Fig. 2) measured Nakajima [1] in 1988. Many additions have been suggested, which
during 4 weeks and logs of the downtime reasons (shown in can be divided in three groups: the ones that adjust the definitions
Figs. 3 and 4). To finish, the team was asked to use the flowchart to of the factors in OEE, the ones that add weights to the factors and
determine the most appropriate improvement strategies and the ones that look at the manufacturing line or entire plant.
determine its implementation feasibility. However, no method for a HMLV job shop machining environment
is present in literature. To fill this gap in literature, and more
4.2. Analysis of results importantly, to meet the needs of European industry, a new OEE
method for HMLV is designed and a process for selecting the best
The results indicated that a large loss of the machine’s area for improvement was developed. The method is machining
availability was due to time losses larger than 5 min. The reasons equipment effectiveness (MEE) and it is designed in such a way
for time loss, as shown in Fig. 4, were related to the absence of that all factors of equipment effectiveness are included. The test of
work/orders at the machines. Also, material was unavailable at the the method at the machining department of VDL ETG Almelo
machines when required. A fault-tree was constructed to find the showed that MEE can be applied and the flowcharts provided
possible problems causing the unavailability of material. It was useful help in determining the best areas for improvement.
concluded that there was no clear overview of the products that are
or can be produced on the Matec 50HV. The evaluation team Acknowledgements
concluded that the following elements needed to be presented in
the product overview: routing, flow, production time, added value/ The authors of this paper would like to acknowledge the work of
costs, fixed costs (material), frequency of orders, stability of ir. Wim Granneman, who participated in the implementation of
demand, outsourcing agreements, batch sizes throughout the MEE and provided important advice.
routing, limitations machines, tools and moulds. The team also got
to the conclusion that this overview of the products can be used as
building blocks for reducing the downtime reason. Proposed References
options for improvement were: always have products available in
stock that can be produced unmanned; produce products in [1] Nakajima S (1989) TPM Development Program: Implementing Total Productive
constant flow independent from fluctuating demand; make sure Maintenance, Productivity Press, Cambridge, MA.
[2] Bulent D, Phil T, Richard G (2000) Overall Equipment Effectiveness as a
that no process before the Matec 50HV’s can be the bottleneck. Measure of Operational Improvement – A Practical Analysis. International
Journal of Operations & Production Management 20(12):1488–1502. http://
4.3. Human aspects dx.doi.org/10.1108/01443570010355750.
[3] Raouf A (1994) Improving Capital Productivity through Maintenance. Interna-
tional Journal of Operations & Production Management 14(7):44–52. http://
As described in the literature, OEE methods should be dx.doi.org/10.1108/01443579410062167.
understood and used at the shop floor level in order to be [4] Groote PD (1995) Maintenance Performance Analysis: A Practical Approach.
Journal of Quality in Maintenance Engineering 1(2):4–24. http://dx.doi.org/
successful. As MEE has less obvious definitions of the performance 10.1108/13552519510089556.
and quality factors than OEE, it is of paramount importance to [5] Ivancic I (1998) Development of Maintenance in Modern Production: Proceedings
continuously assess the level of understanding of the method of 14th European Maintenance Conference, EUROMAINTENANCE’, Du-brovnik,
Hrvatska, October98, 5–7.
among the project participants. Therefore, proper training was [6] Muchiri P, Pintelon L (2008) Performance Measurement Using Overall Equip-
noted to be very important for the successful introduction of the ment Effectiveness (OEE): Literature Review and Practical Application Discus-
method. Furthermore, it was also noted that that the analysis of sion. International Journal of Production Research 46(13):3517–3535. http://
dx.doi.org/10.1080/00207540601142645.
results needs to be kept both simple and transparent. For example,
[7] Lanza G, Stoll J, Stricker N, Peters S, Lorenz C (2013) Measuring Global
adding weighting factors decreased notably the understanding of Production Effectiveness. Procedia CIRP 7:31–36. http://dx.doi.org/10.1016/
the MEE calculation by the operators. As a consequence, it made j.procir.2013.05.006.
the process of analysing results more difficult. The main reason is [8] Oechsner R, Pfeffer M, Pfitzner L, Binder H, Müller E, Vonderstrass T (2002)
From Overall Equipment Efficiency (OEE) to Overall Fab Effectiveness (OFE).
that every machine requires different weight factors, as costs are Materials Science in Semiconductor Processing 5(4–5):333–339. http://
not equally important for all machines. When consulting dx.doi.org/10.1016/S1369-8001(03)00011-8.
managers on this issue, they pointed out that the most important [9] Huang SH, Dismukes JP, Shi J, Qi S, Razzak MA, Bodhale R, Robinson DE (2003)
Manufacturing Productivity Improvement Using Effectiveness Metrics and Sim-
reason for not adding weighting factors is that they are subjective ulation Analysis. International Journal of Production Research 41(3):513–527.
and therefore might create false information. Especially when the [10] Huang SH, Dismukes JP, Shi J, Su Q, Wang G, Razzak MA, Robinson DE (2002)
MEE factors are used in management meetings more detailed Manufacturing System Modeling for Productivity Improvement. Journal of
Manufacturing Systems 21(4):249–259. http://dx.doi.org/10.1016/S0278-
information is required to see which machine is performing 6125(02)80165-0.
according to the goal. Therefore, it was concluded not to use [11] Nachiappan RM, Anantharaman N (2006) Evaluation of Overall Line Effective-
weight factors, and make the team aware that MEE has the ness (OLE) in a Continuous Product Line Manufacturing System. Journal of
Manufacturing Technology Management 17(7):987–1008. http://dx.doi.org/
function of explicitly sensing the behaviour of the machines, but 10.1108/17410380610688278).
that the right interpretation of the signals strongly depends on the [12] Braglia M, Frosolini M, Zammori F (2008) Overall Equipment Effectiveness of a
experience of the managers and operators and their understand- Manufacturing Line (OEEML). Journal of Manufacturing Technology Manage-
ment 20(1):8–29. http://dx.doi.org/10.1108/17410380910925389.
ing of the relations between a given machine and the rest of the
production system.

Please cite this article in press as: Jauregui Becker JM, et al. Improving the overall equipment effectiveness in high-mix-low-volume
manufacturing environments. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.04.126