- Email: [email protected]
Measuring Global Production Effectiveness
Available online at www.sciencedirect.com
Procedia CIRP 7 (2013) 31 – 36
Forty Sixth CIRP Conference on Manufacturing Systems 2013
Measuring Global Production Effectiveness Gisela Lanzaa, Johannes Stolla*, Nicole Strickera, Steven Petersa, Christof Lorenza a Insitute of Production Science (wbk), KIT – Karlsruhe Institute of Technology, Kaiserstraße 12, 76131 Karlsruhe, Germany * Corresponding author. Tel.: +49-721-608-46166; fax: +49-721 608-45005. E-mail address: [email protected].
Abstract Increasingly shorter product life cycles at an increasing number of variations call for productive, reliable and quality-oriented production systems and networks which are able to meet the turbulence of global demand especially at an expected higher frequency of economic crises. The following paper presents the development of a theoretical measure for an evaluation that integrates all aspects of a globally distributed production system. The work is based on the latest enhancements of the classic OEE figure of the TPM concept. © 2013The TheAuthors. Authors.Published Published Elsevier © 2013 by by Elsevier B.V.B.V. Selection and/or peer-review responsibility of Professor Pedro Carmo Cunha Selection and peer-review underunder responsibility of Professor Pedro Filipe doFilipe CarmodoCunha Keywords: Global Production, Overall Equipment Effectiveness, Key Performance Indicators
1. Introduction
2. Overall equipment effectiveness
The sector of machinery and plant engineering is facing new challenges. A growing multitude of variants and an increasing product differentiation due to more customization, shorter product life cycles, uncertainty in demand as well as growing international stress of competition have to go along with an increase in effectiveness [1]. Many companies meet these new challenges with an increasing automation of their production facilities and an ongoing internationalization of their production sites. Automation and linking of production systems lead to complex manufacturing systems which additionally have to go global. The requirements for the developing global production networks are still increasing although the degree of complexity regarding production costs, quality of processes and products is increasing. A commonly used figure to evaluate the efficiency of production systems is the Overall Equipment Effectiveness (OEE). The OEE [2] is a figure that basically refers only to one machine. However, there exist extended concepts, but they are mostly just limited to individual production lines. There is no global extension of this effectiveness concept that defines and summarizes influencing parameters in a global production network.
This paragraph will discuss the Overall Equipment Effectiveness more in detail. The Overall Equipment Effectiveness (OEE) is the traditional evaluation measure of the Total Productive Maintenance (TPM) that has to be maximized and it compares the operating level with the ideal potential of the plant performance. The fundamental idea is based on the conception that this ideal operational potential is reduced by various losses. By using this figure, the reasons for these losses are to be identified, so that corrective actions can be taken accordingly [2], [3]. The productivity figure had been developed by Seiichi Nakajima as part of the TPM. At first, this figure had only been used in the TPM sector but the OEE can now also be used as an independent operational improvement tool as for Lean Production and Six Sigma. The OEE evaluates and improves by now the effectiveness of machining and manufacturing processes for a large number of companies and shows the efficiency of the TPM concept [4], [5], [6]. The OEE is more and more used in many production and assembly lines for series production. With the help of the OEE, productivity and economic benefit can be well described. On the basis of manually or automatically recorded operational and machinery data,
2212-8271 © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Professor Pedro Filipe do Carmo Cunha doi:10.1016/j.procir.2013.05.006
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Gisela Lanza et al. / Procedia CIRP 7 (2013) 31 – 36
the OEE can be calculated very easily for a defined production period [7]. Figure 1 summarizes the key elements and the fundamental influencing parameters of the OEE. Production line
Loss
Overall Equipment Effectiveness (OEE)
Scheduled downtimes
Total useful life
Value adding Operating Time
Speed loss
= Interruptions, breaks, downtimes, maintenance, etc.
Availability x Performance x Quality
Plant shutdown
Availability
Changeover and set-up time
Available Time – Downtime Available Time
Unoccupied time, short stops
Performance
Lower speed
Allocated Time Actual Time Quality
Initial production problems
Quality loss
Net Operating Time
Availability loss
Planned Utilization Time
Operating Time
Calculation for OEE
Output – Rejections Output
Rejections, rectification of defects
Fig. 1. OEE and sources of loss to display the operational behavior according to [1, 7, 8]
According to [2], OEE values around 0,4 are not unusual figures for producing companies. Although individual factors might be rated as very good, this way of calculation leads to a detection of possible misjudgments. "Studies carried out worldwide have revealed that the average OEE in producing companies is at about 60 %" [6]. In consequence, a target level of 85 % represents a clear potential for improvement for many companies [2]. In recent years various changes and extensions to the original OEE figure have been made. The following chapter will describe some of these approaches more in detail. 3. Selected Extensions of OEE In literature as well as in practice, various terminologies have come up which are either related to single plants or have been extended to a holistic view of a complete factory [8]. Table 1 shows a selection of common extensions.
3.1. Total Effective Equipment Productivity (TEEP) While the OEE is using the planned production time as a temporal reference figure (see Fig. 1), the theoretically utilizable calendar time should be integrated into a comprehensive survey [9]. Setting and maintenance activities can in that way be transferred to the time of planned downtimes in order to obtain a higher OEE at the expense of plant utilization. This utilization integrates [10] into the key figure Total Effective Equipment Productivity (TEEP). The planned production time in traditional calculations of the OEE leaves room for definition and interpretation. The TEEP reduces this problem and contributes therefore to a better comparability. On the other hand, as an exclusive key figure, it can lead to the wrong conclusions about the real plant state. At one shift and two shift utilizations without weekend shifts the TEEP cannot reach a value above 50 percent, if the overall operating time is defined as calendar time. It has to be considered that the overall operating time can also be defined as planned production time. Then, only the planned down times are integrated into the TEEP. By looking at the two key figures TEEP and OEE potential problems regarding utilization and equipment effectiveness can be identified. 3.2. Overall Asset Effectiveness and Overall Plant Effectiveness (OAE/OPE) Based on the summary of the illustration of the possibilities for extension of the original basic concept, at first two extensions of the OEE are to be presented for the determination of the Overall Equipment Effectiveness and for the identification of plant losses according to [8]: The Overall Asset Effectiveness (OAE) and the Overall Plant Effectiveness (OPE). While literature deals with them in a limited way, they are used by many companies and industries and have the same meaning with regard to industrial application.
Table 1. Overview of selected key figures TEEP Concept
Calculation
Extension of OEE by integration of planned shutdowns
TEEP
OAE/OPE Relation between actual and possible Output
OAE
O act O th
OPE
Tvalue Tth
EU * A * P * Q
EU: Equipment utilization A: Availability P: Performance Q: Quality
O: Output : overall scheduled time
Tth
Tvalue (
Tth
: value creating time
- planned stops)
OLE
OEEML/ TOEE
Manufacturing system as whole Identification of critical line of several line processes process steps and consideration of decoupling n
OLE
Tth ,op
Tth ,op , n
O n max t i
Tth
Tth ,op ,1
i 1
: overall scheduled operative time
n: index of last station L: index of line t: cycle time
OEEML
TOEE
Onact TLth
t BS TOEE L tL
ALint ALext OEE
BS: bottleneck station : availability losses due to preventive
ALint
maintenance : availability losses due to external effects
ALext
Gisela Lanza et al. / Procedia CIRP 7 (2013) 31 – 36
According to this, a comprehensive OEE can be determined without difficulty with the nominal-actual value ratio for a complete production system, so that the calculation of the effectiveness parameters becomes considerably simpler (simplicity). OAE and OPE differ in the calculation of the parameters concerning the considered items (quantity or time) whereas the OPE in contrast to the OAE is not determined by output quantity but by length of time. In both cases, any losses are considered but an identification of the mentioned weaknesses is not possible. Therefore, a real added value from the extension of the original OEE is not apparent. 3.3. Overall Line Effectiveness (OLE) An alternative possibility for extension is the Overall Line Effectiveness (OLE) by NACHIAPPAN. In this approach the production system is described as the entirety of several process steps to completion of the product. This is in line with the criticism on the OEE that the focus on single machines or plants is not significant because the inter-plant interference is considerable. The result of this process integration is a holistic method of an approach to single line process steps that allows for a simple calculation/measurement of the individual lines-OEE. During calculating one have to consider that the machines are directly interdependent. Thus, the output of a machine is determined by its input. This input, in turn, corresponds to the output of the upstream machines (see [11], p. 992). In doing so, a continuous flow sequence is assumed for n process steps while defectives and parts to be reworked are removed. If the single plants are decoupled the method loses its validity. The corresponding line availability results from the ratio of the actual operating time to the planned holding time where the planned holding time depends on the planned downtimes of the first machine. Overlaps of planned downtimes are not taken into consideration. Furthermore, the approach by NACHIAPPAN takes especially the time dependence between the different plants and machines into account. In case of a continuous production flow, OLE consequently gives very good results. However, the hypotheses concerning operating time definition for intercalated buffers and decoupling have to be abandoned. In addition, the formula presented in table 1 merely focuses on the last process n in the line so that an identification of the critical process steps is problematic.
3.4. Overall Equipment Effectiveness of a Manufacturing Line and Total Overall Equipment Effectiveness (OEEML/TOEE) On this basis, BRAGLIA introduces Overall Equipment Effectiveness of a Manufacturing Line (OEEML). In the first instance, further loss categories are defined to determine afterwards Total Overall Equipment Effectiveness (TOEE). It should be noted, that planned downtimes for preventive maintenance and external sources of line losses are integrated [12]. Furthermore, external line availability losses which are caused due to upstream or rather downstream transportation processes are determined and calculated if thereupon the cycle time of the machine is increased because of the interlinking [12]. Provided that the line is still not operated with optimal productivity the consideration of decoupling by means of buffers can lead to the result that single machines in the line have a shorter cycle time than the real bottleneck workstation. If the buffer capacities are exhausted, the line has to follow the cycle time of the real bottleneck workstation [12]. Since the real bottleneck workstation can vary from the theoretical one, the bottleneck workstation change has to be integrated in the determination of the OEEML. The added value resulting from this extension can therefore be described by the identification of the theoretical and actual bottleneck workstation taking into account decoupling and its influence on the OEEML. Thus, the OEEML allows for the precise determination of the machines influencing the overall effectiveness. By means of counter measures and the setup of buffers the overall system can be influenced positively as a consequence. This represents therefore a real advantage over the OLE. Basically, the approaches to Overall Throughput Effectiveness, Overall Line Effectiveness and Overall Equipment Effectiveness of a Manufacturing Line are therefore very good extensions of the OEE concept whereas the approach to the OEEML can involve a calculation that can be complicated because of the increasing loss categories as well as the positive influence of the counter measures. 4. Global Production Effectiveness Below, an evaluation method is presented that transfers the idea of the overall plant availability to the global production network - Global Production Effectiveness (GPE). It is based on the principles of the OEE and describes all essential determinates in the globally distributed production system. For the development of the GPE the single factors which provide a basis for the integrated key figure are presented at first. Depending on the actual network configuration single determining factors can be
33
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Gisela Lanza et al. / Procedia CIRP 7 (2013) 31 – 36
identified and defined in the global context. Afterwards, these parameters are individually constructed and then transferred into the global comprehensive survey. The company infrastructure provides the network in which the company operates and thus determines its geographic orientation. If the horizon is global, there has to exist a transnational network for supply, production and sales that distributes structures and processes strategically to different locations. For the GPE the production network is particularly significant because the GPE is to be projected up onto several departments coming from the producing machines.
According to [13] the following calculation formulas result for the possible subsystems: ME
n
J
ME S
ME P
VAR
i 1
n 1
min min i 1
n i 1
n
n i 1
Qa
,
VAR i ti
VAR ta
a
n
Qj , j i 1
min
n
i 1
(1)
t BS
(2)
min min k iJ t i , t a
VAR n tn
(3)
1 ti
VARi k iE Qi VARi , ti ti n
min i 1
Manufacturing Effectiveness (ME) represents an essential element of the GPE. With its help the effectiveness of single locations can be measured and additionally an indication of the interconnection between the components of the GPE is provided. Moreover the presented approaches to expansion, regarding the OEE, are taken up at that point again and all subsystems (joining, serial, parallel or expansion, (cp. Fig. 2)) of the manufacturing system are modeled.
i
t i k iJ
VAR i ti i 1
ME E
4.1. Manufacturing Effectiveness
min min
k iE 1 , ti ti
(4)
VAR: variable factor (cp. Fig. 3) Q: Quality n: index of last station a: index of station after joining/expansion t: cycle time BS: bottleneck station
For the calculation of ME the subsystems are modeled in accordance with the equations (1) – (4) and the overall system, in turn, considered as the serial system of the subsystems (cp. Fig. 2: grey shadowed subsystems are elements of the overall serial system). 4.2. Sourcing Effectiveness
J-subsystem
S-subsystem
P-subsystem
E-subsystem
Fig. 2. Exemplary line system with different subsystems (J= joining, S=serial, P=parallel, E=expansion)
The change still exists in the joining in value k. This value indicates how many parts of plant i are needed for the joining in plant a. The same applies to expansion. In addition, a variable factor (VAR) is introduced which is to be proven by means of the flow sheet illustrated in Fig. 3 depending on the available data and the disturbances to be taken into account. Individual machine data Consideration of sheduled shutdowns? Calculation of ME FE
Measurement
No
OEE
Yes Integration of external line losses?
VAR No
TEEP
Yes TOEE
Fig. 3. Flow sheet for calculation of ME
The factors of the OEE are also used to number the sourcing effectiveness (SE) for which availability, performance and quality have to be defined. Availability can be defined as the proportion of on-time delivery of correct quantities by a supplier. The quality degree corresponds to the proportion of good parts delivered and the performance rate is standardized on a scale from 0 to 1 which is based on a comparative calculation regarding stock handling or rather commissioning. The multiplication of these individual values leads to the supplier effectiveness (SupE). For a SE, the individual supplier figures have to be linked and related to each other. Thereby, two conditions have to be distinguished: temporal dependency of the delivery (e.g. JIT) and of different scenarios. Basically, two forms of supplierprovision can be distinguished. With a JIS or JIT delivery the supplier becomes dependent on time. This time dependency can be decoupled by means of stocks. A time-dependent delivery with short-term buffers is not considered here, since this special form is depending on variety and size. In addition, a distinction must only be made between the relevant locations, since the national influencing of suppliers can be ignored. If parts are delivered depending on time, this delivery system can be seen as a joining. A calculation of the SE in this case can therefore be carried out according to formula (1):
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Gisela Lanza et al. / Procedia CIRP 7 (2013) 31 – 36
SEJ
n
min i 1
SupEi Ti S
n
min Ti S i 1
(5)
Ti S : Time for order and Transportation of supplier i
For determining the global total effectiveness, the whole system is divided into subsystems. For the system outlined in figure 4, the following four subsystems arise as an example: SupED,1
4.3. Transportation Effectiveness For the transportation effectiveness (TE) the following total evaluation emerges, considering the transport damages (Q), the speed in relation to the maximum possible speed (L) and, if applicable, the proportional waiting time until the means of transport (V) can be made available, with n means of transport: QT
TE total
n
LT
V iT
i 1
(6)
4.4. Stock Effectiveness For the stock effectiveness (StE), the following formula arises for the three components of the OEE, taking into account any possible damages caused by the stocking (Q), the service level of logistics (L) and, if applicable, a consideration of available storage areas depending on random or fixed storage space allocation (V): StE
V
St
L St Q St
(7)
4.5. Personnel Effectiveness For the personnel effectiveness (PE), the following formula results, taking into account the created/moved good parts (Q), the availability of a staff member minus sick leave and holidays (V) and a standardized productivity index compared with other states (L): PE
V
P
L
P
Q
P
(8)
SupEA,1 SupEA,2 SupEA,3
SupED,1
SE MEB
StEB
SE
D
TEC TEC,1
TEC,2
TEC,3
MED
StED
A
A
C
B
D
Fig. 5. Separation of global network into subsystems
Each of these subsystems will now be modeled according to the presented individual dimensions (see 4.1 – 4.4.) at which ramifications and parallel or serial sequences follow the logic of figure 2 or the equations (1) - (4). Then, the four subsystems are perceived as a serial overall system. 5. Calculation for a hypothetical system To elaborate the applicability in industrial settings, the approach is applied to a hypothetical global manufacturing system (cp. Fig. 4 and 5). Therefore, the two manufacturing systems MEB and MED are assumed to be as illustrated in Figure 6. Furthermore, it is assumed that no scheduled downtimes are considered and plant effectiveness can be calculated as OEE (cp. Fig. 3). MEB
MED 2* 3
1
5
2
3
2 1
5 4
4
4.6. Integrated Key Figure Fig. 6. Sample manufacturing systems to determine ME
The formulation of the GPE is always related to an individual network. Figure 4 shows an exemplary globally networked manufacturing system:
As pointed out in section 4.1 the systems can be transferred to simplified serial system (cp. Fig. 7) by the use of equations (1) to (4). MEB MES,1-2
MED MEP,3-4
OEE5
OEE1
MEP,2*-4
OEE5
Fig. 7. Simplified serial system
Fig. 4. Exemplary global manufacturing system
To demonstrate the results, realistic sample figures have been chosen randomly as depicted in Table 2. The calculations for the subsystems of Fig. 5 have been done
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Gisela Lanza et al. / Procedia CIRP 7 (2013) 31 – 36
according to section 4 and the overall key figure Global Production Effectiveness has been determined. Table 2. Sample calculation for GPE Key Figure
Station-No. 1 2 3 4 5
Station-No. 1 2 3 4 5
Variables
Result
Supplier 1 = 0,9325 = 19,5
Supplier 2 0,9260 19,9
Supplier 3 0,9411 19,8
Q 0,9630 0,9598 0,9230 0,9414 0,9628
L 0,9183 0,9062 0,9184 0,8856 0,9005
V 0,8219 0,8265 0,8008 0,8582 0,8120
0,909
0,930
0,965
= 0,8164
0,6354
0,8401
0,6100
= 0,3256
Supplier 1 = 0,9735 = 37,7
Supplier 2 0,9169 39,9
0,8577
0,8636
0,8715
Q 0,9624 0,9257 0,9432 0,9379 0,9427
L 0,8994 0,9217 0,8895 0,9087 0,9059
V 0,8281 0,8345 0,8432 0,8346 0,8219
OEE 0,7168 0,7120 0,7074 0,7113 0,7018
A 0,9104
B 0,4899
C 0,3256
D 0,3128
0,9104
OEE 0,7268 0,7189 0,6789 0,7155 0,7040
t 19,2 18,8 15,1 19,1 18,3
= 0,6728 = 0,6935 = 0,7154 = 0,6000
0,8677
= 0,6455
t 38,2 34,6 34,6 37,5 34,1
= 0,7364 = 0,7129 = 0,6832 = 0,6208
4,54%
The results illustrate the interdependence of the single elements in a global manufacturing network. In the presented hypothetical case the moderate transportation effectiveness has a high influence on the overall system as the following production is in direct dependency to all previous steps. Furthermore, the dependency of the manufacturing in subsystem D from stock and supplier effectiveness has a significant effect on the overall effectiveness of subsystem D. Even though MED is on a good level the joining relation in subsystem D and the weak stock effectiveness cause a low overall effectiveness. Concluding, the challenges of interlinked systems that are already well known on linked production lines can now be analyzed on manufacturing network level. The structuring of the real network into subsystem and the calculation of the single key figures can help network managers to track the network performance and to derive measures in order to improve the overall network performance. 6. Summary The OEE was used as a fundamental basis for the design of a global assessment concept for the effectiveness of a production network and additionally, further developments have been examined on that basis. Because of the full idea, the advantages of some approaches could be joined to describe a manufacturing
effectiveness (ME). SE has been defined to extend the effectiveness approach to procurement activities. With TE another key figure has been introduced which evaluates the transport processes in the global production network. The associated stock formation has been checked with the developed StE. When organizing a globally oriented company, personnel of different origins have to be employed for the work processes, expressed in PE. Through combining the defined key figures, the concept of a Global Production Effectiveness finally arises. Thereby, the effectiveness of any global production structure can be evaluated and developments can be quantified and controlled.An important aspect that the GPE fails to give is the adaptability and flexibility of the structures. For this purpose, a separate consideration on the basis of dynamic methods (see [14]) is required and is content of further research. References [1] [2] [3] [4] [5] [6] [7] [8]
[9]
[10] [11]
[12]
[13]
[14]
Mourtzis, D.; Doukas, M. Decentralized Manufacturing Systems Review: Challenges and Outlook, Logistics Research, Springer, ISSN: 1865-0368 (2012) Nakajima, S.: Total Productive Maintenance – Introduction to TPM. Productivity Press 1988, ISBN 0-915299-23-2. Lau, P.: Technologieorientiertes Produktionscontrolling zur Steigerung der Anlageneffektivität im Presswerk. Dissertation, Universität Hannover, 2009. May, C.; Schimek, P.: Total Productive Management. 2. Auflage, CETPM Publishing 2009, ISBN 9-783940-775-05-4. Hansen, R.: Overall Equipment Effectiveness – A Powerful Production/Maintenance Tool for Increased Profits. Industrial Press 2001, ISBN 0-8311-3138-1. Ryll, F.; Freund, C.: Grundlagen der Instandhaltung; in: Schenk, M.:Instandhaltung technischer Systeme. Springer-Verlag 2010, ISBN 978-3-642-03948-5. Kreppenhofer, D.; Langer, T.: Effektivitätsermittlung von Produktionssystemen; in: Zeitschrift für wirtschaftlichen Fabrikbetrieb, Carl Hanser Verlag 2009, Ausgabe 10. Muchiri, P.; Pintelon, L.: Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion; in: International Journal of Production Research (2008), Vol. 46 No. 13, S. 3517- S. 3535. Etteldorf, J.: Analyse und Verbesserung der Gesamtanlageneffektivität an automatisierten Produktionsanlagen. Dissertation in Fortschritt-Berichte VDI Reihe 2, Nr. 541, Düsseldorf: VDI Verlag 2000. Hartmann, E.: TPM – Effiziente Instandhaltung und Maschinenmanagement. 3. Auflage, mi-Fachverlag 2007 aus dem Englischen 1992, ISBN 978-3-636-03088-7. Nachiappan, R.; Anantharaman, N.: Evaluation of overall line effectiveness (OLE) in a continuous product line manufacturing system; in: Journal of Manufacturing Technology Management (2006), Vol. 17 No. 7, S. 987 - S. 1008. Braglia, M.; Frosolini, M.; Zammori, F.: Overall equipment effectiveness of a manufacturing line (OEEML); in: Journal of Manufacturing Technology Management (2009), Vol. 20 No.1, S. 8 - S. 29. Muthiah, K; Huang, S.: Overall throughput effectiveness (OTE) metric for factory-level performance monitoring and bottleneck detection; in: International Journal of Production Research (2007), Vol. 45 No. 20, S. 4753 - S. 4769. Lanza, G.; Peters, S.: Integrated capacity planning over highly volatile horizons. in: CIRP Annals - Manufacturing Technology, 2012, Vol. 61, S. 395-398.
Procedia CIRP 7 (2013) 31 – 36
Forty Sixth CIRP Conference on Manufacturing Systems 2013
Measuring Global Production Effectiveness Gisela Lanzaa, Johannes Stolla*, Nicole Strickera, Steven Petersa, Christof Lorenza a Insitute of Production Science (wbk), KIT – Karlsruhe Institute of Technology, Kaiserstraße 12, 76131 Karlsruhe, Germany * Corresponding author. Tel.: +49-721-608-46166; fax: +49-721 608-45005. E-mail address: [email protected].
Abstract Increasingly shorter product life cycles at an increasing number of variations call for productive, reliable and quality-oriented production systems and networks which are able to meet the turbulence of global demand especially at an expected higher frequency of economic crises. The following paper presents the development of a theoretical measure for an evaluation that integrates all aspects of a globally distributed production system. The work is based on the latest enhancements of the classic OEE figure of the TPM concept. © 2013The TheAuthors. Authors.Published Published Elsevier © 2013 by by Elsevier B.V.B.V. Selection and/or peer-review responsibility of Professor Pedro Carmo Cunha Selection and peer-review underunder responsibility of Professor Pedro Filipe doFilipe CarmodoCunha Keywords: Global Production, Overall Equipment Effectiveness, Key Performance Indicators
1. Introduction
2. Overall equipment effectiveness
The sector of machinery and plant engineering is facing new challenges. A growing multitude of variants and an increasing product differentiation due to more customization, shorter product life cycles, uncertainty in demand as well as growing international stress of competition have to go along with an increase in effectiveness [1]. Many companies meet these new challenges with an increasing automation of their production facilities and an ongoing internationalization of their production sites. Automation and linking of production systems lead to complex manufacturing systems which additionally have to go global. The requirements for the developing global production networks are still increasing although the degree of complexity regarding production costs, quality of processes and products is increasing. A commonly used figure to evaluate the efficiency of production systems is the Overall Equipment Effectiveness (OEE). The OEE [2] is a figure that basically refers only to one machine. However, there exist extended concepts, but they are mostly just limited to individual production lines. There is no global extension of this effectiveness concept that defines and summarizes influencing parameters in a global production network.
This paragraph will discuss the Overall Equipment Effectiveness more in detail. The Overall Equipment Effectiveness (OEE) is the traditional evaluation measure of the Total Productive Maintenance (TPM) that has to be maximized and it compares the operating level with the ideal potential of the plant performance. The fundamental idea is based on the conception that this ideal operational potential is reduced by various losses. By using this figure, the reasons for these losses are to be identified, so that corrective actions can be taken accordingly [2], [3]. The productivity figure had been developed by Seiichi Nakajima as part of the TPM. At first, this figure had only been used in the TPM sector but the OEE can now also be used as an independent operational improvement tool as for Lean Production and Six Sigma. The OEE evaluates and improves by now the effectiveness of machining and manufacturing processes for a large number of companies and shows the efficiency of the TPM concept [4], [5], [6]. The OEE is more and more used in many production and assembly lines for series production. With the help of the OEE, productivity and economic benefit can be well described. On the basis of manually or automatically recorded operational and machinery data,
2212-8271 © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Professor Pedro Filipe do Carmo Cunha doi:10.1016/j.procir.2013.05.006
32
Gisela Lanza et al. / Procedia CIRP 7 (2013) 31 – 36
the OEE can be calculated very easily for a defined production period [7]. Figure 1 summarizes the key elements and the fundamental influencing parameters of the OEE. Production line
Loss
Overall Equipment Effectiveness (OEE)
Scheduled downtimes
Total useful life
Value adding Operating Time
Speed loss
= Interruptions, breaks, downtimes, maintenance, etc.
Availability x Performance x Quality
Plant shutdown
Availability
Changeover and set-up time
Available Time – Downtime Available Time
Unoccupied time, short stops
Performance
Lower speed
Allocated Time Actual Time Quality
Initial production problems
Quality loss
Net Operating Time
Availability loss
Planned Utilization Time
Operating Time
Calculation for OEE
Output – Rejections Output
Rejections, rectification of defects
Fig. 1. OEE and sources of loss to display the operational behavior according to [1, 7, 8]
According to [2], OEE values around 0,4 are not unusual figures for producing companies. Although individual factors might be rated as very good, this way of calculation leads to a detection of possible misjudgments. "Studies carried out worldwide have revealed that the average OEE in producing companies is at about 60 %" [6]. In consequence, a target level of 85 % represents a clear potential for improvement for many companies [2]. In recent years various changes and extensions to the original OEE figure have been made. The following chapter will describe some of these approaches more in detail. 3. Selected Extensions of OEE In literature as well as in practice, various terminologies have come up which are either related to single plants or have been extended to a holistic view of a complete factory [8]. Table 1 shows a selection of common extensions.
3.1. Total Effective Equipment Productivity (TEEP) While the OEE is using the planned production time as a temporal reference figure (see Fig. 1), the theoretically utilizable calendar time should be integrated into a comprehensive survey [9]. Setting and maintenance activities can in that way be transferred to the time of planned downtimes in order to obtain a higher OEE at the expense of plant utilization. This utilization integrates [10] into the key figure Total Effective Equipment Productivity (TEEP). The planned production time in traditional calculations of the OEE leaves room for definition and interpretation. The TEEP reduces this problem and contributes therefore to a better comparability. On the other hand, as an exclusive key figure, it can lead to the wrong conclusions about the real plant state. At one shift and two shift utilizations without weekend shifts the TEEP cannot reach a value above 50 percent, if the overall operating time is defined as calendar time. It has to be considered that the overall operating time can also be defined as planned production time. Then, only the planned down times are integrated into the TEEP. By looking at the two key figures TEEP and OEE potential problems regarding utilization and equipment effectiveness can be identified. 3.2. Overall Asset Effectiveness and Overall Plant Effectiveness (OAE/OPE) Based on the summary of the illustration of the possibilities for extension of the original basic concept, at first two extensions of the OEE are to be presented for the determination of the Overall Equipment Effectiveness and for the identification of plant losses according to [8]: The Overall Asset Effectiveness (OAE) and the Overall Plant Effectiveness (OPE). While literature deals with them in a limited way, they are used by many companies and industries and have the same meaning with regard to industrial application.
Table 1. Overview of selected key figures TEEP Concept
Calculation
Extension of OEE by integration of planned shutdowns
TEEP
OAE/OPE Relation between actual and possible Output
OAE
O act O th
OPE
Tvalue Tth
EU * A * P * Q
EU: Equipment utilization A: Availability P: Performance Q: Quality
O: Output : overall scheduled time
Tth
Tvalue (
Tth
: value creating time
- planned stops)
OLE
OEEML/ TOEE
Manufacturing system as whole Identification of critical line of several line processes process steps and consideration of decoupling n
OLE
Tth ,op
Tth ,op , n
O n max t i
Tth
Tth ,op ,1
i 1
: overall scheduled operative time
n: index of last station L: index of line t: cycle time
OEEML
TOEE
Onact TLth
t BS TOEE L tL
ALint ALext OEE
BS: bottleneck station : availability losses due to preventive
ALint
maintenance : availability losses due to external effects
ALext
Gisela Lanza et al. / Procedia CIRP 7 (2013) 31 – 36
According to this, a comprehensive OEE can be determined without difficulty with the nominal-actual value ratio for a complete production system, so that the calculation of the effectiveness parameters becomes considerably simpler (simplicity). OAE and OPE differ in the calculation of the parameters concerning the considered items (quantity or time) whereas the OPE in contrast to the OAE is not determined by output quantity but by length of time. In both cases, any losses are considered but an identification of the mentioned weaknesses is not possible. Therefore, a real added value from the extension of the original OEE is not apparent. 3.3. Overall Line Effectiveness (OLE) An alternative possibility for extension is the Overall Line Effectiveness (OLE) by NACHIAPPAN. In this approach the production system is described as the entirety of several process steps to completion of the product. This is in line with the criticism on the OEE that the focus on single machines or plants is not significant because the inter-plant interference is considerable. The result of this process integration is a holistic method of an approach to single line process steps that allows for a simple calculation/measurement of the individual lines-OEE. During calculating one have to consider that the machines are directly interdependent. Thus, the output of a machine is determined by its input. This input, in turn, corresponds to the output of the upstream machines (see [11], p. 992). In doing so, a continuous flow sequence is assumed for n process steps while defectives and parts to be reworked are removed. If the single plants are decoupled the method loses its validity. The corresponding line availability results from the ratio of the actual operating time to the planned holding time where the planned holding time depends on the planned downtimes of the first machine. Overlaps of planned downtimes are not taken into consideration. Furthermore, the approach by NACHIAPPAN takes especially the time dependence between the different plants and machines into account. In case of a continuous production flow, OLE consequently gives very good results. However, the hypotheses concerning operating time definition for intercalated buffers and decoupling have to be abandoned. In addition, the formula presented in table 1 merely focuses on the last process n in the line so that an identification of the critical process steps is problematic.
3.4. Overall Equipment Effectiveness of a Manufacturing Line and Total Overall Equipment Effectiveness (OEEML/TOEE) On this basis, BRAGLIA introduces Overall Equipment Effectiveness of a Manufacturing Line (OEEML). In the first instance, further loss categories are defined to determine afterwards Total Overall Equipment Effectiveness (TOEE). It should be noted, that planned downtimes for preventive maintenance and external sources of line losses are integrated [12]. Furthermore, external line availability losses which are caused due to upstream or rather downstream transportation processes are determined and calculated if thereupon the cycle time of the machine is increased because of the interlinking [12]. Provided that the line is still not operated with optimal productivity the consideration of decoupling by means of buffers can lead to the result that single machines in the line have a shorter cycle time than the real bottleneck workstation. If the buffer capacities are exhausted, the line has to follow the cycle time of the real bottleneck workstation [12]. Since the real bottleneck workstation can vary from the theoretical one, the bottleneck workstation change has to be integrated in the determination of the OEEML. The added value resulting from this extension can therefore be described by the identification of the theoretical and actual bottleneck workstation taking into account decoupling and its influence on the OEEML. Thus, the OEEML allows for the precise determination of the machines influencing the overall effectiveness. By means of counter measures and the setup of buffers the overall system can be influenced positively as a consequence. This represents therefore a real advantage over the OLE. Basically, the approaches to Overall Throughput Effectiveness, Overall Line Effectiveness and Overall Equipment Effectiveness of a Manufacturing Line are therefore very good extensions of the OEE concept whereas the approach to the OEEML can involve a calculation that can be complicated because of the increasing loss categories as well as the positive influence of the counter measures. 4. Global Production Effectiveness Below, an evaluation method is presented that transfers the idea of the overall plant availability to the global production network - Global Production Effectiveness (GPE). It is based on the principles of the OEE and describes all essential determinates in the globally distributed production system. For the development of the GPE the single factors which provide a basis for the integrated key figure are presented at first. Depending on the actual network configuration single determining factors can be
33
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Gisela Lanza et al. / Procedia CIRP 7 (2013) 31 – 36
identified and defined in the global context. Afterwards, these parameters are individually constructed and then transferred into the global comprehensive survey. The company infrastructure provides the network in which the company operates and thus determines its geographic orientation. If the horizon is global, there has to exist a transnational network for supply, production and sales that distributes structures and processes strategically to different locations. For the GPE the production network is particularly significant because the GPE is to be projected up onto several departments coming from the producing machines.
According to [13] the following calculation formulas result for the possible subsystems: ME
n
J
ME S
ME P
VAR
i 1
n 1
min min i 1
n i 1
n
n i 1
Qa
,
VAR i ti
VAR ta
a
n
Qj , j i 1
min
n
i 1
(1)
t BS
(2)
min min k iJ t i , t a
VAR n tn
(3)
1 ti
VARi k iE Qi VARi , ti ti n
min i 1
Manufacturing Effectiveness (ME) represents an essential element of the GPE. With its help the effectiveness of single locations can be measured and additionally an indication of the interconnection between the components of the GPE is provided. Moreover the presented approaches to expansion, regarding the OEE, are taken up at that point again and all subsystems (joining, serial, parallel or expansion, (cp. Fig. 2)) of the manufacturing system are modeled.
i
t i k iJ
VAR i ti i 1
ME E
4.1. Manufacturing Effectiveness
min min
k iE 1 , ti ti
(4)
VAR: variable factor (cp. Fig. 3) Q: Quality n: index of last station a: index of station after joining/expansion t: cycle time BS: bottleneck station
For the calculation of ME the subsystems are modeled in accordance with the equations (1) – (4) and the overall system, in turn, considered as the serial system of the subsystems (cp. Fig. 2: grey shadowed subsystems are elements of the overall serial system). 4.2. Sourcing Effectiveness
J-subsystem
S-subsystem
P-subsystem
E-subsystem
Fig. 2. Exemplary line system with different subsystems (J= joining, S=serial, P=parallel, E=expansion)
The change still exists in the joining in value k. This value indicates how many parts of plant i are needed for the joining in plant a. The same applies to expansion. In addition, a variable factor (VAR) is introduced which is to be proven by means of the flow sheet illustrated in Fig. 3 depending on the available data and the disturbances to be taken into account. Individual machine data Consideration of sheduled shutdowns? Calculation of ME FE
Measurement
No
OEE
Yes Integration of external line losses?
VAR No
TEEP
Yes TOEE
Fig. 3. Flow sheet for calculation of ME
The factors of the OEE are also used to number the sourcing effectiveness (SE) for which availability, performance and quality have to be defined. Availability can be defined as the proportion of on-time delivery of correct quantities by a supplier. The quality degree corresponds to the proportion of good parts delivered and the performance rate is standardized on a scale from 0 to 1 which is based on a comparative calculation regarding stock handling or rather commissioning. The multiplication of these individual values leads to the supplier effectiveness (SupE). For a SE, the individual supplier figures have to be linked and related to each other. Thereby, two conditions have to be distinguished: temporal dependency of the delivery (e.g. JIT) and of different scenarios. Basically, two forms of supplierprovision can be distinguished. With a JIS or JIT delivery the supplier becomes dependent on time. This time dependency can be decoupled by means of stocks. A time-dependent delivery with short-term buffers is not considered here, since this special form is depending on variety and size. In addition, a distinction must only be made between the relevant locations, since the national influencing of suppliers can be ignored. If parts are delivered depending on time, this delivery system can be seen as a joining. A calculation of the SE in this case can therefore be carried out according to formula (1):
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Gisela Lanza et al. / Procedia CIRP 7 (2013) 31 – 36
SEJ
n
min i 1
SupEi Ti S
n
min Ti S i 1
(5)
Ti S : Time for order and Transportation of supplier i
For determining the global total effectiveness, the whole system is divided into subsystems. For the system outlined in figure 4, the following four subsystems arise as an example: SupED,1
4.3. Transportation Effectiveness For the transportation effectiveness (TE) the following total evaluation emerges, considering the transport damages (Q), the speed in relation to the maximum possible speed (L) and, if applicable, the proportional waiting time until the means of transport (V) can be made available, with n means of transport: QT
TE total
n
LT
V iT
i 1
(6)
4.4. Stock Effectiveness For the stock effectiveness (StE), the following formula arises for the three components of the OEE, taking into account any possible damages caused by the stocking (Q), the service level of logistics (L) and, if applicable, a consideration of available storage areas depending on random or fixed storage space allocation (V): StE
V
St
L St Q St
(7)
4.5. Personnel Effectiveness For the personnel effectiveness (PE), the following formula results, taking into account the created/moved good parts (Q), the availability of a staff member minus sick leave and holidays (V) and a standardized productivity index compared with other states (L): PE
V
P
L
P
Q
P
(8)
SupEA,1 SupEA,2 SupEA,3
SupED,1
SE MEB
StEB
SE
D
TEC TEC,1
TEC,2
TEC,3
MED
StED
A
A
C
B
D
Fig. 5. Separation of global network into subsystems
Each of these subsystems will now be modeled according to the presented individual dimensions (see 4.1 – 4.4.) at which ramifications and parallel or serial sequences follow the logic of figure 2 or the equations (1) - (4). Then, the four subsystems are perceived as a serial overall system. 5. Calculation for a hypothetical system To elaborate the applicability in industrial settings, the approach is applied to a hypothetical global manufacturing system (cp. Fig. 4 and 5). Therefore, the two manufacturing systems MEB and MED are assumed to be as illustrated in Figure 6. Furthermore, it is assumed that no scheduled downtimes are considered and plant effectiveness can be calculated as OEE (cp. Fig. 3). MEB
MED 2* 3
1
5
2
3
2 1
5 4
4
4.6. Integrated Key Figure Fig. 6. Sample manufacturing systems to determine ME
The formulation of the GPE is always related to an individual network. Figure 4 shows an exemplary globally networked manufacturing system:
As pointed out in section 4.1 the systems can be transferred to simplified serial system (cp. Fig. 7) by the use of equations (1) to (4). MEB MES,1-2
MED MEP,3-4
OEE5
OEE1
MEP,2*-4
OEE5
Fig. 7. Simplified serial system
Fig. 4. Exemplary global manufacturing system
To demonstrate the results, realistic sample figures have been chosen randomly as depicted in Table 2. The calculations for the subsystems of Fig. 5 have been done
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Gisela Lanza et al. / Procedia CIRP 7 (2013) 31 – 36
according to section 4 and the overall key figure Global Production Effectiveness has been determined. Table 2. Sample calculation for GPE Key Figure
Station-No. 1 2 3 4 5
Station-No. 1 2 3 4 5
Variables
Result
Supplier 1 = 0,9325 = 19,5
Supplier 2 0,9260 19,9
Supplier 3 0,9411 19,8
Q 0,9630 0,9598 0,9230 0,9414 0,9628
L 0,9183 0,9062 0,9184 0,8856 0,9005
V 0,8219 0,8265 0,8008 0,8582 0,8120
0,909
0,930
0,965
= 0,8164
0,6354
0,8401
0,6100
= 0,3256
Supplier 1 = 0,9735 = 37,7
Supplier 2 0,9169 39,9
0,8577
0,8636
0,8715
Q 0,9624 0,9257 0,9432 0,9379 0,9427
L 0,8994 0,9217 0,8895 0,9087 0,9059
V 0,8281 0,8345 0,8432 0,8346 0,8219
OEE 0,7168 0,7120 0,7074 0,7113 0,7018
A 0,9104
B 0,4899
C 0,3256
D 0,3128
0,9104
OEE 0,7268 0,7189 0,6789 0,7155 0,7040
t 19,2 18,8 15,1 19,1 18,3
= 0,6728 = 0,6935 = 0,7154 = 0,6000
0,8677
= 0,6455
t 38,2 34,6 34,6 37,5 34,1
= 0,7364 = 0,7129 = 0,6832 = 0,6208
4,54%
The results illustrate the interdependence of the single elements in a global manufacturing network. In the presented hypothetical case the moderate transportation effectiveness has a high influence on the overall system as the following production is in direct dependency to all previous steps. Furthermore, the dependency of the manufacturing in subsystem D from stock and supplier effectiveness has a significant effect on the overall effectiveness of subsystem D. Even though MED is on a good level the joining relation in subsystem D and the weak stock effectiveness cause a low overall effectiveness. Concluding, the challenges of interlinked systems that are already well known on linked production lines can now be analyzed on manufacturing network level. The structuring of the real network into subsystem and the calculation of the single key figures can help network managers to track the network performance and to derive measures in order to improve the overall network performance. 6. Summary The OEE was used as a fundamental basis for the design of a global assessment concept for the effectiveness of a production network and additionally, further developments have been examined on that basis. Because of the full idea, the advantages of some approaches could be joined to describe a manufacturing
effectiveness (ME). SE has been defined to extend the effectiveness approach to procurement activities. With TE another key figure has been introduced which evaluates the transport processes in the global production network. The associated stock formation has been checked with the developed StE. When organizing a globally oriented company, personnel of different origins have to be employed for the work processes, expressed in PE. Through combining the defined key figures, the concept of a Global Production Effectiveness finally arises. Thereby, the effectiveness of any global production structure can be evaluated and developments can be quantified and controlled.An important aspect that the GPE fails to give is the adaptability and flexibility of the structures. For this purpose, a separate consideration on the basis of dynamic methods (see [14]) is required and is content of further research. References [1] [2] [3] [4] [5] [6] [7] [8]
[9]
[10] [11]
[12]
[13]
[14]
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