Q Taylor & Francis~ Taytor&FrancisGroup

[NT.

J. PROD. RES., 2003, YOLo 41, No.3, 513-527

Manufacturing productivity improvement using effectiveness metric!~ andsimulation analysis

SAMUEL H. HUANGt*, JOHN P. DISMUKES+, J. SHIt,QI SU+, MOUSALAM A. RAZZAK§, ROHIT BODHALE~ andD. EUGENE ROBINSON I I

Traditional productivity metrics, such as throughput and utilization rate, arenot very helpful for identifying the underlying problems and opportunities forproductivity improvement in a manufacturing system. In this paper, a systematicmethodology is presented for productivity measurement and analysis at thefactory level. Metrics of Overall Equipment Effectiveness (OEE) and OverallThroughput Effectiveness (OTE) are introduced and developed, respectively, forrigorous and quantitative measurement of equipment and system productivity.These metrics are integrated with computer simulation to facilitate rapidanalysis of equipment and manufacturing system productivity, and the investiga-tion of productivity improvement opportunities. The results of this research makepossible the representation of factory level productivity or overall facto~{ effec-tiveness by OTE, and the use of OTE for quantitative benchmarking and com-parison of the productivity of various factories. A real-world manufacturing casestudy is reported to demonstrate how to employ these techniques to improvemanufacturing productivity.

1. IntroductionMetrics for measuring and analysing the productivity of manufacturing facilities

have been studied for several decades. The traditional metrics for measuring pro-ductivity are throughput and utilization rate, which only measure part of the per-formance of manufacturing equipment. They are not very helpful for identifying theproblems and underlying improvements needed to increase productivity. Due tointense global competition, companies are striving to improve and optimize theirproductivity in order to stay competitive. This situation has led to the need for morerigorously defined productivity metrics that are able to take into account severalimportant factors, such as equipment availability (breakdowns, set-ups and adjust-ments), performance (reduced speed, idling and minor stoppages), and quality (defects,rework and yield). The total productive maintenance (TPM) concept, launched bySeiichi Nakajima (Nakajima 1988) in the 1980s, has provided a quantitative

Revision received May 2002.t Intelligent CAM Systems Laboratory, Department of Mechanical, Industrial and

Nuclear Engineering, University of Cincinnati, Cincinnati, OH 45221, USA.t Center for Manufacturing Value Chain Science, College of Engineering, The University

of Toledo, Toledo, OH 43606, USA.§Intelligent Quality Systems, Toledo, OH 43635, USA.~TransSolutions, LLC, Fort Worth, TX 76155, USA.II Pilkington North America, 811 Madison Avenue, Toledo, OH 43697, USA..To whom correspondence should be addressed. e-mail: shuang@gauss.mie.uc.equ

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2003 Taylor & Francis Lid.line

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H. Huang et al.514

metric--overall equipment effectiveness (OEE)--for measuring the productivity ofindividual production equipment in a factory.

The concept of OEE is becoming increasingly popular and has been widely usedas a quantitative tool essential for measurement of productivity in semiconductormanufacturing operations, because of an extreme capacity constrained manufactur-ing environment and an increasing concern about return on capital facility invest-ment. For example, an increase in output by just a few percent would contributesignificantly to the semiconductor manufacturer's ability to recover overheads in theestimated 5 years depreciation time allocated to a fab (Konopka 1996). Researchersin the semiconductor industry have taken the lead in applying OEE to measure andimprove the equipment level productivity, in conjunction with InternationalSEMA TECH at Austin, Texas and the Center for Semiconductor Manufacturingat the University of California, Berkeley (SEMATECH 1999, Busing and Leachman1998, Giegling et al. 1997, Leachman 1997, Bona1 et al. 1996, Ames et al. 1995).More recently, researchers at the University of Toledo, in collaboration with theglass industry, have published analyses of OEE related to flat glass manufacturing,which include analysis of the individual factors (Wang et al. 2000, Dismukes et al.1999, Chandrasekaran 1999). However, quantitative OEE analysis is still in the earlystages of development and limited to productivity behaviour of individual equip-ment.

Recent publications (Scott 1999, Scott and Pisa 1998) have recognized and ana-lysed the need for a coherent, systematic methodology for productivity measurementand analysis at the factory level. As Scott (1999) pointed out, manufacturing is acomplex web of highly interdependent activities: interactions among machines, tools,materials, people, testers, processes, departments, and company. However, too oftenthese 'inter-dependent' activities are viewed in isolation, and there is a lack of co-ordination in deploying available factory resource (people, information, materials,and tools) to manage work efficiently. The gains made in OEE, while importantand ongoing, are insufficient. It is necessary to focus one's attention beyond theperformance of individual tools towards the performance of the whole factory.The ultimate objective is a highly efficient integrated system, not brilliant individualtools. Overall Factory Effectiveness (OFE) is a term about combining activities,relationships between different machines and processes, integrating information,decisions, and actions across many independent systems and subsystems. A literaturesurvey indicates that, at present, there is no single, well defined, proven methodologyfor the analysis of overall factory effectiveness.

Simulation analysis is considered the most reliable method to date in studying thedynamic performance of manufacturing systems. In this paper, an OFE metric,overall throughput effectiveness (OTE), for complex connected manufacturing sys-tems, is developed based on an analysis of OEE metrics. These metrics are integratedwith simulation analysis for manufacturing productivity improvement. Promisingresults were obtained when applying the methodology in a real-world manufacturingcase study.

2. Overall Factory EffectivenessThe term OFE is commonly used in the TPM paradigm to represent the inte-

grative performance measurement of a manufacturing system, combining all activities,relatiopships, information, decisions and actions across many independent subsys-tems. This section first presents the concept of OEE that measures the performance

Effectiveness metrics and simulation analysis 515

of individual equipment. This concept is then extended to the factory level, tenned asOverall Throughput Effectiveness (OTE), which is a typical kind ofOFE metric. Forcalculating the OTE metric of a system, various subsystem types are identified andthe methodologies for evaluating their OTEs are presented.

2.1. Overall Equipment Effectiveness (DEE)OEE has been widely used by manufacturers to determine productivity at the

equipment level. It is usually formulated as a function of a number of mutuallyexclusive components, such as availability efficiency, performance efficiency, andquality efficiency in order to quantify various types of productivity losses, such asbreakdown, set-up and adjustment, idling and minor storage, reduced speed, andquality defect and rework (Nakajima 1988).

The conventional formula for OEE can be written as,

DEE AetTP etTQetT, (1)

TuAeff = "T;' (2)

(a)Tp RavgPelf =~ X -

R (th)

U avg

(3)

PgQefT = ~ (4)

where

AelT Availability efficiency (associated losses include non-scheduleddowntime, breakdowns, set-up and adjustments, etc.),

PelT Performance efficiency (associated losses include idle, reducedspeed, blockage, etc.),

QelT Quality efficiency (associated losses include defects, rework, etc.),T u Equipment uptime,TT Total time of observation (after this, the actual performance

of an equipment or a system can be compared with its theoreticalsituation),

T p Equipment production time,

Ri~g Average actual processing rate for equipment in production foractual product output,

Ri~J Average theoretical processing rate for actual product output,P g Good product output from equipment during T T,Pa Actual product units processed by equipment during TT'

Since equipment might not be operating at its theoretical speed during T p, Ri~gcan be determined by

(5)

516 S. H. Huang et al.

Using Equations (1)-(5) leads to another useful expression for OEE, which is

PgOEE = (th) I.Ravg T t

(6)

LetP (th) - R (th) T (7)a -avg '1',

I

which is the actual attainable product output (units) that could have been producedaccording to the theoretical processing rate in total time T T, OEE can be furthersimplified as

DEE = ~ = good product o~tput (units)p(th) theoretical attainable product output (units) in total time. (8)

a I

By this definition, OEE can be calculated directly from measured P g and pith)without using any other factors. This expression for OEE, which is referred to as theunit-based OEE, now has a straightforward interpretation: OEE is the good productoutput (units) produced by equipment in total time observed, divided by the actualattainable product output (units) that could have been produced according to thetheoretical processing rate in total time observed. This expression lays the founda-tion for measuring the factory level productivity.

2.2. Overall Throughput Effectiveness (OTE)A manufacturing system/factory is usually made up of one or more principal

types of manufacturing system architecture, depending on industry types and whichmanufacturing stages are considered. The principal types of manufacturing systemarchitectures consist of two or more individual types of equipment and can beclassified as the 'series', 'parallel', 'flexible', 'assembly' and 'disassembly' types ofindustrial process integration (Burbidge 1990, 1992). If the OEEs for these principalmanufacturing architectures can be calculated, then OFEs for any manufacturingsystem may be determined. Suppose there is a manufacturing system with n indivi-dual types of equipment, during the observation p~riod of T T, the OEE and P g foreach individual machine tool can be determined b~

OEE(i) = Aeff(i)Peff(i) Qeff(i) t=l,...,n, (9)

-(th) 1-Pg(i)-OEE(i)Ravg(i)TT i-I,...,n. (10)

Note that equation (10) is a rearrangement of equ~tion (6) for one type of equip-ment.

By extending the expression of unit-based OEE (~quation (8)) to the factory level,the overall throughput effectiveness (OTE) during the period T T can be defined as

good product output (units) from factoryOTE = theoretical attainable product output (units) from factory in total time

= :g(F). (II)pith)

a(F)

Analogous to pith), at the equipment level, p~~~) is the theoretically attainableproduct output at the factory level in total time T T, ;given as

Effectiveness metrics and simulation analysis

(th) (th) ( 12)P a(F) = Ravg(FjTT,

where R~~j(F) is the average theoretical processing rate for actual product outputfrom the factory, which is a function of the system interconnectivity.

Similarly, the actual product output (units) and good product output (units) of afactory during the period of T T, P g(F) can be defined as

Pg(F) = (OTE)R~~~(F)TT. (13)

Note that OEE(i) and P g(i) could be random variables in a viewpoint of longperiod of time. The reason is that for different observation periods of T T or even thesame length of observation period starting at different times, in most situations themeasured values of OEE(i) and P g(i) will be different because of the randomness ofmachine tool availability, performance, and yield. Therefore, the values of OEE(i)and P g(i) are not known with certainty before they are measured during the observa-tion period of TT' To be meaningful and useful, the measured values ofOEE(i) andP g(i) must be associated with a total time observed, T T. However, if during theobservation period of T T, machine tool i can reach steady state, then by usingsome statistical approaches, the expected values of OEE(i) and P g(i) can be deter-mined.

2.3. Productivity analysis of subsystemsFive major 'types' of unique combinations, or subsystems, are identified. They

are 'series', 'parallel', 'assembly', 'expansion' and 'complex', with the provision that'rework' can be applied as a modification of each of the basic subsystems, e.g. a seriessubsystem with rework. Among these architectural combinations, series and parallelcombinations are frequently encountered in real-world manufacturing systems.Moreover, other types of combinations can be deduced from the two fundamentaltypes. Therefore, we present the formulae for calculating OTE for the cases of seriesand parallel combined subsystems.

2.3.1. Series-connected subsystemFor a series-connected subsystem consisting of n individual machine tools, based

on the theory of conservation of material flow, during the observation period of T T,the good product output (units) of machine tool n must equal that of the seriesprocess. That is

14)Pg(F) = Pg(n)

where P g(n) is the good product output (units) of machine tool n.Therefore, we have

~

(th) ( )Pg(F) = OEE(n)Ravg(n)TT' 15

In a series subsystem, production is dominated by the slowest machine too1(s) inthe subsystem. Therefore the theoretical average processing rate of a series sub-system in total time TT for actual product output (units) can be determined by

(th) . { (th) } . ( )Ravg(F) =mm Ravg(i) 1= 1,...,n. 16

Using equations (11), (12), (14), (15) and (16), the OTE for the subsystem can bederived as

S. H. Huang et at.518

2.3.2. Parallel-connected subsystemFor a parallel-connected subsystem consisting of n individual machine tools,

based on the theory of conservation of material flow, during the observationperiod of T T, the good product output (units) of all machine tools must equalthat of the parallel subsystem, and the actual product output (units) of all machinetools must equal that of the parallel subsystem. That is

nPg(F) = L Pg(ij

i=1

18)andn

PatE) = L Pari)

Therefore, we have

(19)n

Pa(F) = L (R~~g(;)Tp(;)),;=\

n'\:""" .to (th) ,Pg(F) = L... (O~:E(;)RaVg(;)TT)'

;=1

n

R(th) =~R(th).. (21)avg(F) L.., avg(l)

;=1

Using equations (11), (13), (19), (20), and (21), the OTE for the parallel sub-

system can be derived as

~P g(F) -;h

p<th) -R(th) C!laCE) avg(F) ,

OTE n~ (th)L I Ravg(i,;=1

3. Case studyThe proposed metrics can be integrated with simulation to analyse and improve

manufacturing productivity, as demonstrated by using a real-world industrial casestudy conducted at the Pilkington North America plant in Clinton, Michigan,described as follows.

3.1. System descriptionThe manufacturing system under consideration consists of 15 workstations as

shown in figure 1. Among these 15 workstations, four are automatic machines and

In a parallel-connected subsystem, the production rate is the summation of theproduction rate of each machine tool in the subsystem. Thus, we have

Effectiveness metrics and simulation analysis 519

~, .I

: Inconung Ready:: Materials :' '

I .,

: IncomIng Ready:: Materials:' '

, lib Deflasher , I

[~~=~ -' ---,- --_t ,- I: : Buffer III :, I

--~~ '--- ~ I

~~

---,- ---*~=~,- : : Buffer III :

!._-' [=~~

1" IIa Deflasher 11 1~=~

IIIb Trimmer

1 '. I ,

I Va H-Seal

." 1

\

:- -v~~; 01;;s- Wi~d~~- WIP --~H1 ,'- '

I ;:::~:::::~::::::::~14 !: Input Buffer IX :

I II ~

IX Insp.

I---~ ~ ~r---r t

: : Output Buffer IX III___~

X Insp. 2

,,---' ~ r---r 1

~ : Output Buffer X :I___~ J

XI Water Tester

"r1 OEM

! End

Figure Process flowchart.

11 are manually operated workstations. There is only one type of part assembled inthis assembly line. The raw material and in-process parts from other production linesare always available during each shift. The operators pick up the raw material andin-process parts from a material rack and feed them into the Moulder. Products fromthe Moulder go through two operations in series, Deflashing and Trimming. Parallel

520 S. H. Huang et at

to these three steps is an identical set of workstations performing the same opera-tions. Following the parallel trimming workstations, parts are fed from both sets ofequipment into the same Router, based on a first-in first-out (FIFO) order, and arethen processed in one of two parallel H-Seal tables. The last sequence of operationsis performed through a set of six workstations in series: Spreader, Anti-theft tabinstallation, Gap check, Inspection 1, Inspection 2, and finally Water Tester. Bufferracks are located before and after inspection 1 workstation and after inspection 2with a capacity of 10 parts in each to store in-process parts temporarily. Each work-station has a production capacity of one part. Parts are manually transportedbetween workstations to be fed based on the first-in first-out (FIFO) rule.

3.2. Data collectionData collection is the most challenging and time-consuming task in doing system

simulation analysis. Consistent with the progressive-refinement approach, data wereobtained from the factory in the following sequence.

(1) Define the overall process/part flow.(2) Develop a description of operation.(3) Define incidental details and firm up data values.

The process flow chart, together with the description of the operations, provideda good data document that was expanded as the case study proceeded. Based on thisdocument, a basic model was built. Once the basic model was built and tested,additional details of the process such as downtimes, shift times and defect ratesfor each workstation are added and the values of processing times are firmed up.The basic model is built, first, using the theoretical processing times for each work-station as the information on actual processing times is not available at that time.Later, this information was obtained by actually timing all the operations in the

DTFreq.

(8)E(8217)E(8217)

0000

once00000000

DTDuration

(s)600

144000000

360000000000

UPP

Ca Yield

(sjpart) (%)B(I.27,2.51,119,150) 84B(I.27,2.51,119,150) 75B(I.27,1.98,86,139) 98B(I.27,1.98,86,139) 98B(I.28,4.09,25,160) 99B(I.28,4.069,25,160) 99B(2.21,3.86,25,33) 100B(I.69,3.08,120,193) 98B(I.69,3.08,120,193) 98B(I.135,2.151,15,29) 100B(I.18,1.185,36,57) 99B(10.9,8.41,12,16) 100B(0.986,1.95,80,330) 100B(I.31,1.46,85,199) 100B(2.58,8.94,75,150) 97

First DT

(s)72003600

0000

720000000000

C1h

(s/part)130135108108606028

147147191915

16614496

Moulder-aMoulder-bDeflash-aDeflash-bTrim-aTrim-bRouterHseal-aHseal-bSpreaderAnti-TheftGapcheckInspection 1Inspection 2Water Test

Note: B(p, q, min, max)---Beta distribution, where p = lower shape factor, q = ,(x)-Exponential distribution.

Table I. System data input for the simulation model.

higher shape factor;

521Effectiveness metrics and simulation analysis

assembly line in addition to gathering historical data on equipment unscheduleddowntimes (DT) and yield (Quality). The basic input data set for the assemblyline based on collected information is listed in table 1.

In table 1, Cth is the theoretical processing time equivalent to the Takt time,which is the best achieved speed of part manufacturing. The actual processingtime (Ca) is determined from the conducted time studies. Yield and unscheduleddowntime (DT) information are based on historical information over a period ofone month of production.

4. Simulation modelAlthough experimenting with the real assembly line would be ideal, this is seldom

feasible. The cost associated with changing a system, such as the number of work-stations, system parameters, number of operators in each workstation, etc, may bequite high, both in terms of capital required to implement the change and the lostoutput resulting from the disruption. Trying multiple changes with an existingsystem is usually impractical. In real-world systems, many things do not happenin exactly the same way each time they occur. Even in the most highly automatedprocesses, equipment downtimes, material handling device failures and other situa-tions combine to create an environment of uncertainty. Once human factors areincluded, the potential variation increases dramatically. Simulation is uniqueamong decision tools in the ability to cope with these variations and to provideestimates of the influence of these variations on the performance of the system.Problems that occur in dynamic and stochastic systems (those containing randomly

'igure

2. Snapshot of the simulation model.

522 S. H. Huang et at.

~

.

Figure 3. Snapshot of productivity metric information.

occurring events) become increasingly difficult to analyse as the number of stochasticvariables and their interdependencies increase. Under these circumstances, simulationmodels provide superior analysis compared with mathematical or other analyticalmodels (Banks et at. 2000).

The simulation model for the assembly line is built with a discrete event systemsimulator-ProModel and run on Pentium-based personal computers underWindows NT. The OEE and OTE functions described in section 2 are embeddedinto the simulation model. Figure 2 is a snapshot of the simulation model by whichseveral different manufacturing scenarios are tested. During each simulation run, thevalue of OEE for each workstation and the value of OTE for the assembly line aredynamically calculated, as shown in figure 3. The run length of each manufacturingscenario is 40 hours and each manufacturing scenario runs for five replications. Thewarm-up period is 5 hours. A simulation output report lists information on theworkstation such as percentage downtime, OEE, amount of defective products, inaddition to system OTE and other results.

5. Result analysisA base case simulation model is constructed according to the input data listed in

table I, in order to serve as a reference for comparing sensitivity analysis results. Theoutput from the base-case simulation reported results which, when interpreted, pro-vided a useful set of information on AetT, P etT, QetT and DEE (see table 2). The OTEof the base case was found to be 0.85.

Effectiveness metrics and simulation analysis 523

upp

Moulder-aMoulder-bDeflash-aDeflash-bTrim-aTrim-bRouterHseal-aHseal-bSpreaderAnti-TheftGapcheckInsp.lInsp.2Water Test

Aeff

0.920.2011.001.001.000.981.001.001.001.001.001.001.001.00

QetT

0.840.750.980.980.990.991.000.980.981.000.991.001.001.000.97

OEE*

0.620.150.510.110.280.060.150.670.150.100.100.080.880.760.49

* OTE = 0.85.

Table 2 Base-case results.

The base-case run provided a general description of what may be considered asthe inherent capability of the existing system. This is now taken as the productiontarget for any improvement tasks to be implemented on the system when lossesoccur. When multiple losses occur at various locations in the system, managingthe improvement decision becomes a concern. The various improvement scenariossuggested may be tested using simulation, with the advantage of automaticallygauging production improvement through the built-in OEEjOTE metrics.

As a demonstration, several losses are assumed to have occurred in the system, allat the same production shift. The randomly selected losses are limited to having animpact on availability and quality efficiencies only. Although performance efficiencyis not directly manipulated, it fluctuates from the base-case in response to otherlosses occurring in the equipment or in upstream or downstream locations. Datain table 3 list the results of the simulation after introducing random losses to thebase-case. Losses are reflected in AelT and QelT in highlighted cells. OTE for this runwas only 0.49. In other words, the introduced losses decreased OTE by(0.85 -0.49)/0.85 = 36% from the base-case.

To investigate the significance of various improvement opportunities on thesystem with the losses, three groups of simulation runs are generated, each with amutually exclusive set of improvement scenarios. Groups 1 and 2 target availabilityand quality, respectively, each at a single workstation. Improvement scenarios inGroup 3 also include mutually exclusive sets, but target a combination of randomlyselected losses in two locations.

Improvements on availability efficiency (Group 1) are conducted by eliminatingthe downtime losses at the selected workstations one at a time, therefore providingfive cases, as shown in table 4. The improvement is reflected by an increase inavailability (AelT) which increases OEE and is eventually expected to increase OTE.

By comparing simulation results of the potential improvement scenarios, visualinspection of results in the above table suggests that eliminating losses at Moulder-ato reach the inherent AelT of 0.92 will greatly enhance productivity by increasing

PelT

0.801.000.520.110280.060.150.680.150.100.100.080.880.760.51

524 S. H. Huang et at.

Moulder-aMoulder-bDeflash-aDeflash-bTrim-aTrim-bRouterHseal-aHseal-bSpreaderAnti-TheftGapcheckInsp.lInsp.2Water Test

0.830.980.340.080.180.040.100.410.090.060.050.040.530.440.30

0.410.080.330.080.180.040.090.400.090.060.050.040.510.440.28

1.001.001.00.1.001.001.001.001.00.1.00.

.OTE = 0.49.

Table 3. Status during system losses

Table 4. Group I improvement scenario (Aeff)'

OTEby about 31%. While Moulder-b is found to have a lower Aeff (0.13) comparedwith Moulder-a (0.66), the range of availability improvement in Moulder-a to reachbase-case status is greater; hence, a larger increase is witnessed. Further analysessuggest that, with almost the same magnitude of independent improvement in Aeff ofthe three workstations-Router, Inspection 1 and Water Tester-improvements onthe Router contribute the most to increase in OTE. Case 3, which addresses theRouter, has an increase of 14% by increasing Aeff from 0.96 to 0.98. On the otherhand, Cases 4 and 5 show almost no change in OTE. This may be the effect ofbuffering at Inspection 1 and the Water Tester, which shielded the workstationsagainst the downtime losses.

Improvements on quality (Group 2) are studied through four independent casesshown in table 5 using the same subscript notations previously used. Results fromthe analyses suggest that the position of the workstation in the system layout playsan important role in determining the impact of improvement. Loss elimination inRouter and Anti-Theft (cases 3 and 4) increased OTE by 14 and 16%, respectively,while in cases 1 and 2 OTE increased by less than 10%. Referring to figure 1, theRouter and Anti-Theft are found to be in series connected alignment with the systemmaterial flow. The sensitivity of a series connected subsystem as opposed to a parallel

Effectiveness metrics and simulation analysis 525

Table 5. Group 2 improvement scenario (Qeff)'

Table

6. Group 3 improvement scenario (multiple).

connection is explained by the ability of one workstation in the parallel subsystem toovercome the loss experienced by the alternative workstation. Workstations in aseries subsystem, however, transfer the loss downstream to be reflected on theentire system.

The last group of sensitivity analyses comprises three cases, each with multipleimprovements per simulation run, as shown in table 6. Based on conclusions fromGroup 1 and 2 simulation runs, one may expect that an improvement on Aeff atMoulder-a and on Qeff at Anti-Theft should provide the highest increase in OTE.However, results in table 6 show that the expected combination (case 2) increasesOTE by only 37%. Less than 51% is gained by the improvement on Aeff and Qeffboth on Moulder-a (case 1).

Such behaviour agrees with the discussion and conclusions of Huang et al.(2002). The equipment with the maximum OEE acts as the bottleneck of thewhole manufacturing system. When the maximum OEE is higher than those ofother units in the system, it will mostly determine the system performance. Whenthe OEE value for Moulder-a is small, the change of Anti- Theft's Qeff can still showa greater impact on the OTE of the system. When Moulder-a's OEE is increasedfrom 0.41 to a high value of 0.60, the impact of Anti-Theft on the OTE is over-whelmed.

6. ConclusionsIn this paper, the effectiveness metrics for calculation and analysis of equipment

and system productivity for complex-connected manufacturing systems have beendeveloped. These effectiveness metrics are then embedded into a simulation soft-ware-ProModel as built-in functions so that the simulator is able to calculateautomatically OEE and OTE for equipment and the system without any extra

526 S. H. Huang et at

effort. To demonstrate how to use the proposed metrics and simulation analysis toimprove manufacturing productivity, a real-world assembly line has been chosen as acase study. Several group simulation runs are generated to investigate the signifi-cance of various improvement opportunities. The experimental results show that theproposed approach is very effective in identifying the problems and underlyingimprovements needed to increase productivity.

Significantly, the results of this research now make possible the representation offactory level productivity or overall factory effectiveness by OTE, and the use ofOTE for quantitative benchmarking and productivity comparison of various fac-tories. Hitherto, this has not been possible because of the inability to represent,quantitatively, the factory level productivity in terms of the OEE of many types ofequipment and/or workstations making up the factory.

,

AcknowledgementsThis research was jointly supported by the National Science Foundation under

grant DMI-9713743, and by Pilkington, North America under an industrial grant.Significant interaction with Mr Edward Kopkowski of Pilkington in the design andexecution of the case study is acknowledged. We also express our appreciation of thesupport provided by managers and operators from Pilkington's production facilityat Clinton, Michigan.

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