International Journal of Engineering Business Management
Special Issue: Innovations in Pharmaceutical Industry
OEE Evaluation of a Paced Assembly
Line Through Different Calculation and
Simulation Methods: A Case Study
in the Pharmaceutical Environment
Regular Paper
Filippo De Carlo
1,*, Maria Antonietta Arleo
1and Mario Tucci
11 University of Florence - Department of Industrial Engineering
* Corresponding author E-mail: filippo.decarlo@unifi.it
Received 16 Jun 2014; Accepted 26 Aug 2014
DOI: 10.5772/59158
© 2014 The Author(s). Licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract Modern production systems must guarantee high performance. Increasingly challenging international competition, budget reductions for the health sector and constant technological evolution are just three of the many aspects that drive pharmaceutical companies to continuously improve the productivity of their lines. The scientific literature has for many years been proposing calculation models for estimating the productivity of a machine. One of the most famous, and still used, is overall equipment effectiveness (OEE). This allows the calculation the valuable output considering the six ‘big losses’. The limitations of this approach are noticeable when considering a production line instead of a single machine. Numerous researchers have proposed alternative methods or changes in OEE, to be able to cover the widest spectrum of possible cases.
In this study, we wanted to evaluate how such theoretical models related to OEE are actually able to represent the world of tight production flows or whether, in these cases, a more complex type of simulation should be preferred. To do this, we carried out a case study of a production
line in the pharmaceutical industry, and the results showed that the simulation approach gives better results because of the peculiarities not considered by the theoretical models.
Keywords Overall Equipment Effectiveness, Un-paced Production Line Efficiency, Simulation
1. Introduction
In the industrial sector it is increasingly common to employ methods and tools to measure production performance. There are various reasons why, in recent years, there has been a steady increase in the adoption of these techniques. The main reason is the need to quantify the achievement of the objectives set by the companies and, consequently, to identify areas of improvement [1]. In the literature there are many papers that deal with the measurement of system performance [2] [3] [4] [5]. They
emphasize th the ways in features of th the productio performance widely sprea productivity many metho improve the fields [7]. The perfor management particular, it and compare which elemen potential. As a product machines tha coming ou measuremen machines. Th widely used such indicat approaches a capacity util operation of the causes o resolve them an index th features of a In 1998, Nak equipment e implementat Based on this materials in measure the provides a machine with this differenc losses, whic parameters: quality (Q). The availabil of producti performance non-measura scraps and re the OEE para will never be The use of O fairly widesp heir historical which they he target mark on field in par from the f ad until at leas is so crucia ds have been performance rmance mea t, focus on qui is essential t e it to the theo nts make it les tion process is at contribute t t of the nt is primaril hroughput ra for this purpo tors are less are preferred lization provi a machine, wi of inefficiencie m. For this rea
hat would en production sy kajima [10] int effectiveness ion of total p s concept, the nternational ( efficiency of measure of h respect to th ce are associa ch are usuall availability lity parameter ivity due t efficiency tak able stops, wh eworks. These ameter is less e equal to theo OEE to quantif pread in the l evolution ov had to adapt ket; first of all rticular this sh financial to st the end of t al in product developed re of a line in asures used ick inefficiency to measure ac oretical one, in ss effective tha s generally com to the product same proce ly concerned ate and capac ose [8]. For low
effective and [9]. However ide a genera ithout giving es or any gui son the need nclose the m ystem. troduced the (OEE), a key productive ma e semiconduct (SEMI), defin equipment. T the actual p he theoretical o ated with the ly defined in
(A), perform r (see Figure 1 to breakdow kes into accou hile the qualit e productivity than one, as oretical produc
fy the efficienc production
ver the years t to the chan l, globalizatio hifted the focu
different asp he 1980s [6]. S tion managem ecently in ord various indus in produc y identificatio ctual perform n order to ide an its hypothe mposed of sev tion of the pro ess, perform with indivi city utilization w production d more struc r, throughput l measure of information a idance on how was felt to de main character concept of ov y element for aintenance (TP tor equipment ned an index The OEE indic productivity
one. The caus e so-called six
n terms of t mance (Ep) 1) refers to the wns and set unt the minor ty index consi y losses mean actual produc ction. cy of a machi sector. There and nging n. In us of pects, Since ment, er to strial ction on. In mance ntify etical veral oduct mance idual n are rates, tural and f the about w to efine ristic verall r the PM). t and x to cator of a ses of x big three and e loss tups, r and iders n that ction ne is e are seve form whi hav som Figu Subt due defe oper This mod in a show com [11] such gene The mod mea rath mor The next liter a co is p desc the 2. O A fi inef insid freq com that eral aspects th mulation, uns ich it could be e tried to over me changes to t ure 1. Outline tracting from th to speed losse ctive parts, we rating time (NT) s paper analys del and of som a pharmaceuti ws great int mparing both b , [12], to asse h as total pr erally improv purpose of dels are suff asuring the pe her, if other in re appropriate remainder of t section summ rature, then se ouple of its im presented. In cribed, while i results and so OEE evolution irst difficulty fficiencies of th de the refer quently in the mplex machine t the control
hat make this suitable for e applied. For
rcome the lim the classical fo
of OEE calcula he loading time es, and the time
e obtain the o ) and, at the end
ses the applic me of its evolu
cal company. terest in the batch and cont ss the benefit roductive ma e the manufac the study is ficient and u erformance of nstruments, in e. f this paper is marizes the ev ection 3 illustr mprovements. section 5, the in the last sect ome concludin in scientific li in the use o he system con rence six big analysis of th es. In these ca system of the s parameter, i the different this reason se mitations of OE ormulation of ation according e (LT) the down e taken for the operating time d, the valuable t cability of the utions to a pr The pharmac calculation tinuous produ ts of integrate anufacturing cturing proces s to verify w useful for de the productio n particular si s organized a volution of OE rates the theor In section 4, t e experiment tion there is a ng remarks. terature of OEE is fou nsidered are u g losses [8]. he production ases, in fact, e machine w in its classical t contexts in everal authors EE, proposing Nakjima. g to Nakajima. n time, the time e production of (OT), the net time (VT) classical OEE roduction line ceutical sector of OEE, for uction models d approaches [13], and to ss [14]. whether these escribing and on process or, imulation, are as follows: the EE in scientific ry of OEE and the case study al results are discussion of
und when the unlikely to fall This occurs n data logs of it is common would provide l n s g . e f t E e r r s s o e d , e e c d y e f e l s f n e
error codes for each stop, which are often difficult to classify in the Nakajima paradigm. It is often necessary to contact the machine manufacturer for clarification and explanation; this, unfortunately, is almost never immediate and is sometimes impossible.
Furthermore, Jeong and Phillips [15] have shown that OEE is not very suitable in capital-intensive sectors. In these areas, in fact, in order to have an immediate return on investment, the machines should be utilized to their maximum potential. It is therefore appropriate to consider each type of loss, even those related, for example, to the scheduled preventive maintenance (PM) or to the closures of plants during holidays. These elements are not considered in the classical version of OEE, which was conceived in a manufacturing environment. According to Jeong and Phillips, therefore, the right time at which the calculation of OEE should be made is not the loading time but, rather, the total calendar time.
In addition, de Ron and Rooda [16] have introduced a new version of the OEE parameter, in order to measure the actual performance of a production system, without considering all the inefficiencies that come from outside and that do not depend directly on them (operator skills, availability of materials, etc.). They define the E parameter that distinguishes cases in which a machine is inserted and integrated in a production process from those in which it is considered an element in itself. According to the authors, the conditions of starving and blocking, causing slowdowns in an independent machine, should not be considered in the calculation of inefficiencies. The OEE, therefore, measures the performance of a specific machine inserted into a wider production environment. Material handling, the presence of buffers, and production queues, however, significantly impact on its performance; for this reason, as well as having an index that measures the efficiency of each machine, you must also have an index representative of the entire line.
In fact, the main limitation of OEE is that it generally cannot be used for the calculation of the efficiency of an unbalanced production line. For this reason, several authors have proposed modifications to the classical formulation of OEE.
Brandt and Taninecz [17], for example, have introduced a parameter called the overall plant efficiency, which takes into account the efficiency of three elements: the workspace, the people and, of course, the machines. Braglia et al. [8] have studied how to calculate the efficiency of a production line, introducing a new metric called OEEML (overall equipment effectiveness of a manufacturing line). The main advantage of this method
is its possibility of evaluating a global parameter of an entire production line.
Caridi et al. [18] used the OEE parameters to calculate the rate of a balanced paced line without decoupling points, taking into account how the quality parameter impacts negatively on the pace of the line. The main limitation of this approach is that it needs a balanced line.
The analytical approach of OEE, despite the proposed changes, has several limitations, and so several researchers directed their interest towards a different approach [1], namely, the simulation. In literature, in fact, there are many works that demonstrate such interest [19], in particular in the production field [20] [21], for improving line effectiveness [22], for a more efficient plant layout [23], or for management of the entire supply chain [24].
Simulation is defined as the process that allows experiments to be performed on a specifically developed model, rather than on the real system. A simulation model, therefore, is a descriptive model of a process or of a system, built thanks to some of its typical parameters (production speed of a station, production or waiting times, etc.). As a descriptive model of a real system, it can be used to perform experiments, to evaluate hypothetical changes to the real system, to compare different alternatives, and to urge the system ‘in vitro’. This experiment has the advantage of not having any real impact on the system, although many of these simulations are time consuming and require information that is not always readily available [25].
Despite these disadvantages, the simulation is considered to be an indispensable method of problem solving [26] in different application contexts, outstandingly necessary in the following cases [25]:
• testing of a complex system;
• definition and design of a new system;
• heavy investments required for the implementation of a proposed change to a new or existing system; • the need to have a tool that can show the various
stakeholders involved the effects of specific solutions for a system.
The use of performance indicators is widespread in all industries, especially in those with a high level of difficulty in achieving high profits [27]. One of these is the pharmaceutical industry, which has high profitability and, at the same time, a remarkable need for high investments. These are linked both to the development phase of a new drug (the time and cost required for the introduction of a new drug into the market are, in fact, extremely long) and to the production phase.
3. OEE and O OEE is a ver become a r production s according to time the unp availability, e The corporat reason for th obtain a synt a machine. One of the m the unavaila determinatio information involves a continuous essential to h hardware sys [28]. As is known reducing the of scraps tha particular, is does not t considered p performance Caridi et al. an alternativ to determine always to str queried whe have high le argue that th difficult to ob an undesira necessary to required one The authors taking into a each machin production r the following �: rhythm (p ��: standard ��: availabili ���: performa OEEML descri ry important reference in system. The o quality stan productive tim efficiency and te interest for t his can be fo thetic and intu major problem ability of the on. Searching and contro considerable and profitab have prepared stem, such as n, one of the value of OEE at are detected an element of ake into ac properly, esp value for the have tried to e model [18]. whether, for rive for the pe ther, even if n evels of produ he balancing btain: to avoid able reduction o introduce a . calculate th account the e ne downstream
rate of the line g equation:
� �
��� ace) of the pac time of the bo ity of the bottl
ance efficiency iption model, so mu the efficienc available tim dards, is calc mes, related to quality, are su this parameter und essential uitive index o s in the use of information g for useful l system of effort in ter ble OEE use, d an appropr the one descri parameters th E is that of qua
d only at the e f which the cla count. Indee ecially when entire produc account for t The objective a paced line, t erfect balance. not perfectly b uctivity. Some of the lines is d productivity n of overall an additional e cycle time effect that the m of the bottl e. This concep ��� �����∏�������� ced line ottleneck leneck y of the bottle uch so that it cy measure me to produc culated. From the paramete ubtracted. r is significant lly in the nee of the behaviou f OEE is relate necessary fo data within f the equipm rms of time. , therefore, riate software ibed by Singh hat contribute ality. The pres
end of the lin assical OEE m ed it should we search f ction line. this effect thro e of this study
the best soluti The authors h balanced, lines e authors [29] s undesirable y losses genera efficiency, l capacity to of a paced e quality inde leneck has on pt is expresse � neck t has of a ction, m this ers of t: the ed to ur of ed to or its the ment For it is and et al. es to sence ne, in model d be for a ough y was on is have s can [30] and ating it is o the line, ex of n the d by (1) ��: �: bo �: n �: in bott The and with The defi equ star stru is im prod for i asso and auth prev redu Just defi real the avai The the b Figu the c For of t loss theo whi quality of the ottleneck stati number of stati ndex for evalu
tleneck. presence of t � implies the h the bottlenec evolution o ines a new ipment effect ting point of ucture of the s mportant to duction station its operation. ociated with, blocking. A hors, must be ventive mainte uction in the av t for the con ine a new ind
machine, cal classic OEE m ilability due to OEEML is th bottleneck: ����� ure 2. An altern calculation of O the calculatio the theoretica es that are up oretical bottle ich is identifie bottleneck ion index ions of the line uate the quality the productivi e best allocatio ck moved to th f OEE propo parameter tiveness of a the authors sources of inef separate the n from those w To the latter for example, further mod e made to acc enance tasks o vailability of th sideration of dex, represent led OEEM. It multiplied by o preventive m ���� � � en obtained b � ������� � native scheme o EEM on of OEEML, al bottleneck pstream and d eneck differs ed in the line e y downstream ity losses conn on strategy is he end of the l osed by Brag called OEE manufacturin is the definit fficiency; they losses direc which are gen
category belo the condition dification, acco count for the
on a machine he entire line ( these losses, tative of the e t is obtained b y���, which maintenance. ��� ��� by considering ��� ��� �� � of time classific
, you start fro (TBN) multi downstream in from the rea considering m the nected to�, �� not balanced, line. glia et al. [8] EML (overall ng line). The tion of a new y argue that it tly due to a nerated in line ong the losses ns of starving ording to the losses due to that causes a (see Figure 2). , the authors efficiency of a by calculating is the loss of (2) g the OEEM of ��� (3)
cation, used for
om the OEEM iplied by the n the line. The al one (RCO), the situations � , ] l e w t a e s g e o a s a g f ) f ) r M e e , s
of blocking a upstream of finally those above is ap (asynchronou pace on the slowdown, upstream b unavoidable reduced. The simulati analytical me of dealing representativ the way in investigated. instead of t evaluate alt implement th tool for eval production appropriate representativ system, and t The simulatio both to creat representation Nowadays applications, for the graphi In order for and relevant the system u passes the ve • the ver model which implem reflects • the val actually Both phases use of the mo The simulatio different leve • determi • continu The differen simulation d randomness. the model e and starving, TBN, those downstream pplicable to us lines, or wi e line. Each n can recover buffer. In t in the pre on approach, ethods presen with pro ve of a real sy n which you It allows y the real syste ternative scen hem in pract luating the pe process); it key perf ve of specific the compariso on models requ te a suitable n of reality, there are m most of which ic representatio the simulatio information a under consid erification and ification phas created refle is that, as a mentation on the initial ide lidation phas y representativ are obviousl odel as a supp on models can els: inistic vs. stoc uous vs. discre nce between depends on We have a evolves over and in fact i between TBN of RCO. The synchronous ith buffers) th non-bottlenec its inefficie this way e evious model however, is nted here. It d oblems, defi ystem. The sim
formalize t you to inves em, giving y narios witho tice. It is, the erformance of
allows the formance in
operating se on of alternativ
uire the use of (and often la , and as a many comm h also provide on of the mode on model to p about the choi deration, it is d validation sta
se allows us t ects the anal result of its the appropr as of the expe se verifies th ve of the real s ly necessary c port tool. n be classified chastic simulat ete simulation deterministi the presenc deterministic time, only on identify the lo N and RCO, e model prese lines. Other here is not a ste
k station, aft encies thanks efficiency lo l, are drasti different from defines a new ining a m mulation mod the reality to tigate the m ou the abilit out the need
refore, a valu f a system (e. identification ndicators (K ettings of the ve solutions. computer soft arge and com calculation mercial simul
important sup el and its result provide impo ices to be mad necessary th ages [31]: to ensure that lyst's initial i construction riate softwar ert;
hat the mode system. conditions for d according to tion ic and stoch e or absence simulation w n the basis o osses and ented rwise eady ter a s to osses, ically m the way model del is o be model ty to d to uable .g., a n of KPIs) real tware, plex) tool. ation pport ts. rtant de on hat it t the idea, and e, it el is r the o two hastic e of when of its featu simu diffe type whi its v The simu mad in corr gene exam of a The acco chro The whi stati 4. C The pack The Figu Eng Figu ASM The med vari othe seco are Such cont the for t of t loca a b anot The mac abou ures and i ulation, the p erent behavio e, the variable ile in the discr variables, vary discrete even ulation in wh de through va well-defined respond to th erate a variati mples of even customer and advancemen ording to the onological ord majority of t ich means th istical analysis Case study case study kaging line of scheme of o ure 3 accordi gineers (ASME ure 3. Operatio ME symbology packaging lin dicinal produ ious steps req er materials ondary flows conveyed on h materials a tainment of th information p the union of th the pallet, rea ations. The sta blistering mac
ther boxing m line just desc chine line, C, ut 400 vials p nitial condit presence of ra ours. In a sim e values vary w rete one, the st y only in certai nt simulation i hich the time ariables that ch
instances o hose in whic ion of the state nts in the servi d the completi nt of time, in occurrence o der in which th the discrete ev hat the mod s to reach vali presented in a pharmaceut operation of ng to Americ E) symbology. on diagram of ne consists of ucts undergo quired to be are needed are added to the packagin are, for exam he vials, the b pamphlets to i he boxes, and ady for trans ations of the lin
chine, a box machine and a cribed is not p is the bottlene er minute. Bo tions. In th andom variab mulation of th with continuit tate of the syst in time instan is a special kin evolution of hange their v of time. The ch events occ e of the model ice field inclu ion of the serv this example of these two e hey occur. vent models a del outputs d conclusions n this paper tical company the line is re can Society o the line in ac a main stream , station by packaged. In : for this r o the main flo ng line in desi mple, the bli boxes for the insert in each d so on, up to portation to ne are essentia xing machine, palletizer. perfectly balan eck, with a pr oth machines u he stochastic bles results in he continuous y during time tem, and then nts. nd of discrete the system is alue instantly ese moments cur [25] that l. For instance ude the arrival vice requested e, takes place events, in the are stochastic, will need a s. refers to the y. epresented in of Mechanical ccordance with m in which the station, the n this process reason, other ow. The latter ignated areas. isters for the blister packs, box, the tape the formation the customer ally a labeller, , a shrinker, nced: the third roductivity of upstream and c n s e, n e s y s t e, l d. e e , a e n l h e e s r r . e , e n r , , d f d
those downstream have higher productivity, varying between 430 and 440 ampoules per minute.
The line includes the presence of some buffers, which are able to decouple the different stations. Important in particular are the buffers adjacent to the bottleneck. The buffer upstream allows exploitation of the greater productivity of the machines preceding the bottleneck (A and B) and ensures that the bottleneck is never starving (problems such as failures to upstream machines set ups, etc., generating a lack of material for the bottleneck, are avoided). The buffer downstream, instead, avoids the bottleneck never being in the condition of blocking. In fact the critical station can work even when the downstream machine is no longer able to work (for a failure or any setup), continuing to produce and to store the pieces worked right in the downstream buffer. The goal of the buffers adjacent to the bottleneck, therefore, is to avoid the blocking of production for reasons dependent from other working stations.
The line has the main decoupling buffer just upstream of the machine C. It is supervised by the control logic that constantly checks his level of filling and stops, if necessary, either the upstream or downstream machine. The machines at the bottom of the line have high productivity that is obviously not fully exploited because the bottleneck sets its lower pace to the entire line. 5. Results
The methods presented in the third section were applied to the packaging line of a pharmaceutical company in order to assess its performance.
The application of each method was preceded by a phase of production log analysis, necessary to obtain useful data for the calculation of productive performance. This phase required considerable effort, mainly related to the need to determine the times of production and those of machine downtime. These values were derived by analysing the codes associated with the states of the machines, although this relation is not always trivial and immediate because the difficulty of the interpretation of some codes is somewhat cryptic.
The classic OEE was the first model applied. Starting from the log data of the production line, we prepared the necessary information for the calculation of �, �� and � and, in particular, the load times, failures, setup times, and the time lost due to non-measurable stops and to loss of quality. The database used included an opening time of the factory of approximately three months (89 days) from which, given non-working days (holidays, Sundays) and work shifts, we had a loading time of 48 days.
We then performed the availability parameter calculation, considering only the efficiency losses related to faults and setups. Thus, for each machine of the line, we could evaluate the operating time. Table 1 shows the values gained.
Machine � �� � ��� A 74.4% 74.3% 100% 55.3% B 76.6% 77.6% 99.2% 59.0% C 68.9% 66.0% 99.1% 45.1% D 92.4% 100% 98.4% 90.9% E 99.0% 100% 100% 99.0% F 96.4% 100% 100% 96.4%
Table 1. Availability, performance efficiency, quality and OEE
for each of the six machines of the line obtained with the application of the Nakajima approach
The last three stations, D E and F, have very high experimental values of Ep as they never showed reductions in their rate of production. It should be noted that stations are largely oversized and, in the calculation, all the cases in which a station was stopped for blocking or starving were eliminated.
The application of the model proposed by Caridi et al. enabled the reduction of the quality index going upstream in the line to be taken into account. The OEE values of the machines are shown in Table 2.
Machine � �� � ��� A 74.4% 74.3% 96.7% 53.5% B 76.6% 77.6% 96.7% 57.5% C 68.9% 66.0% 97.5% 44.3% D 92.4% 100% 98.4% 90.9% E 99.0% 100% 100.0% 99.0% F 96.4% 100% 100.0% 96.4%
Table 2. Availability, performance efficiency, quality and OEE
for each of the six machines of the line obtained with the Caridi et al. [18] approach
To evaluate the OEEML [8] it was necessary to separate inefficiencies due to individual machines of the line from the external ones (blocking, starving and preventive maintenance). This difference has an impact mainly on the values of Ep, which were obtained by eliminating the efficiency losses related to blocking and starving. To take into account preventive maintenance, however, we analysed the maintenance plan of the line, from which we could derive the values of ���. Table 3 shows the values of OEEM for each machine of the line.
Machine � �� � ��� ���� A 74.4% 75.6% 100.0% 99.0% 55.7% B 76.6% 78.9% 99.2% 99.0% 59.4% C 68.9% 77.1% 99.1% 98.0% 51.6% D 92.4% 100% 98.4% 99.0% 90.0% E 99.0% 100% 100% 99.5% 98.5% F 96.4% 100% 100% 99.5% 95.9%
Table 3. Availability, performance efficiency, quality, PM
availability and OEEM for each machine in the line, obtained by applying the method proposed by Braglia et al. [8]
To compare packaging l creation of a required a ca and through the informati level of detai We used sto was built in its specific p considers the which are t materials (la location, and Figure 4 sho very useful simulation so Figure 4. Sim highlighted wi that define the the line (A, B, C
Before usin verification validation w characteristic results provid Figure 5 sho the actual machine C fo Figure 5. Real of a specific ba from the prod the simulation
e these resul line were fin a simulation m areful analysi the producti ion needed to il appropriate ochastic discr Rockwell Ar packaging sim e main featur true material abels, tapes, d dimension of ows a graphic to describe t oftware. mulation model ith different col e behaviour of e C, D, E, F) ng the simu and validat was perform c parameters ded by the mo ws, as an exa and simulate or a specific ba vs. simulated t atch of the C ma duction log whi n results. lts, the perf nally measur model of the is of the line, on logs, neces describe the l to our needs. ete event sim ena simulatio mulating mod
res of the rea l flow, the u wraps), ma f buffers. cal representa the system, o of the packag lours identify th each of the mac
ulation mode tion were c ed consideri of the line, b odel with the r ample, the com
ed cumulativ atch.
trend of the cum achine. The real le the simulate
formances of red through line. This act both on the ssary to obtai line according mulation: a m on software, u dules. The m al packaging use of secon achine reliab tion of the m obtained with
ging line. The he parts of the m
chines that mak
el, the steps carried out. ng the diffe by comparing real ones. mparison betw ve production mulative produ l data were obta d data derives the the tivity field in all g to a model using model line, ndary bility, model h the areas model ke up s of The erent g the ween n of uction ained from Refe prod simu In th failu hori due inte grap exce emp com Tabl for a from the s Tab outp Tabl obta In perf com data erring to Fig duction of the ulated case. he graph, of c ure, given the izontal axis. Y to the interac r-operational phs it is clear eption for a g ptying of the mpared to reali Minute 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 le 4. Real vs. si a specific batch o m the productio simulation resul le 5 shows the put data of the
le 5. Availabili
ained with outpu
order to atta formance, Tab mpared to the r a (these are sh gure 5, Table e C machine b course, you m e relative sho You can still o ction between
buffers. From that the trend reater speed i line, visible ity. Real cumula productio 7 25 37 43 73 85 91 164 248 248 255 274 274 334 406 491 569 imulated trend of the C machin n log, while the lts. e value of ava e simulation m Machine A B C D E F
ity for each of ut simulation da ain a comple ble 6 shows t results of the hown in Table 4 shows th both for the re may not notice ortness of the observe a non the different m m the analysi ds are compar in the phases in the simu tive n Sim cum prod 1 1 1 1 of the cumulati ne. The real data e simulated dat ailability evalu model. � 88,3% 75,1% 89,3% 99,5% 99,6% 97,5%
the six machin ata ete represent the availabilit simulation mo 1). e cumulative al and for the e any stop for e time on the n-linear trend machines and is of the two rable, with an of filling and ulation model mulated mulative duction 406 479 483 562 570 641 671 720 731 788 799 879 958 1038 1117 1196 1275 ive production, a were obtained ta derives from
uated with the
nes of the line
tation of the ty of the line odel with real e e r e d d o n d l , d m e e e e l
Table 6. Real v The simulat management taken into acc lines quality end of the p distributed al stringent qua packaging, al product, is n parts at the e contained in controls on continuously identify and soon as possi our case study presence of th number of via From the ope each machin experimental efficiency w interaction of setups, the maintainabili The simulat managers be of every sing obtained for entire line ob The simulatio with an exp company, ca theoretical pr that the real p value obtaina simulated one for industria company’s KP simulation res A critical an section. 6. Discussion The applicat shown how t Real Simulated vs. simulated av tion approach typical of the count. While i controls are c process, in the
long the entire ality controls t
lso, as the fina not suitable fo end of the line boxes and p the quality o at intermedia discard (or re ible. In practic y relate to the he label of the als inserted in e erational poin ne were introd l values. A were obtain f each station scheduled s ity values of e ion results w cause, in addi gle machine, th the entire pr btained by the on results were perimental pr alculated as th roductivity. Fo production is able. The exper
e, but slightly l al confidential
PI was the way sults and the th nalysis of the n and conclusi ion of the mo the simulation � 53,2% d 51,5% vailability for th h allows th e pharmaceuti in most produ arried out alm e pharmaceut e line. This is typical of this al link in the or the identific e, when the pr allets. It is qu of the produc ate points of ework) non-co ce, the quality
physical integr e medicine, the each package, nt of view, the duced by takin Availability a ned experim
with the othe stops and th each machine. were very us ition to provid hey enabled a roduction line simulation m e compared wit roductivity KP he ratio betw or instance a va 60% of the ma rimental value lower (the valu ity). The com y to verify the heoretical meth results is pro on odels presente n approach is he packaging lin he non-confor ical industry t uction or packa most entirely a tical field they mainly due to area. The stag chain of maki cation of defe roducts are alr uite necessary ct are carried the line, so a ompliant piece controls typic rity of the vials e correctness o and so on. e quality valu ng them from and perform mentally by er, introducing he reliability
seful for the ding the behav an OEE value t e. The OEE of model is 61.20% th the real data PI in use in ween real and alue of 60% m aximum theore is very close t ue is not given mparison with quality of both hods. ovided in the ed previously more suitabl ne rmity to be aging at the y are o the ge of ing a ective ready that d out as to es as cal of s, the of the es of m the mance the g the and line viour to be f the %. a and n the d the means etical to the n here h the h the next y has le for repr The mod been anal simu mor the The anal effo Mos nece veri The anal alon mac of e upst the them The that real and the give the allo ope been thes patt adh A fu buff tren its p Figu bottl resenting the problems re dels, both clas n faced in our lytical model ulation, howe re meaningful line can hypot simulation a lytical one: to rt is necessary st of the tim essary parame ification and v main elem lytical models ng the line. chines and en each other ( tream stops, a worked piece m in the down e analysis of t the simulat l production, d the model i simulation m e a better repr packaging ws definition ning and clo n derived th se speed valu terns of Figu heres to reality urther compa fer upstream nd of this buff performance in ure 6. Number
leneck in the rea
packaging lin elated to the sical and its e r case study. A ls are to be ever, makes it l to the busine thetically be c approach is m o have a true y, both in term me has been
eters and adju validation pha ent that dif s is the prese
The buffers nsure that they
(i.e., when th a machine can es from the up nstream buffer the results ( tion model c , due to a m in the start-u model has be resentation o line. The si n of speed re osing of batch hrough line ues, the diffe ure 5 are r y even more. arison can be of the bottle fer in the real n the simulatio r of pieces in al case ne considered e application volutions, in f Although the e considered t possible to o ess because ev considered. more demand e and reliable ms of time and spent identi usting the mod ases.
fferentiates si ence of decou
allow separ y can work in here are dow n work anywa pstream buffe r). (see Figure 5 annot perfec mismatch bet up phase. For een modified f the actual fu imulation so lated to the p hes. This info observation. erences betw educed and made by co eneck. Figure case, while Fi on model. the buffer up in this study. of the OEE fact, have also results of the d quite well, obtain results very aspect of ding than the e model huge d competency. fying all the del during the imulation by upling points ration of the ndependently wnstream or ay picking up r and putting 5) has shown tly represent tween reality r this reason d in order to unctioning of oftware used phases of the ormation has Introducing ween the two the pattern nsidering the 6 shows the igure 7 shows pstream of the . E o e , s f e e . e e y s e y r p g n t y n o f d e s g o n e e s e
Figure 7. Nu bottleneck in th As well as t changed, pas estimate of 5 the model ha the experim simulation. With regard have been a b The classical low, for the real data. Th calculation m The second buffers, has that scraps reworked. A account the reducing it a this is not su As a matter mentioned, immediately procedures. The third mo the lack of an in the reliabil To sum up, w helpful with approach is and well bal manage imb the effects of The simulatio any ‘abnorm be a valuable in all cases i encoded ster umber of piece he simulated ca the model, th ssing from th 58.80%. Thus, as led to furthe ental value r to the theore bit too far from l OEE calculat bottleneck of his is explaine model. model, in a worsened the proceed alo As has been s quality index according to t uitable for ou of fact in th the defectiv extracted fr odel, lastly, pr ny modelling lity of the resu we can say tha h individual well suited t lanced paced alances of the
buffers. on, on the con mal’ situations e alternative t in which the eotype. es in the buffe ase e OEE value he previous 6 this addition er reduction o recorded in t etical models, m experimenta
tion gave resu the line, whe ed by the lack addition to n e situation be ong the line stated previou x of every up the downstrea ur pharmaceut he pharmaceu ve parts are rom the line rovides a high of the buffer c ults. at the classica machines, to scenarios w lines, while t e stations but ntrary, is suita
and, for this to analytical m system is not er upstream o of the entire 1.20% to the nal modificatio
of the gap betw the KPI, and , their predict al reality. ults that were en compared k of buffers in not managing ecause it assu e without b usly, it takes pstream station am machines, tical environm utical industry e identified with autom er value, altho causes a limita al OEE approa
and the sec with synchron the third can t does not inc able for mode reason, prove models, espec t reducible to f the line new on of ween d the tions e too with n the g the umes being into n by , but ment. y, as and mated ough ation ach is cond nized also clude elling es to cially o any To perf prod perf imp app com syst The prov the sele defi met and mor mut whi oper avai expe instr adv edit on eval mod cont mod An of th they inte and cont From can no c OEE pha Con imp enab prod It w mod and resu conclude, w formance mea duction or pa formance, id provement act ropriate tha mprehension o tem. analytical me vide some est
line. In any cting the mo ine its scope. thods have sig
effectiveness re depth the tual relations ich they intera
rational buff ilable appro enditure of ti ruments with antage of sim t one of the in output, with luation of fo dels. Once the
tinues with m dification and
increased use he productivi y allow the rep
r-operational rework (more text. m the point of say that in th comparisons b E and simu rmaceutical n nsidering futu portance of d ble representa duction line, a will thus be
dels that, unlik can afford b ults. we can say asures able to ackaging line dentifying p tions. Some m an others, of more typi ethods have p timate of the p case, it is im odel and in If the choice gnificant adv s. If, howeve behaviour of between the act with each fer, then the oach. Simula ime and cost h the greate mulation is tha nput values, in hout going t rmulae as re e model is bu many possibil improvement e of theoretical ity of a line w presentation o buffer, opera e realistic man f view of the o he scientific lit between OEE, ulation appr nor in the manu ure developme developing ne ation of the p also taking int possible to ke simulators, better analysi that the av o assess the e is essential fo problems an measure mod because t ical elements proven to alwa production pe mportant to making assu is well thoug vantages in te r, we must i the line, hig machines and h other and w e simulation ation, despit t, is still toda est effectiven at it is possib n order to ver through any equired by th uilt, therefore lities for prom t of the line. l models for t will be possib of unbalanced ted under pol nagement for originality of terature there , OEEML, oth roaches, neit ufacturing fie ents, this stud ew analytical performance e to account the apply accura , are much mo is and comp vailability of efficiency of a or monitoring nd planning dels are more they enable s of the real ays be able to erformance of be careful in umptions that ght out, these rms of speed investigate in ghlighting the d the ways in with the inter-is the best e the high ay one of the ness. Another ble to directly rify the effects intermediate he theoretical e, the support mpt analyses, the evaluation ble only when lines, with an licies of scrap the industrial this work, we are currently her variants of ther in the ld. dy shows the models that efficiency of a buffer effects ate analytical ore immediate arison of the f a g g e e l o f n t e d n e n -t h e r y s e l t , n n n p l e y f e e t a s. l e e
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