A SIMULATION FRAMEWORK TO HELP IN LEAN MANUFACTURING INITIATIVES

Fernando SevillanoBusiness Management Department

Universidad Camilo Jose Cela (Madrid, Spain)Email: fsevillano@ucjc.edu

Miguel Serna, Marta Beltran and Antonio GuzmanComputing Department, ETSII

Universidad Rey Juan Carlos (Mostoles, Spain)Email: mserca@gmail.com, marta.beltran@urjc.es and antonio.guzman@urjc.es

KEYWORDS

Lean Manufacturing, Manufacturing Simulation, Simula-tion framework, Value Stream Map.

ABSTRACT

Lean Manufacturing initiatives try to understand where thesources of waste are in a manufacturing process in order tominimize or avoid them and to add value to the stakehold-ers. The improvement actions related to lean methods arealways incremental, fast and easy; mainly based on peo-ple’s experience and on simple tools. For example, a ValueStream Map (VSM) is created to have a visual representa-tion of the material and information ¤ows involved in theproduction process, allowing improvement teams to detectwhere is the waste introduced and where is the value added.

The integration of simulation with VSM could consider-ably improve the results obtained in lean projects, helpingthe decision makers in adding dynamic information to theusually considered static pictures of the processes. The in-formation obtained from what-if simulations allows to de-tect improvement opportunities, to prioritize them, to an-alyze the best implementation alternative, and to quantifythe possible bene£ts of the proposed actions.

This work presents the LeanSim framework, an easy touse tool based on Matlab and Simulink, capable of inte-grating a lean method such as VSM with simulation. Fur-thermore, a case study of an improvement project at a milkproduction plant is presented to illustrate the utilization ofthis new framework on a real environment.

INTRODUCTION

The key concepts in the current competitive and globalizedcontext are reducing costs while increasing quality. Fur-thermore, only ¤exible organizations with fastest adapta-tion time acquire a competitive advantage and survive intoday’s demanding markets.

This changing scenario leads to new manufacturingparadigms capable of providing ef£ciency, responsiveness

and quality such as Lean Manufacturing (Shah and Ward,2007).

The lean paradigm involves dynamic, continuous andcustomer-focused improvement processes designed toeliminate waste and to create value to the organizationstakeholders (enterprise, workers, society and customers).All these processes are based on a group of more than ahundred methods and tools (Alukah and Gill, 2008), all in-cremental and knowledge-based, trying to take advantageof the ideas, experience and skills of everyone in the or-ganization. And some have been speci£cally developedto Lean Manufacturing, while others are general methodswhich can be used in this kind of environment too.

In general, all these tools promote, in some way, a wholere-thinking of how to produce to eliminate waste. But al-most all of them are based on ”pencil and paper”, becauseone of the challenges of the lean paradigm is to use onlysimple, fast and easy tools (Field, 2001). And the determi-nation and prioritization of the improvement opportunitiesis based exclusively on the experience of the people com-posing the improvement team and on a set of well-knownand easy KPIs (Key Performance Indicators).

This kind of declaration of principles has traditionallyexcluded simulation of the group of tools considered in leaninitiatives. It is usually thought that simulation demandsa large investment of time and resources and that expertknowledge of simulation methods and tools is needed. Al-though some recent works have pointed out the importantadvantages of using simulation to improve the results oflean projects (Lian and Van Landeghem, 2002; Evans andAlexander, 2007; Deif, 2010; Hoe et al., 2010), it is not awidely used practice yet.

The main contribution of this paper is to propose a £rstprototype for a simulation framework based on Matlab andSimulink, LeanSim, to support lean initiatives, speci£cally,those based on Value Stream Map (VSM) methods, al-though the proposed framework could be easily extendedin the future to work supporting another lean methods andtools. The proposed framework has demonstrated to bevery useful in experimenting with all the possible manufac-turing system changes to £nd the best improvement options

Proceedings 25th European Conference on Modelling andSimulation ©ECMS Tadeusz Burczynski, Joanna KolodziejAleksander Byrski, Marco Carvalho (Editors)ISBN: 978-0-9564944-2-9 / ISBN: 978-0-9564944-3-6 (CD)

before their implementation (a what-if simulation can helpin understanding if a speci£c modi£cation really implies animprovement) and in prioritizing their implementation.

The rest of this paper is organized as follows. Section 2introduces the basic concepts of Value Stream Map meth-ods and discusses previous works focused on improvingtheir performance using some kind of simulation. Section3 explains in depth the LeanSim, the simulation frame-work proposed to support VSM initiatives and discussesall the details related to its design and utilization. Section4 presents a real case study to exemplify the use of the pro-posed framework and to verify its utility in lean initiatives.And £nally Section 5 summarizes the most important con-clusions and lines for future research.

BACKGROUND

Value Stream Map (VSM) is a lean method used to improvethe ¤ow of materials and information necessary to producea product or a service, eliminating the waste and addingvalue (Rother and Shook, 1999).

This tool is based on drawing the current VSM diagramshowing these ¤ows, and the activities or processes in-volved in the production as well as the KPIs selected tomeasure their performance, typically, the Cycle Time (CT),the Change Over Time (C/O) and the Uptime for each ac-tivity or process. For the whole process, from end to end,the Total Lead Time and the Value Added Time are usu-ally taken into account too. The materials ¤ow is generallydrawn from left to right, while information ¤ow generallygoes right to left. It is important to remark that the symbolsused to create this kind of diagrams are standardized andthey are easily drawn and recognized.

After creating the initial diagram, the involved improve-ment team, in which each area or stakeholder of the processmust be represented, critique the current situation and thedesired situation is formulated drawing the future VSM di-agram. This new diagram is only a tool for graphically rep-resenting changes that could be made, therefore, to identityall the possible improvement activities usually called for-mal kaizen events.

The comparison between the current VSM diagram andthe future VSM diagram helps the improvement team toprioritize these kaizen events and to propose an action planto reach the desired situation for the process. Obviously,there are always many different options and alternativesto reach this state and the team should carefully considerall the involved variables and criteria (cost in time and re-sources, possible bene£ts, risks, etc) to choose the bestmethod to perform the transition from the current state tothe desired state.

Some recent works have proven that simulation couldbe a powerful tool to select the best alternative for movingtoward the desired future state, for example (Solding andGullander, 2009) or (Paju et al., 2010), because VSM itselfdoes not improve anything. It is only an analysis tool, the

action plan proposed by the improvement team is the ac-tual improvement tool and a what-if simulation could sig-ni£cantly enhance this action plan. And there are somesoftware solutions that integrate VSM methods and simula-tion, such as Process Simulator as a Microsoft Visio plug-in(ProModel, 2011) or Simcad and its Value Stream Analyzer(Createsoft, 2011), but all of them with limited functional-ities.

Simulation is one of the most valuable tools for processimprovement, and its value typically increases as the pro-cess to be improved becomes more complex and the deci-sions of the improvement team are more complicated. Butto the moment, simulation is perceived as a too dif£cult andslow tool to be used in lean projects, and unfortunately, the£rst attempts to combine both types of methods are stillsimple and unmature.

PROPOSED FRAMEWORK: LEANSIMIn this section the LeanSim simulation framework, basedon Matlab and Simulink, is presented. The main goal ofthis framework is to provide lean improvement teams witha simple, fast and easy tool capable of guiding them in theirdecisions. In this £rst prototype, the simulation frameworkis focused on supporting the VSM method, but the frame-work architecture has been developed to be easily scaledin order to support another lean tools related to the man-ufacturing ¤ow such as Kanban, Takt time calculations ordesign of cell layouts.

ChallengesThe goal of this work is to propose a framework that al-lows users to quickly create VSM diagrams and to providethem the capability of simulating ceratin parts of these di-agrams to identify inef£ciencies and to determine the bestcorrection actions in lean initiatives. A number of chal-lenges need to be addressed to reach this kind of simulationframework, the most important:

• Capability of rapidly and easily creating modular andscalable models parametrized to a speci£c process ata speci£c plant at a certain moment, if possible, reuti-lizing previous and/or pre-built models.

• If the models need to be built from scratch, the mod-eling process should not be a time-consuming processand no high expertise in simulation techniques shouldbe required.

• The simulations need to be completely data driven andthe data needs to be available from different kinds ofinputs and in different standard formats. The staticpicture of the process, drawn with pencil and paperin traditional lean methods, must be easily completedwith dynamic information.

• Capability of integration with other models, frame-works and applications.

The challenges mentioned above lead us to the selectionof Matlab and Simulink (Mathworks, 2011) as the basis forthe LeanSim framework. In fact, the proposed simulationenvironment is composed of a set of Matlab functions anda customized Simulink library (called leansimlib).

The main advantage of using Simulink is in its graphi-cal user interface (GUI) for building models as block dia-grams. Furthermore, models can be created using differ-ent approaches: assembling the model directly in Simulinkusing the standard available blocks or creating customizedblocks, using Matlab M-£les, programming S-functions orinterfacing Simulink with other simulation tools and appli-cations.

The graphical philosophy based on ”drag and drop”makes it easy to get started building hierarchical modelswithout deep knowledge about simulation techniques, andpreliminary results can be rapidly obtained. But, at thesame time, Simulink offers powerful tools to model com-plex systems allowing a tight integration with the Matlabenvironment and all its capabilities (for example to de£nemodel inputs the OPC toolbox may be used, the simulationoutputs can be managed for analysis and visualization, theHLA/DIS toolbox can be used to build distributed simula-tions, etc).

Preliminary De£nitions and ConceptsFor many years, simulation has been used to analyzeproduction and logistics problems. In many cases,Commercial-off-the-shelf Simulation Packages (CSP) sup-port the development and visualization of simulation mod-els in this kind of contexts.

The standard SISO-STD-006-2007 2.0 de£nes a set ofInteroperability Reference Models (IRM) to create a com-mon framework and to allow users to create distributedsimulations for manufacturing applications consisting ofCSPs and their models easily (Simon et al., 2007).

The simulation framework proposed in this work hasbeen designed to be compatible with this standard in orderto use distributed simulation in the future if it is needed torun complex projects based on distributed models. There-fore, the same terms and de£nitions have been used:

• Model (M). A model describes and represents a realworld system.

• Time (T). It represents a speci£c simulation time in amodel. Time is an integer value and in this standarddoes not have speci£c units to measure it.

• Event (E). An event speci£es an instantaneous systemtransition between two different states at a time T.

• Entity (e). An entity is something that is processed,for example, a raw material or a product. It goesthrough some queues and activities representing themanufacturing system and it is de£ned by its at-tributes.

• Queue (Q). It is a queue of entities managed withsome speci£c queuing discipline (LIFO, FIFO, etc),and the typical example is a plant buffer storing theinventory between two processes.

• Activity (A). It is a time consuming action, processor step with a known duration. The activity starts andends with an event.

• Resource (R). A resource is something that is neededby an activity to begin. In manufacturing environ-ments, typically the machines and operators.

• Data Structure (D). It is similar to a resource butthere are modeling differences in terms of semantics.For example, it can be an inventory record, a produc-tion order or a bill of materials.

Therefore, in a traditional VSM diagram the material¤ows represent entities movements, the inventories arequeues, the processes are activities, the machines and oper-ators are resources and the information (automatic or man-ual) and kanbans are data structures.

LeanSim ArchitectureAs it has been said before, the this £rst prototype of theLeanSim framework is focused only in supporting VSMprojects. Therefore, a Simulink library has been created(leansimlib.mdl) with three categories of blocks (£gure 1).Initially, only a limited number of blocks has been de£nedinside each category but new blocks can be added in thefuture:

• Activities. Three blocks have been included in thiscategory, a static activity, an input-from-Matlab ac-tivity and a dynamic activity. The traditional VSMdata boxes used to show the processes KPIa are notneeded in the LeanSim framework to give this in-formation, because each activity is de£ned by its at-tributes through the Simulink parameters for eachblock.

• Drawings. The typical symbols used to create a VSMdiagram have been included in this block category, ini-tially: automatic information ¤ow, customer, kaizenevent, manual information ¤ow, other (empty box formiscellaneous information), production control, pullraw (for materials ¤ow), push raw (for materials ¤ow),time line and truck.

• Queues. Two blocks have been included in this cate-gory, a FIFO queue and a LIFO queue.

LeanSim can be used to draw traditional VSM diagramswith Simulink without using simulation, as any other sim-ple drawing tool. But if the improvement team decides touse simulation to guide the VSM method, the activities andqueues are the blocks that can be simulated.

Figure 1: LeanSim Library and Sublibraries

In these cases, it has to be considered that a tradi-tional VSM diagram rarely summarizes all the informationneeded to run an accurate and complete simulation. TheLeanSim framework expands the information typically in-cluded in a VSM diagram so that it contains all the informa-tion needed to run a simulation. For example, each activityis de£ned by its CT, C/O and Uptime, but also by its associ-ated resources (machines and operators) including informa-tion about possible sharing, and by the number of entitiesthat the activity can process simultaneously. Therefore, theactivities and queues blocks are con£gurable, with a rightclick on one of these blocks, the con£guration window canbe opened to de£ne the block parameters (one example isshown in £gure 2).

Some drawing symbols without simulation capabilitiesinclude con£guration parameters through this kind of win-dow too, because they can help in completing the VSM di-agram information. For example, the time line include twoparameters, the Total Lead Time and the Value Added Timeand the information ¤ows (automatic and manual) includea Frequency parameter (by shift, daily, weekly, etc).

Queue blocks. A queue block is designed to store en-tities. It has an input port and an output port, it tries tooutput an entity but if the output port is blocked, it keepsstoring it. When the output port is open, the entities leavethe queue. The difference between the two initially de£nedqueues, FIFO queue and LIFO queue are only in the queue

management policy, FIFO (First In First Out) or LIFO (LastIn First Out).

The con£guration window in this category is very sim-ple because only two attributes are de£ned. The £rst, theQueue length. A queue block is able to store up to L entitiesat the same time, if the queue stores exactly this number ofentities, the queue is full and cannot accept new entities un-til new space is available. An the second, the Average WIP(Work in Progress) in hours, to quantify the mean delay ofentities inside the queue.

Activity blocks. For the blocks in this category, the con-£guration window is divided in three areas: the perfor-mance attributes area, the resource attributes area and thecapacity area (£gure 2).

The performance attributes are the CT, the C/O and theUptime (all in seconds), while de resource attributes arethe Number of machines, Availability of machines (as apercentage), Number of operators and Availability of oper-ators (again as a percentage). Finally, the capacity attributeis only one, the Number of entities which can be processedat the same time by this activity.

The differences between the three initially de£ned activ-ity blocks are mainly in the method used to give values tothe performance attributes and in their simulation capabili-ties:

• Static activity: All the performance attributes values

Table 1: De£nition of the Considered Line (L105) Activities AttributesActivity CT (s) C/O (s) Uptime (%) Machines Machines av. (%) Operators Operators av.(%) Number of entities

Liquid £lling 0.18 600 57 1 100 4 100 1Sealing/caping/labeling 0.2 60 99 2 100 2 100 1

Baling 0.15 300 99 1 100 2 100 4Packing 0.14 3600 98 6 100 3 100 40

Figure 2: Example of Block Con£guration Window: StaticActivity Block Attributes

Table 2: De£nition of the Considered Line (L105) QueuesAttributes

Queue Management Queue Length Average WIP (h)Bales inventory FIFO 192.000 24Pallets inventory FIFO 300.000 24

are statically £xed by the user in the same way thatthe resource attributes. In fact, this kind of activityis a drawing symbol and corresponds to the traditionalprocess symbol in a VSM diagram, without simulationcapabilities.

• Input-from-Matlab activity: The performance at-tributes are linked to variables de£ned in the Matlabenvironment and they are completely dynamic. There-fore, they can be the result of some computation, orthey can be obtained from an external informationsource such as an Excel datasheet or an OPC server.

• Dynamic activity: In this case, the activity is actuallya Simulink subsystem. The block is only a high levelabstraction, but with a double-click increasing levelsof detail can be simulated to obtain the performanceparameters for the considered activity and to performwhat-if simulations.

CASE STUDYIn this section, the LeanSim framework is used to identifyand to prioritize the formal kaizen events present in the £-nal part of an enriched liquid milk manufacturing process.

Speci£cally, the VSM method has been used at a milk pro-duction plant in order to improve the £lling/packing line(coded L105) for the one liter plastic bottle format, becausethis line has been the bottleneck of the whole manufactur-ing process during the last year.

A lean multi-functional team is created with 8 people(utility specialists, process specialists, product specialists,quality manager and production manager, this last as theteam leader) during 3 months to develop this VSM project.They £rst de£ne the project scope and objectives and col-lect all the needed process information. Tables 1 and 2summarize the features of the line to improve and £gure3 shows the initial VSM diagram built with the LeanSimframework. It has been simpli£ed only to show the con-sidered line in order to ease the understanding of this casestudy and it only shows the essential information related tothe main activities (the information ¤ows and other detailsare not included in this version). It can be seen that fouractivities are taken into account by the lean improvementteam: the liquid £lling, the sealing/capping/labeling, thebaling (in bales of 4 bottles) and the packing (in pallets of10 bales).

Initially, the VSM diagram has been created as a tra-ditional draw, without simulation capabilities. Once thekaizen events have been identi£ed (in this speci£c case,three of these events are going to be explored), some partsof the diagram have been transformed to take advantage ofthe what-if simulation capabilities of the proposed frame-work. The three kaizen events are related to the liquid £ll-ing activity uptime, to the sealing/caping/labeling activitywastes and to the packing activity (and associated invento-ries) bottleneck. Therefore, two Input-from-Matlab activityblocks have been used to model and simulate the liquid £ll-ing and sealing/capping/labeling activities, and a Dynamicactivity block has been selected for the packing activity.With this transformation the simulation results can be usedto de£ne and to prioritize all the possible improvement ac-tions shown in the £gure as kaizen events. The two activ-ities modeled with Input-from-Matlab activity blocks usedata from an external Excel datasheet to accurately analyzeall the possible improvement actions. The packing activityhas been modeled in detail with a SimEvents model be-cause the whole packing process needs to be redesigned.

All these speci£c models are out of the scope of this pa-per, but the important conclusion is that a future VSM di-agram has been created with the help of these simulationsand concrete actions to perform the transition from the cur-rent state to the desired state have been de£ned:

Figure 3: Simpli£ed Initial VSM Diagram for Manufacturing Line L105

1. Redesign the packing activity and its inventories to re-duce the WIP average time of the bale inventory from24 hours to 8 hours. This improvement decreases theTotal Lead Time from an initial value of 2 days (48hours) to 32 hours.

2. Increase liquid £lling machine uptime from 57% to70% solving the most important unsterility causes.This can be done by involving the £lling machinemanufacturer in some updating and maintenancetasks, implementing TQM and installing new visualcontrols at the plant.

3. Implement a new cell for the sealing/caping/labelingactivity to smooth the line, establishing cleaner andmore organized work areas (with the 5S method) andavoiding unnecessary material and operator move-ments.

CONCLUSIONS AND FUTURE WORKSimulation has been used in the past for training and edu-cation in the context of Lean Manufacturing, but it is notusually considered as a lean tool.

In this work a simulation framework based on Matlaband Simulink, LeanSim, has been presented. The £rstgoal of this framework is to demonstrate that simulationcan bring important bene£ts to lean initiatives allowing theidenti£cation, de£nition, evaluation and prioritization ofthe improvement actions.

The £rst version of the LeanSim framework has been de-signed to support Value Stream Map projects. A VSM di-agram presents only a static view of the process and fails

in capturing the dynamic interactions between all the in-volved agents. Simulation adds the fourth dimension toVSM, time, transforming a static snapshot on a much morecomplete and powerful what-if analysis tool. It allows thelean team, not only to determine which improvements canmove the system towards the desired state, furthermore, toquantify the amount of improvement that can be expected.

All these advantages have been illustrated with a realcase based on a VSM project in an enriched milk manu-facturing process. The LeanSim framework has demon-strated to be a useful decision-making tool in fast improve-ment events, avoiding exclusively human-dependent deci-sions based only on qualitative methods and past experi-ences.

We are currently working in many interesting lines re-lated to this work. The LeanSim library is being completedwith new blocks, templates and functionalities in order toimprove its usability in VSM projects and to support an-other lean methods. And when the library is £nished, ourintention is to create a website where it can be freely avail-able for download and for receiving feedback from users.Finally, we are developing a methodology to model manu-facturing processes on multiple levels of detail using hier-archical modeling with the LeanSim framework.

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AUTHOR BIOGRAPHIESFERNANDO SEVILLANO received the master degreein economics and business management from Univer-sity Complutense of Madrid, Spain, in 1994 and thePhD degree from the Computing Department, Rey JuanCarlos University, Madrid, Spain in 2010. He has beenworking as consultant and manager in different IT com-panies and he is currently collaborating with Universidad

Camilo Jose Cela as a part time professor. His currentresearch interests at the GAAP research group are en-terprise organization and strategy, information systems,continuous improvement and simulation. His email isfsevillano@ucjc.edu and his personal webpage athttp://www.gaapsoluciones.es/sevillano/.

MIGUEL SERNA is captain of the Spanish army andreceived the master degree in Information Technologyand Communication Systems for Defense in 2007. He iscurrently working in the GAAP research group at the ReyJuan Carlos University, £nishing his PhD thesis relatedto interoperability in military and industrial environmentsand to distributed simulation. His email address ismserca@gmail.com.

MARTA BELTRAN received the master degree inelectrical engineering from University Complutense ofMadrid, Spain, in 2001, the master degree in industrialphysics from UNED, Spain in 2003 and the PhD degreefrom the Computing Department, Rey Juan Carlos Uni-versity, Madrid, Spain in 2005. She is currently workingwith this department as a lecturer. She is the leaderof the GAAP research group and has extensively pub-lished in high-quality national and international journalsand conference proceedings in the areas of computerarchitecture and parallel and distributed systems. Hercurrent research interests are high performance computing,simulation and heterogeneous systems. Her email ismarta.beltran@urjc.es and her personal webpageat http://www.gaapsoluciones.es/beltran/.

ANTONIO GUZMAN received the master degree inapplied physics from University Autonoma of Madrid,Spain, in 1999 and the PhD degree from the ComputingDepartment, Rey Juan Carlos University, Madrid, Spainin 2006. He is a coauthor of more than 20 papers ininternational conference proceedings and journals. Heis currently an associate professor in the ComputingDepartment of Rey Juan Carlos University and his currentresearch interests are high performance computing, perfor-mance modeling, interoperability and security. His emailis antonio.guzman@urjc.es.