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Utilising Simulation toEnhance Value StreamMapping: A ManufacturingCase ApplicationThomas McDonald , Eileen M. Van Aken &Antonio F. RentesVersion of record first published: 04 Aug 2010.

To cite this article: Thomas McDonald , Eileen M. Van Aken & Antonio F. Rentes(2002): Utilising Simulation to Enhance Value Stream Mapping: A ManufacturingCase Application, International Journal of Logistics Research and Applications: ALeading Journal of Supply Chain Management, 5:2, 213-232

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International Journal of Logistics

ISSN 1367-5567 Print/ISSN 1469-848X online © 2002 Taylor & Francis Ltd

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DOI: 10.1080/1367556021014869 6

International Journal of Logistics: Research and ApplicationsVol. 5, No. 2, 2002

* Correspondence: Eileen Van Aken, 250 Durham Hall (0118), Virginia Tech, Blacksburg, VA

24061, USA; E-mail: evanaken@vt.edu

Utilising Simulation to EnhanceValue Stream Mapping: AManufacturing Case Application

THOMAS McDONALD,1 EILEEN M. VAN AKEN1* &ANTONIO F. RENTES2

1Virginia Tech, Blacksburg, VA 24061, USA, 2University of S Äao Paulo, S Äao Carlos,SP 13560, Brazil

ABSTRACT Value stream mapping is prescribed as part of the lean productionportfolio of tools and has been applied in a variety of industries. This paper describesan application of value stream mapping, enhanced by simulation, to a dedicatedproduct line in an engineer-to-order motion control products manufacturing plant.This paper makes two primary contributions: an application of value stream mappingin an actual setting and the use of simulation to answer questions that could not beaddressed only using the static view provided by value steam mapping. This paperdescribes both the current state and the future state for the product line, as well as theanalysis and results obtained from simulation. We conclude with a discussion offuture research and applications in this area.

Introduction

Faced with ever-increasing challenges such as the globalisation of the market-place, increased competition and increased customer expectations, organisa-tions are pursuing strategies to improve overall performance and com-petitiveness in the global market. A variety of improvement methodologiesand approaches are available to organisations, many of which have yieldedencouraging implementation successes. Lean production (also calledlean manufacturing, or lean thinking, as popularised by Womack & Jones(1996)) is increasingly being implemented as a potential solution for many

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214 T. McDonald et al.

organisations, particularly within the automotive (Day, 1998; Jones, 1999;Womack et al., 1990; Womack & Jones, 1996) and aerospace (Abbett & Payne,1999; Peters & Lodge, 1999; Womack & Fitzpatrick, 1999) manufacturingindustries, as evidenced by the case study applications presented at the LeanEnterprise Institute’s 1999 Lean Summit. Although a number of principlesand tools appear to be derived from just-in-time, cellular manufacturing andworld-class manufacturing, lean production has emerged relatively recentlyas an approach that integrates different tools to focus on the elimination ofwaste and produce products that meet customer expectations (Hines &Taylor, 2000; Womack & Jones, 1996).

The term lean thinking, created by Womack & Jones (1996), was used toname the thinking process of Taiichi Ohno and the set of methods describingthe Toyota Production System. Concepts related to lean thinking werepopularised in the book The Machine that Changed the World, which illustratesthe significant performance gap between the Japanese and western automo-tive industries (Womack et al., 1990). James-Moore & Gibbons (1997) definekey areas of focus, each with associated principles, within the lean productionapproach: flexibility, waste elimination, optimisation, process control andpeople utilisation. These areas of focus and principles can be operationalisedusing specific tools and techniques. A number of authors have defined theportfolio of tools/techniques to implement lean production (Emiliani, 2000;Hines et al., 2000; Rother, 1998). Some tools may be more focused on the entireorganisation as the unit of analysis, such as total productive maintenance,while others, such as value stream mapping (VSM), may be more focused ona product value stream.

In this paper, we focus on VSM for two primary reasons. First, thereappears to be a need for additional and detailed examples of this tool indifferent types of actual production settings. In particular, there appears to bea lack of applications of VSM to production processes having parallel processsteps (versus less complex processes with only serial steps). Second, wewanted to explore how other modelling tools, such as simulation, could beused in some cases to augment VSM.

A value stream is defined as all the value-added and non-value-addedactions required to bring a specific product, service, or combination ofproducts and services, to a customer, including those in the overall supplychain as well as those in internal operations. VSM is an enterpriseimprovement technique to visualise an entire production process, represent-ing information and material flow, to improve the production process byidentifying waste and its sources (Rother & Shook, 1999). The value streammap is created using a predefined set of icons (shown in Figure 1). VSMcreates a common language about a production process, enabling morepurposeful decisions to improve the value stream.

Rother & Shook (1999) define a structured approach for improving avalue stream. The first step is to identify the relevant product families andselect one as the target for improvement. The next step is to construct acurrent state map for the product value stream, using information gatheredfrom the actual production process. The third step in the VSM process is tomap the future state. Rother & Shook (1999) identify eight questions that mustbe answered to construct the future state map (as shown in Table 1). The first

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Value Stream Mapping 215

five questions are concerned with ª basicº issues related to the construction ofthe future state map, the next two address technical implementation detailssuch as the control system (ª heijunkaº ) to define non-mapping details, whilethe eighth question addresses the improvement actions needed for transitionfrom the current to the future state. Lastly, an implementation plan is createdto implement the future state.

FIGURE 1. Value Stream Mapping Icons (Rother & Shook, 1999).

TABLE 1. Design Questions for Future State (Rother & Shook, 1999)

Future-state questions

Basic 1. What is the takt time?

2. Will production produce to a finished goods supermarket or directly to

shipping?

3. Where can continuous flow processing be utilised?

4. Is there a need for a supermarket pull system within the value stream?

5. What single point in the production chain will be used to schedule production?

Heijunka 6. How will the production mix be levelled at the pacemaker process?

7. What increment of work will be consistently released from the pacemaker

process?

Kaizen 8. What process improvements will be necessary?

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216 T. McDonald et al.

In many cases, the future state can be designed using these questions ina straightforward manner, using only the manual approach prescribed inRother & Shook (1999), to create a feasible future state that can beimplemented quickly. However, in some cases, defining the future state for aprocess may be difficult using only the value stream map. For example,predicting the inventory flows and levels throughout the production processis not possible with only static models. In these cases, simulation, anextensively used process-modelling tool, can be used to reduce uncertaintyand create consensus by visualising dynamic views of the process for a givenfuture state. Additionally, simulation can be used to explore alternative futurestates generated by different responses to the eight design questions.

Next, we present an application of VSM to a dedicated production line ina small manufacturing plant. A core product family within this plant wastargeted for significant throughput improvement without increased capitalequipment investment. In this paper, we provide a description of the currentand future state of the product value stream. We also demonstrate howsimulation can be integrated with VSM to visualise better dynamic features ofthe future state before implementation.

Description of Case Application

This application was implemented within a high-performance motion controlproducts manufacturing plant in the southeast US. This plant is one of severalwithin a larger corporation in the motion control industry having world-wideoperations. The identity of the organisation is protected; however, we shallrefer to the plant as Industrial Motors (IM). Motors manufactured in the IMplant are used in applications in the machine tool, medical products, andaerospace and defence industries.

The IM plant was facing increased pressures, both externally andinternally, to improve the performance of a specific product line Ð the ABproduct line; these challenges are summarised in Table 2. Each day seemed tobring another crisis related to schedule, production, or engineering changes,creating a chronic reactive mode. Faced with these challenges, the IM plantengaged in a number of integrated improvements to revolutionise its productdesign, engineering and production processes. A major targeted initiative wasto increase throughput capability for the AB product line without incurringadditional capital expense.

The expectations for improvement set by plant leadership for the ABproduct line included:

d Production of 80 motors/day, based on perceived market needs (currentproduction throughput is 67 motors/day).

d Manufacturing lead-time of 3 days (current lead-time is 8 days).d In-service quality of 99.9% (current in-service quality is 97%).d On-time delivery of 99% (current on-time delivery is below 70%).

Clearly, the most significant improvements needed for this production line,based on the expectations and goals listed above and current performance, arein manufacturing lead-time and on-time delivery percentage. These perform-ance dimensions were expected to be impacted through the application of an

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Value Stream Mapping 217

improvement approach focused on eliminating non-value-adding work andwaste (particularly due to unnecessary inventory which impacts lead-time). Inaddition, VSM was selected as an analysis tool because of its explicit inclusionof both information and material flow, which enables an effective representa-tion of alternative production control systems (such as Materials RequirementsPlanning (MRP)-type systems, kanbans, etc.). Therefore, because of thestructured approach, focus on waste elimination and the need to meet statedimprovement goals, VSM was viewed by the design team as an effective andappropriate modelling tool, when compared with other tools, to analyse thecurrent state and to define a future state for the production line.

Value Stream Mapping Process and Results

To execute the VSM approach, a design team was assembled with people fromdifferent parts of the organisation having critical information and knowledge:production, engineering, buying/planning and scheduling (production con-trol). The mission of this team, chartered by plant leadership, was to definethe current state and desired future state for the AB product line.

Current State Mapping

The AB product line manufactures 14 types, or families, of standardisedmotors. Customised motors (referred to as ª same-as-exceptº ) have minorcustomisations over a standard platform (as opposed to major unique customdesigns). The customisations that occur inside a standard product family do notlead to significant differences in processing times and set-up times. Across eachmotor family, there are moderately different processing and set-up times.

A weekly MRP schedule based on customer needs is generated for eachprocess step in the AB product line and is used to push orders throughproduction. As shown in Figure 2, the current state map, each motor is

TABLE 2. Summary of Challenges for AB Product Line and the IM Plant

Externally-focused Challenges Internally-focused Challenges

d Increased number of suppliers for this

product ® creating more choices for

the customer

d Ability of competitors to meet customer

requirements at a lower cost and better

service

d Poor delivery performance to the

customer ® repeated missed delivery

dates

d Lack of definition of standard AB part or

product flow

d Lack of clear and consistent process for

scheduling and rescheduling work

d Discrepancy between quoted and actual

lead-times ® increased pressure to reduce

lead-times

d Despite high in-service quality, internal scrap

and rework rates were poor

d Strong functional boundaries between

engineering, materials and production ® lack

of understanding of the impact of actions in

one group on the others

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218 T. McDonald et al.

FIG

UR

E2.

AB

Pro

du

ct L

ine

Cu

rren

t S

tate

Ma

p.

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Value Stream Mapping 219

manufactured as three parallel subassemblies: stator, rotor and end-bell.There is a saw process that cuts the part for the rotor assembly and statorassembly, therefore, the saw step processes twice the number of parts as otherprocesses. The stator subassembly is manufactured on one batch-and-queueprocess, with the following three process steps: sawing, machining thehousing and assembling the stator. The rotor subassembly is manufactured ona second, parallel, batch-and-queue process with the following three processsteps: sawing, machining the shaft and rotor assembly. The end-bellsubassembly is manufactured from raw materials. The sawing, machining,rotor assembly and end-bell assembly process steps operate two shifts, whilethe stator assembly operates three shifts. The three subassemblies are thencombined and tested prior to shipment.

Data were collected for the current state following the approachrecommended by Rother & Shook (1999). For example, data collection beganat shipping, working backwards in the production process to raw materials orsuppliers, collecting ª snapshotº data on inventory levels.

The line at the bottom of the current state map in Figure 2 representsmanufacturing processing time and ª inventory timeº (i.e. inventory levelsconverted to time). The observed production lead-time is approximately 8days and 2.8 hours, while the observed value-added time is only 152 minutes.The lead-time is calculated based on the sum of the processing and inventorytimes and was determined using the critical path, which is the statorsubassembly line. The processing (cycle time) and set-up times shown inFigure 2 were determined based on a weighted average of the most frequentlyproduced motor families.

The cycle times shown in the data boxes in Figure 2 were determined bythe sum of the processing time at each resource inside the process step. Forexample, in the stator assembly line, there are six operators (resources) workingin series on each shift. The total cycle time for this entire process step is 76minutes Ð the time for all six operators to complete all processing activities on agiven part. However, it should be noted that this time does not represent thecapacity of stator assembly. Parts are being moved (and completed at the end ofstator assembly) on average every 12.6 minutes (the capacity of this processstep), not considering breakdowns and set-up times.

To convert the inventory amount to time, the current state production rateof 67 motors/day was used Ð the number of units in inventory was divided by67 to determine time, as shown in the lead-time line in Figure 2. Where there areparallel processing steps, the critical path was used for inventory time.

In this effort, because the design team was interested first and foremostin improving manufacturing responsiveness (lead-time) to the customer, theteam focused on identifying and removing waste, including unnecessaryinventory, in internal operations of the production line. Therefore, in thisanalysis and improvement effort, the team did not analyse or develop futurestate recommendations on supply chain issues, including defining quantitiesof raw material inventory (shown as ª n/aº in Figure 2) or supplier lead-timefor parts. Since production does not currently ª buy to orderº , but insteadbuys strategically and/or by opportunity, the amount of raw material was notconsidered relevant at this time for this analysis. Following the current effortdescribed in this paper, future improvement could be realised by focusing on

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220 T. McDonald et al.

supply chain issues to improve the total value stream (e.g. as described inTaylor & Brunt, 2001), including establishing stronger partner relationshipswith suppliers to implement strategies to reduce raw material and partsinventories as well as reducing supplier lead-time and costs. This approach offirst focusing on internal operations, which often leads to uncoveringproblems and opportunities in the supply chain, is consistent with casestudies reported in Womack & Jones (1996).

Future State Mapping

As discussed previously, defining the future state involves addressing theeight questions in Table 1. The design team successively developed consensuson the answers to these questions. The features of the future state for the ABproduct line are described in more detail using the questions below, butbriefly, the major difference between the current and future state is theproduction control strategy, as seen by comparing the current state (Figure 2)with the future state map (Figure 3).

Question 1: What is the takt time? ª Takt timeº is the unit production pace thatmust be met to match the customer requirements. It must be calculated basedon the demand rate. Thus, the equation for takt time is:

Takt time =Available work time per shift

Customer demand rate per shift.

The throughput required for the AB product line was 80 motors/day,assuming a fully staffed production over two shifts of 8 hours each. Thisresults in a takt time of 12 minutes/motor:

Takt time =16 h * 60 min/h

80 motors=

12 min

motor.

Question 2: Will production produce to a finished goods supermarket or directly toshipping? A ª supermarketº is a buffer of ready-to-ship products strategicallylocated at the end of the production process from which shipping can pull theproduct (Rother & Shook, 1999). A kanban would then be released to replenishthe supermarket. Producing directly to shipping indicates that only thenecessary number of products is manufactured and then shipped.

In this application, because the production line produced both standardand customised motors, the design team decided to produce to a supermarketfor standard motors and by customer order (direct to shipment) forcustomised motors (see Figure 3).

Question 3: Where can continuous flow processing be utilised? In this application,the differing characteristics of customised motors compared to standardmotors starts at the saw process. Therefore, the proposed future state specifiescontinuous flow of production, without controlled inventories, from the sawprocess to the final motor assembly. The production line will act as an

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Value Stream Mapping 221

FIG

UR

E3.

AB

Pro

du

ct L

ine

Fu

ture

Sta

te M

ap

.

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222 T. McDonald et al.

integrated cell, using a Constant Work-in-Process (CONWIP) system (Hopp &Spearman, 1996) to control internal production. In this application there willbe a limited and constant number of carts, which will be used to transportmaterial and other items specified in each kanban. The quantity of availablecarts will automatically control the maximum volume of inventory in theproduction line. If there are no available carts at the beginning of theproduction line, the introduction of production kanbans is interrupted. Thedesign team specified five rules to maintain control over production:

(1) If, at the beginning of the line, material and engineering information arenot available, production is not started (kanban is not released).

(2) The work transferred from one process step to another inside theproduction line must be a complete transfer lot (as defined by thekanban).

(3) The sequence of batch entrance should be maintained, as much aspossible, beyond the stator and rotor assembly process steps.

(4) A limited and constant number of carts will be used in the productionline.

(5) The exit sequence at the stator assembly, slower than the rotor assembly,determines the final sequence of motor assembly.

Question 4: Where will we need a supermarket pull system within the valuestream? A pull system supermarket is a clearly defined interface betweenprocess steps in a production process. All items required for productionshould be present in the supermarket, creating continuous flow from thesupermarket forward.

Four supermarkets were defined. The first supermarket (shown as A inFigure 3) was designed to provide very responsive delivery of standardisedproducts to customers. Once a standard product is shipped, the correspond-ing production kanban card will be returned to production control, where itwill be forwarded to the heijunka box (defined in question 5). The secondsupermarket (shown as B in Figure 3) will be for end-bells, which are readyto be placed on the motor. Transportation/production kanbans will be used tomove end-bells from the production exit station. Internal to the productionstation, this kanban will be used like a regular production kanban. The thirdsupermarket will be located before the end-bell process (shown as C in Figure3), for the stock of material to be used in the end-bells process step. Materialreplacement will be made using transportation kanbans. Finally, the fourthsupermarket (shown as D in Figure 3) will be at the beginning of theproduction line to keep items that are regularly used in production. Items thatare not used regularly in standardised motors will be available at the lineentrance on a scheduled basis established by production control, not by usingthe supermarket concept.

Question 5: What single point in the production chain should be the pacemakerprocess? The pacemaker process is the process to which production isscheduled; everything before it is pulled from the pacemaker process andeverything after is continuous flow. The pacemaker process for the AB

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Value Stream Mapping 223

product line will be the saw, because, as mentioned previously, the productdifferentiation begins at this process.

In the future state, a heijunka box (Rother & Shook, 1999) will be installedto schedule the pacemaker process. The kanbans to be introduced in theheijunka box will be forwarded to a controller by production control.Production control, before inserting a kanban in the heijunka box, will check forthe availability of material and information (projects, process charts, etc.)necessary for production. If the information and material are available, thekanban will be placed in the production sequence. The production sequence tomatch product demand will be defined in question 6.

As shown in Table 1, the first five questions define the basic conceptualdesign of the production line. As shown in Figure 3, the carts will determine theWork-in-Process (WIP) volume. The stator assembly is slower than the parallelrotor assembly line and will require three shifts in order to fulfil the dailyrequired production. The rotor assembly and the motor assembly will requireonly two shifts. Withdrawing kanbans from the heijunka box and inserting theminto the production line will occur throughout the two shifts. The pace of thisinserting will be the takt time (12 minutes). This pacing process is made at thebeginning of the production line, before the saw process. After this pacedinsertion, the resources, when they have material to be processed, must work asfast as possible, considering their production capacity.

The future state was designed to provide sufficient inventory forproduction at stator assembly on the third shift. This translates to 8 hours ofinventory, based on processing time for stator assembly (recall that statorassembly will be the only process step staffed on the third shift). Carts loadedwith material to be processed will be located at the entrance of the statorassembly, as shown in Figure 3. This same amount of inventory will beavailable at the entrance to the motor assembly at the beginning of the first shifton the next day. Translated to time, this represents 5.5 hours (or two-thirds of ashift) based on the pace of motor assembly process step. It was expected thatthis inventory level in front of the motor assembly would be reduced to zerobefore the end of the second shift. The assembled standardised motors are sentto the supermarket, whereas the customised motors are shipped directly.

Question 6: How will the production mix be levelled at the pacemaker process? Theguideline for answering this question is to distribute evenly over time theproduction of different products at the pacemaker process. To distributeproduction evenly, multiple smaller batches of each product need to bescheduled. This is counterintuitive to the batch-and-queue scheduling method(Womack & Jones, 1996), but allows for shorter lead-times, flexibility in productmix and smaller inventories of work-in-process and finished goods.

Based on the capacity analysis in Table 3 and the historic motorproduction mix, it is possible to determine the batch size and the productionfrequency of each type. Rother (1999) proposed an approach to calculate thepossible number of set-ups and define the ª every part everyº (EPE) Ð thecalculations to apply this approach are defined below:

d Average throughput time = (Average processing time)/(Availableresources)

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224 T. McDonald et al.

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Value Stream Mapping 225

d Total throughput time = (No. of parts per day) * (Average throughput time)d Total available time = (No. of shifts per day) * (8 h * 60 min)d Available time for set-up = (Total available time) ± (Total throughput

time)d No. of set-ups possible per day = (Available time for set-up)/(Average set-

up time)d Minimal set-up frequency = Min (No. of set-ups possible per day)d EPE = Number of families of motors/Minimal set-up frequency.

Considering the capacity analysis, the design team determined that thereshould be seven set-ups per day, based on the minimum number of set-upspossible, defined by stator assembly at seven. However, to develop additionalproduction capability, the design team decided to target eight possible set-upsper day for the line (and in particular at stator assembly). This set-upreduction at stator assembly was considered to be a relatively easyimprovement effort based on an analysis of the current procedures and set-uptime at stator assembly (see footnote to Table 3). Given eight possible set-upsper day and 14 motor families, the production frequency for each family,rounding up to the nearest integer, will be 2 days (EPE 2 days). Therefore, onaverage seven different families will be produced each day.

An analysis was conducted to determine the expected product mix tomeet 80 motors/day, using the historical production mix of the 14 motorfamilies. Because EPE 2 days, there will be 160 motors to be produced eachcycle (80 motors/day over 2 days). Table 4 shows the quantity of motorsneeded over the cycle (2 days) for each motor family Ð this quantityrepresents the batch size for each family. Because the quantity required foreach family is a multiple of two, the design team decided to use a transfer lotsize of two, as shown in Table 4. Therefore, each kanban will order theproduction of two motors, and the number of kanbans required is shown in thelast column of Table 4.

Question 7: What increment of work (the ª pitchº ) will be consistently released to thepacemaker process? The guideline is to release and withdraw small incre-ments (called the ª pitchº ) of production at the pacemaker process. The pitchis calculated by multiplying the takt time by the finished goods transferquantity at the pacemaker process. Pitch becomes the basic unit of theproduction schedule for a product family.

Given a takt time of 12 minutes, and considering that the transfer lot sizeis two motors, then the pitch should be 24 minutes. Therefore, the heijunka boxshould be divided in spaces equivalent to 24 minutes, and this will be thefrequency of kanbans introduced in the line.

Question 8: What process improvements will be necessary? Answering thisquestion forces the team to identify improvements needed to implement thefuture state. These improvements are shown as ª kaizen burstsº in the futurestate map. In general, the types of improvements identified are those feasibleto implement in the same time frame as the future state design Ð typicallyrelatively short term. Based on case presentations at the 1999 Lean Summit,

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226 T. McDonald et al.

many future state designs appear to have a 3± 6-month time frame comparedto the current state Ð therefore, the improvements identified in this questionare those which can typically also be addressed in the same time frame.

Although there is room for improvement in the actual processing and set-up times in the AB product line, the design team concluded they were ingeneral sufficient to support production of 80 motors/day. However, a criticalfactor considered for implementation of the future state is the existing volumeof rework, particularly within stator assembly. In addition, as discussed earlier,set-up time for stator assembly is an improvement needed for realising thefuture state, although, as discussed, this was perceived as a relativelystraightforward improvement. The stator assembly has by far the longest set-up time, and is the limiting factor determining the number of set-ups possibleon the production line, as shown in the calculations in Table 3. The kaizen burstin the future state map in Figure 3 highlights these improvement areas.

Several questions emerged in the future state mapping process that couldnot be answered solely with the static future state value stream map. Thesequestions, why they were relevant to this case application and how they wereaddressed using simulation are discussed next.

TABLE 4. Required Production Mix by Motor Family Type

Motor Family

Required Quantity

of Motors

per Cycle1

Transfer

Lot Size

Required

Number of

Kanbans

A 16 2 8

B 36 2 18

C 26 2 13

D 20 2 10

E 6 2 3

F 8 2 4

G 10 2 5

H 30 2 15

I 4 2 2

J 2 2 1

K 2 2 1

L 2 2 1

M 2 2 1

N 2 2 1

Totals 166 83

1 Each cycle corresponds to the EPE 2 days.

Enhancing Value Stream Mapping with Simulation

Most of the applications of VSM do not appear to utilise simulation Ð thereappears to be a perception within the VSM community that developing a usefulsimulation model is a lengthy and time-consuming process, not well-aligned

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Value Stream Mapping 227

with the relatively quicker cycle time of the VSM process. Simulation may notbe viewed as worth the time because a company can simply rearrange aproduction line quickly, see if it works and, if not, move it back. In reality,particularly using recent simulation software, results can not just improve thequality of solutions, but do so in a relatively short time frame (Chan, 1995; Chan& Abhary, 1996; Chan & Jiang, 1999; Mize et al., 1992; Tabucanon et al., 1998;Zahir et al., 2000).

Furthermore, we propose that there may be cases, as was found in thisapplication, where simulation is not just feasible in the same time frame as theuse of VSM, but is, in fact, important to provide information about thedynamic nature of the production process. This type of information cannot beobtained using VSM alone because of the static nature of this tool. For thisapplication, there were several areas of uncertainty associated with the futurestate, centred around the fundamental question of the amount and variationin WIP throughout the system. Several more specific follow-on questions canthen be answered:

(1) What is the maximum WIP at each processing step?(2) What is the variation in WIP across shifts throughout the system?(3) How many CONWIP carts will be needed to support the level of WIP?(4) How much space is needed in each holding area?(5) When does motor assembly resume takt time pace, given the build-up of

inventory on the third shift?

These questions are not straightforward to answer with VSM alone because ofthe number of different motor families produced, the different processingtimes and set-up times for each processing step, the use of a different numberof shifts in the line and the fact that there are parallel processing pathsthrough the production line. These factors together create a complexity thatcannot be addressed using VSM alone.

Arena® performs discrete event simulation (Kelton et al., 1998) and wasused in this application to simulate the future state of the AB product line. Thesimulation model required approximately 40 hours of development time. Thefuture state model was developed using the average processing and set-uptimes shown in Table 3 and the production sequence for motor families shownin Table 4 (Family A± Family N). Figure 4 illustrates a simplified view of thesimulation model’s representation of the production line Ð for the purpose ofclarity, some information has been removed.

The simulation model was run for 500,000 minutes, or approximately 347simulated days, to determine when steady state conditions were achieved. Anoutput file was created containing information on the number in the system.Using Arena Output Analyser, the data were batched using a batch size of 10observations. The batched file was evaluated using a moving average todetermine the end of the transient period. From the moving average graph,we determined the transient state ended by approximately 16,000 minutes. Awarm-up period of 17,280 minutes, or 12 days, was used. The model was runfor a total of 362 880 minutes (approximately 252 days), where 12 daysrepresented the warm-up period, leaving 240 days to provide data forsimulation results.

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228 T. McDonald et al.

FIG

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E4.

Are

na

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Sh

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of

Fu

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Sta

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od

el.

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Value Stream Mapping 229

Figure 5 shows the simulated level and variation of WIP inventorythroughout the system, starting at day 13. These data are based on just onereplication, however, 200 replications of the future state simulation modelwere also generated to form a 95% confidence interval for the amount ofinventory with a half-width interval within 5% of the expected value. Thisresult was not significantly different from the results reported below,therefore, for the purposes of this analysis, results from the single replicationwill be used.

As seen in Figure 5, there is wide variation in WIP inventory acrossprocessing steps and across shifts. Based on the results in Figure 5, the specificquestions noted earlier can be addressed as discussed below.

(1) What is the maximum WIP at each processing step? The maximum WIP forthe entire production line occurs at the entrance to motor assembly (58pieces as shown in Figure 5). The next highest WIP occurs at the entranceto the stator assembly (32 pieces). The other processing steps haveinventory levels of less than 10 pieces. This information enables the designof the physical layout of the production line (space needed Ð see question4) as well as equipment needs (carts Ð see question 3).

(2) What is the variation in WIP across shifts throughout the system? Based on ananalysis of the simulation results, the highest levels of inventory occur atthe end of the third shift, as expected, in particular at the entrance to themotor assembly. As seen in Figure 5, there is a repeating cycle of peakinventory for motor assembly at the end of the third shift. This cycle is dueto the production sequence of motor families utilised in the simulationmodel. Changing the production sequence would impact this profile Ð itsmagnitude and the differences in peaks within the cycle; this could beexplored further using the simulation model.

(3) How many CONWIP carts will be needed to support the level of WIP? Thenumber of carts needed is based on the maximum inventory in the systemat any time, and will be half the number of maximum pieces because thetransfer lot size (and kanban container size) is two. As discussed in theprevious question, the maximum inventory occurs at motor assembly at theend of the third shift (58). However, at these peak times, there is also a smallamount of inventory in other processing steps, such that the total is 61.Rounding up, the total number of carts required for the future state is 32.

(4) How much space is needed in each holding area? The amount of inventory atthe entrance to each processing step from Figure 5 can easily be translatedinto a corresponding number of carts and space required.

(5) When does motor assembly recommence takt time pace, given the build-up ofinventory on the third shift? Until WIP reaches zero at motor assembly, thepace is faster than takt time pace. After the occurrence of the maximumpeaks, as shown in Figure 5, the worst case is that motor assembly returnsto takt time pace early in the second shift. Understanding this resultconfirms the ability to operate without a third shift on motor assembly.Second, this result highlights a potential alternative future strategy thatcould be pursued. Specifically, resources could be reallocated to havemore assemblers available in the first shift than the second shift in orderto resume takt time pace even more quickly.

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FIG

UR

E5.

Sim

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Wo

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Value Stream Mapping 231

The simulation model in this application was created based on the future statevalue stream map. There were several simplifications made in the representa-tion of the production line for the purpose of this paper. A number of featureswere not considered in the simulation model: machine breakdowns, rework,employee breaks, fluctuations in product demand (quantity or mix), variationin actual set-up times and differences in cycle times for customised versusstandardised motors. Although these factors can be conceptually shown in thefuture state map, their dynamic characteristics can only be represented insimulation models where variation over time and interrelationships amongthe factors can be shown. These factors could easily be integrated into thesimulation model to refine further the model and explore additional analysesand what-if questions. For example, a what-if question representing analternative future state for this application that could be explored is: Howmuch improvement is needed in stator assembly set-up and processing timeto utilise only two shifts?

Conclusions

This case study application has demonstrated that simulation analysis can bea useful and important part of VSM. Although we are not proposing thatsimulation always be utilised with VSM, it can form an integral part of thetool set. Specifically, as found in this case, when there is product complexity(leading to differences in processing and set-up times across productvariants), parallel processing steps and/or different number of shifts usedacross a production line, simulation can provide important information tocomplement that obtained from future state mapping. Furthermore, simula-tion facilitates process visualisation, creating a shared consensus about theprocess and where improvements can be made.

There are several areas for future research and application to build on thework described here. First, the integration of VSM icons into simulationsoftware would facilitate the development of simulation models whenappropriate and reduce the time required. A second area for future researchis to explore how VSM can be combined with other process analysis tools toshow information not currently made explicit on current or future state maps.Lastly, this work and approach can be extended to supply chain management.There have been a few applications of VSM to the supply chain (versusinternal operations only), however, additional applications are needed.Furthermore, we propose that this approach would be applicable to supplychain analysis and design in order to model this type of complex environmentand explore design alternatives.

Acknowledgements

This research was supported by a research contract (Agreement #MOA2599)to the second author and by a FAPESP (FundÄaçao de Apoio Áa Pesquisa doEstado de S Äao Paulo) (process #97/8154 ± 2) research grant to the third author.The authors would like to thank Dr Kimberly Ellis at Virginia Tech, Dr PatrickKoelling at Virginia Tech and Mr Dirk Van Goubergen at Ghent University fortheir feedback on earlier versions of this work. We would also like to thankthe anonymous reviewers for their comments.

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