2. DISTRIBUTED SUPPLY CHAIN SIMULATION AS A DECISION SUPPORT TOOL
• Random changes to the system state are difﬁcult to portray, tics operations at its own site, where users interact with the
even though there has been signiﬁcant effort to understand, system. Also, planning and scheduling systems (eventually
control, and reduce variability in the system. APS procedures) are logically separated from the (simu-
• Precise prediction of the evolution of the system over time lated) operations. The simulation models interact with each
is not possible. other and exchange data with the planning and scheduling
systems in the same way as the real manufacturing or logis-
Many of these limitations can be at least partially over- tics operations of the supply chain. Detailed model infor-
come with a “good” representation of the operational sys- mation (application codes and data) is encapsulated within
tem in a computer model by applying discrete event sim- each (either planning or operational) model. The partici-
ulation (DES) technology. After running a simulation, we pating corporations only need to deﬁne essential data ﬂows
know “how execution would be,” assuming certain val- from one supply chain node to another. In the background,
ues for critical parameters. Commercial simulation tools the modeling and analysis system initiates a remote model
for analyzing supply chains have been released in recent invocation. Data representing the simulated material and
years, for example, the Supply Chain Analyzer by IBM . information ﬂow between supply chain operations are then
Simulation models typically are driven by the release exchanged as messages during the simulation run. These
of materials into the system. These input releases, how- messages can be transmitted through a network (e.g., the
ever, are difﬁcult to generate in today’s pull environments Internet) connecting the participating corporations.
with the frequent phase-in of new products. The systems Three critical issues for implementing such a distributed
represented by the simulation models are ultimately driven supply chain simulation are (1) the speciﬁcation of the in-
by customer demand scenarios. While simulation models terfaces between models, (2) the mechanism for support-
are quite useful in understanding the interactions between ing intermodel communication, and (3) distributed model
supply chain components, they generally incorporate a synchronization.
relatively crude abstraction of the associated planning Satisfying these requirements involves developing a
processes. supply chain reference model either implicitly or explic-
The most straightforward way of translating customer itly. Examples of similar efforts are described in Gong and
demand into feasible input release rates is to integrate McGinnis , Narayanan et al. , and Park et al. .
the underlying APS procedure(s) into the simulation (see In a similar effort, the Manufacturing Engineering Lab-
Fig. 2). In such a way, the simulation is made much more oratory of National Institute of Standards and Technology
realistic, and active experimentation with alternative sup- is developing an architecture for the seamless integration
ply chain management strategies becomes possible. of manufacturing simulation systems, manufacturing soft-
Distributed simulation comes into the picture when the ware applications, and manufacturing data repositories .
model to be assessed is an entire supply chain and the
detailed information required is geographically dispersed
or partners do not want to share sensitive data (such as 3. Technical Feasibility of the Framework
dispatching rules or the nature of their other customer de-
mands) in one simulation model. It also allows evaluating 3.1 Interoperability and Reusability
structurally different alternatives rather than just different
conﬁguration parameters fed to a single simulation model. In our case, the integration of a set of independent simula-
This has been identiﬁed as one of the key challenges to be tion models and APS procedures to form a high-ﬁdelity
tackled when it comes to complex supply chain scenario supply chain simulation is accomplished by adopting
optimization . the standards of the high-level architecture (HLA). The
HLA has been adopted by the Object Management Group
2. Distributed Simulation Framework (OMG) and the Institute of Electrical and Electronics En-
gineers (IEEE) as a standard for the interoperability of
The idea behind this distributed simulation framework is simulations (1516-2000).
to combine technology for the interoperability of simula- HLA is an architecture for the reuse and interoperation
tion models with APS to create synergies between state-of- of simulations . In HLA terms, each simulation model
the-art planning and scheduling software systems and ad- (which in our case represents either an operational node or
vanced simulation technology, as well as overcome short- an APS procedure within the supply chain) is referred to as
comings faced when applying one of these technologies in- a federate, while a collection of such federates makes up a
dividually. An earlier version of this framework has been federation. HLA supports the possibility of distributed col-
described in Lendermann, Gan, and McGinnis , who laborative development of a complex simulation applica-
presented additional examples to illustrate the necessity of tion as well as the reuse of capabilities available in different
incorporating planning procedures into a simulation. simulations. Thus, a set of simulation and planning mod-
In a distributed simulation framework, each participat- els, possibly developed independently and implemented
ing corporation/company in the supply chain is able to run using different languages and hardware platforms, can be
its own simulation model of manufacturing and/or logis- put together to form a large federation of simulations.
Volume 79, Number 3 SIMULATION 127
3. Lendermann, Julka, Gan, Chen, McGinnis, and McGinnis
Feasible execution plan: How • Real history of the
execution should be... Plan production/logistics
Operational • How the execution
execution • KPIs
Figure 1. Use of advanced planning and scheduling (APS) systems to generate input releases for the execution of manufacturing
and/or logistics operations. KPI = key performance indicator.
Extended scope of simulation model
Feasible execution plan: How • Real history of the
execution should be... Plan production/logistics
Operational • How the execution
execution • KPIs
Conventional scope of
Figure 2. Conventional simulation scope and extended scope for a pull environment. APS = advanced planning and scheduling;
KPI = key performance indicator.
128 SIMULATION Volume 79, Number 3
4. DISTRIBUTED SUPPLY CHAIN SIMULATION AS A DECISION SUPPORT TOOL
The standard provides a common technical frame- internal behavior (and other sensitive data) of a simulation
work for the integration of simulation and planning mod- model is completely invisible to the outside world (i.e., the
els. It comprises three components: the HLA interface other federates). Further details on this issue can be found
speciﬁcation, federation rules, and the object model tem- in Lutz .
plate (OMT). An interface speciﬁcation, known as run- Using HLA, each federate must deﬁne the information
time infrastructure (RTI), deﬁnes how federates inter- it will share with others. Even though HLA can hide infor-
act with the federation and with one another and sup- mation that a corporation does not want to share, it lacks
ports federation execution. It provides a set of ser- the capability to share a subset of the sensitive data with a
vices to the federates for data interchange and synchro- subset of corporations that make up the supply chain. This
nization in a coordinated fashion. The services are de- limitation can be resolved by a technique called Hierarchi-
ﬁned in six categories: federation management, declara- cal HLA . This approach shows signiﬁcant potential
tion management, object management, data distribution of being further developed to resolve other technical chal-
management, ownership management, and time manage- lenges of distributed supply chain simulation such as im-
ment. The RTI can thus be viewed as a distributed op- proving the scalability of the simulation and relaxing the
erating system providing services to support interopera- synchronization requirements between federates.
ble simulations executing in distributed computing envi-
ronments (see www.cc.gatech.edu/computing/pads/tech- 3.3 Execution Time
highperf.html). Each federate deﬁnes the objects and in-
teractions that are shared in the OMT. The responsibilities Lengthy execution time is a major concern when it comes to
of the federate and its relationship with the RTI are de- large-scale supply chain simulation that involves more than
scribed by the federation rules. one corporation. Any one federate that runs slowly (typi-
cally because of the complexity of its model) will hinder
3.2 Data Encapsulation and Message Exchange the progress of the whole supply chain simulation.
between Simulation Models To tackle this problem, internal parallelism between the
bottleneck federates can be exploited using a parallel feder-
To encapsulate the operation of each individual element of ate architecture . This architecture partitions the bot-
the supply chain (or its model) and yet have the models tleneck federate to form logical processes (LPs) that are
interact, an interface speciﬁcation is required. Analogous simulated in parallel on a shared-memory multiprocessor
to an application program interface (API), the speciﬁcation system. It integrates a parallel discrete event simulation
should be complete yet concise. At present, we know of (PDES) protocol  and HLA-based distributed simu-
no industry standard describing this type of speciﬁcation lation and facilitates the formation of a hybrid-distributed
for supply chain interactions. In addition to the speciﬁca- simulation that consists of both sequential and parallel fed-
tion of the interfaces, there must be a method by which the erates. With this parallel federate architecture, the perfor-
interface speciﬁcation is communicated to each participat- mance of the overall supply chain simulation can thus be
ing model and enforced in the operation of the distributed improved signiﬁcantly.
These requirements are satisﬁed by using the HLA in- 4. Implementation Approaches
frastructure. HLA provides a means for each individual
element of the supply chain to deﬁne data it is willing to Depending on the operational or strategic challenges to be
share, using the OMT. Each element will thus have a sim- tackled, two alternative implementation approaches have
ulation object model (SOM) that deﬁnes the shared object been identiﬁed and developed. The framework enables de-
and interaction classes. Using the Uniﬁed Modeling Lan- velopment of the supply chain simulation from scratch,
guage (UML), these key interactions can be identiﬁed, and adding additional layers of granularity over time (top-down
the objects and messages to be shared between nodes in approach). It also provides mechanisms to integrate exist-
the supply chain simulation can be speciﬁed. ing complex simulation models with each other and reﬁne
An example of a shared object is an order, which con- them over time to create high-ﬁdelity simulations (bottom-
tains information about the items being ordered and sub- up approach).
sequently shipped from a supplier and transported by a
transportation node. An example of a message is an in-
4.1 Top-Down Approach
ventory status enquiry from a planning module to a source
for a product ordered by a customer. Together, the SOMs The top-down approach would be chosen if strategic chal-
form a federation object model (FOM) for the entire sup- lenges are to be addressed or detailed simulation models
ply chain simulation. For example, if a factory is willing to are not already available for the different supply chain el-
share its inventory status with its partners, it will deﬁne a ements. The starting point for this approach is the entire
factory object class with inventory status as one of the at- supply network (i.e., all critical manufacturing and logis-
tributes. The inventory status is then made available to the tics elements of the supply chain), each represented by one
partners through the factory object class publication. The simulation model. These models can be representations of
Volume 79, Number 3 SIMULATION 129
5. Lendermann, Julka, Gan, Chen, McGinnis, and McGinnis
factories, warehouses, and/or transportation units as simple • Many complex operational dependencies between suppli-
as possible (e.g., a simple lead-time random distribution as ers and customers are necessary, with signiﬁcant potential
a function of capacity and capacity load). Thus, if an exist- for global optimization.
ing simulation model is not available, a less detailed, more • The need for the optimization of sequence and capacity uti-
aggregate model can be used that, in most cases, would lization in manufacturing is high, and therefore the ﬂexi-
still be better than a deterministic model. All simulation bility regarding capacity adaptations (e.g., because of high
models are running on the same local-area network (LAN) capital costs) is low.
at the same geographical location, although they can rep- • Manufacturing activities are standard, and their parame-
resent (i.e., critical suppliers’ and customers’) operations terization in master data might be difﬁcult but not impos-
sible; therefore, participation of the shop ﬂoor at planning
at other locations.
and scheduling is rather low.
This approach would be chosen if the main objective is
to optimize the overall supply chain structure rather than • The bills of materials/recipes are not too complex and easy
execution details within the individual models. In this case,
initial model building can be accomplished rapidly, and (at • The logistics content of the value-added operations is
least qualitative) results can be obtained quickly.
The greatest advantage, however, is the possibility of • The nonrepetitive labor content of the value-added opera-
tions is low.
ﬂexible model development and reﬁnement, as shown in
Figure 3: each of the simulation models can be reﬁned in- • The number of customer orders to be handled is large.
dividually and asynchronously, provided the FOM remains
unchanged. Individual models can have different levels of The tremendous potential beneﬁts of an application of
granularity to some extent. Reference models can easily this kind of framework across supply chains can be sum-
be replaced by more realistic models that represent the ac- marized as follows:
tual factory, warehouse, or transportation unit. Individual • More realistic experimentation with the system can be ac-
models can even be physically shifted from the original complished because the dynamic behavior of the supply
site to the sites (i.e., customers/suppliers) they represent chain and stochastic uncertainties are taken into account,
and run on computers that are connected to the original and APS algorithms are integrated with the simulation.
site through the Internet. These external parties can then • Collaborative supply chain enhancement becomes possi-
further develop and maintain their models and execute the ble across globally distributed locations without having to
entire simulation by their own as well. Such an approach disclose sensitive company data.
is well suited to applications in which the federates are • Fast results from simulation rather than projections from
initialized using current actual data, rather than through a historical data can be used to support decision making.
conventional “warm-up” simulation. • High ﬂexibility accounts for today’s frequent changes
of business requirements and marketplaces: supply chain
4.2 Bottom-Up Approach structures can be changed very easily, and the framework
is not hampered by growth limitations (i.e., it is scalable).
The bottom-up approach would be chosen if operational
challenges are the principal concern. The starting point is
the detailed simulation model of one element of a supply 6. Relevance to the Semiconductor Industry
chain such as a factory. The motivation for such an ex-
tension of the simulation model beyond the factory’s own The semiconductor industry is subject to many of the char-
“four walls” would be the need for a more realistic “be- acteristics mentioned above. Most important, it has com-
havioral response” of the suppliers and/or customers for a plex production processes and complex interdependencies
more realistic simulation of the factory’s operations, with- between different business nodes in the semiconductor sup-
out having to share critical execution data in one model. ply chain. These nodes include the wafer fabs, the assem-
Other steps of further enhancing this kind of supply bly and test (A&T) facilities, the logistics partners (trans-
chain simulation could then be automation of data input portation, warehousing, and distribution), and the ﬁnal cus-
and/or incorporation of scheduling procedures, as illus- tomers. Semiconductor supply chains have a global reach,
trated in Figure 4. and the supply chain nodes individually face intense com-
petition. Efﬁcient and effective supply chain operation—
5. Application in Industry the coordinated actions of all the supply chain partners—is
a critical component of competitiveness. Clearly, supply
The framework as described in this paper is applicable to chain management (SCM) is one key to competitiveness
industries having the following characteristics: in the global semiconductor marketplace.
Semiconductor supply chain management must over-
come some distinct problems. One of the most fundamen-
• A mass-production environment is needed that is subject
tal difﬁculties is that the different parties in the supply
to high variability and stochastic uncertainties across the
supply chain. chain may be both partners and competitors or that some
130 SIMULATION Volume 79, Number 3
6. DISTRIBUTED SUPPLY CHAIN SIMULATION AS A DECISION SUPPORT TOOL
Ref Factory [parameters]
W/H Warehouse [parameters]
Figure 3. Top-down approach for implementation of distributed simulation framework
of them (e.g., A&T) may serve multiple competing supply dent players in the semiconductor market; they may not
chains. It is the rule today, rather than the exception, that no wish to be part of an exercise in which a single mono-
one company owns the entire supply chain. For that reason, lithic model is created as their interests may change with
supply chain partners may be unwilling to share (or legally the ever-changing business environment. Also, they may
prevented from sharing) their detailed operational informa- choose not to divulge conﬁdential information to other par-
tion and plans, which can greatly complicate SCM. Today, ties or have different levels of information sharing with
many of the decisions that affect supply chain performance different parties in the supply chain. Our distributed mod-
are made individually by the various supply chain partners, eling framework addresses all the above issues. It provides
with limited coordination. a mechanism to simulate complex supply chain scenar-
Individually, the semiconductor supply chain partners ios with a high degree of ﬁdelity using already available
(especially fab and A&T) have complex behavior that is simulation models. It also addresses the issue of selected
difﬁcult to model analytically. Thus, analytic or closed- information sharing among different parties.
form models of the entire supply chain are unlikely to cap- In the subsequent sections, we discuss the Supply Chain
ture its dynamic response capabilities. Furthermore, the Operations Performance Evaluator (SCOPE), a decision-
different parties in the supply chain often are indepen- support prototype based on the framework and dealing with
Volume 79, Number 3 SIMULATION 131
7. Lendermann, Julka, Gan, Chen, McGinnis, and McGinnis
? Assembly & Test
Scheduling System B
Scheduling System A
Assembly & Test
Scheduling System B
Simulation Model Simulation Model Simulation Model
Input data Input data Input data
Figure 4. Bottom-up approach for implementation of distributed simulation framework
the operations of a semiconductor supply chain. We de- and synchronization between and among the federates. As
scribe the modeled semiconductor supply chain, the func- mentioned, in the HLA parlance, the set of federates inter-
tionality of each node, and how its behavior has been mod- acting with each other is called a federation to distinguish
eled in a prototype. We discuss the design and structure this type of system simulation from the more traditional
of SCOPE and how it is conﬁgured to simulate various monolithic simulation model.
supply chain scenarios. We also discuss how SCOPE ana-
lyzes the simulations and reports various key performance 7.1 Wafer Fab and Assembly and Test
The manufacturing process of the wafer fab simulation is
7. Modeling a Semiconductor Supply Chain based on the Sematech wafer fabrication model . The
Sematech model uses several ﬁles to deﬁne the manufactur-
The semiconductor supply chain shown in Figure 5 has ing processes: the process ﬂow, rework, tool set, operator
been modeled in SCOPE. The supply chain consists of set, and volume release ﬁles. The process ﬂow ﬁle deﬁnes
two wafer fabs, an A&T facility, a warehouse, a distribu- the workﬂow of products in terms of the steps through
tion center, a planning entity, a transportation provider, and which wafer lots must ﬂow. For each step, the ﬁle deﬁnes
multiple customers. The functions performed by each of the machine set and operator set needed, processing time
these nodes in the supply chain are described below. incurred, and so on. The rework ﬁle deﬁnes rework se-
Based on our distributed modeling framework, each of quences for a wafer product and is similar to the process
the facilities (wafer fab, A&T, warehouse, and distribution ﬂow ﬁle in format. The tool sets ﬁle contains information
center) as well as the transportation provider is modeled about the tool sets (or machine sets) used, including the
using discrete event simulation; the six simulation models number of machines in the set (machines within a set are
and the planning model are given HLA “wrappers” to cre- identical), downtime, and so forth. The operator sets ﬁle is
ate federates. The HLA RTI handles the communication similar to the tool sets ﬁle. Examples of information about
132 SIMULATION Volume 79, Number 3
8. DISTRIBUTED SUPPLY CHAIN SIMULATION AS A DECISION SUPPORT TOOL
Figure 5. Scope of the modeled semiconductor supply chain. A&T = assembly and test.
the operator sets are number of operators within an oper- ponents are transferred for the burn-in process, followed by
ator set, break time, and so on. Finally, the arrival rate of a series of post-burn-in tests and then inspection and pack-
each wafer lot is given by the volume release ﬁle. Sam- aging. Figure 7 shows the manufacturing ﬂow used in the
ple data sets (Table 1) were acquired from Sematech for model. The A&T facility keeps the produced microchips in
modeling the wafer fab. its inventory, supplies to the warehouse, or supplies directly
Figure 6 shows an example of a process ﬂow from a to the customers. More details of the simulation models can
logic factory of the Sematech data model. This ﬂow is one be found in Turner et al.  and Sivakumar and Chong
of the shortest among all the Sematech data models. The .
ﬂow is drawn based on the machine view, in which a node Both the wafer fab and A&T models were integrated
represents a machine and a directed edge represents a step with a parallel simulation technology to achieve faster run-
transition. The number beside the edges is the step number times. HLA wrappers were added to these models, which
of the ﬂow. When there is more than one step transition were developed prior to the current work, to make them
from the source to destination machine, all step numbers interoperable as federates in a federation.
will be tagged beside the edges (e.g., transitions 5 and 12
in Figure 6). 7.2 Warehouse and Distribution Center
The wafer fab produces wafers for the A&T facility
based on a predeﬁned forecast. When wafers complete pro- The warehouse and distribution center models have the
cessing, they are shipped to the A&T facility. same basic structure: shipments arrive at receiving and are
The A&T model was developed based on data sets avail- unloaded; unit loads are put away into storage; when or-
able from various industrial projects undertaken at the Sin- ders are released for shipping (either a distribution center
gapore Institute of Manufacturing Technology (SIMTech, replenishment or a direct customer order), the unit loads are
formerly Gintic Institute of Manufacturing Technology). retrieved from storage and assembled to form a shipment;
These data sets were converted to the Sematech format the transport federate is called to pick up the shipment;
used for the wafer fab. The A&T has three main facilities, and the shipment is handed off to the transport federate.
namely, the preassembly, assembly, and test operations. The basic model represents the labor (and associated ma-
In preassembly, diced wafers are afﬁxed to the lead frame terial handling) resource available for receiving, put-away,
and cured. The wire-bonding process follows, in which the picking, and packing, and these activities take an amount
dies are bonded to the leads of the lead frame. After wire of time that is sampled from an appropriate distribution.
bonding, the die is molded and routed through deﬂashing, The warehouse and distribution center models communi-
laser marking, and plating processes. Finally, the trimming cate with the transportation federate to receive and ship
and forming process involves punching the molded com- the product (in addition to communicating with the plan-
ponents from the lead frame. Singulated components are ning federate to provide inventory status) and to receive
tested and graded, and after the ﬁrst series of tests, the com- customer and replenishment orders.
Volume 79, Number 3 SIMULATION 133
9. Lendermann, Julka, Gan, Chen, McGinnis, and McGinnis
Table 1. Sematech data sets
Data Product Number of Number of
Set Type Routes Process Steps
1 Nonvolatile memory 2 486
2 ASIC and memory 7 1981
3 Memory, various types 11 4718
4 Microprocessors 2 111
5 ASIC 24 4176
6 ASIC and pilot line 9 2541
No of steps: 19
Operator: No 6, 13 7, 14
Figure 6. Process ﬂow from the Sematech data model
Pre Wafer Wafer
-Assembly Mounting Loading
Die Wire Trim &
Assembly Attach Molding Deflash Plating
Testing Burn-in Packing Ship
Figure 7. Modeled process ﬂow in an assembly and test (A&T) facility
The warehouse and distribution center simulation mod- 7.3 Planning Module
els were implemented using Silk, a Java-based discrete
event simulation tool , and were designed speciﬁcally The planning module mimics planning systems or planning
to be used as federates in a distributed simulation system. algorithms used in the supply chain and provides the single
While the demonstration prototype models are fairly sim- point of contact for customer orders. The planning entity
ple (e.g., only one product is modeled), because they were interacts with the customers, receives orders from them,
developed using object-oriented design and programming and arranges for their fulﬁllment. It checks the inventory
principles, they are readily adapted and extended. levels of the distribution center, the warehouse, and the
134 SIMULATION Volume 79, Number 3
10. DISTRIBUTED SUPPLY CHAIN SIMULATION AS A DECISION SUPPORT TOOL
A&T to decide which entity should fulﬁll the customer comprising two multiprocessor machines (4 processor and
order. The decisions are taken based on the customer order 8 processor) and a workstation. The two wafer fabs feder-
dates and the production and transportation lead times. An ates and the A&T federate ran on the 4-processor machine,
order is rejected if the due date cannot be met after taking and the rest of the federates ran on the 8-processor machine.
into consideration all possible fulﬁllment routes. In addition to the simulation and planning federates already
described, SCOPE incorporates additional components: a
7.4 Transportation Provider monitor, a visualization tool, and a set of services on an
In the demonstration prototype, transportation is modeled
as a very simple process—the transportation time for any 8.1.1 The Monitor
shipment is a function of the distance traveled, with a
random component. The transportation federate is imple- The monitor is simply an HLA federate that receives all the
mented in Silk. HLA messages associated with orders and shipments and
One beneﬁt of the distributed approach to modeling the writes corresponding data to a federation log ﬁle, main-
supply chain is that individual federates can be elaborated tained in an MS SQL 2000 database.
without affecting other federates. At the present time, for
example, the transportation federate is being completely re- 8.1.2 The Visualization Tool
designed to incorporate long-haul backbone transport net- The visualization tool is a Web page that uses a pre-
works for sea, air, and rail freight. Over-the-road transport hypertext process server to query the federation log ﬁle,
will continue to be modeled as a function of the distance compute certain KPIs, and present the results in a graphical
traveled. form. The user of the visualization tool can be anywhere,
and in fact, there can be multiple visualization tools run-
7.5 Customer ning at one time, each examining a different KPI.
The customer federate models multiple customers. A cus- 8.1.3 Authentication Server
tomer orders a ﬁxed lot size of a product. The orders are
received with an interarrival time. The interarrival time SCOPE was deployed using a framework described in
varies exponentially with a speciﬁc mean and variance, as Julka et al.  and shown in Figure 8. There are two
deﬁned by the user. A customer’s location is deﬁned by a components of the deployment framework: authentication
geo-code, which can be speciﬁed during conﬁguration of server (AS) and company server (CS). In the present case,
the simulation and stay constant throughout the simulation. all the CS (marked as computer A, B, C, and D) were con-
nected through a LAN. The services provided by the two
7.6 Modeling Summary components include the following:
Two key points about the demonstration prototype are 1. federate information and management (FIM),
worth noting. First, some of the federates were developed
from preexisting discrete event simulation models (which 2. authentication module (AM),
had been developed over a period of several years, involv-
ing a changing development team), and some of the fed- 3. simulation conﬁguration module (SCM),
erates were developed speciﬁcally for the demonstration.
4. invocation and termination module (IM),
Second, some of the federates are implemented in Java,
some in C++. The distributed modeling approach we have 5. simulation information module (SIM).
taken does not obsolete legacy models or require a standard
The use of HLA as the integration platform for the
8.2 SCOPE Conﬁgurations
distributed models has accommodated a variety of model
sources and programming languages. The evidence is SCOPE helps in the study of the semiconductor supply
strong that this approach can, in fact, be used successfully chain by enabling the user to perform supply chain exper-
to integrate models created independently by different sup- iments with the conﬁgurable distributed simulation model
ply chain partners. and the ﬂexible performance evaluation module. Each of
the nodes has a geographical location that goes into the
8. Prototype: Supply Chain Operations calculation of material transportation lead times. The other
Performance Evaluator (SCOPE) speciﬁc conﬁgurations available at the various federates in
8.1 Structure of SCOPE the simulation are as follows:
Figure 8 shows the overall structure of SCOPE. For our ini- 1. Wafer fab and A&T: These federates remain the most
tial demonstrations, SCOPE was run on a 10-mbps LAN complex of all the federates in the simulation. Apart
Volume 79, Number 3 SIMULATION 135
11. Lendermann, Julka, Gan, Chen, McGinnis, and McGinnis
Figure 8. Overall deployment structure of the Supply Chain Operations Performance Evaluator (SCOPE)
from changing process conﬁgurations, as mentioned 4. Transportation provider: The parameters available
in the Sematech data standards, changes can also to the user to conﬁgure the supply chain simulation
be made in the rules governing inbound and out- include at present the ﬂeet size and mean shipping
bound materials. The policies governing calculation delay. The latter is also inﬂuenced by the distance be-
of lot sizes based on forecasted demand and replen- tween the different nodes (based on their geo-codes).
ishment of material in downstream distribution net-
work nodes can also be changed.
8.3 Supply Chain Performance Evaluation
2. Warehouse and distribution center: The parameters
that can be changed include initial inventory lev- Critical analysis of the data generated after a simulation is
els, number of units per pallet, number of picking of outmost importance for a study. Furthermore, the choice
teams, put-away teams and receiving teams, the re- of KPIs in a simulation of the entire supply chain in itself is
supply rate (rate at which inventory is pushed out a complex problem. The performance indices of the various
to the downstream entity), and the resupply amount nodes that are computed and presented by the performance
(amount sent with each resupply). evaluation module are mentioned below. These indices are
computed as the simulation progresses and can be observed
3. Planning module: The sequence in which inventory in real time. Alternatively, they can be recorded after the
levels are checked from among the A&T facility, entire simulation is over in the form of a report.
warehouse, and distribution center to decide the sub-
sequent award for a customer order can be set in the 1. Wafer fab, A&T, warehouse, distribution center: The
planning module. performance of these nodes in the supply chain
136 SIMULATION Volume 79, Number 3
12. DISTRIBUTED SUPPLY CHAIN SIMULATION AS A DECISION SUPPORT TOOL
Execution time (sec)
1 2 3 4 5 6
Supply chain model
Figure 9. Execution speed on local-area network (LAN) and wide-area network (WAN)
simulation is gauged by the amount of inventory at (United Kingdom), and communicated through the Inter-
the node, the rate at which orders are received, and net. The benchmarking was restricted to the wafer fab and
the average lead time for the orders. the A&T models only. Six realistic supply chain scenar-
ios constructed from the Sematech modeling data standard
2. Planning module: The number of active customer and past industrial projects were used. The scenarios var-
orders in the supply chain plotted against time is ied with regard to the number of wafer fabs involved and
presented in the visualization associated with the wafer product types supplied to the A&T facility.
performance of this entity. The service level of the Figure 9 shows the performance achieved. As observed,
supply chain, which is computed based on the per- simulation scenarios executed across the Internet com-
centage of rejected orders, is also presented for this pleted in less than an hour. This illustrated the feasibility for
module. distributed supply chain strategic/tactical-level optimiza-
3. Logistics provider: The total shipments, average de- tion with simulated time horizons of months or years. Ba-
livery time between various nodes, plot of active sic supply chain scenarios that involve critical partners can
shipments, and rate of shipments are the perfor- be conﬁgured and simulated at geographically distributed
mance indices associated with the logistics provider. sites, in contrast to the conventional approach of having
all the nodes in a single location, without the issue of sim-
ulation speed. Sophisticated “what-if” scenarios can then
8.4 Performance of Supply Chain Prototype be simulated and analyzed using key performance indi-
cators of the supply chain. Such a tool can thus be used
As discussed in Section 3, distributed simulation based on for decision making in supply chain reengineering and
HLA offers the advantages of data shielding, interoper- management.
ability, and reusability of the simulation model. Another
advantage is that larger models can be constructed as sev- 9. Conclusions and Future Work
eral submodels running on separate computers intercon-
nected by a network, rather than on a single computer. In The prototype illustrates the feasibility of distributed sim-
the latter case, the model size is constrained by resource ulations using both legacy and purpose-built models writ-
availability of a single computer. Model size can thus scale ten in a variety of programming languages and running
much better using distributed simulation technology. In on different platforms. We have presented how a semicon-
this section, we benchmark the performance of our supply ductor supply chain can be modeled using this approach.
chain prototype running as a distributed simulation on a Such a distributed model can be used to perform various
LAN and a distributed simulation on a wide-area network supply chain experiments and provide invaluable decision
(WAN). The WAN models were installed at two remote support for supply chain reengineering and management.
sites in Singapore, as well as in a site at Oxford University Future work includes identiﬁcation of speciﬁc supply chain
Volume 79, Number 3 SIMULATION 137
13. Lendermann, Julka, Gan, Chen, McGinnis, and McGinnis
management problems that cannot be addressed by analyt-  McLean, C., and F. Riddick. 2000. The IMS mission architecture for
ical models and single monolithic simulation models. distributed manufacturing simulation. In Proceedings of the 2000
The work described in this paper is a result of collab- Winter Simulation Conference, Orlando, FL, pp. 1539-48.
 Kuhl, F., R. Weatherly, and J. Dahmann. 1999. Creating computer
oration between SIMTech and Georgia Tech to develop simulation systems: An introduction to the high level architec-
the basic methodology and computational tools. The na- ture. Englewood Cliffs, NJ: Prentice Hall.
ture of our collaborative efforts will now change focus to  Lutz, R. 1998. High level architecture object model development and
address the potential impact in industry. In particular, the supporting tools. SIMULATION 71 (6): 401-9.
 Cai, W., S. J. Turner, and B. P. Gan. 2001. Hierarchical federations:
future work will engage one or more industrial partners An architecture for information hiding. In Proceedings of the 15th
to develop industrial prototypes and extend the business International Workshop on Parallel and Distributed Simulation,
operations aspect of the framework to allow seamless inte- pp. 67-74.
gration of manufacturing and inbound/outbound logistics.  Ji, Z., B. P. Gan, S. J. Turner, and W. Cai. 2001. Parallel federates:
A collaborative research project between SIMTech and An architecture for hybrid distributed simulation. In Proceedings
of the 5th International Workshop on Distributed Simulation and
Nanyang Technological University, aiming at enhancing Real-Time Applications, pp. 97-104.
security and robustness of distributed supply chain tech-  Gan, B. P., and S. J. Turner. 2000. An asynchronous protocol for
nology, is currently ongoing. Some challenging research virtual factory simulation on shared memory multiprocessor sys-
issues such as efﬁcient synchronization among tightly cou- tems. Journal of the Operational Research Society 51:413-22.
 Sematech. 1997. Sematech modeling data standards, version 1.0.
pled federates, ability to detect and recover from simulation Technical report, Sematech, Inc., Austin, TX.
crashes, and selective information sharing/hiding will be  Turner S. J., C. C. Lim, Y. H. Low, W. Cai, W. J. Hsu, and S. H.
resolved in this project. Huang. 1998. A methodology for automating the parallelization
of manufacturing simulations. Paper presented at the 12th Work-
shop on Parallel and Distributed Simulation (PADS ’98), May,
10. Acknowledgments Bonff, Alberta, Canada.
 Sivakumar, A. I., and C. S. Chong. 2001. A simulation based anal-
The authors thank Prof. Appa Iyer Sivakumar (Nanyang ysis of cycle time distribution, and throughput in semiconductor
Technological University, Singapore) and Chin Soon backend manufacturing. Computers in Industry 45:59-78.
Chong (Singapore Institute of Manufacturing Technology,  Healy, K. J., and R. A. Kilgore. 1997. Silk: A Java-based process
simulation language. In Proceedings of the 1997 Winter Simula-
Singapore) for their inputs. They also thank Prof. Stephen J. tion Conference, Atlanta, GA, pp. 475-82.
Turner and Prof. Cai Wentong (School of Computer En-  Julka, N., D. Chen, B. P. Gan, S. J. Turner, and W. Cai. 2002. Web-
gineering, Nanyang Technological University, Singapore) based conﬁguration and control of HLA-based distributed simu-
for their inputs. This work was partly funded by the Sin- lations. In Proceedings of the International Conference on Scien-
gapore National Science and Technology Board (now the tiﬁc & Engineering Computation (IC-SEC 2002), Singapore, pp.
Agency for Science, Technology & Research [A*STAR])
and by the W. M. Keck Foundation through a grant to the
Georgia Institute of Technology.
Peter Lendermann is a senior scientist in the Production and
Logistics Planning Group at the Singapore Institute of Manufac-
11. References turing Technology (SIMTech), Singapore.
 Archibald, G., N. Karabakal, and P. Karlsson. 1999. Supply chain Nirupam Julka is a research engineer in the Production and
vs. supply chain: Using simulation to compete beyond the four Logistics Planning Group at the Singapore Institute of Manufac-
walls. In Proceedings of the 1999 Winter Simulation Conference,
turing Technology (SIMTech), Singapore.
Phoenix, AZ, pp. 1207-14.
 Banks, J., F. Azadivar, D. M. Ferrin, J. W. Fowler, D. W. Halpin,
A. M. Law, G. T. Mackulak, M. Manivannan, and W. S. Murphy Boon Ping Gan is a research engineer in the Production and
Jr. 2001. Panel session: The future of simulation. In Proceedings Logistics Planning Group at the Singapore Institute of Manufac-
of the 2001 Winter Simulation Conference, Washington, DC, pp. turing Technology (SIMTech), Singapore.
 Lendermann, P., B. P. Gan, and L. F. McGinnis. 2001. Distributed sim- Dan Chen is a research engineer in the Production and Logis-
ulation with incorporated APS procedures for high-ﬁdelity supply tics Planning Group at the Singapore Institute of Manufacturing
chain optimization. In Proceedings of the 2001 Winter Simulation
Conference, Washington, DC, pp. 1138-45. Technology (SIMTech), Singapore.
 Gong, Dah-Chuan, and L. F. McGinnis. 1996. Towards a manufac-
turing metamodel. International Journal of Computer Integrated Leon F. McGinnis is the Eugene C. Gwaltney professor of man-
Manufacturing 9 (1): 32-47. ufacturing systems in the School of Industrial and Systems Engi-
 Narayanan, S., D. A. Bodner, U. Sreekanth, T. Govindaraj, L. F. neering, Georgia Institute of Technology, Atlanta.
McGinnis, and C. M. Mitchell. 1998. Research in object-oriented
manufacturing systems simulations: An assessment of the state of Joel P. McGinnis is a software engineer working for Northrup
the art. IIE Transactions 30 (9): 795-810.
Grumman. He was previously a research assistant in the School
 Park, J., S. A. Reveliotis, D. A. Bodner, C. Zhou, J. F. Wu, and L. F.
McGinnis. 2001. High-ﬁdelity virtual prototyping of 300 mm fabs of Industrial and Systems Engineering, Georgia Institute of Tech-
through discrete event systems modeling. Computers in Industry nology, Atlanta.
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