Distributed Supply Chain Simulation as a Decision Support Tool for ...

SIMULATION http://sim.sagepub.com

Distributed Supply Chain Simulation as a Decision Support Tool for the Semiconductor Industry Peter Lendermann, Nirupam Julka, Boon Ping Gan, Dan Chen, Leon F. McGinnis and Joel P. McGinnis SIMULATION 2003; 79; 126 DOI: 10.1177/0037549703255635 The online version of this article can be found at: http://sim.sagepub.com/cgi/content/abstract/79/3/126

Published by: http://www.sagepublications.com

On behalf of:

Society for Modeling and Simulation International (SCS)

Additional services and information for SIMULATION can be found at: Email Alerts: http://sim.sagepub.com/cgi/alerts Subscriptions: http://sim.sagepub.com/subscriptions Reprints: http://www.sagepub.com/journalsReprints.nav Permissions: http://www.sagepub.com/journalsPermissions.nav

Downloaded from http://sim.sagepub.com at PENNSYLVANIA STATE UNIV on April 16, 2008 © 2003 Simulation Councils Inc.. All rights reserved. Not for commercial use or unauthorized distribution.

Distributed Supply Chain Simulation as a Decision Support Tool for the Semiconductor Industry Peter Lendermann Nirupam Julka Boon Ping Gan Dan Chen Singapore Institute of Manufacturing Technology (SIMTech) 71 Nanyang Drive Singapore 638075, Singapore [email protected] Leon F. McGinnis Joel P. McGinnis School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332-0205 The need for better understanding, control, and optimization of supply chains is being recognized more than ever in the new economy. Simulation holds a great potential in portraying the dynamic evolution of supply chains and providing appropriate decision support to address challenges arising from high variability and stochastic uncertainty. Realizing high-fidelity supply chain simulation will require integration of individual supply chain component simulation models and planning systems, shielding to prevent sensitive data from being shared indiscriminately, and even the geographical distribution of the supply chain component models. The authors discuss various conceptual and technical issues that have been successfully addressed to realize a prototype of distributed semiconductor supply chain simulation as well as implementation approaches that can be pursued. The prototype emulates a semiconductor supply chain consisting of two wafer fabs, an assembly and test facility, a distribution center, a warehouse, a supply chain planning module, a logistics provider, and customers. Keywords: Supply chain, simulation, distributed, semiconductor, decision making

1. Introduction and Motivation

chain planning system to incorporate a high-fidelity representation of every constraint, every possible behavior of all supply chain components, or every possible contingency in the environment. In this setting, feasibility of supply chain plans is a significant issue. Today’s state-of-the-art advanced planning and scheduling (APS) systems take information about customer demand and historical information about supply chain performance and generate material planning and control decisions that are intended to be feasible (see Fig. 1). Because of their deterministic nature, we realize the limitations of pure planning approaches at the moment of actual execution. These issues are particularly critical for the semiconductor industry:

Excellence in manufacturing and logistics operational execution requires the timely and effective translation of customer demand into material control decisions across the entire supply chain. This challenge is complicated by the range of products, complex processes at each stage of the supply chain, suppliers and customers who also may be competitors, third-party logistics, and a variety of technical, business, and economic constraints. Coordinated supply chain operational planning is essential to know how execution should be done to make the product at the lowest possible cost and deliver it to the customer on time. However, it is unrealistic to expect a supply

SIMULATION, Vol. 79, Issue 3, March 2003 126-138 © 2003 The Society for Modeling and Simulation International DOI: 10.1177/0037549703255635

| | | | | |

• The operational performance can be assessed only based on the real history of the system. But parameters in the past cannot be changed any more. • Experimentation with the real system is often disruptive, seldom cost-effective, and sometimes just impossible.


DISTRIBUTED SUPPLY CHAIN SIMULATION AS A DECISION SUPPORT TOOL

• Random changes to the system state are difficult to portray, even though there has been significant effort to understand, control, and reduce variability in the system. • Precise prediction of the evolution of the system over time is not possible.

Many of these limitations can be at least partially overcome with a “good” representation of the operational system in a computer model by applying discrete event simulation (DES) technology. After running a simulation, we know “how execution would be,” assuming certain values for critical parameters. Commercial simulation tools for analyzing supply chains have been released in recent years, for example, the Supply Chain Analyzer by IBM [1]. Simulation models typically are driven by the release of materials into the system. These input releases, however, are difficult to generate in today’s pull environments with the frequent phase-in of new products. The systems represented by the simulation models are ultimately driven by customer demand scenarios. While simulation models are quite useful in understanding the interactions between supply chain components, they generally incorporate a relatively crude abstraction of the associated planning processes. The most straightforward way of translating customer demand into feasible input release rates is to integrate the underlying APS procedure(s) into the simulation (see Fig. 2). In such a way, the simulation is made much more realistic, and active experimentation with alternative supply chain management strategies becomes possible. Distributed simulation comes into the picture when the 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 dispatching rules or the nature of their other customer demands) in one simulation model. It also allows evaluating structurally different alternatives rather than just different configuration parameters fed to a single simulation model. This has been identified as one of the key challenges to be tackled when it comes to complex supply chain scenario optimization [2]. 2. Distributed Simulation Framework The idea behind this distributed simulation framework is to combine technology for the interoperability of simulation models with APS to create synergies between state-ofthe-art planning and scheduling software systems and advanced simulation technology, as well as overcome shortcomings faced when applying one of these technologies individually. An earlier version of this framework has been described in Lendermann, Gan, and McGinnis [3], who presented additional examples to illustrate the necessity of incorporating planning procedures into a simulation. In a distributed simulation framework, each participating corporation/company in the supply chain is able to run its own simulation model of manufacturing and/or logis-

tics operations at its own site, where users interact with the system. Also, planning and scheduling systems (eventually APS procedures) are logically separated from the (simulated) operations. The simulation models interact with each other and exchange data with the planning and scheduling systems in the same way as the real manufacturing or logistics operations of the supply chain. Detailed model information (application codes and data) is encapsulated within each (either planning or operational) model. The participating corporations only need to define essential data flows from one supply chain node to another. In the background, the modeling and analysis system initiates a remote model invocation. Data representing the simulated material and information flow between supply chain operations are then exchanged as messages during the simulation run. These messages can be transmitted through a network (e.g., the Internet) connecting the participating corporations. Three critical issues for implementing such a distributed supply chain simulation are (1) the specification of the interfaces between models, (2) the mechanism for supporting intermodel communication, and (3) distributed model synchronization. Satisfying these requirements involves developing a supply chain reference model either implicitly or explicitly. Examples of similar efforts are described in Gong and McGinnis [4], Narayanan et al. [5], and Park et al. [6]. In a similar effort, the Manufacturing Engineering Laboratory of National Institute of Standards and Technology is developing an architecture for the seamless integration of manufacturing simulation systems, manufacturing software applications, and manufacturing data repositories [7]. 3. Technical Feasibility of the Framework 3.1 Interoperability and Reusability In our case, the integration of a set of independent simulation models and APS procedures to form a high-fidelity supply chain simulation is accomplished by adopting the standards of the high-level architecture (HLA). The HLA has been adopted by the Object Management Group (OMG) and the Institute of Electrical and Electronics Engineers (IEEE) as a standard for the interoperability of simulations (1516-2000). HLA is an architecture for the reuse and interoperation of simulations [8]. In HLA terms, each simulation model (which in our case represents either an operational node or an APS procedure within the supply chain) is referred to as a federate, while a collection of such federates makes up a federation. HLA supports the possibility of distributed collaborative development of a complex simulation application as well as the reuse of capabilities available in different simulations. Thus, a set of simulation and planning models, possibly developed independently and implemented using different languages and hardware platforms, can be put together to form a large federation of simulations. Volume 79, Number 3


SIMULATION

127

Lendermann, Julka, Gan, Chen, McGinnis, and McGinnis

Customer Demand

APS .

Feasible execution plan: How execution should be...

Input release

Plan Operational execution

• Real history of the production/logistics system/network • How the execution was... • KPIs

Supply chain

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

Customer Demand

APS .

Feasible execution plan: How execution should be...

Input release

Plan Operational execution

• Real history of the production/logistics system/network • How the execution was... • KPIs

Supply chain

Conventional scope of simulation model 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



The standard provides a common technical framework for the integration of simulation and planning models. It comprises three components: the HLA interface specification, federation rules, and the object model template (OMT). An interface specification, known as runtime infrastructure (RTI), defines how federates interact with the federation and with one another and supports federation execution. It provides a set of services to the federates for data interchange and synchronization in a coordinated fashion. The services are defined in six categories: federation management, declaration management, object management, data distribution management, ownership management, and time management. The RTI can thus be viewed as a distributed operating system providing services to support interoperable simulations executing in distributed computing environments (see www.cc.gatech.edu/computing/pads/techhighperf.html). Each federate defines the objects and interactions that are shared in the OMT. The responsibilities of the federate and its relationship with the RTI are described by the federation rules. 3.2 Data Encapsulation and Message Exchange between Simulation Models To encapsulate the operation of each individual element of the supply chain (or its model) and yet have the models interact, an interface specification is required. Analogous to an application program interface (API), the specification should be complete yet concise. At present, we know of no industry standard describing this type of specification for supply chain interactions. In addition to the specification of the interfaces, there must be a method by which the interface specification is communicated to each participating model and enforced in the operation of the distributed simulation. These requirements are satisfied by using the HLA infrastructure. HLA provides a means for each individual element of the supply chain to define data it is willing to share, using the OMT. Each element will thus have a simulation object model (SOM) that defines the shared object and interaction classes. Using the Unified Modeling Language (UML), these key interactions can be identified, and the objects and messages to be shared between nodes in the supply chain simulation can be specified. An example of a shared object is an order, which contains information about the items being ordered and subsequently shipped from a supplier and transported by a transportation node. An example of a message is an inventory status enquiry from a planning module to a source for a product ordered by a customer. Together, the SOMs form a federation object model (FOM) for the entire supply chain simulation. For example, if a factory is willing to share its inventory status with its partners, it will define a factory object class with inventory status as one of the attributes. The inventory status is then made available to the partners through the factory object class publication. The

internal behavior (and other sensitive data) of a simulation model is completely invisible to the outside world (i.e., the other federates). Further details on this issue can be found in Lutz [9]. Using HLA, each federate must define the information it will share with others. Even though HLA can hide information that a corporation does not want to share, it lacks the capability to share a subset of the sensitive data with a subset of corporations that make up the supply chain. This limitation can be resolved by a technique called Hierarchical HLA [10]. This approach shows significant potential of being further developed to resolve other technical challenges of distributed supply chain simulation such as improving the scalability of the simulation and relaxing the synchronization requirements between federates. 3.3 Execution Time Lengthy execution time is a major concern when it comes to large-scale supply chain simulation that involves more than one corporation. Any one federate that runs slowly (typically because of the complexity of its model) will hinder the progress of the whole supply chain simulation. To tackle this problem, internal parallelism between the bottleneck federates can be exploited using a parallel federate architecture [11]. This architecture partitions the bottleneck federate to form logical processes (LPs) that are simulated in parallel on a shared-memory multiprocessor system. It integrates a parallel discrete event simulation (PDES) protocol [12] and HLA-based distributed simulation and facilitates the formation of a hybrid-distributed simulation that consists of both sequential and parallel federates. With this parallel federate architecture, the performance of the overall supply chain simulation can thus be improved significantly. 4. Implementation Approaches Depending on the operational or strategic challenges to be tackled, two alternative implementation approaches have been identified and developed. The framework enables development of the supply chain simulation from scratch, adding additional layers of granularity over time (top-down approach). It also provides mechanisms to integrate existing complex simulation models with each other and refine them over time to create high-fidelity simulations (bottomup approach). 4.1 Top-Down Approach The top-down approach would be chosen if strategic challenges are to be addressed or detailed simulation models are not already available for the different supply chain elements. The starting point for this approach is the entire supply network (i.e., all critical manufacturing and logistics elements of the supply chain), each represented by one simulation model. These models can be representations of Volume 79, Number 3


SIMULATION

129


factories, warehouses, and/or transportation units as simple as possible (e.g., a simple lead-time random distribution as a function of capacity and capacity load). Thus, if an existing simulation model is not available, a less detailed, more aggregate model can be used that, in most cases, would still be better than a deterministic model. All simulation models are running on the same local-area network (LAN) at the same geographical location, although they can represent (i.e., critical suppliers’ and customers’) operations at other locations. This approach would be chosen if the main objective is to optimize the overall supply chain structure rather than execution details within the individual models. In this case, initial model building can be accomplished rapidly, and (at least qualitative) results can be obtained quickly. The greatest advantage, however, is the possibility of flexible model development and refinement, as shown in Figure 3: each of the simulation models can be refined individually and asynchronously, provided the FOM remains unchanged. Individual models can have different levels of granularity to some extent. Reference models can easily be replaced by more realistic models that represent the actual factory, warehouse, or transportation unit. Individual models can even be physically shifted from the original site to the sites (i.e., customers/suppliers) they represent and run on computers that are connected to the original site through the Internet. These external parties can then further develop and maintain their models and execute the entire simulation by their own as well. Such an approach is well suited to applications in which the federates are initialized using current actual data, rather than through a conventional “warm-up” simulation. 4.2 Bottom-Up Approach 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 chain such as a factory. The motivation for such an extension of the simulation model beyond the factory’s own “four walls” would be the need for a more realistic “behavioral response” of the suppliers and/or customers for a more realistic simulation of the factory’s operations, without having to share critical execution data in one model. Other steps of further enhancing this kind of supply chain simulation could then be automation of data input and/or incorporation of scheduling procedures, as illustrated in Figure 4. 5. Application in Industry The framework as described in this paper is applicable to industries having the following characteristics: • A mass-production environment is needed that is subject to high variability and stochastic uncertainties across the supply chain.

• Many complex operational dependencies between suppliers and customers are necessary, with significant potential for global optimization. • The need for the optimization of sequence and capacity utilization in manufacturing is high, and therefore the flexibility regarding capacity adaptations (e.g., because of high capital costs) is low. • Manufacturing activities are standard, and their parameterization in master data might be difficult but not impossible; therefore, participation of the shop floor at planning and scheduling is rather low. • The bills of materials/recipes are not too complex and easy to configure. • The logistics content of the value-added operations is significant. • The nonrepetitive labor content of the value-added operations is low. • The number of customer orders to be handled is large.

The tremendous potential benefits of an application of this kind of framework across supply chains can be summarized as follows: • More realistic experimentation with the system can be accomplished because the dynamic behavior of the supply chain and stochastic uncertainties are taken into account, and APS algorithms are integrated with the simulation. • Collaborative supply chain enhancement becomes possible across globally distributed locations without having to disclose sensitive company data. • Fast results from simulation rather than projections from historical data can be used to support decision making. • High flexibility accounts for today’s frequent changes of business requirements and marketplaces: supply chain structures can be changed very easily, and the framework is not hampered by growth limitations (i.e., it is scalable).

6. Relevance to the Semiconductor Industry The semiconductor industry is subject to many of the characteristics mentioned above. Most important, it has complex production processes and complex interdependencies between different business nodes in the semiconductor supply chain. These nodes include the wafer fabs, the assembly and test (A&T) facilities, the logistics partners (transportation, warehousing, and distribution), and the final customers. Semiconductor supply chains have a global reach, and the supply chain nodes individually face intense competition. Efficient and effective supply chain operation— the coordinated actions of all the supply chain partners—is a critical component of competitiveness. Clearly, supply chain management (SCM) is one key to competitiveness in the global semiconductor marketplace. Semiconductor supply chain management must overcome some distinct problems. One of the most fundamental difficulties is that the different parties in the supply chain may be both partners and competitors or that some




Factory [parameters]

Ref M/F

D/C D/C M/F M/F

M/F M/F

LAN

W/H W/H

Warehouse [parameters]

M/F M/F Ref D/C

M/F D/C M/F

M/F M/F

W/H

M/F D/C

Internet Figure 3. Top-down approach for implementation of distributed simulation framework

of them (e.g., A&T) may serve multiple competing supply chains. It is the rule today, rather than the exception, that no one company owns the entire supply chain. For that reason, supply chain partners may be unwilling to share (or legally prevented from sharing) their detailed operational information and plans, which can greatly complicate SCM. Today, many of the decisions that affect supply chain performance are made individually by the various supply chain partners, with limited coordination. Individually, the semiconductor supply chain partners (especially fab and A&T) have complex behavior that is difficult to model analytically. Thus, analytic or closedform models of the entire supply chain are unlikely to capture its dynamic response capabilities. Furthermore, the different parties in the supply chain often are indepen-

dent players in the semiconductor market; they may not wish to be part of an exercise in which a single monolithic model is created as their interests may change with the ever-changing business environment. Also, they may choose not to divulge confidential information to other parties or have different levels of information sharing with different parties in the supply chain. Our distributed modeling framework addresses all the above issues. It provides a mechanism to simulate complex supply chain scenarios with a high degree of fidelity using already available simulation models. It also addresses the issue of selected information sharing among different parties. In the subsequent sections, we discuss the Supply Chain Operations Performance Evaluator (SCOPE), a decisionsupport prototype based on the framework and dealing with

Volume 79, Number 3


SIMULATION

131


Reports

?

Assembly & Test

?

Scheduling System B

Simulation Model Input data

Wafer fab

?

Scheduling System A


Assembly & Test

?

Distributor

Scheduling System B



Figure 4. Bottom-up approach for implementation of distributed simulation framework

the operations of a semiconductor supply chain. We describe the modeled semiconductor supply chain, the functionality of each node, and how its behavior has been modeled in a prototype. We discuss the design and structure of SCOPE and how it is configured to simulate various supply chain scenarios. We also discuss how SCOPE analyzes the simulations and reports various key performance indicators (KPIs). 7. Modeling a Semiconductor Supply Chain The semiconductor supply chain shown in Figure 5 has been modeled in SCOPE. The supply chain consists of two wafer fabs, an A&T facility, a warehouse, a distribution center, a planning entity, a transportation provider, and multiple customers. The functions performed by each of these nodes in the supply chain are described below. Based on our distributed modeling framework, each of the facilities (wafer fab, A&T, warehouse, and distribution center) as well as the transportation provider is modeled using discrete event simulation; the six simulation models and the planning model are given HLA “wrappers” to create federates. The HLA RTI handles the communication

and synchronization between and among the federates. As mentioned, in the HLA parlance, the set of federates interacting with each other is called a federation to distinguish this type of system simulation from the more traditional monolithic simulation model. 7.1 Wafer Fab and Assembly and Test The manufacturing process of the wafer fab simulation is based on the Sematech wafer fabrication model [13]. The Sematech model uses several files to define the manufacturing processes: the process flow, rework, tool set, operator set, and volume release files. The process flow file defines the workflow of products in terms of the steps through which wafer lots must flow. For each step, the file defines the machine set and operator set needed, processing time incurred, and so on. The rework file defines rework sequences for a wafer product and is similar to the process flow file in format. The tool sets file contains information about the tool sets (or machine sets) used, including the number of machines in the set (machines within a set are identical), downtime, and so forth. The operator sets file is similar to the tool sets file. Examples of information about




Figure 5. Scope of the modeled semiconductor supply chain. A&T = assembly and test.

the operator sets are number of operators within an operator set, break time, and so on. Finally, the arrival rate of each wafer lot is given by the volume release file. Sample data sets (Table 1) were acquired from Sematech for modeling the wafer fab. Figure 6 shows an example of a process flow from a logic factory of the Sematech data model. This flow is one of the shortest among all the Sematech data models. The flow is drawn based on the machine view, in which a node represents a machine and a directed edge represents a step transition. The number beside the edges is the step number of the flow. When there is more than one step transition from the source to destination machine, all step numbers will be tagged beside the edges (e.g., transitions 5 and 12 in Figure 6). The wafer fab produces wafers for the A&T facility based on a predefined forecast. When wafers complete processing, they are shipped to the A&T facility. The A&T model was developed based on data sets available from various industrial projects undertaken at the Singapore Institute of Manufacturing Technology (SIMTech, formerly Gintic Institute of Manufacturing Technology). These data sets were converted to the Sematech format used for the wafer fab. The A&T has three main facilities, namely, the preassembly, assembly, and test operations. In preassembly, diced wafers are affixed to the lead frame and cured. The wire-bonding process follows, in which the dies are bonded to the leads of the lead frame. After wire bonding, the die is molded and routed through deflashing, laser marking, and plating processes. Finally, the trimming and forming process involves punching the molded components from the lead frame. Singulated components are tested and graded, and after the first series of tests, the com-

ponents are transferred for the burn-in process, followed by a series of post-burn-in tests and then inspection and packaging. Figure 7 shows the manufacturing flow used in the model. The A&T facility keeps the produced microchips in its inventory, supplies to the warehouse, or supplies directly to the customers. More details of the simulation models can be found in Turner et al. [14] and Sivakumar and Chong [15]. Both the wafer fab and A&T models were integrated with a parallel simulation technology to achieve faster runtimes. HLA wrappers were added to these models, which were developed prior to the current work, to make them interoperable as federates in a federation. 7.2 Warehouse and Distribution Center The warehouse and distribution center models have the same basic structure: shipments arrive at receiving and are unloaded; unit loads are put away into storage; when orders are released for shipping (either a distribution center replenishment or a direct customer order), the unit loads are retrieved from storage and assembled to form a shipment; the transport federate is called to pick up the shipment; and the shipment is handed off to the transport federate. The basic model represents the labor (and associated material handling) resource available for receiving, put-away, picking, and packing, and these activities take an amount of time that is sampled from an appropriate distribution. The warehouse and distribution center models communicate with the transportation federate to receive and ship the product (in addition to communicating with the planning federate to provide inventory status) and to receive customer and replenishment orders. Volume 79, Number 3


SIMULATION

133


Table 1. Sematech data sets Data Set

Product Type

Number of Routes

Number of Process Steps

1 2 3 4 5 6

Nonvolatile memory ASIC and memory Memory, various types Microprocessors ASIC ASIC and pilot line

2 7 11 2 24 9

486 1981 4718 111 4176 2541

1

0

Start

16

2 9

3

17 18

8, 15 4

10

11

End

5, 12

Product: Logic No of steps: 19 Operator: No

7, 14

6, 13

Figure 6. Process flow from the Sematech data model

Pre -Assembly

Wafer Mounting

Assembly

Die Attach

Testing

Electrical Testing

Wafer Loading

Wafer Saw

Wire Bond

Molding

Burn-in

Deflash

Vision Inspection

Plating

Packing

Trim & Form

Ship

Figure 7. Modeled process flow in an assembly and test (A&T) facility

The warehouse and distribution center simulation models were implemented using Silk, a Java-based discrete event simulation tool [16], and were designed specifically to be used as federates in a distributed simulation system. While the demonstration prototype models are fairly simple (e.g., only one product is modeled), because they were developed using object-oriented design and programming principles, they are readily adapted and extended.

7.3 Planning Module The planning module mimics planning systems or planning algorithms used in the supply chain and provides the single point of contact for customer orders. The planning entity interacts with the customers, receives orders from them, and arranges for their fulfillment. It checks the inventory levels of the distribution center, the warehouse, and the




A&T to decide which entity should fulfill the customer order. The decisions are taken based on the customer order dates and the production and transportation lead times. An order is rejected if the due date cannot be met after taking into consideration all possible fulfillment routes. 7.4 Transportation Provider In the demonstration prototype, transportation is modeled as a very simple process—the transportation time for any shipment is a function of the distance traveled, with a random component. The transportation federate is implemented in Silk. One benefit of the distributed approach to modeling the supply chain is that individual federates can be elaborated without affecting other federates. At the present time, for example, the transportation federate is being completely redesigned to incorporate long-haul backbone transport networks for sea, air, and rail freight. Over-the-road transport will continue to be modeled as a function of the distance traveled. 7.5 Customer The customer federate models multiple customers. A customer orders a fixed lot size of a product. The orders are received with an interarrival time. The interarrival time varies exponentially with a specific mean and variance, as defined by the user. A customer’s location is defined by a geo-code, which can be specified during configuration of the simulation and stay constant throughout the simulation. 7.6 Modeling Summary Two key points about the demonstration prototype are worth noting. First, some of the federates were developed from preexisting discrete event simulation models (which had been developed over a period of several years, involving a changing development team), and some of the federates were developed specifically for the demonstration. Second, some of the federates are implemented in Java, some in C++. The distributed modeling approach we have taken does not obsolete legacy models or require a standard programming platform. The use of HLA as the integration platform for the distributed models has accommodated a variety of model sources and programming languages. The evidence is strong that this approach can, in fact, be used successfully to integrate models created independently by different supply chain partners. 8. Prototype: Supply Chain Operations Performance Evaluator (SCOPE) 8.1 Structure of SCOPE Figure 8 shows the overall structure of SCOPE. For our initial demonstrations, SCOPE was run on a 10-mbps LAN

comprising two multiprocessor machines (4 processor and 8 processor) and a workstation. The two wafer fabs federates and the A&T federate ran on the 4-processor machine, and the rest of the federates ran on the 8-processor machine. In addition to the simulation and planning federates already described, SCOPE incorporates additional components: a monitor, a visualization tool, and a set of services on an authentication server. 8.1.1 The Monitor The monitor is simply an HLA federate that receives all the HLA messages associated with orders and shipments and writes corresponding data to a federation log file, maintained in an MS SQL 2000 database. 8.1.2 The Visualization Tool The visualization tool is a Web page that uses a prehypertext process server to query the federation log file, compute certain KPIs, and present the results in a graphical form. The user of the visualization tool can be anywhere, and in fact, there can be multiple visualization tools running at one time, each examining a different KPI. 8.1.3 Authentication Server SCOPE was deployed using a framework described in Julka et al. [17] and shown in Figure 8. There are two components of the deployment framework: authentication server (AS) and company server (CS). In the present case, all the CS (marked as computer A, B, C, and D) were connected through a LAN. The services provided by the two components include the following: 1. federate information and management (FIM), 2. authentication module (AM), 3. simulation configuration module (SCM), 4. invocation and termination module (IM), 5. simulation information module (SIM).

8.2 SCOPE Configurations SCOPE helps in the study of the semiconductor supply chain by enabling the user to perform supply chain experiments with the configurable distributed simulation model and the flexible performance evaluation module. Each of the nodes has a geographical location that goes into the calculation of material transportation lead times. The other specific configurations available at the various federates in the simulation are as follows: 1. Wafer fab and A&T: These federates remain the most complex of all the federates in the simulation. Apart Volume 79, Number 3


SIMULATION

135


Figure 8. Overall deployment structure of the Supply Chain Operations Performance Evaluator (SCOPE)

from changing process configurations, as mentioned in the Sematech data standards, changes can also be made in the rules governing inbound and outbound materials. The policies governing calculation of lot sizes based on forecasted demand and replenishment of material in downstream distribution network nodes can also be changed. 2. Warehouse and distribution center: The parameters that can be changed include initial inventory levels, number of units per pallet, number of picking teams, put-away teams and receiving teams, the resupply rate (rate at which inventory is pushed out to the downstream entity), and the resupply amount (amount sent with each resupply). 3. Planning module: The sequence in which inventory levels are checked from among the A&T facility, warehouse, and distribution center to decide the subsequent award for a customer order can be set in the planning module.

4. Transportation provider: The parameters available to the user to configure the supply chain simulation include at present the fleet size and mean shipping delay. The latter is also influenced by the distance between the different nodes (based on their geo-codes).

8.3 Supply Chain Performance Evaluation Critical analysis of the data generated after a simulation is of outmost importance for a study. Furthermore, the choice of KPIs in a simulation of the entire supply chain in itself is a complex problem. The performance indices of the various nodes that are computed and presented by the performance evaluation module are mentioned below. These indices are computed as the simulation progresses and can be observed in real time. Alternatively, they can be recorded after the entire simulation is over in the form of a report. 1. Wafer fab, A&T, warehouse, distribution center: The performance of these nodes in the supply chain




Execution time (sec)

2500.00

2000.00

1500.00

Distributed (LAN)

1000.00

Distributed (WAN)

500.00

0.00 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 the node, the rate at which orders are received, and the average lead time for the orders. 2. Planning module: The number of active customer orders in the supply chain plotted against time is presented in the visualization associated with the performance of this entity. The service level of the supply chain, which is computed based on the percentage of rejected orders, is also presented for this module. 3. Logistics provider: The total shipments, average delivery time between various nodes, plot of active shipments, and rate of shipments are the performance indices associated with the logistics provider. 8.4 Performance of Supply Chain Prototype As discussed in Section 3, distributed simulation based on HLA offers the advantages of data shielding, interoperability, and reusability of the simulation model. Another advantage is that larger models can be constructed as several submodels running on separate computers interconnected by a network, rather than on a single computer. In the latter case, the model size is constrained by resource availability of a single computer. Model size can thus scale much better using distributed simulation technology. In this section, we benchmark the performance of our supply chain prototype running as a distributed simulation on a LAN and a distributed simulation on a wide-area network (WAN). The WAN models were installed at two remote sites in Singapore, as well as in a site at Oxford University

(United Kingdom), and communicated through the Internet. The benchmarking was restricted to the wafer fab and the A&T models only. Six realistic supply chain scenarios constructed from the Sematech modeling data standard and past industrial projects were used. The scenarios varied with regard to the number of wafer fabs involved and wafer product types supplied to the A&T facility. Figure 9 shows the performance achieved. As observed, simulation scenarios executed across the Internet completed in less than an hour. This illustrated the feasibility for distributed supply chain strategic/tactical-level optimization with simulated time horizons of months or years. Basic supply chain scenarios that involve critical partners can be configured and simulated at geographically distributed sites, in contrast to the conventional approach of having all the nodes in a single location, without the issue of simulation speed. Sophisticated “what-if” scenarios can then be simulated and analyzed using key performance indicators of the supply chain. Such a tool can thus be used for decision making in supply chain reengineering and management. 9. Conclusions and Future Work The prototype illustrates the feasibility of distributed simulations using both legacy and purpose-built models written in a variety of programming languages and running on different platforms. We have presented how a semiconductor supply chain can be modeled using this approach. Such a distributed model can be used to perform various supply chain experiments and provide invaluable decision support for supply chain reengineering and management. Future work includes identification of specific supply chain Volume 79, Number 3


SIMULATION

137


management problems that cannot be addressed by analytical models and single monolithic simulation models. The work described in this paper is a result of collaboration between SIMTech and Georgia Tech to develop the basic methodology and computational tools. The nature of our collaborative efforts will now change focus to address the potential impact in industry. In particular, the future work will engage one or more industrial partners to develop industrial prototypes and extend the business operations aspect of the framework to allow seamless integration of manufacturing and inbound/outbound logistics. A collaborative research project between SIMTech and Nanyang Technological University, aiming at enhancing security and robustness of distributed supply chain technology, is currently ongoing. Some challenging research issues such as efficient synchronization among tightly coupled federates, ability to detect and recover from simulation crashes, and selective information sharing/hiding will be resolved in this project. 10. Acknowledgments The authors thank Prof. Appa Iyer Sivakumar (Nanyang Technological University, Singapore) and Chin Soon Chong (Singapore Institute of Manufacturing Technology, Singapore) for their inputs. They also thank Prof. Stephen J. Turner and Prof. Cai Wentong (School of Computer Engineering, Nanyang Technological University, Singapore) for their inputs. This work was partly funded by the Singapore National Science and Technology Board (now the Agency for Science, Technology & Research [A*STAR]) and by the W. M. Keck Foundation through a grant to the Georgia Institute of Technology.

[7] McLean, C., and F. Riddick. 2000. The IMS mission architecture for distributed manufacturing simulation. In Proceedings of the 2000 Winter Simulation Conference, Orlando, FL, pp. 1539-48. [8] Kuhl, F., R. Weatherly, and J. Dahmann. 1999. Creating computer simulation systems: An introduction to the high level architecture. Englewood Cliffs, NJ: Prentice Hall. [9] Lutz, R. 1998. High level architecture object model development and supporting tools. SIMULATION 71 (6): 401-9. [10] Cai, W., S. J. Turner, and B. P. Gan. 2001. Hierarchical federations: An architecture for information hiding. In Proceedings of the 15th International Workshop on Parallel and Distributed Simulation, pp. 67-74. [11] Ji, Z., B. P. Gan, S. J. Turner, and W. Cai. 2001. Parallel federates: An architecture for hybrid distributed simulation. In Proceedings of the 5th International Workshop on Distributed Simulation and Real-Time Applications, pp. 97-104. [12] Gan, B. P., and S. J. Turner. 2000. An asynchronous protocol for virtual factory simulation on shared memory multiprocessor systems. Journal of the Operational Research Society 51:413-22. [13] Sematech. 1997. Sematech modeling data standards, version 1.0. Technical report, Sematech, Inc., Austin, TX. [14] Turner S. J., C. C. Lim, Y. H. Low, W. Cai, W. J. Hsu, and S. H. Huang. 1998. A methodology for automating the parallelization of manufacturing simulations. Paper presented at the 12th Workshop on Parallel and Distributed Simulation (PADS ’98), May, Bonff, Alberta, Canada. [15] Sivakumar, A. I., and C. S. Chong. 2001. A simulation based analysis of cycle time distribution, and throughput in semiconductor backend manufacturing. Computers in Industry 45:59-78. [16] Healy, K. J., and R. A. Kilgore. 1997. Silk: A Java-based process simulation language. In Proceedings of the 1997 Winter Simulation Conference, Atlanta, GA, pp. 475-82. [17] Julka, N., D. Chen, B. P. Gan, S. J. Turner, and W. Cai. 2002. Webbased configuration and control of HLA-based distributed simulations. In Proceedings of the International Conference on Scientific & Engineering Computation (IC-SEC 2002), Singapore, pp. 822-5.

Peter Lendermann is a senior scientist in the Production and Logistics Planning Group at the Singapore Institute of Manufacturing Technology (SIMTech), Singapore.

11. References [1] Archibald, G., N. Karabakal, and P. Karlsson. 1999. Supply chain vs. supply chain: Using simulation to compete beyond the four walls. In Proceedings of the 1999 Winter Simulation Conference, Phoenix, AZ, pp. 1207-14. [2] 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 Jr. 2001. Panel session: The future of simulation. In Proceedings of the 2001 Winter Simulation Conference, Washington, DC, pp. 1453-60. [3] Lendermann, P., B. P. Gan, and L. F. McGinnis. 2001. Distributed simulation with incorporated APS procedures for high-fidelity supply chain optimization. In Proceedings of the 2001 Winter Simulation Conference, Washington, DC, pp. 1138-45. [4] Gong, Dah-Chuan, and L. F. McGinnis. 1996. Towards a manufacturing metamodel. International Journal of Computer Integrated Manufacturing 9 (1): 32-47. [5] Narayanan, S., D. A. Bodner, U. Sreekanth, T. Govindaraj, L. F. McGinnis, and C. M. Mitchell. 1998. Research in object-oriented manufacturing systems simulations: An assessment of the state of the art. IIE Transactions 30 (9): 795-810. [6] 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 through discrete event systems modeling. Computers in Industry 1528:1-20.

Nirupam Julka is a research engineer in the Production and Logistics Planning Group at the Singapore Institute of Manufacturing Technology (SIMTech), Singapore. Boon Ping Gan is a research engineer in the Production and Logistics Planning Group at the Singapore Institute of Manufacturing Technology (SIMTech), Singapore. Dan Chen is a research engineer in the Production and Logistics Planning Group at the Singapore Institute of Manufacturing Technology (SIMTech), Singapore. Leon F. McGinnis is the Eugene C. Gwaltney professor of manufacturing systems in the School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta. Joel P. McGinnis is a software engineer working for Northrup Grumman. He was previously a research assistant in the School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta.

