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Distributed Supply Chain Simulation as a Decision Support ...

  1. 1. 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 peterl@SIMTech.a-star.edu.sg 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 repre- sentation of every constraint, every possible behavior of all Excellence in manufacturing and logistics operational exe- supply chain components, or every possible contingency cution requires the timely and effective translation of cus- in the environment. tomer demand into material control decisions across the In this setting, feasibility of supply chain plans is a entire supply chain. This challenge is complicated by the significant issue. Today’s state-of-the-art advanced plan- range of products, complex processes at each stage of the ning and scheduling (APS) systems take information about supply chain, suppliers and customers who also may be customer demand and historical information about supply competitors, third-party logistics, and a variety of techni- chain performance and generate material planning and con- cal, business, and economic constraints. trol decisions that are intended to be feasible (see Fig. 1). Coordinated supply chain operational planning is es- Because of their deterministic nature, we realize the sential to know how execution should be done to make the limitations of pure planning approaches at the moment of product at the lowest possible cost and deliver it to the cus- actual execution. These issues are particularly critical for tomer on time. However, it is unrealistic to expect a supply the semiconductor industry: • The operational performance can be assessed only based | on the real history of the system. But parameters in the SIMULATION, Vol. 79, Issue 3, March 2003 126-138 | past cannot be changed any more. © 2003 The Society for Modeling and Simulation International | • Experimentation with the real system is often disruptive, | DOI: 10.1177/0037549703255635 | seldom cost-effective, and sometimes just impossible. |
  2. 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 [1]. 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 [4], Narayanan et al. [5], and Park et al. [6]. 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 [7]. 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 [2]. 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 [8]. 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 [3], 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. 3. Lendermann, Julka, Gan, Chen, McGinnis, and McGinnis APS. Customer Demand Feasible execution plan: How • Real history of the execution should be... Plan production/logistics system/network Operational • How the execution was... execution • KPIs Input release 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 APS. Customer Demand Feasible execution plan: How • Real history of the execution should be... Plan production/logistics system/network Operational • How the execution was... execution • KPIs Input release 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
  4. 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 [9]. 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 [10]. 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 [11]. 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 [12] 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. simulation. 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. 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 to configure. 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 significant. 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. 6. DISTRIBUTED SUPPLY CHAIN SIMULATION AS A DECISION SUPPORT TOOL M/F Ref Factory [parameters] D/C D/C M/F M/F LAN M/F M/F W/H W/H Warehouse [parameters] M/F M/F D/C Ref 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 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. 7. Lendermann, Julka, Gan, Chen, McGinnis, and McGinnis Reports ? Assembly & Test ? Scheduling System B Simulation Model Input data Wafer fab ? Scheduling System A Assembly & Test Scheduling System B ? Distributor 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 indicators (KPIs). The manufacturing process of the wafer fab simulation is 7. Modeling a Semiconductor Supply Chain based on the Sematech wafer fabrication model [13]. 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. 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. [14] and Sivakumar and Chong of the shortest among all the Sematech data models. The [15]. 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. 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 1 0 16 Start 2 3 9 17 18 8, 15 4 10 11 5, 12 End Product: Logic No of steps: 19 Operator: No 6, 13 7, 14 Figure 6. Process flow from the Sematech data model Pre Wafer Wafer Wafer Saw -Assembly Mounting Loading Die Wire Trim & Assembly Attach Molding Deflash Plating Bond Form Electrical Vision Testing Burn-in Packing Ship Testing Inspection 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 [16], 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. 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 authentication server. 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. [17] 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 programming platform. 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. 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. 12. DISTRIBUTED SUPPLY CHAIN SIMULATION AS A DECISION SUPPORT TOOL 2500.00 Execution time (sec) 2000.00 1500.00 Distributed (LAN) 1000.00 Distributed 500.00 (WAN) 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 (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. 13. Lendermann, Julka, Gan, Chen, McGinnis, and McGinnis management problems that cannot be addressed by analyt- [7] 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. [8] 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 [9] 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. [10] 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. [11] 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- [12] 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. [13] 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 [14] 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. [15] 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, [16] 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- [17] 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. 822-5. 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. [1] 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. [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 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. 1453-60. [3] 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. [4] 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- [5] 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 [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 of Industrial and Systems Engineering, Georgia Institute of Tech- through discrete event systems modeling. Computers in Industry nology, Atlanta. 1528:1-20. 138 SIMULATION Volume 79, Number 3