A strategic decision support system for supply network design and management in the semiconductor industry

Computers ind. Engng Vol. 29, No. 1~., pp. 443--447, 1995 Copyright © 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0360-8352/95 $9.50 + 0.00

Pergamon 0360-8352(95)00114-X

A Strategic Decision Support System for Supply Network Design and Management in the Semiconductor Industry Jos~ Manuel Padillo 1, Ricki Ingalls 2, and Steven Brown 3

~CIMSystems Research Center, Department of Industrial and Management Systems Engineering Arizona State University, Tempe, AZ 85287-5106 2SEMATECH, 2706 Montopolis Drive, Austin, TX 78741-6499 3Motorola, Inc./SEMATECH, 2706 Montopolis Drive, Austin, TX 78741-6499 ABSTRACT This paper presents a strategic decision support system (DSS) which has been conceptualized and designed by SEMATECH* to assist the large semiconductor manufacturing organization in managing its extensive supply chain network. This DSS has been named "Manufacturing Enterprise Model" or "MEM". MEM ties each factory and its primary metrics to the rest of the business enterprise to assess how changes in wafer fabrication affect other factories, the distribution system, and customer deliveries. The model is intended to be used to evaluate future factory concepts and to assist business planners in strategic decisions about product allocation and major resource/facility planning. INTRODUCTION The design and management of manufacturing supply networks poses a continuous challenge for large industrial from. In the case of the large semiconductor organization these tasks are further complicated by the globalization of markets and operations. This makes the allocation of resources a very difficult problem. For most industrial companies, the manufacturing operation is the largest, the most complex, and the most difficult to manage component of the fu'm. Because of this complexity, it is essential for firms to have a comprehensive manufacturing strategy to aid in organizing and managing the fn'm's manufacturing systems [1,2,3]. To this purpose SEMATECH developed a decision support system, the Manufacturing Enterprise Model (MEM), to assist the large semiconductor manufacturing organization in managing its extensive supply chain network. The primary objective of SEMATECH's Manufacturing Enterprise Model is to tie each wafer fabrication factory (fab) and its primary metrics to the rest of the business enterprise in order to evaluate the effect that any changes in the lab may have on the entire enterprise. MEM will be used by strategic planners to assist in strategic decisions about product allocation and major resources and facilities planning.

AN EXTENSIVE DEFINITION OF MEM MEM is defined as a decision support system (DSS) because it aids decision makers to confront ill-structured problems through direct interaction with data and analysis models [4]. MEM's basic analytic power is provided by a series of mathematical programming models that evaluate the complex planning problems of production-distribution networks. Figure 1 shows the model's main elements: Mathematical m'o2ramming model - The mathematical models for MEM can be programmed using any commercially-available algebraic modeling language. These languages facilitate the formulation of large scale models by using special constructs (e.g., indexing, macros, etc.) that greatly reduce the number of variable declarations. Moreover, these languages link the problem formulation with the data sets in a manner that can be processed by optimization solvers. * SEMATECH (Semiconductor Manufacturing TECHnology) is a consortium of U.S. semiconductor manufacturers working with industry, universities, and national labs on vital technical research in the area of semiconductormanufacturing. 443

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Optimization solver - Optimization solvers are computer programs that perform the numerical calculations necessary to solve mathematical programming models. MEM requires these specialized computer programs in order to efficiently perform the large number of computations demanded by the analysis of the network configuration. Input information - Although the real network configuration problem (or manufacturing enterprise problem) involves a substantial number of variables and constraints, only a small fraction of these variables and constraints truly dominates the real decision problem. Thus, the type of input information required by MEM is highly aggregated and the model provides the capability to organize and maintain the data files that are used repeatedly. MEM captures the enterprise by specifying relevant characteristics about the critical components of the enterprise (e.g., key resources, products, etc.). Also, the model parameters define the planning horizon for the model and the interest rate to calculate the discounted cash flow of each configuration alternative. End-user interface and report generator - Due to the complexity of MEM and due to the diverse background of its users, this system is a very "user-friendly" application. MEM provides a highly interactive user interface and powerful graphic capabilities. In addition, the actual mathematical model and solver are concealed from the planner so as not to distract from the analysis function. Furthermore, MEM generates several reports that indicate the best network configuration or policy for a given business scenario. The income statement/cash flow report is the most encompassing. It provides the total revenue for each of the fLrm's products (at discounted dollars) in each period, and also the total costs for each plant in the enterprise (these total costs can be divided into operating, resource, production, inventory, and transportation costs).

FACTORY

]

OUTPUT

Production levels and cost ] Inventory levels and cost ] • Plant size & utilization (space]

REPORTS

• Total

PRODUCT

• •

coStS

•

l

BUSINESS

Production levels Production location Revenue Cycle time

• Cash flow • Income statement

ON Les

MODEL

PARAMETERS

Mathematical Model &

Time horizon definition ] Working calendar definitior~ Discounting rate ]

Optimization Solver

FACTORY

of product • Factory life (including expansion stages) • Location • Set-up cost • Operating cost • Cycle time by product • Total Inventory capacit, . Inventory carrying cost., • Flow

INPUT

• Cost

DATA

Figure I

PRODUCT

KEY

• Demand

• Availability

• Product qualification • Revenue • Resource consumption • Probe test yield • Chips per wafer • Binning • Unit cost • M a x . and Min. inventor

•

Initial cost

RESOURCES .

Space consumption . Operating cost

FOUNDRIES ISubcoatracto~) capacity . Delivery time . Yield

• Production • Unit price

TRANSPORTATION • Modes • Routes

• .

Cycle time Unit cost

MEM Input Data Requirements and Output Report Generation

THE MEM PLATFORM A. MEM Environment The Manufacturing Enterprise Model has been developed using the Manager for Interactive Modeling Interfaces (MIMI), a product of Chesapeake Decision Sciences, Inc., that allows development

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of planning and scheduling applications using the latest operations research techniques. MIMI is written in the "C" language with graphic interfaces in OSF Motif. A Windows/NT version will be available in 1995. MIMI runs on 17 different platforms ranging from 386/486 PC's (SCO UNIX) to IBM mainframes (VM/CMS and MVS/TSO). MIMI supports workstations such as the HP 9000 Series 700 (HP-UX), DECstation (Ultrix), DEC VAX Family (VMS), DEC Alpha AXP (Open VMS and OSF/1), DG AViiON (DG/UX), IBM RS/6000 (AIX) and Sun SPARCstation (SunOS with Motif). In general, hardware requirements on RISC platforms are a minimum of 32 megabytes RAM (64 megabytes preferred), 100 megabytes of free hard disk space and a color monitor. Any terminal operating as an X Terminal (including PC emulation packages) will serve as the user interface on a local area network. B. MIMI Description • Graphical User Interface MIMI's Graphical User Interface (GUI) provides facilities for the on-line creation of dialogs, menus, presentation graphics, hypertext help, and graph-based modeling interfaces. Customized, graph-based interfaces can be created for any part of the MIMI database including input data, model formulation, model structure, solutions, and reports. • Database MIMI was designed to coexist with other databases. MIMI's database (or data concentrator) was developed to provide integrated, in-memory, indexed support for a wide variety of computer-intensive planning and scheduling tools. The database consists of sets (ordered lists) and tables. MIMI borrows from both hierarchical and relational database concepts. Specialized, indexed sparse data structures are provided for large matrices, bills of materials, product flows, etc. The database has a strong object-oriented flavor with frames, methods, and inheritance. Extensive data manipulation capabilities are provided via set algebra, matrix algebra, and a macro language. • External Database Links MIMI has flat file interfaces for importing/exporting sets and tables that will be used for MEM. Generic, dynamic embedded SQL links are available for Oracle, Ingres, and DEC's RDB at additional cost. Links to other SQL database managers can be provided upon request for additional cost. The MEM model utilizes the ability of the MIMI data base to read flat files. These flat files are in ASCII and .CSV formats. The MEM model also utilizes the ability of the MIMI database to place reports into flat files. • Mathematical Programming Linear, nonlinear, or mixed-integer programming formulation of any size can be modeled in MIMI. Model size is only limited by available computer memory. Formulations, models, and solutions are stored in memory in the MIMI database, with direct links to the CPLEX optimizer. The CPLEX optimizer also provides Mixed Integer Programming (MIP) capabilities. A Successive Linear Programming (SLP) algorithm is provided for large-scale nonlinear problems. Robust Optimization (RO) routines for planning under risk and uncertainty are also included. • Expert System MIMI's expert system shell (written in "C") is based on first-order predicate logic with IF..THEN.. production rules which reside in MIMI sets. It supports both forward and backward chaining and operates much like PROLOG in backward mode. The expert system shell is an integral part of the MIMI database and understands MIMI sets, subsets, and tables. The MIMI expert system also supports learning. THE MEM ALGORITHM MEM is designed to answer eight questions that relate to long-range planning. The questions represent two key aspects of long-range semiconductor production planning: cost and cycle time. The underlying data in MEM supports all eight questions, but may be applied differently depending on the particular inquiry. The first question is "Which products should we manufacture in which facilities to meet demand at minimum cost, without violating capacity?" This question is the typical product allocation question. All

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demands are required to be met, and all resource constraints are known. This question allocates products to locations based on net present value (NPV) least-cost calculations. The second question is "If we cannot meet demand with current capacity, which products should we manufacture in which facilities to make the most money, without violating capacity?" This question differs from question 1 in that it allows MEM to determine whether meeting the demand is profitable for the company. If so, the demand will be met. In question 1, all demand must be met. The third question is "If we cannot meet demand with current resources, what resources can we add (and when) at the least cost to meet demand, without building new facilities?" It is assumed that the number and size of the facilities are fixed, but that there may be space in which to add new resources. This question will buy new (or retire old) resources and place them in the facilities where it is the most cost-effective at the appropriate time. Adding resources can continue until the facility runs out of space. The fourth question is "If we cannot meet demand with current four-walls capacity, what new facilities can we add at least cost to meet demand?" The model will add (or retire) resources and add, expand, or retire production facilities. Like question 1, all of the demand must be met. Like question 3, resources will be added or retired. MEM uses the least-cost expansion, which can include opening a new facility or expanding a current facility. The user must input the possible facility configurations. The fifth question is "Allowing for expansion and addition of facilities, how can we maximize profit if demand does not have to be met?" In essence, this combines questions 2 and 4. It allows for all of the resource and facility manipulation that occurs in question 4, but it will consider the cost of such expansions against the profit generated by the demand decisions described in question 2. This is the ultimate of the strategic planning questions if the user is concerned about maximizing the profit of the corporation, as it is evaluated using NPV calculations. Questions six through eight use the same information as questions one through five, but the primary focus of the strategic planner is to minimize the product cycle time. The definition of cycle time in MEM is the production time in days, from start of manufacturing until completion of the sellable product and the calculation is keyed to the utilization of key resources. The sixth question is "How can we meet demand by minimizing cycle time when facilities and resources are fixed?" A parallel question to question 1, this question assumes fixed capacity and minimizes cycle time by effectively allocating products to facilities and choosing short cycle time transportation options. The seventh question is %Vith a limited amount of money to spend on resources, how can we add resources to meet demand and minimize cycle time?" Like question 3, this question uses the available cash for capital to purchase new resources. In this case, however, it does it to minimize the cycle time. The eighth question is 'With a limited amount of money to spend on resources and facilities, where and when should we add resources and facilities to meet demand and minimize cycle time?" Like question 4, this question will allow the purchase or retirement of resources, and the purchases, expansion or closing of facilities. However, the decisions are made on the basis of minimizing the cycle time instead of maximizing the NPV calculation. T H E MODEL STRUCTURE MEM is built upon a model structure that is specific to the semiconductor industry. This structure allows the model to run faster than a generic model that addresses the same problem. It also gives the users, the strategic planner in a semiconductor corporation, a basis for relating to the model and its data. The first part of the MEM structure is based on the broad manufacturing steps for a semiconductor product. These include wafer fabrication (slicing wafers and printing circuits), wafer sort/probe (testing circuits on the wafer), assembly (slicing individual chips from the wafer), test (testing and binning each chip based on speed, etc.), and distribution (where completed products are sold).

Fab

bl""rH Sort

Basic Process Flow

Assembly

14

Test

I-'t°'°'n° Center

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Each of the five stages is a discrete entity in the model and cannot he further subdivided. There are five types of parameters provided by the user to create a model: product data, location data, major resources (key manufacturing equipment), transportation, and time periods. AN EXAMPLE Since MEM has just recently been developed, it has not been impleraented and used on a regular basis in any of the SEMATECH member companies. Several companies, including Digital, National Cash Register, and Motorola, are actively evaluating MEM and its use within their company. This section is meant to give the reader a sense of what type of problems MEM addresses in a real-world context. As previously stated, MEM is primarily built to handle multi-plant, multi-commodity medium-tolong-range strategic manufacturing planning for the semiconductor industry. We have found that companies who have shown the most interest in MEM are facing one of the following issues: • The first issue is characteristic of the ASIC (Application-Specific Integrated Circuit) semiconductor business. ASIC devices are semi-custom, make-to-order chips that are used for specialty applications. Broadly characterized, the ASIC business is a low-volume, high-mix business and the main competitive weapon in manufacturing is cycle time. The complexity of the ASIC supply chain is not hard to imagine. Good planning on the part of the ASIC manufacturer will provide capacity in times of high fluctuating demand. Although not a universal strategy, many ASIC manufacturers use foundries to lessen the effect of high variance in the demand on the company-owned facilities. In tests, MEM has proven to handle this environment quite well. • The second issue is characteristic of some of the larger semiconductor companies with large product portfolios. Although not build-to-order businesses, these businesses deal with a wide range of product offerings in plants across the world. Many of these companies are growing quickly and want to use MEM to manage the growth of the company efficiently. To these companies, bringing new facilities on line in a timely fashion and reallocating new products to all facilities is important. At a time when new fabs can cost well in excess of $1 billion, building a fab earlier than required can unnecessarily cost the company hundreds of millions of dollars.

CONCLUSION In conclusion, SEMATECH's MEM promises to provide the semiconductor industry with a stateof-the-art decision support system that could help achieve great improvements in the planning of manufacturing-distribution networks. In an industry where technology innovation had been sufficient to allow profitability, this type of tool was not required. Because the days of living solely on technology for profit are quickly coming to an end, MEM and related systems will become critical for success in the semiconductor industry. ACKNOWLEDGMENTS The authors wish to thank Dr. Jose Pablo Nuno of Arizona State University for his insightful help and guidance throughout the project. REFERENCES [1] Fine, C. H., & Hax, A. C. (1985). Manufacturing strategy: A methodology and an illustration. Interfaces, 15(6), 28-46. [2] Hayes, R. H., & Wheelwright, S. C. (1984). Restoring our compe_titive edge. New York: Wiley. [3] Skinner, W. (1969). Manufacturing-missing link in corporate strategy. Harvard Business Review. May-June, 136-145. [4] Sprague, R.H., Jr., & Watson, H.J. (Ed.). (1986). Decision support systems: Putting theory into practice. Englewood Cliffs, NJ: Prentice-Hall.