A Semiconductor Supply Chain Model with Non-Stationary Parameters

1. A Semiconductor Supply Chain Model withNon-Stationary Parameters INFORMS Miami Beach, Florida November 4-7, 2001 Guillermo Gallego Columbia University Department of Industrial Engineering and Operations Research, City of New York ggallego@ieor.columbia.edu Bala Ramachandran, Kaan Katircioglu IBM T.J.Watson Research Center Yorktown Heights, NY 10598 rbala@us.ibm.com, kaan@us.ibm.com Sandy Anderson, Tracey Pilkinton, George Wood IBM Microelectronics Division Burlington, Vermont

2. Environment: Semiconductor manufacturing/distribution supply chain has a global multi-echelon structure... Wafer Test Bond and Card Test Card Assembly Die Final Module Module Assembly Stock Stock Final Stock Substock typically 1.5-3 months typically 1-2 weeks typically 1 week = Mfg/Assembly/Test location = Physical stocking location

3. Environment: Semiconductor Part Proliferation Wafer Mfg & Test Die Stock Bond & Assembly Burn-In, Test & Sort Choices: Module Stock Design Level Packaging Options: Tests: Organization, Addressing, etc. Tests Results: Wafer Sorts (quality) Reliability: Pass/Fail (random) Speed Sort: Random test results

4. Environment: Market Conditions & Trends • Moore’s law continues to hold (i.e. 50% cost reduction in every 18 months) • Seasonal gyrations in demand/supply balance • Power in hands of customers • Increasingly stringent service requirements (JIT, shorter order-to-delivery cycle times) • Contracts becoming common • Constructive partnerships are rare • Product life cycles are short and prices decline fast • Commodity products have low margins

5. Environment: Wafer Fabrication • Objectives: • Reduce cycle times & WIP inventory • Improve yields (very significant cost driver) • Meet service commitments at die stock • Maximize process learning • Minimize unit production costs (Achieve high capacity utilization) • Maximize quarterly vs. long-term profit • Conflicting operations strategies: • Start only what is needed vs. fill the line to capacity (Push vs. Pull) • Keep lots moving vs. allow engineering holds • Many Complex constraints

6. Environment: Bond, Assembly & Test • Multiple objectives: • Minimize unit production costs • Meet service commitments at module stock • Maximize quarterly profit • Keep lines running smoothly • Conflicting operations strategies: • Build to order vs. build to plan • Minimize lot size vs. minimize setups • Limit number of part numbers vs. offer as many parts as possible • Constraints: • Mfg bottleneck (e.g. tester equipment) • Long lead time parts (e.g. lead frames)

7. Environment: Inventory Management Are these targets consistent? Planning (monthly/quarterly): Set target service levels: “x% to customer request date” Set target inventory levels: “y DOS at module finished stock” Execution (daily/weekly): Take corrective actions Track service and inventory levels Check if inventory and service levels are within acceptable ranges Determine causes for discrepancies

8. Model: Framework A multi-echelon system FAB Wafer FAB FAB FAB Sector 1 Sector 2 Test Sector 3 Sector 4 Device inventory Forecast Bias Forecast Error Bond & Test (sort) Ship Mark Assembly Module inventory

9. Model: Assumptions • A system in series (at least 8 echelons) • Single product, single location at each echelon • Non-stationary parameters: • Demand is random and has time varying distribution • Lead times are random and have time varying distribution • Manufacturing yields are random and have time varying distribution • Costs are deterministic and time varying • Market prices are deterministic and time varying • Inventory management is done through a non-stationary order-up-to policy at each echelon • Life cycle of a product is relatively short and random (careful inventory obsolescence management is required) • Objective: • Minimize overall inventory (FGI+WIP) and achieve a target customer service • Customer service: On time shipment to customer request date

10. Model: Demand and Inventory Control • Total Lifecycle modeling with - weekly forecast updates - daily orders, receipts and sales - periodic updates of market size • Demand Model based on a Martingale method for forecast updates • Order-up-to level is state-dependent and forecast dependent

11. Model: Lifecycle Analysis at the Design Stage • Determine appropriate inventory policies at the device and module levels at different stages of the lifecycle to achieve service targets • Evaluate Tradeoffs between obsolescence risk and customer service • Determine overall profitability of product • Estimate value of required investments as a function of time

12. Model: Tactical Analysis during the Product Lifecycle • Assess product profitability, market potential to determine future strategy • Determine strategy for build-plan, based on uncertainty in demand • Assess trade-offs between obsolescence risk and customer service DGR = 40 DGR duration = 35 DGR = 40 DGR duration = 20

13. Model: Parametric Sensitivities Nine stage model with random lead times, yields and non-stationary demand

14. Model: Data Elements

15. Model: Scenario Analysis

16. Model: Safety stocks adjusted for delays Two stage model with stationary demand Optimizing safety stock to achieve a service lower bound of 90%,

17. Summary • Semiconductor supply chain is a complex process with multiple stages, each with random cycle times and yields • Developed stationary model to set stock policies at the module and device levels • Stationary model assists is analyzing drivers of supply chain performance • Developed Non-stationary model to capture supply chain dynamics • Used to set varying inventory policies at different stages of the product lifecycle • Models assist in analyzing tradeoffs between inventory costs and customer service • Developed methodology to tune safety stocks in a multi-stage system, accounting for process backlogs