Oct. 12, 2006

1. Oct. 12, 2006 王啟泰 Chi-Tai Wang Ph.D. in Industrial & Operations Engineering, University of Michigan (Ann Arbor) team member of ”IBM Operational Framework for Advanced Supply Chain Planning” ctwang@us.ibm.com 802-769-4423 (US office) 0912-839-803 (Taiwan cell) Large-scale Supply Chain Optimization at IBM’s Microelectronics Division

2. Quiz … I’m loving it ! sub sub sub C dem =40 D B dem=30 dem=30 30% 20% 50% A bins to B, C, and D 50%, 30%, and 20% of the time, respectively. A What is the minimum number of A we need to start so that all the demands are satisfied ?

3. Snap Shot of Supply Chain Planning Systems BOM ! cycle time ! demand ! manufacturing release ? inventory ! SCP system substitution ? work-in-process ! vendor shipment ? receipts ! customer shipment ? build options ! processing time ! yield ! avail. assets known info./constraints suggested actions  +

4. Supply Chain Planning from Manufacturing Process Point of View:Snap Shot of Electronics Manufacturing Processes finished good X finished good Y finished good Z finished good W demand part 8 part 9 part 10 cycle time 50% 60% 70% 30% 30% 40% 20% part 5 part 6 part 7 part 4 (grade A) part 4 (grade B) part 4 (grade C) 60% 10% 30% yield inventory policy data date effective part 4 (untested) = BOM part 3 backorder penalties = binning = alternate BOM part 2 build cost = substitution part 1

5. Supply Chain Planning from Geological Point of View Sweden IBM Bromont, QC Switzerland Germany Italy Canada US Japan Shippac IBM East Fishkill, NY UK China SPIL, ASE, Amkor Endicott Philippines IBM Poughkeepsie, NY Amkor, Chartered Altis China IBM Burlington, VT today’s IBM semiconductor worldwide supply & demand network supply sites vendor sites demand geographies

6. Problem Solving Approach 1: Heuristic-based Technologies Heuristic-based solution technologies Most rely on bill-of-material explosion / implosion paradigms to create plans; some of them may solve simple mathematical programs to better handle difficult manufacturing scenarios. 80 2006-05-27 A yield 0.80 process time 7 days 2006-05-20 100 cycle time 3 days 100 2006-05-17 B process time 5 days yield 0.50 200 2006-05-12 • (pros) Can meet requirements from many manufacturing industries; short solution time. • (cons) Unable to optimally solve difficult scenarios such as complicated material substitutions, multiple manufacturing processes & locations, rework, etc.

7. Problem Solving Approach 2: Optimization-based Technologies Optimization-based solution technologies Create plans by capturing the essence of manufacturing processes in a mathematical formulation. • Material balance at stocking points • Backorder conservation • Resource availability • Costs & penalties • (pros) Offer unprecedented power to solve very complicated and difficult business requirements optimally. • (cons) Practical use in supply chain planning is often limited by time and hardware concerns … either memory is insufficient to load the entire problem, or it would take days, if not weeks, to find an optimal solution. Quite often, human decisions are part of the solution process (e.g., determining which part of the problem will be solved by optimization). • ( Currently at IBM, a linear program with size 2 million variables, 1 million rows and 5 million non-zero entries would generally take 20 to 40 minutes to be solved. )

8. complex substitutions 30 40 30 sub Module sub sub C D E F 60% 40% 80% 20% 10 B Optimal Solution build: 100 P substitutions: 10 of A for B 10 of A for C 10 Device A B C alternate BOMs 30% X Y 50% 20% P1 P4 P2 P3 P Z U IBM’s patented problem decomposition approach to deliver the speed and quality required for solving large scale supply chain problems • First, the entire manufacturing process is divided into a number of “stages” such that there exist little or no interactions between any two of these stages. (“card,” “module” and “device” have been the stages used to decompose the problem for IBM’s semiconductor manufacturing business) • Then, at each stage, the data is further divided such that complex manufacturing scenarios are processed by LP-based solvers while the less • complex scenarios are processed by heuristics-based solvers. A

9. IBM’s patented problem decomposition approach to deliver the speed and quality required for solving large scale supply chain problems heuristic LP card pegging pegging heuristic LP module pegging pegging heuristic LP device • Proven industrial strength solution technology; no human interventions required; running multiple times on a daily basis to manage IBM’s demand-supply networks. • Problems with 80K parts, 50K demands, 40K pieces of work-in-process and 120K capacity requirements are solved less than 4 hours.

10. Articles for Follow-ups / References “In 1999, it improved its use of assets by $80 million.” Lyon, P., Milne, R.J., Orzell, R., and Rice, R., “Matching Assets with Demand in Supply-Chain Management at IBM Microelectronics,” Interfaces, vol. 31, no. 1, pp. 108-124. • Won IBM a finalist status for the INFORMS 2000 Franz Edelman Award for Achievement in Operations Research and the Management Sciences (INFORMS stands for “Institute for Operations Research and the Management Sciences”). Wang, C., Chang, C., Degbotse, A., Fordyce, K., Hegde, S., Milne, R.J., Orzell, R.A., Patil, S., and Smith, S., “IBM Operational Framework for Advanced Supply Chain Planning: Cutting Edge Functions for Managing Next-Generation Demand-Supply Networks,” International Congress on Logistics and SCM Systems, Kaohsiung (Taiwan), 2006. Denton, B., Forrest, J., and Milne, R.J., “Methods for Solving a Mixed Integer Program for Semiconductor Supply Chain Optimization at IBM,” to appear in Interfaces, vol. 36, no. 5, Sept.- Oct., 2006. • Winning paper of the INFORMS 2005 Daniel H. Wagner Prize for Excellence in Operations Research Practice.