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Service Supply Chain Transformation at KLA-Tencor John Nunes Director of Strategic Consulting, MCA Solutions Supply Chain World – North America 4 - 6 April.

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Presentation on theme: "Service Supply Chain Transformation at KLA-Tencor John Nunes Director of Strategic Consulting, MCA Solutions Supply Chain World – North America 4 - 6 April."— Presentation transcript:

1 Service Supply Chain Transformation at KLA-Tencor John Nunes Director of Strategic Consulting, MCA Solutions Supply Chain World – North America 4 - 6 April 2005 Anaheim, California

2 Agenda Aftermarket Opportunity Semiconductor Overview KLA-Tencor Change Roadmap Some Preliminary Results Lessons Learned

3 Session Overview In many capital equipment sectors, the service supply chain has become increasingly important to business success. Experience shows that the key to success is finding the right balance between supply chain investments and customer satisfaction. This presentation will demonstrate how semiconductor equipment maker KLA- Tencor found the right balance. How the development of benchmarks and targeted metrics was key to transforming service supply chain management from a source of customer dissatisfaction into a competitive differentiator will also be explained.

4 Evolution of Technology Innovation Differentiation Commoditization The supply of aftermarket parts is a $400B business that covers everything from toner cartridges to cruise ship engines. Sales of such items are an important source of profit for companies that sell durable equipment. Indeed, many of those in the Fortune 100 rely on the aftermarket for up to 40 percent of their profits. The McKinsey Quarterly, February 2005.

5 Aftermarket Revenue Opportunity Service revenue contribution is growing. –Revenue over the Product Life Cycle is high-margin/low risk Pressure continues to mount on Service Revenue streams to match Product profit contributions. –In many industries Service lags While pressure mounts for Service to achieve the same contribution, investment in IT Solutions lags Service contribution provides some down cycle - insulating performance. –Source of differentiation, customer acquisition & retention Source: AMR Research

6 Mfg vs. Service Supply Chain CharacteristicMfg Supply ChainService Supply Chain Item DemandsVery HighLow: 1 to 2 a year Demand EventsPredictable; standard forecasting tools Random Response TimesManufacture to order2 and 4 hour Delivery NetworkContinued simplificationIncreasing in complexity Profit MarginsDecreasingIncreasing Inventory StrategyPostponement/JITPre-position IT investmentVery highUnder-funded

7 Who is KLA-Tencor? The world leader in yield management and process control solutions for semiconductor mfg. Because yield improvement is key to increasing manufacturing productivity and profitability, KLA- Tencor has outperformed the semiconductor capital equipment segment as a whole. KLA-Tencor technology is used by every major semiconductor manufacturer around the world.

8 Semiconductor Manufacturing Leading-Edge Technology –Latest generation Materials, Process, and Equipment Technology provide the capability to build integrated circuits with 70 nanometer critical dimensions Capital Intensive Manufacturing –The cost of a state-of-the-art 300mm wafer fabrication facility can exceed $3B and the revenue impact from an hour of machine down time can exceed $1M Epic Boom and Bust Cycles –The seeds of the next downturn are being sown as we speak Captive Supply Chain/Sourcing –Copy Exactly! philosophy drives single-source supply constraints Shrinking Customer Base –Continuing trend toward foundry manufacturing –10 companies make up 80% of industry revenue

9 Wafer Fabrication Food Chain Growing aftermarket opportunity for Wafer Fab Equipment Manufacturers.

10 Rising Share of New Fab Spending

11 Global Wafer Fabs: 375 and growing!

12 Change Roadmap

13 The Problem – May 2001 Poor parts availability is a major customer complaint and threatens to impact future business. Past investments in inventory and service supply chain improvements have failed to achieve sustained results. Cost of poor operational performance is creating significant drain on service profitability. Service profitability is key to success during downturn.

14 Change Drivers Business Performance –Cyclical pressure on service profitability –Low profitability relative to benchmarks –Poor ROI on past efforts Competitive Environment –Competitor service and support offerings Customer Pressure –Poor performance relative to competition –Relentless focus on cost reduction

15 Change Roadmap

16 Deteriorating Inventory Performance Inventory growth outpaced installed base growth…. …and was more than 3 times higher than best in class* performance *PRTM/PMG 2003 Service Supply Chain Benchmark Study for electronics equipment manufacturers % Growth from Jan 2002 Baseline … while field inventory targets were growing uncontrollably

17 Performance Benchmarking Cross-industry benchmarks provided limited insight to relative performance due to a wide range of customer requirements and performance metrics; fill rate is simply too broad of a measure. Within-industry benchmarks were skewed toward process equipment with a heavy reliance on consumable parts. The key to success was understanding customer requirements and measuring current performance relative to customer benchmarks.

18 Metrology vs. Process Tools <10% of Aftermarket Revenue from Spare Parts/Consumables Target Fill Rate 65% in 4 hours <10% consumables 70% of active parts are repairable ~0.5 part per tool per month –5,000 pieces per month >70% of Aftermarket Revenue from Spare Parts/Consumables Target Fill Rate 85% in 4 hours >70% consumables 20-30% of active parts are repairable ~20 parts per tool per month –400,000 pieces per month Wafer Process Equipment Manufacturer Metrology & Inspection Equipment Manufacturer

19 Parts Contribution to Unscheduled Downtime The role of service planning is to minimize equipment downtime related to parts. The industry standard metric is Mean Down Awaiting Part (MDAP). In an informal survey of International Sematech members, the benchmark for MDAP% was defined as <1% of total machine time. Historically, KLA-Tencor had not been able to achieve this benchmark. SEMI E10 Equipment State Stack Source: SEMI E10-90

20 Job completion Time (downtime) Time Mean Down Awaiting Part (MDAP) On-site repair Repair job completed, machine is up Parts arrive CSE orders additional parts if necessary Customer calls CSE arrives with some or all of the required parts On-site diagnosis Remote Diagnosis Machine fails Parts Availability Logistics Transportation CSE Response Time Repair Time Unscheduled Service Event Source: Cohen, M.A., Zheng, Y., Agrawal, V., (1997) Service parts logistics: a benchmark analysis. IIE Transactions 29, 627-639.

21 Change Roadmap

22 Root Cause Summary Forecasting capability of existing planning and execution systems (ERP/DRP) was not designed to be effective in an extreme low usage environment. Field inventory positioning model did not achieve an optimum balance between fill rate performance and inventory investment. Poor interface between multiple planning and execution systems created data integrity and timing issues. Past improvement efforts had relied on short-term actions which prioritized material by location, customer or product. These actions could not be sustained when other priorities arose.

23 Challenge of Low Failure Rate Planning Of 15,000 Active parts, 75% had 1 demand or less in the past 12 months. Of 5,000 Parts with Demand, 3,000 had demand of 3 or less in the past 12 months.

24 Forecasting Service Part Demand A stable installed base population of 100 parts may have one failure during the previous 12 months, and 5 during the next 12 months. This is not a trend. The same installed base population of 100 parts may have 1 failure in 12 months at one location but 5 at another and 0 at another. This does not mean that there should be no stock at the location with zero demand. The Pick the Best Forecast Method approach assumes that historical patterns will repeat. This creates a tendency to pick inappropriate forecasting methods that attempt to predict trends and seasonality. Traditional Forecast Concepts Do Not Apply to Service Parts

25 Change Roadmap

26 Service Supply Chain Challenge A) Current system capability Service Supply Chain Cost Service Performance 99% 60 70 80 90 100 110 92%94%96%98% B A D B) Achieving current process entitlement D) Shifting the curve with Multi-Echelon Optimization methods C How can you improve service levels without dramatically increasing inventory? C) Improving service with increased cost

27 Service Supply Chain Strategy Part Sourcing Tied to Service Programs –Reserve last part for contract/warranty customers Multi-Echelon Fulfillment Process –Local/Regional/Global service targets to achieve MDAP goal Global Demand/Inventory Visibility –Ability to source parts globally 7 x 24 x 365 Service Planning and Optimization (SPO) –World-class spares planning system Configured Installed Base Database –Detailed knowledge of local installed base at the component level Global Supply Chain Capability –Sourcing, Quality, Test, Reverse Logistics

28 Multi-Echelon Fulfillment Model Distribution Center Customer Regional Depot Local Depot Factory/ Supplier 95% Regional Target < 24 hour response 95% Regional Target < 24 hour response 65% Local Target < 4 hour response 65% Local Target < 4 hour response 98.5% Global Target < 72 hour response 98.5% Global Target < 72 hour response 99.5% Global Target < 96 hour response 99.5% Global Target < 96 hour response Part Wait CalculatorMulti-Echelon Fulfillment Targets The optimal balance between inventory and MDAP requires a multi- echelon approach to order fulfillment. Customer requirements for equipment up-time and part wait time (MDAP) drive the selection of appropriate service levels.

29 Phase I: Segmentation & Business Rules Parts are categorized into material class groups based on cost and demand frequency. Demand by group is then summed and groups are added until the target performance is achieved. Target includes safety factor for supply chain gaps. Stock levels are then determined based on local population and global & local demand history for the each part.

30 Phase II: True Multi-Echelon Optimization The mathematics of multi-echelon optimization were developed to deal explicitly with low/sporadic demand parts and complex fill rate requirements. The business rules approach falls apart rapidly under these conditions with too much reliance on simple rules of thumb and existing business practices. Positioning business rules did not allow for a part to be local at one forward location and regional at another forward location.

31 Overall Improvement Roadmap New mgmt team takes over SSCM Disable Linear Forecasting Models Implement field stocking model based on Material Class Begin pilot of Multi-Echelon Optimization System (SPO) Full integration of Multi-Echelon Optimization Capability (SPO) 3PL Global outsourcing and integration Implement single ERP for inventory and order mgmt Develop improved configured installed base database 3PL IT Integration Stabilize Process & Performance

32 Change Roadmap

33 Field Target Trend

34 Product X Fill Rate Improvement

35 Improvement in Key Service Metrics

36 Q4 02 Q4 04 Service Supply Chain Expense The mix is moving in the right direction. Excess expense at its lowest point in 3 years.

37 Lessons Learned Use Performance Benchmarks to get buy-in for change at the highest levels in the organization. Customer benchmarks are more useful than competitive benchmarks. While fill rate is a useful metric for competitive comparison in service supply chain management, it isnt always meaningful to a customer. The pain generated by poor performance must be greater than the pain generated by the remedy. Begin with an 80% solution - early wins drive momentum for continued change. Establish a performance measurement system early on. When performance improves its easy to forget how bad it used to be.

38 Further Reading J.J. Chamberlain and J. Nunes, Service Parts Management: A Real Life Success Story, Supply Chain Management Review, September 2004, pp. 38-44. M.A. Cohen, N. Agrawal, V. Agrawal, Achieving Breakthrough Service Delivery Through Dynamic Asset Deployment Strategies (to be published). R. Wise and P. Baumgartner, Go Downstream: The New Profit Imperative in Manufacturing, Harvard Business Review, September-October 1999, pp. 133-141. M.J. Dennis and A. Kambil, Service Management: Building Profits After the Sale, Supply Chain Management Review, January-February 2003, pp. 42- 49. R.G. Bundschuh and T.M. Dezvane, How to make after-sales services payoff, The McKinsey Quarterly, 2003 Number 4, pp. 116-127. T. Gallagher, M.D. Mitchke, and M.C. Rogers, Profiting from spare parts, The McKinsey Quarterly, February 2005.

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