Service Supply Chain Transformation at KLA-Tencor

John NunesDirector of Strategic Consulting, MCA Solutions

Supply Chain World – North America 4 - 6 April 2005 Anaheim, California

Agenda

• Aftermarket Opportunity

• Semiconductor Overview

• KLA-Tencor Change Roadmap

• Some Preliminary Results

• Lessons Learned

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.

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.

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

24%

45%

20%

76%

55%

80%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Revenue Profit IT Expenditure

Service Product

Source: AMR Research

Mfg vs. Service Supply Chain

Characteristic Mfg Supply Chain Service Supply Chain

Item Demands Very High Low: 1 to 2 a year

Demand Events Predictable; standard forecasting tools

Random

Response Times Manufacture to order 2 and 4 hour

Delivery Network Continued simplification Increasing in complexity

Profit Margins Decreasing Increasing

Inventory Strategy Postponement/JIT Pre-position

IT investment Very high Under-funded

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.

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

Wafer Fabrication Food Chain

• Growing aftermarket opportunity for Wafer Fab Equipment Manufacturers.

Rising Share of New Fab Spending

Global Wafer Fabs: 375 and growing!

Change Roadmap

• Defi ne the problem

• Collect and Analyze data

• I dentif y the root cause

• Plan and implement the solution

Problem Data Eff ects StandardizePost

Mortem

• Determine the impact of the solution on the problem

• Make the solution permanent

• Allow the team to learn f rom the problem solving experience

• Output: Problem Statement, Target Statement

• Output: Qualifi -cation of the Problem

• Output: Verifi ed Root Cause

• Output: Solution I mplemen-tation Plan

• Output: Quantifi -cation of the results

• Output: Revised standards, procedures or policies

• Output: I dentifi cation of one key strength and one key weakness

Cause Solution

1 2 3 4 5 6 7

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.

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

Change Roadmap

• Defi ne the problem

• Collect and Analyze data

• I dentif y the root cause

• Plan and implement the solution

Problem Data Eff ects StandardizePost

Mortem

• Determine the impact of the solution on the problem

• Make the solution permanent

• Allow the team to learn f rom the problem solving experience

• Output: Problem Statement, Target Statement

• Output: Qualifi -cation of the Problem

• Output: Verifi ed Root Cause

• Output: Solution I mplemen-tation Plan

• Output: Quantifi -cation of the results

• Output: Revised standards, procedures or policies

• Output: I dentifi cation of one key strength and one key weakness

Cause Solution

1 2 3 4 5 6 7

Deteriorating Inventory Performance

Inventory growth outpaced installed base growth….

100

105

110

115

120

125

Q102 Q202 Q302 Q402 Q103 Q203 Q303

Installed Base

Inventory

…and was more than 3 times higher than best in class* performance

Inventory as % installed base (normalized)

-

0.50

1.00

1.50

2.00

2.50

3.00

3.50

KLA Tencor Best In Class

*PRTM/PMG 2003 Service Supply Chain Benchmark Study for electronics equipment manufacturers

% Growth from Jan 2002 Baseline

0

10

20

30

40

50

60

70

80

May-99 J un-99 J ul -99 Aug-99 Sep-99 Oct-99 Nov-99 Dec-99 J an-00 Feb-00 Mar -00 Apr -00 May-00 J un-00 J ul -00 Aug-00 Sep-00 Oct-00 Nov-00 Dec-00 J an-01 Feb-01 Mar -01 Apr -01 May-01 J un-01

…while field inventory targets were growing uncontrollably

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.

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

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.

UnscheduledDowntime

ScheduledDowntime

Production Test

Productive Time

Standby/Engineering

Non-scheduled

Total Time

168 hrs/week

Equipm

entD

owntim

e

Production

Time

Equipm

entU

ptime

Equipm

ent Utilization

Capability

Production U

tilizationC

apability

SEMI E10 Equipment State StackSource: SEMI E10-90

Job completion Time (downtime)

Time

Mean Down Awaiting Part (MDAP)

On-site repair

Repair jobcompleted,machine is up

Parts arrive

CSE orders additional parts if necessary

Customercalls

CSE arrives with some or all of the required parts

On-site diagnosis

RemoteDiagnosis

Machinefails

• 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.

Change Roadmap

• Defi ne the problem

• Collect and Analyze data

• I dentif y the root cause

• Plan and implement the solution

Problem Data Eff ects StandardizePost

Mortem

• Determine the impact of the solution on the problem

• Make the solution permanent

• Allow the team to learn f rom the problem solving experience

• Output: Problem Statement, Target Statement

• Output: Qualifi -cation of the Problem

• Output: Verifi ed Root Cause

• Output: Solution I mplemen-tation Plan

• Output: Quantifi -cation of the results

• Output: Revised standards, procedures or policies

• Output: I dentifi cation of one key strength and one key weakness

Cause Solution

1 2 3 4 5 6 7

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.

0600

1,2001,8002,4003,0003,6004,2004,8005,4006,0006,6007,2007,8008,4009,0009,600

10,20010,80011,40012,000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49>=

50

Total Pieces Requested During Past 12 Months

Num

ber o

f Par

ts P

lann

ed

0%5%10%15%20%25%30%35%40%45%50%55%60%65%70%75%80%85%90%95%100%

Perc

ent o

f Tot

al P

arts

Number of Parts

% of Total

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.

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 PartsTraditional Forecast Concepts Do Not Apply to Service Parts

Change Roadmap

• Defi ne the problem

• Collect and Analyze data

• I dentif y the root cause

• Plan and implement the solution

Problem Data Eff ects StandardizePost

Mortem

• Determine the impact of the solution on the problem

• Make the solution permanent

• Allow the team to learn f rom the problem solving experience

• Output: Problem Statement, Target Statement

• Output: Qualifi -cation of the Problem

• Output: Verifi ed Root Cause

• Output: Solution I mplemen-tation Plan

• Output: Quantifi -cation of the results

• Output: Revised standards, procedures or policies

• Output: I dentifi cation of one key strength and one key weakness

Cause Solution

1 2 3 4 5 6 7

Service Supply Chain Challenge

A) Current system capability

Ser

vice

Su

pp

ly C

hai

n C

ost

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?

How can you improve service levels without dramatically

increasing inventory?

C) Improving service with increased cost

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

Multi-Echelon Fulfillment Model

Distribution Center

Cu

sto

mer Regional

Depot

LocalDepot

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

Local Installed Tools: 500

Parts Per Tool Per Month: 0.50

Target Requests Cycle-Time Hrs Lost Local 65.0% 163 4 650

Region 95.0% 75 24 1,800 < 48 97.0% 5 48 240 < 72 98.5% 4 72 270 < 96 99.5% 3 96 240 < 168 100.0% 1 168 210

Total 250 Total Loss 3,410 Available 364,800

MDAP 0.93%

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.

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.

VALUE 0 N 1 2 3 4 5 6A G G G R R R R LB G G G R R R L LC G R R R R L L LD R L L L L L L LE R L L L L L L L

0 0 1-2 3-4 5-6 7-8 9-10 11-12

VELOCITY

MONTHS WITH DEMAND DURING THE PAST YEAR

0

150

300

450

600

750

900

1050

1200

1350

1500

1650

1800

1950

2100

2250

2400

2550

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3000

B6

C6

A6

D6

E6

B5

C5

C4

D1

E1

D2

D3

D4

D5

E2

E3

E5

E4

DN

EN

C3

C2

B3

B4

A5

C1

B2

D0

A4

A3

E0

A2

CN

B1

C0

Z6

B0

A1 N T6

T5

A0

Z4

BN

T3

T4

Z2

T1

Z5

T2

Z1

Z3

AN

T0

Z0

TN

ZN

Material Class Categories

Lin

es

Re

qu

es

ted

in

Q1

FY

20

02

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

% o

f T

ota

l L

ine

s R

eq

ue

ste

d in

Q1

FY

20

02

Lines % of Total

LOCAL

REGIONAL

GLOBAL

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.

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

70% 75% 80% 85% 90%

Target Fill Rate at Forward LocationsT

arg

et In

ven

tory

Inve

stm

ent

($M

) Segments & Business Rules SPO Strategy

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

70% 75% 80% 85% 90%

Target Fill Rate at Forward Locations

Tar

get

Inve

nto

ry In

vest

men

t ($

M) Segments & Business Rules SPO Strategy

Positioning business rules did not allow for a part to be “local” at one

forward location and “regional” at another

forward location.

Positioning business rules did not allow for a part to be “local” at one

forward location and “regional” at another

forward location.

Overall Improvement Roadmap

Q3 01 Q4 01 Q1 02 Q2 02 Q3 02 Q4 02 Q1 03 Q2 03 Q3 03 Q4 03 Q1 04 Q2 04

New mgmt team takes over SSCM

New mgmt team takes over SSCM

Disable Linear

Forecasting Models

Disable Linear

Forecasting Models

Implement field stocking model

based on Material Class

Implement field stocking model

based on Material Class

Begin pilot of Multi-Echelon Optimization

System (SPO™)

Begin pilot of Multi-Echelon Optimization

System (SPO™)

Full integration of Multi-Echelon Optimization

Capability (SPO™)

Full integration of Multi-Echelon Optimization

Capability (SPO™)

3PL Global outsourcing

and integration

3PL Global outsourcing

and integration

Implement single ERP for inventory and order mgmt

Implement single ERP for inventory and order mgmt

Develop improved configured

installed base database

Develop improved configured

installed base database

3PL IT Integration

3PL IT Integration

Stabilize Stabilize Process & Process &

PerformancePerformance

Change Roadmap

• Defi ne the problem

• Collect and Analyze data

• I dentif y the root cause

• Plan and implement the solution

Problem Data Eff ects StandardizePost

Mortem

• Determine the impact of the solution on the problem

• Make the solution permanent

• Allow the team to learn f rom the problem solving experience

• Output: Problem Statement, Target Statement

• Output: Qualifi -cation of the Problem

• Output: Verifi ed Root Cause

• Output: Solution I mplemen-tation Plan

• Output: Quantifi -cation of the results

• Output: Revised standards, procedures or policies

• Output: I dentifi cation of one key strength and one key weakness

Cause Solution

1 2 3 4 5 6 7

Field Target Trend

Historical Thresholds

0

10

20

30

40

50

60

70

80 Normalized

Product X Fill Rate Improvement

0

100

200

300

400

500

600

700

800

900

1000

1100

1200

Jul-01 Aug-01 Sep-01 Oct-01 Nov-01 Dec-01 Jan-02 Feb-02 Mar-02 Apr-02 May-02 Jun-02

40.0%

45.0%

50.0%

55.0%

60.0%

65.0%

70.0%

75.0%

80.0%

85.0%

90.0%

95.0%

100.0%

Lines Local Regional Global Virtual LWS

Improvement in Key Service Metrics

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

22%

24%

26%

28%

30%

Q102 Q202 Q302 Q402 Q103 Q203 Q303 Q403 Q104

% I

mp

rove

me

nt

fro

m D

ec

20

01

Ba

selin

e

Local Fill Rate Regional Fill Rate Global Fill Rate Supply Chain Cost (% of Rev)

Repair17%

Logistics16%

Excess37%

Materials23%

Overhead7%

Q4 02Q4 02

Overhead7%

Materials39%

Excess8%

Logistics21%Repair

25%

Q4 04Q4 04

Service Supply Chain Expense

• The mix is moving in the right direction.• Excess expense at its lowest point in 3 years.

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 isn’t 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 it’s easy to forget how bad it used to be.

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.