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Transcript of Service Supply Chain Transformation at KLA-Tencor John Nunes Director of Strategic Consulting, MCA...
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
2700
2850
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.