The document discusses the importance of an agile and optimized inventory management strategy for businesses, emphasizing how effective inventory control can lead to increased customer satisfaction, loyalty, and profitability. It addresses the challenges of implementing inventory optimization software, including costs and the need for continuous improvement, while highlighting the significance of managing the product lifecycle and utilizing data for forecasting. The document concludes with a focus on the role of people, processes, and technology in achieving a successful inventory management strategy.

1. Global Inventory Management Strategy,
Design & Execution:
Optimisation & Flexibility Amidst Constant Change
Joel Marusiak
IM Solutions Manager - EMEA
February 8th 2013

2. Your future, our history
• Your business is time sensitive?
• Your product is service critical?
• You require an agile partner that
addresses all your needs?
You answered YES to these questions?
Then join our customers and chose
Neovia Logistics to manage your value chain.

3. Your business, our expertise

4. Why is Spare Parts Inventory so
important?
Initial
Sales
Revenue
Aftermarket
Revenue
Initial
Sales
Margin
2x-5x
Opportunity
4x-10x
Aftermarket
Sales Margin
Opportunity
0 5 10 15 yrs 0 5 10 15 yrs
• Right Part
• Right Place
• Right Time
• Product / Plant up-time
• Customer Satisfaction
• Loyalty
• Profitability
• Competitive Advantage
Inventory is the core of any Supply Chain

5. Inventory Optimisation Software:
Expectation V’s Actual Results
Benefit Value in €
Expectation:
• Return on investment
• Immediate optimisation of inventory
• Quick wins
Time
Reality of software implementation:
• High cost – >1.5 x software
• Long, iterative process
• Continuous Improvement
• Trial & error
• Optimised point unknown
Define standardised
processes
Fill gaps in the
systems functionality
Change existing
system
Understand world class inventory
management philosophy
Understand
existing system
Measure &
Monitor Impact
Stabilisation
Refine
parameter
Implement standard
processes
Measure
baseline
Small iterative
changes

6. Overall Inventory Optimisation:
Managing the product lifecycle
New Part
Introduction
Statistical
Forecasting
End of life
Management
• Risk: Customer dissatisfaction / obsolescence
• Limited / No system functionality
• Risk: Balance between service & inventory under optimised
• Mature parts managed by global, multi-echelon system
• System configurations set to fulfil targets
• Exception management
• Expertise for refining configurations
• Risk: High obsolescence
• Limited / No system functionality

7. New Part Introduction
Issue Impact
Global New Model Launch High customer expectation
Aftermarket support
High fill rate required
critical to brand perception
1000’s of new parts Huge obsolescence risk
No demand history No system forecasting
solution
Collaboration &
Communication
Identify
new parts
Set statistics
Collect data on
similar parts
Must be customised to Client-specific data and goals
Replenish
& deploy
Constant introduction of small number
of new items also require attention to
manage overall parts profile

8. Overall Inventory Optimisation:
Managing the product lifecycle
Mature parts – statistical forecasting

9. Is your system capturing and
using data effectively?
PART# ABC PART# DEF
Monthly Demand 100 pieces 100 pieces
Cost per piece €10 €10
Lead Time 30 days 30 days
Criticality (Service) Same Same
Which part should have greater safety stock? - Same?
Monthly Customer Order Lines 2 lines @ 50 pieces 20 lines @ 5 pieces
Now which part should have greater safety stock?
Part DEF impacts more customers with
Using order lines & pieces-per line to forecast &
lower inventory investment
plan inventory provides the foundation to
optimising both inventory & service

10. “Dialling” the system, optimising
your inventory
FFiillll
Fill
RRaattee
Rate
IInnvveennttoorryy
Inventory
IInnvveessttmmeenntt
Investment
OOppeerraattiioonn
Operation
WWoorrkkllooaadd
Workload
MMaasstteerr
Master
DDaattaa
Data
SSyysstteemm
System
CCoonnffiigguurraattiioonn
Configuration
Facility
Planning
FFaacciilliittyy
PPllaannnniinngg
SSuupppplliieerr
Supplier
SScchheedduulleess
Schedules
IIMM IM AAnnaallyysstt
Analyst
Workload
WWoorrkkllooaadd
BBuussiinneessss
Business
SSttrraatteeggyy
Strategy
SSaalleess
Sales
DDeemmaanndd
Demand
PPrroodduucctt
Product
MMaasstteerr
Master
Data
DDaattaa
CCoonnffiigguurraattiioonn
Configuration
BBiillll Bill OOff
Of
DDiissttrriibbuuttiioonn
Distribution
PPrriiccee
Price
BBrreeaakkss
Breaks
10
SSiimmuullaattiioonn
Simulation
Holistic View
Optimisation
HHoolliissttiicc VViieeww
OOppttiimmiissaattiioonn
Global GGlloobbaall Parts PPaarrttss PPooppuullaattiioonn
Population
Forecast
DDeemmaanndd DDrriivveenn
CCoonnssttrraaiinntt CCoonnssiiddeerraattiioonn
DDiissccrreeeett EEvveenntt PPrroocceessssiinngg
Order
Processing
FFoorreeccaasstt
Stocking
Evaluation
Receipts &
Deployments
Requirement
Planning
Demand Driven
Constraint Consideration
Discreet Event Processing
Supply chain impacts known
prior to Execution

11. Simulation Results:
Choosing a strategy
Simulation Establishes Optimal Service V’s Inventory Exchange Curve & Supply Chain view
Stocked
Items
Service /
Fill Rate
Average
Inventory
Inv.
Turns
€20
€18
€16
€14
€12
€10
88% 90% 92% 94% 96% 98% 100%
Service / Fill Rate
Average Inventory
(in millions)
Simulation Scenario
#1 – Baseline 26,708 89.4% €18.181 3.69
#2 – Inventory Strategy 20,931 89.4% €11.187 5.98
#3 – Fill Rate Strategy 23,542 98.6% €18.195 3.67
#4 – Optimised Inventory 21,715 94.3% €13.430 4.99
Cash Flow
& Space
Customer
Service
Optimised
performance
Facility sizing
& capital
Return On
Investment
11

12. “The goal of forecasting is not to predict the future but to tell you what
you need to know to take meaningful action in the present.”
Managing varying demand types:
Demand Forecast
System defined forecast models at SKU level:
Paul Saffo, Harvard Business Review
Agile & Flexible Forecasting
Demand Forecast
Intermittent / Erratic
Time
Random
Time
Seasonal / Cyclic
Time
Trend
Time
Demand Trip
Time
Outliers
Time
Campaign / Surge
Time

13. Change
In the modern economy and worldwide market
place, what could change the status quo?...
Everything!
Economic crisis - “Great Recession”
Earthquakes
Government policy
Tsunami
Competition
Changing supply base
Market Shifts & Expansion

14. Overall Inventory Optimisation:
Managing the product lifecycle
End of life management

15. End of product Life
Supersession • Supersession at SKU level
• Forecast tracks demand changes
• Responsiveness to change can be varied
Flexibility enables forecast accuracy, optimised inventory and
minimises obsolescence
Declining Growth Rate
Time
• Forecast remaining product support period
• Investigate commercial opportunity
• Decide whether to purchase ATB
All Time Buy
• Supersession start date
• Demand capture on all levels of the chain
• Sales order allocation
• Forecasting
• Replenishments (DRP/Purchasing & Deployments)
• Chain breaks

16. 16
People, Process & Technology:
Core to Flexible and Agile Inventory
Management
Strategy
Execution
Performance
Monitoring
People
Technology Process

17. “Big Bang” Return On Investment
Benefits:
• Rapidly optimised inventory
• Cash flow increased
• Maximised customer service
• Minimal disruption to supply chain
• Immediate Return On Investment
Benefit Value in €
Time
Strategy
Execution
Performance
Monitoring
The true value of world class inventory
planning technology can only be realised
when tightly managed by experts using
best-practice processes
People
Actual Results
• Inventory -20%
• Maintained fill >95%
“Great
Recession”
• Inventory -7%
• Fill rate +3%
• Inventory Turns +65%
50% Sales
Growth
• Inventory -42%
• Facing (local) fill rate +13%
• Inventory turns + 49%
Network
Restructuring

18. Thank you for listening
18
Joel Marusiak
IM Solutions Manager – EMEA
Neovia Logistics
Please feel free to get in touch…
joel.marusiak@neovialogistics.com
Strategy
Forecasting
& Inventory
Planning
Replenishment
& Deployment
Client
Collaboration
Performance
Monitoring &
Analytics