Beyond The Traditional Network
Analysis
Track 3 Session 2
Joseph Shaw
Alex Scott
Title: Sr. Network Modeling Analyst
Title: Managing Consultant
Company: Ahold USA
Company: IBM
Email: Joseph.Shaw@AholdUSA.com Email: alexscott@us.ibm.com
Website: www.aholdusa.com
Website: www.ibm.com
Phone: 508-977-5093
Phone: 312-320-9841
2
Abstract
So you are challenged with managing a large portfolio of
products and a complex set of vendors, customers and
distribution locations. How do you make sense of this all
and streamline your supply chain? This session takes
you beyond the pin-on-a-map network analysis and
examines factors such as sourcing strategies, inventory
optimization, route planning and more. We will also
review a grocery case study that involved the analysis of
sourcing effectiveness, evaluation DC investment
opportunities, and relocation of legacy facilities to get
the most out of their supply chain network.
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Agenda
 Overview - Network Modeling and Analysis
 Managing Model Complexity
 Baseline Modeling
 “What If” Scenario Analysis
 Inventory Optimization
 Route Optimization
 Key Takeaways
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 Ahold USA is a major grocery retailer in the
North East, ranked # 5 in the US
- 4 Retail Divisions, with over 740 stores in 11 states
- Supported by 20 DC’s (internal & external)
- Over 110K Associates
- Private fleet drives over 66 million miles annually
- In addition, it also is the US’s largest on-line home
delivery grocery company, Peapod
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Ahold USA Continental Structure
About 60m people
live in this area.
About 1/5 of the
total US
population
4 Divisions, 744 Stores
$23B sales, 11 States
20 Warehouses (includes 3rd party)
Stop & Shop NE = 215 stores
Stop & Shop NY = 171 stores
Giant-Maryland = 180 stores
Giant-Martins
= 178 stores
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Network Modeling and Analysis
General Supply Chain Management
Planning Issues
 What is the optimal number,
location and capacities of
suppliers, plants and
production lines & processes?
 What is the right number,
location and size of
consolidation centers, forward
DCs, cross-docks, etc.?
 What are the trade-offs between
- Inbound and outbound
transportation costs, duties,
tariffs, etc.
- Transportation costs and
warehousing and inventory costs
- Costs and service levels
Overall Objectives
• Evaluate the current DC network to identify
opportunities to optimize warehousing and
distribution
•
•
•
Which dry DC should service what region?
What is the value of co-locating different
product types?
Which demand should be cross-docked?
• Evaluate the long-term DC network strategy
•
•
•
Are the DCs & cross-docks the right
number, location and size?
What is the cost & service impact of adding
X-number of stores?
What is the impact of switching a vendor?
Vendor
Stores
DC
Cross Dock
Vendor
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Stores
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Overall Approach
Ahold utilized specialized network design software and premium
consulting services with proven track record to help with this analysis
Data Collection
Data Analysis
& Validation
Baseline
Modeling
Analyze
Alternative
Scenarios
Optimize with
current DCs
Final Summary
Evaluate
future DC
options
Products
Business
Rules
Plants /
Vendor
Network Design and
Analysis Focus
Software
Project
Scope
Freight
Costs
DCs
Demand
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Managing Model Complexity
Overall Network Snapshot
Suppliers
DCs
Customers
Network Specifications
Value
Number of Suppliers
> 5,000
Number of SKUs
˜90,000
Number of Store Locations
> 750
Number of DCs
16
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Managing Model Complexity
As we move downstream, the complexity of the supply chain increases
exponentially
Vendors
Supplier
Capacity
Product
Flow
Rules
Consolidation
Product
Categorization
Cross Docks
DCs
16
Consolidation Centers
and DCs
Cross Docks
Warehousing
Restrictions
Sourcing
Policies
Warehouse
Capacity
Warehouse
Capability on
Handling
Products
Customers
Multiple
Carriers
90,000
Products
5,000
Vendors
Stores
750
Store Locations
Demand
Pattern
Zone Base
Transport Rates
for Product Set
Utilization
and
Limitations
• Complexity
• Uncertainty
• Risk
Additionally, these
variables and
business rules
change over time
which makes the
problem dynamic
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Simplification is the Answer
Baseline
Simulation
Baseline
• Reflects the current reality.
• There is a tolerance level
for the
accuracy due to the assumptions made
during modeling and due to the dynamics
of the business situations (Changes in the
environment over time)
Scenario 2
Scenario 1
Reality
(Actual)
Simulation
Baseline
Scenarios
• Validate the improvements gained by
simulating the changes made to the
baseline and provide directional strategy
Performance Metric 2
Specialized SC simulation software will be used as an analysis tool to identify
the best case (most practical) solutions in order to improve the current
performance metrics under the given constraints
Customer / Stores aggregation
Pareto on products
10000000
9000000
8000000
7000000
6000000
5000000
4000000
3000000
2000000
1000000
0
Illustrative
90,000 Products  15,000 Critical Products
P_M57457_1
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Performance Metric 1
Focus on the bigger picture
Product aggregation
Total parts after product prioritizations  15,000 Products
Product Aggregation by Vendors  5,000 Products
Consolidation by
3 digit zip code
Products belong to basic 16 commodity categories
Further product aggregation by
product category and vendor
location (Zip)  1700 Products
1700 Unique Items
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Baseline Model
Actual (Accounting)
Lane
Total CC
Cases
Miles (Distance)
Baseline Model
Total Cost
P2W
NA
P2W ASC
NA
P2W Non-ASC
NA
Total CC
Cases
% Variance
Miles (Distance)
Total Cost
$
216,729,734
$
4,950,358
Suffied to Freetown
$
3,940,452
Inbound to Richmond
$
2,408,748
ASC Outbound
$
8,893,080
Cost
W2W
NE
N/A
N/A
NY
N/A
N/A
GC
N/A
N/A
GL
N/A
N/A
$
44,118,000
202,802,893
157,211,545
7,054,791
$
44,028,506
0.20%
$
37,320,000
148,382,005
115,024,810
10,242,613
$
37,403,875
-0.22%
116,413,431
90,242,970
11,380,336
$
41,484,172
14,610,424
W2C
Grand Total
TBD
8,399,110
TBD
$
34,478,646
101,756,708
78,881,169
3,848,169
$
34,578,527
$
124,809,726
569,355,037
441,360,494
32,525,909
$
385,524,372
-0.29%
• Validation: Model results typically within 5% of actual costs
• Scenario analysis
• Supply Chain parameters varied one at a time to study impact of change
• Scenario selection performed during baseline phase
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Typical “What If” Scenarios
No
Potential
Industry
Benefits
Scenario Name
1
Change Consolidation Center location to indicate optimal
based on cost and service factor determination
2
All products sourced from all DCs
•Determine the optimal flow path for all products based on cost 3-6% Cost
•Specific product flow requirements
3
Co-location of various products
•Products group / categories sources from one single location
•Cost of switching a vendor
2-8% Cost
4
Optimal number of DCs based on current state constraints
(Number of DCs based on current options available)
2-7% Cost
Opportunity
1-9% Cost
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Optimal DC Location
Customer by Demand
• Optimal DC location is based on supply and
demand patterns
• Results for multiple options are shown below
Scenario Results
Total Costs
Savings
40%
29%
Baseline
Bethlehem
31%
30%
Hartford
(Second Best)
Chester
(Optimal)
2 Locations
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Inbound Shipments Consolidation
In this scenario, a consolidation center is placed, where the customers (Western
USA) send all the products. Purpose of the consolidation center is for vendor to
have fewer more utilized outbound shipments to other warehouses.
Potential
Location for
Consolidation
Center
Vendors
Customers
Warehouse
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Inbound Shipments Consolidation
Baseline
Scenario
 Potential cost reduction of an entire supply chain cost (dependant on
industry)
 Savings primarily due to additional truck utilization for all inbound
shipments
 Additional inventory held at the consolidation center, but setting up a pull
based replenishment will decrease the overall inventory across the network
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Optimal Number Of DCs
TOTAL COST
Millions $
Fixed Warehouse Operating Cost
Transportation Cost
Optimal Cost
Optimized Baseline Costs
Reduce 1DC of
each type
Use 2 DCs of
each type to
serve entire
network
Use 1 DCs of
each type to
serve entire
network
 Transportation Costs increases as the number of warehouses increase
 Fixed operating costs of all warehouses (collectively) drops as the number
of warehouses reduce across the entire network
 Optimum point depicts the best option based on total cost
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Focus On Inventory Analysis
 Typically, when warehouses are consolidated /
reduced to serve a market, the net inventory in
the system will decrease
 This decrease in inventory is not linear and will
depend on internal and external factors such as
lead times, demand seasonality, number of
SKUs, etc
 Consolidated warehouses
can be sized based on
data outputs in order to
handle demand variations
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Options For Route Optimization
 Changes to the network impact the
route and carrier structure out of each
DC
 In an environment with significant LTL,
less aggressive
3.3%
2.0%
Logistics savings
Inventory considerations
Common systems, leveraged volume, SC synergies
Probability
of success
(~20% of project savings)
Degree of difficulty
Shipment Consolidation (~45% of project savings)
Sequentially
3.7%
1.0%
Warehouse synergies & consolidation (~35% of project savings)
2.4%
Overall % of Logistics Cost Savings
multi-stop, and backhaul opportunities,
this can be one of the largest
opportunities for cost savings
more aggressive
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Technology Enabled Innovation
Traditional Supply Chain Drivers
Quality
Current Business Requirements
Plant
2nd DC
DC
Cust.
Cost
Time
• Traditional tradeoffs
• Economies of scale
• High demand growth
Internal Factors
• Globalization
• Sourcing (LCC)
• Cost of Capital
• Customize Product
External Factors
• Fuel cost
• Labor rates
• Global COEs
With evolving business requirements under current conditions, our approach
focuses on customizing a solution for a specific industry or geographic market.
/ customer
aggregation to adapt
to decisions for
flexibility
P(Erf)
Customer Satisfaction
Coverage
Cost-Efficiency
Customer
Matrix
Efficiency
I
Cost
Performance
technology to better
understand al
tradeoffs and impacts
of decisions
2 Customizing product
Cost of Delivery
1 Use of tools /
Strategic
Customization
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Key Takeaways
 Using a holistic approach to analyzing your supply chain can
identify and quantify cost savings opportunities associated
with sourcing and distribution changes to your network
- Once a model is in place, evaluating future scenarios can be done in
a matter of hours, not days
 Reducing complexity and focusing on only the important
distribution characteristics can help to ease the burden of the
analysis
 Multiple types of technology can be used to approach
different supply chain problems (i.e. network design,
shipment routing and consolidation, multi-tier inventory
targets)
 As computing power increases and memory expands,
modeling and analysis will require less and less simplification
and reduction of complexity, particularly as technology
moves to the cloud
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Questions?
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APPENDIX
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Performance Metric 1
Objectives Of Baseline Modeling
Scenario 2
Simulation Baseline
• Reflects the current reality.
• There is a tolerance level
for the
accuracy due to the assumptions made
during modeling and due to the dynamics
of the business situations (Changes in the
environment over time)
Scenario 1
Reality
(Actual)
Simulation
Baseline
Scenarios
• Validate the improvements gained by
simulating the changes made to the
baseline and provide directional strategy
Performance Metric 2
Specialized SC simulation software will be used as an analysis tool to identify
the best case (most practical) solutions in order to improve the current
performance metrics under the given constraints
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