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Strategic Supply Chain Planning:
The Combined Value of Network
Design and Inventory Optimization
An Optiant and Insight White Paper
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The Combined Value of Network Design and Inventory Optimization
Table of Contents
Introduction
1
Supply Chain Design and Optimization Solutions
1
Supply Chain Network Design
2
Uncertainty and Variability
2
Inventory Optimization
3
The Need for Both Types of Solutions
4
Successfully Integrating the Two Approaches
4
Conclusion
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Introduction
Customer requirements and global
competition have made supply chain design
more challenging, complex and mission
critical than ever before. Numerous trends
have compounded this increase in complexity,
including: mass customization of product(s),
product line and SKU proliferation,
compressed product lifecycles, globalization
and outsourcing of operations, increased
power of key retail and distribution channels
and company mergers and acquisitions.
In the past decade, manufacturing and
distribution firms have more readily
embraced technology to grapple with their
complex supply chain processes and
accomplish specific strategic goals. Initially,
in order to respond to the increased need
for automation of regular activities and
processes, most technology solutions were
focused on either installing or upgrading
transactional and operational decision support
capabilities. More recently, however, the
focus has been different. With the increased
pace of change driving a need for business
flexibility, corporations are increasingly
turning to Supply Chain Design and
Optimization solutions to develop strategies
and tactics to satisfy customer demand
while balancing limitations on supply and
the need for operational efficiency. The
result of the Supply Chain Design and
Optimization process are strategies and
plans that optimize corporate performance
in the areas of revenue growth, cost
containment and ultimately, profitability
and shareholder value.
Supply Chain Design and
Optimization Solutions
Supply chain planning software for modeling
and optimizing strategic supply chain
parameters—Supply Chain Design and
Optimization solutions—can be divided into
two general areas: 1) Supply Chain Network
Design (a.k.a. "network design"); and 2)
Inventory Optimization. Network design
plans processing locations—suppliers,
manufacturing facilities, DCs, and transit
lanes—based on costs and expected supply
& demand. Meanwhile, inventory
optimization plans inventory locations and
quantities, and resulting planning
approach based on costs and supply &
demand uncertainty.
From a modeling and output standpoint,
the focus of each solution is different.
Yet, importantly, these solutions address
complementary sets of strategic supply
chain issues. These issues can cut across a
variety of critical business decisions that
supply chain executives and managers
regularly face: network rationalization,
postponement strategies, make vs. buy,
allocation of capital, make to stock vs.
make to order, etc. Because of the
complementary and interdependent nature
of the factors driving these issues, network
design and inventory optimization solutions
should be used together in an integrated
manner to realize maximum benefit.
Strategic Supply Chain Planning
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Supply Chain Network Design
The focus of network design is to model and
optimize the physical supply chain network
and the flow of materials through the
network. In doing so, network design captures
the costs of the supply chain with a "total
landed cost" perspective, and applies solvers
to determine optimal answers. The answers
output by these models are optimal facility
locations and the throughput of those facilities
(factories, warehouses, etc.), and optimal
transport lanes and routing of goods between
the facilities to most cost effectively satisfy
customer demand. Full network design is
generally strategic in nature and, as such,
encompasses a long-term planning horizon
(1-3 years). Network design also supports
shorter term, more tactical decisions, such as
how to serve new markets or distribute new
products given an existing network.
Because of its focus on the facilities and their
material handling capabilities, network design
is accomplished by analyzing demand across
major product categories or groupings (e.g.,
the product family level). Supply chain
locations are connected, or linked, by
transportation processes when conducting
network design analysis. Capacity, sourcing,
demand (aggregated at the product family
level), fixed and variable costs, and
transportation are some of the primary costs
and constraints considered by network design.
Notably and by necessity, network design
assumes that demand and time as deterministic,
and therefore does not robustly consider the
impacts of uncertainty and variability on the
supply chain.
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Strategic Supply Chain Planning
Uncertainty and Variability
Uncertainty and variability are key drivers
of risk to your supply chain strategies.
Ignoring the consequences and costs of
these risks can greatly reduce the overall
flexibility and efficiency of the supply
chain, as well as significantly degrade
customer service. Companies employ an
array of approaches to insulate customers
from supply chain uncertainty and variability,
but by far the most common is the use
safety stock, also known as buffer inventory.
This inventory is used to buffer against the
uncertainty and variability of supply
(procurement lead-times, processing
lead-times, and transport lead-times) and
of demand (e.g., forecast error, seasonality).
The inventory-driven costs caused by safety
stock can be significant in businesses that
regularly deal with uncertainty, variability,
and risk of inventory obsolescence.
Conversely, the cost of lost business,
service penalties and expediting due to
sub-optimally managed safety stocks can
be significant as well.
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Inventory Optimization
Inventory optimization is the second type
of modeling and optimization capability.
Inventory optimization is a relatively new
approach and technology, specifically
focused on modeling uncertainty and
variability and minimizing the risks they
impose on the supply chain. Because of this
need to capture and quantify these risks,
inventory optimization employs stochastic
methods of analysis and optimization.
Advanced inventory optimization solutions
also consider crucial interdependencies
across supply chain processes, meaning
that they can model and optimize multiechelon/multi-stage supply chain networks.
Similar to network design, inventory
optimization models the supply chain with
a "total supply chain cost" perspective, and
uses optimization-based solvers to output
optimal answers. The outputs of inventory
optimization are optimal inventory
locations (stocking points) and optimal
inventory amounts (target inventory levels)
required to achieve customer service targets,
and drive planning approach decisions. This
type of design is both strategic and tactical
in nature, therefore the modeling horizon is
typically quarterly, monthly, and sometimes
weekly. This planning is accomplished at
the product line, potentially down to the
SKU level (as appropriate), in order to
capture the effects of item-level variability.
This approach also enables inventory
optimization to model and capture the
correlation of demand streams, and the
benefits of risk-pooling at appropriate
points in the supply chain. Inventory
optimization utilizes a process flow
approach to connecting the supply chain—
nodes represent process steps, each with an
associated time and cost. Demand and
demand variance, supply lead-times and
their variances, direct and indirect costs,
target services levels, and inventory
holding costs are some of the primary
costs and constraints considered by
inventory optimization.
Comparison of Network Design and Inventory Optimization
Network Design
Inventory Optimization
Optimized Outputs
Facility locations
Throughput
Transport lanes
Inventory locations
Inventory levels
Customers service levels
Cost Perspective
Total Landed Cost
Total Supply Chain Cost
Planning Horizon and Frequency
Annual/As Needed
Monthly/Quarterly/Seasonal
Level of Aggregation
Major Product
Categories/Families
Product Line/SKU
Treatment of Uncertainty
Deterministic
Stochastic
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The Need for Both Types of Solutions
As stated earlier, network design and
inventory optimization solutions address
complementary sets of strategic supply
chain issues, and that they should be used
together in an integrated manner to realize
maximum benefit. The reasons for this are
two-fold.
The first reason is the need to understand
all of the costs and operating parameters
that are likely to impact supply chain strategy
and performance. Network optimization
primarily supports decisions concerning the
trade-off between fixed costs (facilities,
equipment, etc.) and variable operating
costs (direct materials, direct labor, etc.).
Inventory optimization primarily supports
decisions concerning variable costs that
are often “hidden” in traditional financial
statements, like inventory carrying costs
and working capital tied up in the form of
inventory. Inventory optimization also
supports decisions concerning the types of
operations or capabilities at the various
facilities (warehousing, cross-docking,
make-to-stock vs. make-to-order, etc.), and
the costs associated with those functions.
For example, analysis might recommend a
postponement (a.k.a., late-stage product
customization) strategy that requires the
insertion of packaging or handling
capabilities downstream in the supply
chain. These functions impact both direct
costs, as well as inventory holding costs,
and are incorporated into inventory
optimization. Only by utilizing both
network design and inventory optimization
will managers achieve a full understanding
of ALL supply chain costs and related
performance factors.
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The second reason relates to the interaction
of these cost and performance factors.
Using network design and inventory
optimization in an integrated fashion not
only allows the capture and quantification
of all supply chain cost elements, it enables
the understanding of their linkages and
interdependencies. The reality is that
network structure decisions impact inventory
deployment strategies, and vice versa.
Implementing one solution without the
other overlooks these interdependencies,
and may cause managers to overlook large
cost reduction opportunities or design supply
chains that are not flexible enough to meet
changing customer requirements.
Successfully Integrating the Two
Approaches
Integrating supply chain network design
and inventory optimization is essential to
achieving strategies, plans and results that
accurately represent the realities and optimize
the performance of your business. There are
methodologies and approaches, combined
with best-in-class modeling tools, which
can be used to get the most out the
integrated modeling and optimization
process. This joint modeling process
centers around a hierarchical approach,
where supply chain network design and
inventory optimization are performed in
sequence. The hierarchical planning
approach consists of four steps, linked with
a feedback loop:
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Hierarchical Supply Chain Planning Approach
Generate Optimized
Networks
Optimize Inventory For
Each Network
Data Collection
Evaluate and Adjust
As Necessary
1) Gather data
2) Generate multiple optimized
network scenarios
3) Optimize inventory for each
network scenario
4) Evaluate, adjust and feedback
as necessary
This integrated approach results in better
outputs that are ultimately more practical
for the customer to implement. The first
step is data collection. While the data
requirements for network design and
inventory optimization are similar, there are
a few additional inventory-related variables
required for inventory optimization. In
most cases the additional data elements,
described here at a high level, are target
service levels, inventory carrying costs,
forecast uncertainty, lead times and lead
time uncertainties. More detailed and/or
additional data may be required depending
on the level of analysis to be performed.
Because the network with the lowest total
landed cost may not result in the lowest
total supply chain cost option, multiple
networks need to be considered. The second
step in the joint process involves creating
these multiple network scenarios. To create
these scenarios, constraints should be placed
on the networks that are likely to impact
the optimal inventory levels. For example,
the number of inventory locations is an
important driver of total inventory levels.
Advanced network modeling tools allow the
user to set a maximum number of locations
at a given level in the supply chain (e.g. set
the maximum number of distribution
centers in the US to 2). Another key driver
of inventory is lead-time. Alternative
network scenarios should consider more
costly, but faster processing and transportation
options. It may be necessary, in some cases,
to use the results from inventory optimization
to adjust network-modeling assumptions or
create new scenarios. For example, the
inventory levels recommended for certain
locations by the inventory optimization
might exceed the warehouse space supported
by the fixed cost assumptions in the network
optimization. Each scenario will be an
optimized network, some with specific
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constraints applied, and an associated
optimal inventory strategy. By evaluating
the total supply chain cost (the sum of the
total landed cost and the inventory driven
costs), you can then select the most costeffective supply chain.
Finally, it is important to note that there
is no single mathematical approach or
software tool in which both types of
planning can be captured in one model, thus
providing an optimal combined solution. In
other words, there is no tool that can
simultaneously solve both problems with
"one click" of the optimize button. This is
due to the unique nature of the problems
being solved, the data required, and
the mathematical algorithms required to
effectively construct the problem and
optimize the outputs.
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Conclusion
Now, more than ever, companies require
supply chains that are both flexible and
efficient. Flexibility to respond to a rapidly
changing marketplace, and efficiency to
achieve the goals of cost containment and
corporate profitability. But lean, adaptive
supply chains don’t just happen—they have
to be designed. Combining the latest
technologies and processes for supply chain
network design and inventory optimization
is a powerful means to achieving your
supply chain design goals. Network
design delivers an optimal supply chain
infrastructure, and inventory optimization
delivers optimal inventory deployment
strategies. Each approach is powerful on
its own, but by using them together
managers will unlock hidden opportunities
and capture maximum value from their
supply chain strategies and tactics.
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About INSIGHT, Inc.
INSIGHT software and consulting provide
optimization-based planning and scheduling
to solve the supply chain management
issues of the world's top companies.
INSIGHT solves these latest, critical, and
most complex problems for 40% of Fortune
Magazine’s top fifty, 70% of Business
Week’s most profitable corporations, and
70% of the companies judged to have the
best supply chains. Clients often select
INSIGHT when other solutions have failed.
These clients rely on INSIGHT to gain the
greatest competitive advantage from the
best answer. Clients include Abbott
Laboratories, BASF, BPAmoco, Clorox, HP,
ExxonMobil, Ingram Books, Johnson &
Johnson, Kellogg, Levi Strauss, Motorola,
Pennzoil, PepsiCo, Perrier, Pfizer, Procter &
Gamble, Unilever, and Toyota. The
X-System®, a proprietary optimization
engine, powers a network of planning and
scheduling solutions. Examples include crew
scheduling, production planning, design of
global supply chains, and transportation
planning within TMS® from MercuryGate
International. Increasingly, INSIGHT
provides optimization components, partnering
with third party software providers to deliver
best-of-breed solutions. Call INSIGHT
offices in Virginia at (703) 366-3061 or in
Oregon at (541) 388-6998. On the Web, visit
www.INSIGHT-MSS.com.
About Optiant, Inc.
Optiant is a pioneer in the emerging market
of supply chain design and optimization.
Optiant's flagship software solution,
the PowerChain™ suite, enables global
organizations to design and configure
optimal supply chains, solving real-world
problems today. PowerChain determines
cost-effective inventory placements and
integrates supply chain strategy into sourcing
and distribution processes for quantifiable
results in less than ninety days. Optiant has
proven that intelligent inventory policies
improve customer service levels and provide
rapid ROI by driving out millions of dollars
of associated costs from complex supply
chains. Our roster of blue-chip clients,
including Global 1000 discrete manufacturers, utilizes Optiant's expertise to design
competitive supply chains and innovative
inventory policies. PowerChain solutions are
built on patent-pending optimization
technology based on more than ten years
of leading-edge research and industry
partnerships at the Massachusetts Institute
of Technology. Optiant is a privately held
company headquartered in Boston, Mass.,
and can be reached at www.optiant.com
or (781) 238-8855.
4 Van de Graaff Drive
Burlington, MA 01803
781-238-8855
www.optiant.com
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