The document discusses the urgency of inventory optimization for global manufacturers facing supply chain uncertainties and complexities. It highlights traditional inventory management techniques' limitations and the need for advanced methods to improve inventory visibility and control, particularly in response to volatile demand and globalization. Several strategies, including the use of new technologies and collaborative replenishment systems, are proposed to enhance inventory management effectiveness across extended supply networks.

1. SETLabs Briefings
VOL 5 NO 3
Jul-Sept 2007
Inventory Optimization:
A Necessity Turning to Urgency
By Greg Scheuffele and Anupam Kulshreshtha
Counter your supply chain uncertainties
with inventory optimization techniques
and technologies
A key concern for global manufacturers
today is reducing inventory and inventory
driven costs across their supply and distribution
on inventory management across extended
supply chains.
In this context, manufacturers have
networks [1]. Pressure to cut inventories continues difficulty reducing inventory with traditional
to build for several reasons. Manufacturers no or even advanced inventory management
longer manage linear or stable supply chains. techniques. Today’s global manufacturers have
They juggle vast supply networks. Globalization largely hit limitations in leveraging material
of the supply network and supply base drive requirements planning and management
higher inventories and make cutting inventory processes and systems to cut inventories. Even
more difficult. Globalization among consumers advanced inventory management techniques,
is putting pressure on product availability, such as sales and operations planning or
prompting manufacturers, distributors and developing demand-pull replenishment systems
retailers to upgrade their stock keeping with suppliers using Kanbans, have been either
policies. Emerging market consumers are embraced or found to deliver less impact on
becoming as demanding as those in developed overall inventory reduction than anticipated.
markets. These challenges are exacerbated In the last few years a new paradigm
by manufacturers’ own product development has emerged: where one finds operations teams
decisions. The drive to innovate and increase and planning teams of the manufacturer applying
the rate of new product introductions leads to the latest techniques and technologies to improve
high rates of new technology adoption for next inventory visibility, control and management
generation products, putting enormous pressure across the extended supply network.
1

2. Uncertainty
Reduce through
forecast accuracy
Hedge through
increased inventory
Address head-on
through flexibility
Figure 1: Addressing Uncertainty through IO Techniques Source: Infosys Research
We call this collection of efforts as levels; to enhance service levels and supply
Inventory optimization. Inventory optimization availability; and to establish the right product
helps discrete manufacturers control inventory inventory mix and level in each geography and
driven costs and address today’s demand channel. Many manufacturers also focus on
volatility and supply chain complexity. inventory as part of shifting their operations
We discuss several trends in inventory to achieve demand-pull replenishment across
optimization including opportunities to apply their supply network – hoping to achieve
this new technology to solve more that just high the performance demonstrated by leading
inventory costs. Inventory optimization can manufacturers who have succeeded in this such
enable smarter product launches, lower direct as Dell, Procter and Gamble, Nokia, and Toyota
material cost of goods and faster manufacturing Motor [2].
and distribution execution. A key driver of the renewed focus on
inventory lies in the recognition that traditional
NEED FOR OPTIMIZING INVENTORIES techniques are failing to reign in inventories in
There are several reasons manufacturers are the wake of increased supply chain complexity.
increasing focus on optimizing inventory by This complexity is characterized by increased
applying the latest tools and techniques for uncertainty. Demand is more volatile and
inventory control. Traditionally, competitive therefore less predictable. This is true not
pressure has always driven manufacturers to only for aggregate demand but for forecasting
seek enhanced capabilities to reduce inventory splits and volumes across channels and
2

3. markets. Traditionally, three strategies have IO Techniques
been employed by manufacturers to address IO techniques apply rigorous and discrete
uncertainty: (a) increase inventory levels to analysis to analyzing inventory performance.
hedge against uncertainty, (b) develop supply They then use the analysis to identify product-
chain flexibility to be more responsive to specific changes to inventory stocking and
uncertainty, (c) improve forecast accuracy so that replenishment policies; to identify the supply
less uncertainty propagates to the manufacturing network configuration; or, to correlate
floor. Inventory optimization techniques and inventory investments to item revenue or profit
technologies map to the flexibility and accuracy generation.
strategies [Fig. 1]. On the planning side, a key inventory
What is driving the dramatic increase optimization technique is profit-driven analysis,
in the complexity (and therefore uncertainty) where the profit each individual product
of managing large supply and distribution contributes is ranked; a pareto distribution is
networks? Globalization is one big driver, the developed to separate high profit products from
evolution of emerging markets such as China lower profit products; and inventory holding
and India present new challenges in effective policies are adjusted to cut inventory on low
product distribution with low inventory levels. ranked products and increase inventory on high
Globalization of supply networks means that ranked products, resulting in an intelligently
key functions such as R&D, product design and applied net inventory reduction.
manufacturing are now geographically spread On the execution side, manufacturers
out, which hampers inventory reduction efforts, are striving for IO by applying improvement
that are often best executed by a cross functional concepts based on lean principles, and by
team working together very closely. Increased expanding the use of collaborative and demand-
rates of new product introduction and product pull replenishment schemes such as vendor- or
innovation are also driving complexity into supplier-managed inventory to drive highly
supply networks. Finally, because increases in precise replenishment and fulfillment activity.
transportation and logistics options have made These techniques are also benefiting from
careful control and planning of in-transit or improved supply chain planning and control.
pipeline inventory difficult, manufacturers are Lean seeks to optimize inventory by driving out
tending instead to let inventory drift upwards. non-value added inventory management tasks
in the factory or warehouse and by improving
INVENTORY OPTIMIZATION DEFINED planning and control at a granular level across
Inventory Optimization (IO) is the application each manufacturing or distribution step. Vendor-
of a range of latest techniques and technologies or supplier- managed inventory schemes seek to
for improving inventory visibility, control, share risk and offload inventory ownership.
and management across an extended supply
network. As we will illustrate later in this paper, IO Technologies
these techniques and technologies are driving A key inventory optimization technology is the
improvements beyond what traditional inventory IO engine. IO engines reveal opportunities to cut
management techniques – even advanced inventory by analyzing inventory performance
techniques – have been able to deliver. holistically - looking at data from across the
3

4. “Classic” Inventory “Advanced” Inventory Inventory Advantage of Inventory
Management Management Optimization Optimization over prior methods
Material Constraint based IO Engine Better characterizes demand
Requirements planning (APS) uncertainty and lead time
Planning (MRP) variability
Advanced modeling
Integrates with MRP and APS
Days of Supply rules ABC Classification Profit-Driven Rationalizes inventory with
for setting inventory Analysis minimal impact on revenue /profit
levels
Cycle counting Materials Closed loop planning Provides more predictable control
Management system and analytics with over material flows
inventory control via Enables faster re-configuration of
exception management supply chain
Supports smoother absorption and
handling of unexpected supply or
demand swings impact
No control over Resorts to Chase Optimizes considering Better synchronization between
production Scheduling techniques production and Production and dispatch
transportation batches
Table 1: Different Types of Inventory Management and Source: Infosys Research
Control Techniques
extended supply network. They integrate with (non-linear, algorithmic, etc.) models that
Advanced Planning and Scheduling systems are then solved to identify optimal inventory
and Material Requirements Planning systems to policies, stocking locations, or quantities. The
incorporate policy updates into the supply chain uncertainties addressed by IO engines include:
planning cycle. IO engines identify ‘smarter’ demand uncertainty (or forecast error), cycle
inventory holding rules and replenishment time variability and replenishment lead time
policies that increase overall supply chain variability. The output of running an IO engine
planning accuracy. “Smarter” typically means is fed back to the ERP, constraint based planning,
applying these policies at a more discrete level, or other discrete planning system, adjusting
such as at an item /stock keeping location inventory policies as a finite level. IO engines
combination level instead of just at the item have a range of applications, from modeling and
level. optimizing safety stock across the supply chain
IO engines characterize supply network to identifying optimal re-order point sizes in
uncertainty present in a variety of specific steps environments with highly erratic demand.
or links in manufacturing and distribution The rise in interest in IO engines is
processes using advanced mathematical likely linked to their increased ease of use and
4

5. accessibility over time. In the last few years, ways to solve complex mathematical equations
a new generation of technology and related in acceptable time durations. This progress is a
software vendor community has developed key reason for manufacturers to have applied IO
around IO engines [3]. The latest IO engines techniques across a wide range of problems in
bring the computational horsepower to solve their supply, manufacturing, and distribution
very large optimization problems quickly. networks.
We make a distinction between
inventory optimization engines as a new APPLICATION AREAS
technology and a broader collection of concepts, IO techniques and technologies are being applied
techniques and technologies called inventory within both supply chain planning and execution
optimization. processes. Manufacturer are using these enhanced
In spite of the significant advantages capabilities to cut inventory, enhance service levels
available from the latest IO techniques and and maximize return on investments by setting the
technologies, leveraging them effectively requires right inventory levels in the right production lines
much more data than traditional or “advanced” and distribution channels.
inventory handling techniques. This is a valid Application of an IO approach depends
concern some manufacturers express when on a deep understanding of the existing
planning to leverage these techniques. A second environment around the supply chain. An
concern is frequently raised around the complexity accurate knowledge of the various cost
of the calculations, formulae and mathematics elements of the supply chain, along with a good
employed. For traditional techniques and even understanding of existing lead time and demand
for most advanced approaches, the computations variability is required to model the process
required are limited to simple calculations and correctly. Modeling also demands a proper and
straightforward mathematical formulae, which accurate definition of the optimization objective.
are simple to communicate and explain to teams Some suitable objectives can be minimization of
outside the supply chain function including costs, maximization of revenue or maximization
upper management. For inventory optimization of profit. One also needs to define business related
however, typically higher order calculations, constraints in unambiguous terms. Examples of
including complex equations in algebraic such constraints are the process capacities of
or calculus forms, are used. Understanding various plants, demand limitations of various
and deriving meaningful results from these customers and service time commitments
calculations requires a deeper mathematics between different chain partners.
background and greater computing power. A mathematical representation of
Despite all the complexity, IO these objectives and constraints represents the
techniques and technologies are gaining model for the subject supply chain. Typically,
ground and finding more and more applications factors that drive the business constraints or
because of recent advances in information the defined optimization objective, or both are
technology and far greater computational power nonlinear in nature. In addition, the optimized
available at the disposal of today’s supply value of the IO objective function is point in
chain architects. Similar advancements on the time value that changes over time as component
operations research front have also led to newer parameters in the function change. The same
5

6. is true for the constraints as well. For example, Inventory Reduction in Plant Operations /
assume there is a constraint on total man hours Assembly Lines
available for production. As the number of The production operations are subject to
production units increase, the man hour per various process center capacities. In typical
unit shall decrease at some rate, resulting in non assembly line scenarios, the process capacities
linearity in the constraint. Characterizing this of successive work centers interact with each
dynamic is difficult using traditional analysis, other in a complex fashion and have substantial
inventory optimization is an ideal approach impact on the quantities of WIP or staged
for the complex mathematics this entails. In component inventory for each such work
addition, there is variability in demand and in center. To reduce inventory at each work
fulfillment and replenishment cycle times which center, one needs to optimize the inventory
by themselves can be difficult to characterize. requirements across the processes in a
Thankfully, advancements on the mathematics holistic fashion keeping in mind the required
and IT technology fronts have made it possible manufacturing throughput rate and existing
to capture these situations in great detail and product availability /service commitments the
solve them efficiently. manufacturer must achieve.
Non-linearity in factors that drive business constraints
or optimization objectives call for the adoption of
mathematics-intensive inventory optimization approach
Inventory optimization techniques Inventory requirement can be
can be applied to specific areas across a broad optimized by balancing the assembly line
range of supply chain planning and execution for a smooth work flow. One also needs to locate
activities. The needs, constraints, participation, the most critical resource or bottleneck in
process changes and benefits of each supply chain the assembly line. This bottleneck defines
partner will vary depending on the industry the maximum throughput rate through the
and type of optimization problem. Some newly assembly line with minimum inventory
emerging and proposed applications of IO requirements. The decision has the potential
technology to specific operational issues and to influence the process batch size, transfer
supply chain problems are explained in the batch size and the buffer capacities for each work
following sections. center. At a macro level, these decisions also
The following lists emerging areas of impact the lot sizes that are required to be
applying IO technologies to address complex procured from suppliers and provided to the
supply chain and inventory issues: next stage in the supply chain.
6

7. Inventory Reduction Across Transportation to impact pipeline and safety stock inventory
Networks levels. Many procurement decisions and
Each stage in the supply chain has transportation activities only indirectly drive inventory
options available with different cost structures. levels because decisions are made prior to
The choice of each transfer mode, or combination actual sourcing execution or because of the
thereof, impacts the inventory needed to be longer cycle times associated with tactical
carried in pipeline between those stages. A procurement. While performing the source
simultaneous optimization of the engagement selection as a strategic initiative, little can be
of various transportation modes and the level done on inventory optimization as a tactical
pipeline inventories can have significant impact exercise. The mix of procurement from various
on the overall working capital invested in sources provides significant benefit if optimized
supply chain inventory at any given time. Trade along with the inventory requirement for each
offs are involved on two fronts while making possible configuration of procurement mix from
the transportation decisions. On one hand, various suppliers.
From inventory reduction in plant operations to
transportation networks, via sourcing policies to lot size
optimization — inventory optimization technologies can
address the most complex inventory issues
transportation costs are compensated against Inventory Reduction via Lot Size Optimization
the pipeline inventory. On the other hand, Cycle inventories can be reduced by decreasing
transportation choices also impact customer the lot sizes used in production and distribution
responsiveness and thus the service level replenishment. In production, optimal lot sizes
(product availability) commitments. A careful depend on the fixed cost of forming the lots. A
selection of the right configuration of modes, problem arises when different produced units
inventory holding in pipeline and service level have different optimal lot sizes for production
commitments can optimize and improve the but share the same work center resource or
overall cost incurred in the chain. transportation resource. Considering these
limitations and the cost structure in place, the
Inventory Reduction via Changes to Sourcing lot sizes for different produced units can be
Policies optimized so as to utilize the available resources
Low cost country sourcing strategies open to the maximum. This concept can be applied
up several options for applying inventory across multiple manufacturing work centers
optimization. The key is identifying which and transportation resources via custom
procurement decisions are significant enough optimization routines that look across the supply
7

8. Application Area Approach with Inventory Advantages over Incremental Value
Optimization traditional approaches Realized
Inventory reduction Finding out the Critical System runs at the maximum Provides an effective tool for
in plants / resource and following capacity without unnecessary assembly line operations and
assembly lines the bottleneck approach inventory through out the line a control mechanism for plant
related inventory
Inventory Finding best configuration Results in relatively lower Reduces transportation and
reduction across of transportation modes safety stocks due to lesser distribution costs
transportation (and costs) along with lead times and proper Can increase actual product
networks required safety stocks, exploitation of available availability level with no
utilizing service times to transport modes net increase in pipeline
optimum inventories
Inventory reduction Finding best configuration Better utilization of available A comparatively longer term
via changes to of sourcing options and sourcing options. Easy to cost reduction technique that
sourcing policies quantities with safety evaluate multiple sourcing can simultaneously considers
stock requirements policies procurement and holding cost
Inventory reduction Finding joint lot sizes for a Better overall reduction in An integrated inventory
via lot size suitable group of products inventory and transportation reduction approach for product
optimization sharing similar resources costs families,
and transportation Optimizes inventory with
schedules suitable MRP data
Risk Pooling Finding right size of Better overall reduction in A time tested approach,
inventory by analyzing the inventory across geographies Emerges as an efficient
demand patterns across technique for controlling
geographies for group of inventory in distribution
items function
Inventory reduction Finding requirement Significant reduction in the Value realized in the
via common schedule and quantities of component inventory across procurement of components
component components common to a multiple finished products with for ATO or similar
planning group of products common component parts environment,
Inventory reduction Postponing the product Allows mitigation or A trusted inventory reduction
via postponement differentiation to later elimination of early WIP stage technique, casts drastic
stages in chain by inventories impact on supply chain
involving customer No additional inventory complexity by reducing
preferences at later needed to handle varying number of products
stages customer preferences
Table 2: Inventory Optimization vs. Traditional Approaches Source: Infosys Research
chain, producing a globally optimal result. An Risk Pooling
effective optimization function would consider Safety stocks across various geographies,
local lot size optimality, the arrival rate of work over various time periods and for different
in-process lots from each upstream process, and product groups can be aggregated to reduce
the storage or staging capacity at each resource. the total stock carried for providing a pre-
8

9. defined service level. Many companies like Dell, This enables the organizations to check the
HP and Amazon practically optimized their impact of variations in the consumer demands
safety stocks over different dimensions to for product variants, very early in the chain
exploit the benefit of risk pooling. If the and thus avoiding the need to carry safety
demand patterns across geographies or stocks for different variants in the initial
product groups are independent, the variance stages of the supply chain. Both postponement
for the aggregated demand will be less than and component commonality push the
the summation of individual variances. In product differentiation towards downstream
this scenario, IO techniques and technologies in the chain and facilitate aggregation till later
are applied to identify (a)which geographies stages.
or product groups are optimal candidates for Applying IO techniques here is
safety stock aggregation, (b) which inventory similar to the Risk Pooling application. IO can
holding locations are optimal for risk-pooling identify (a) which products are optimal for
from a cost standpoint, and (c)the level of postponement, (b) where postponement should
safety stock inventory to hold in the risk-pooled take place physically for least cost, and (c) the
location. The same explanation holds good level of postponement inventory to carry.
for aggregation over various time periods and
for different product groups as well. EXAMPLES OF MANUFACTURERS
APPLYING INVENTORY OPTIMIZATION
Inventory Reduction via Common Component These examples of manufacturers applying
P lanning inventory optimization illustrate real world
In a discrete manufacturing environment, a success with the concepts, technologies, and
lot of inventory is held as components across techniques.
various products. Thousands of components One major manufacturer of personal
required for various products have significant communications devices is undertaking a
commonality across products. Aggregation of broad initiative to enable sell-side supplier
such components is another form of pooling, managed inventory for its major customers.
with more relevance to high tech and discrete This manufacturer’s existing product
manufacturing environment. Since the same fulfillment model is based on a classic multi-tier
component is required for various products, distribution channel. Before adopting inventory
the demand at the component level is more optimization techniques, the manufacturer’s
predictable and requires less safety stock as distribution network was unsynchronized
compared to a simple addition of safety stocks and contained sequential layers of distribution
for the same component without considering leading to limited visibility, excess inventory,
commonality. stock outs, cycle time delays and difficulty
establishing new retail relationships.
Inventory Reduction via Postponement In some markets, the manufacturer
Postponement refers modifying a manufacturing faced a 72% retail shelf stock-out rate. In
process so that more of the operation is done addition, its forecasts did not adequately account
closer to the customer and on more of a just-in- for the potential demand if stock outs could be
time basis. addressed.
9

10. To address these issues, the introduced because it could not ramp up
manufacturer, developed a supplier managed new product introduction fast enough. The
inventory (SMI) approach that eliminated manufacturer’s overall goal was enabling higher
distribution tiers, review cycles and order actual product availability levels during new
distortion and improved demand response. It product introductions. It used the following
also developed business and system models for approach to apply an IO engine to help determine
collaborative inventory forecasting with channel how to properly stage pre-build and pipeline
partners and customers, including short, medium inventories:
and long term forecasts of consumption,
replenishment and aggregate sales. • First, it identified a series of product
The manufacturer approached IO by lifecycle stages (Product launch phase,
deploying replenishment planning and execution Ramp up phase, Maturity phase,
solution to support SMI with data shared across End-of-Life phase), each with its own
customers and channel partners allowing the target service level. Demand
company to simultaneously improve planning characteristics for each phase were also
and execution processes. In addition, for each identified, viz.,
SMI replenishment process the manufacturer • Forecast accuracy
deployed, a replenishment lot size optimization • Demand growth
was performed using several months of • Demand variability (based on actual
historical data. demand)
After deploying its solution with • Demand price sensitivity
several customers, the manufacturer realized
significant benefits. With a single channel • Second, it developed a supply chain
partner and distributor in Southeast Asia model that characterized these
- the manufacturer was able to achieve: dimensions across timeframes
corresponding to the expected duration
• A $31 million increase in revenues of each lifecycle stage
• A $40 million improvement in cash flow • Finally it ran the IO engine to optimize
• Recovery of demand lost due to retail inventory target settings to incorporate
stock outs – meeting this demand meant in planning and procurement policies for
increased sales volume and market share. each product at key phases of its product
lifecycle. For the old generation product,
The second example is that of a inventory targets were adjusted for
diversified industrial products manufacturer. its end-of-life phase. For the new
The manufacturer has a range of industrial generation product, inventory targets
products that include industrial solvents and were adjusted for product launch and
machine tools. It faced complexity in managing ramp-up phase.
transition from the older generation product to
its newer generation replacement. It frequently By optimizing inventory targets and
overbuilt its old generation product for several replenishment policies in this manner, the
weeks after the new generation product was manufacturer was able to deliver minimum
10

11. cost and maximum distribution coverage for and efforts to induce flexibility in the supply
both products. By allowing service levels chain are still necessary but not sufficient
to vary with naturally occurring shifts in to manage the growing multi-dimensional
demand variability across lifecycle stages, the complexity. A suggested approach is to adopt
manufacturer was able to reduce inventory inventory optimization concepts, techniques,
by more than 18% with no impact on product and technologies.
availability. Inventory optimization is a powerful
In yet another case, a power tools problem solving approach backed by advanced
manufacturer leveraged an IO engine working technology. The concepts, techniques, and
along side Material Requirements Planning technologies of inventory optimization help
(MRP) and Advanced Planning and Scheduling model, characterize, and account for supply
(APS) systems to enable granular planning of chain uncertainty. This uncertainty is a key
WIP inventory positions (location, target buffer reason manufacturers maintain higher than
quantities, replenishment policies) throughout needed inventory levels. Inventory is a buffer
Inventory optimization is the new problem solving
mantra for all supply chain related issues
its supply chain [5]. The IO engine determined against the uncertainty related to variable
a globally optimal placement of inventory, processing and replenishment lead times, erratic
considering its cost at each stage in the supply demand and forecast bias or error,
chain and also the service level targets and The key to effectively leveraging
replenishment lead times that constrain each inventory optimization lies in viewing it as a
inventory location. problem solving approach. A specific set of
constraints and parameters has to be identified
CONCLUSION and modeled to characterize supply chain
The increased complexity of manufacturing and behavior. An objective function is to be derived
distribution among global manufacturers due from the model to isolate the parameter requiring
to greater variability and uncertainty across optimization. Finally, higher order mathematics
the supply chain suggests that a new approach are to be applied to solving the function, often
to controlling and reducing inventory levels is aided by an IO engine or large scale computing
required. Pressures and trends impacting a capacity. Using this approach, changes can to
manufacturer’s ability to effectively manage be effected to inventory planning, inventory
inventory at a global level are increasing. stocking, replenishment, and transportation
Traditional methods such as accurate forecasting processes. Also this could lead to defining
11

12. the underlying operational policies at a very management practices Outdated,
granular level. Aberdeen Group, March 2005.
4. Matthew Menner and Dan Harmeyer,
REFERENCES Developing Creative Supply Chain
1. Sensing and Shaping Demand Strategies in Highly Competitive
in a Consumer-driven Marketplace, Markets” Conference Proceedings,
Electronics Supply Chain Assoication presented by at the 2006 SCOR Supply
Report, 2006. Chain World North America Conference.
2. Kevin O’Marah, The Supply Chain Boston, MA USA. March 29, 2006.
Top 25: Supply Chain Leadership for the 5. Sunil Chopra and Peter Meindl, Supply
21st Century, AMR Research, Nov 9, 2006. Chain Management’ Third Edition,
3. Beth Enslow, Are Your Inventory Prentice Hall, March 2006.
12

13. Author profile
GREG SCHEUFFELE
Greg Scheuffele is a Principal and Solutions Manager in Infosys’ High Tech and Discrete Manufacturing practice. He
manages Infosys’ portfolio of solutions for inventory design, optimization, and replenishment. He can be contacted at
Greg_Scheuffele@infosys.com.
ANUPAM KULSHRESHTHA
Anupam Kulshreshtha PhD, is a Consultant with the Analytics Services practice of Infosys’ Domain Competency Group.
He can be reached at Anupam_K@infosys.com.
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