The document discusses the significance of using a stochastic multistage, multi-inventory model for optimizing inventory management in supply chains. It highlights how traditional inventory planning methods, based on outdated rules of thumb, can lead to inefficiencies, while modern approaches facilitate better visibility into demand and interdependencies among inventory targets across locations. By leveraging advanced algorithms and data connectivity, organizations can continuously improve their inventory strategies, enhance customer service levels, and minimize working capital requirements.

1. Thought Leadership
Supply Chain Management
INVENTORY OPTIMIZATION
A TECHNIQUE FOR IMPROVING OPERATIONAL
INVENTORY TARGETS

2. The stochastic multistage, multi-inventory model recognizes a simple fact: inventory
targets at one location affect, and are affected by, targets at different locations. To
minimize the need for excess safety stock, this model uses an efficient computational
process where multiple mathematical equations are solved simultaneously to reveal
optimal inventory targets.

3. EXECUTIVE SUMMARY
ON THE ROAD TO OPTIMIZATION
For years, supply chain planners have used outdated
inventory management models, resulting in supply chain
inefficiencies that their companies can ill afford in today’s
ultracompetitive environment.
To boost efficiency, run leaner, and This paper examines how the stochas- Inventory optimization
drive down the cost of supply, many tic multistage, multi-inventory model
organizations are exploring inventory supports inventory optimization. It also
augments existing ERP
optimization – a technique for improving looks at how the SAP® Enterprise and advanced planning and
operational inventory targets to Inventory Optimization application by
address the realities of today’s highly SmartOps uses the model to help sup-
scheduling systems, helping
complex, ever-evolving global supply ply planners execute more effectively in planners improve operational
chains. a highly collaborative enterprise envi-
ronment that extends beyond organiza-
inventory targets.
tional boundaries.
Overview
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4. INVENTORY MANAGEMENT HAS CHANGED
HAS YOUR APPROACH CHANGED WITH IT?
Traditional approaches
to inventory planning –
based largely on rules
of thumb rather than
formal data analysis –
lead to out-of-stock
situations and supply
inefficiencies.
Traditional approaches to inventory (APS) systems, helping planners staggering number of variables, con-
planning are based largely on rules improve operational inventory targets straints, and what-if scenarios, this
of thumb. Planners develop a set of with item-location-period granularity for model empowers planners to manage
heuristics for determining safety stock finished goods, intermediates, or raw the complexity of today’s supply
levels, and these rules get handed down materials and components. The result chains. This differs from traditional
from one generation to the next. Some is better visibility into demand, enabling inventory management models that
organizations also use rudimentary planners to modify inventory to reduce depend on deterministic, discrete-time
analysis, where items are weighted on carrying costs without negatively im- inventory theory in the following ways.
an A-B-C scale with the top priority pacting customer service levels and
of keeping A-level items in stock. The sacrificing product availability. This Molecular, Not Atomic
assumptions made for this sort of analy- empowers organizations to improve Traditional inventory management views
sis, however, are typically revisited only performance and more effectively meet the supply chain according to a single-
rarely. In fast-changing environments, the terms of service-level agreements. stage, single-item model that can be
this approach leads to out-of-stock situ- characterized as atomistic. This model
ations and supply chain inefficiencies. The Science sees each stock item and each stage
or stock location (such as a distribution
The complexities and uncertainties rep- The science behind inventory optimiza- center) as isolated entities with few if
resented by today’s constantly evolving tion involves stochastic (probabilistic) any meaningful dependencies.
global supply chains demand a dynamic multistage, multi-inventory modeling
new approach, and that’s called inven- where a sophisticated algorithm is used The multistage model, in contrast, views
tory optimization. The inventory optimi- to assess vast amounts of historic and the supply chain in terms of molecules –
zation approach augments existing real-time information while accounting which are made up of atoms. This re-
enterprise resource planning (ERP) and for multiple variabilities and interdepen- flects the reality of today’s multistage
advanced planning and scheduling dencies. By enabling the analysis of a supply chains that include suppliers,
4 SAP Thought Leadership – Inventory Optimization

5. numerous manufacturing stages, sub- Variable, Not Unvarying planning functionality for understanding
contractors, vendor-managed inventory, Traditional models lack the mechanisms inventory liabilities, maintaining service
central warehouses, and multiple distri- to dynamically capture changing values levels, and operating according to sup-
bution centers. This model recognizes or correct for time-varying errors. The plier and vendor-managed inventory
a simple fact: inventory targets at one multistage model, by contrast, is flexi- business models.
location affect, and are affected by, ble enough to continuously incorporate
targets at other locations. To minimize the results of ongoing data analysis. Flexibility
the need for excess safety stock, this This analysis helps identify forecast An inventory optimization application
model uses an efficient computational errors and biases so that planners can needs to be flexible enough to accom-
process where multiple mathematical correct values and modify assumptions modate different industries – such as
equations are solved simultaneously to over time, thus supporting continuous consumer products, chemicals, manu-
reveal optimal inventory targets. improvement and closing the loop on facturing, wholesale distribution, and
inventory management. high tech. A useful feature in this
Dynamic, Not Static respect would be reporting tools that
To accommodate the reality of con- The Business Requirements can help validate, analyze, and improve
stantly changing demand, inventory industry-specific supply chain informa-
planners often cobble together sets of The purpose of the multistage model is tion regarding demand, supply, and pro-
unconnected static models to ensure to help planners in real-world supply duction elements.
proper levels of safety stock. In con- planning scenarios. Any application that
trast, the multistage model is designed seeks to leverage the multistage model Data Input Connectivity
to be nonstationary or time varying to must meet the following requirements. Better outputs require better inputs.
accommodate shifting demand. Excess Thus, an inventory optimization applica-
inventory from previous periods can be tion should include data connectivity
used to satisfy demand in future lower-
No planner knows every- modules that can work with a wide
demand periods, making the model thing, and the models that range of data sources to transform and
intertemporally consistent. The non- load raw data easily and automatically.
stationary character of the model also planners work with should These modules should also accommo-
allows organizations to model season- reflect this reality. date the supply chain network structure
ality, promotions, and end-of-quarter itself, which is often the most challeng-
spikes. It also accommodates the fact ing aspect of data connectivity.
that uncertainty is greater when looking Enterprise Readiness
farther ahead in time – something tradi- Many inventory optimization applica- Built-In Intelligence
tional approaches cannot do. tions – designed as desktop tools for The ability to access data is important.
offline analysis – fail to address the fact But planners also need to work with
Data-Driven, Not Assumption-Based that supply planning is a collaborative that data. An inventory optimization
The multistage model recognizes that activity that extends beyond enterprise application, then, must support robust
organizations work only with finite his- boundaries. They are neither scalable analysis to help planners understand
torical data. Traditional models, on the nor capable of automating planning pro- issues involving forecast bias and accu-
other hand, start with an idealized cesses. What’s needed is an applica- racy, supplier uncertainty, schedule
assumption that planners possess tion that leverages the multistage model adherence, and more.
complete knowledge of demand distri- at an enterprise level. Requirements
bution. No planner knows everything, include visibility across multiple ERP
and the models that planners use applications and planning systems,
should reflect this reality. along with support for global inventory-
SAP Thought Leadership – Inventory Optimization 5

6. Approval Workflow or the SAP Advanced Planning & Opti- In the end, these capabilities enable
Planners need to review updated inputs mization component. In either case, it your planners to optimize inventory
(such as forecasts), perform due dili- enables planners to dynamically deter- levels throughout the organization,
gence, and formally approve any modi- mine optimal demand-driven, time- helping you improve customer service
fied targets in order to avoid problems phased inventory targets for every item levels while minimizing working capital
downstream. An inventory optimization at every location throughout your supply requirements. This makes you more
application should accommodate this chain. Specifically, planners can: efficient and far more competitive.
Find Out More
To learn more find out more about
Many inventory optimization applications – designed inventory optimization and how SAP
as desktop tools for offline analysis – fail to address Enterprise Inventory Optimization can
help your organization compete more
the reality that supply planning is a collaborative effectively, contact your SAP represen-
activity that extends beyond enterprise boundaries. tative today or visit us online at
www.sap.com/solutions
/solutionextensions.
requirement with automated workflow • Coordinate capacity, inventory,
and alerts that allow planners to man- demand, lead time, and product avail-
age by exception. ability variables to gauge how much
inventory should be carried by item,
Continuous Improvement location, and time period
Supply chains are in constant flux. This • Leverage a multistage modeling
is why inventory optimization applica- approach to calculate the relationships
tions need to support continuous im- among inventories, service levels,
provement processes. Planners should capacity, and costs across all stocking
have the ability to play out what-if sce- locations and stages – and across
narios and study the ramifications of different types of supply chains within
proposed actions. organizations and beyond enterprise
boundaries to support supplier- and
The Application vendor-managed inventory
• Set and manage targets such as
The SAP Enterprise Inventory Optimi- safety stocks more frequently at
zation application addresses these a more granular level, supporting
requirements, helping organizations lean processes
strike the right balance between service • Accurately track and streamline inven-
levels and inventory investment. The tory positions throughout the order-to-
application can be used on a stand- cash value chain, using advanced
alone basis, if desired, or as an inte- algorithms that eliminate waste and
grated part of the SAP ERP application help the organization run lean
6 SAP Thought Leadership – Inventory Optimization

8. 50 097 312 (09/10)
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