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Part III:
Applications
Part III includes four chapters in which we explore supply chain modeling applications and
supporting concepts:
Chapter 8. Strategic and Tactical Supply Chain Planning
Chapter 9. Operational Supply Chain Planning
Chapter 10. Inventory Planning
Chapter 11. Supply Chain Decision Making Under Uncertainty
Optimization models for strategic and tactical planning are similar in their holistic analysis of a
company’s supply chain. Both types of models are intended to optimize integrated decision
making across purchasing, manufacturing, distribution, and transportation activities. Thus, it is
appropriate and convenient to discuss them together in Chapter 8. The principal difference is that
optimization models for strategic planning include redesign options, such as the location of new
distribution centers (DCs) or the acquisition of a manufacturing firm, whereas such options are
treated as fixed and given when considering the company’s tactical plans.
Optimization models for operational supply chain planning are usually much more myopic in their
scope and more detailed in their descriptions of the decisions to be analyzed. In Chapter 9, we
consider a variety of operational modeling applications. We also address the design of modeling
systems to support operational planning and their integration with other systems used by the
company to acquire and communicate data.
Inventory planning, which is important at all levels of decision making, is discussed as a standalone topic in Chapter 10. The topic merits special attention because inventory models and
solution methods are much different than optimization models and methods. Artistry is required to
integrate them. Chapter 10 also contains descriptions of successful modeling applications in
which inventory decisions were given priority.
In Chapter 11, we examine modeling approaches that explicitly treat uncertainties faced by the
decision maker. The goal of these stochastic models is to explicitly identify contingency plans and
hedging strategies for dealing with uncertainties. Although the models have been successfully
applied to a few supply chain planning problems, they are too sophisticated for today’s typical
planning situations. Still, we believe stochastic modeling approaches are becoming more realistic
as well as more important and therefore well worth our attention.
Chapter Ten:
Inventory Planning
A company may hold inventories of raw materials, parts, work-in-process, or finished products for
a variety of reasons, such as the following:
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To create buffers against the uncertainties of supply and demand
To take advantage of lower purchasing and transportation costs associated with
high volumes
To take advantage of economies of scale associated with manufacturing
products in batches
To build up reserves for seasonal demands or promotional sales
To accommodate products flowing from one location to another (work-in-process
or in transit)
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To exploit speculative opportunities for buying and selling commodities and other
products
Metrics describing the performance of a company’s inventory management practices can be
important signals to shareholders regarding the efficiency of the company’s operations and hence
its profitability. Figure 10.1 illustrates this point. The ratio of sales to inventory for Ford and
General Mills improved almost threefold between 1975 and 1994, conveying the notion that
supply chain management in these companies improved significantly over that period. 1 Studies
have shown, however, that improvements in inventory management over the past 20 years has
been uneven across industries and companies. Thus, most managers have an ongoing need for
better inventory practices.
In this chapter, we study models for inventory management with an emphasis on approaches for
integrating inventory decisions with other supply chain decisions. This perspective, which is
sometimes overlooked by managers responsible for controlling inventories, is crucial because
inventory costs are only one element of total supply chain cost. In some industries, such as
consumer products that have rapid turnover, inventory costs may be less than 10% of total supply
chain cost. In other industries, such as electronics products made of expensive components with
long manufacturing times, inventory costs may exceed 20%. Moreover, inventory costs and the
extent of management’s concern for inventory control will depend on the cost of capital, which will
continue to vary over economic cycles.
In addition to direct inventory costs, the firm incurs indirect costs associated with each stockkeeping unit (SKU) held in stock. These costs are for physical and human resources needed to
administer, store, count, and order the item. For retailing companies, aircraft manufacturers, and
other companies with tens of thousands of items in stock, such indirect inventory costs can be
quite large. This suggests that such firms should frequently review their product lines with an aim
to retiring or consolidating outdated, redundant, or unprofitable items.
Recently, attention has focused on creating business processes and implementing new
technologies that reduce or eliminate inventories, mainly by reducing or eliminating uncertainties
that make them necessary. Better coordination of activities across company functions and
between the company and its vendors and customers can greatly reduce uncertainties. Specific
measures include the following:
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Improving the accuracy of forecasts by developing better forecasting models and
by promoting better communication between supply chain managers and
marketing and sales personnel
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Sharing supply chain information with vendors, third-party transportation
providers, and other suppliers
Installing RFID and other electronic devices that more accurately track physical
inventories
Consolidating the number of locations where products are held and reducing
product variety
Postponing product customization to later stages of the supply chain
Despite such efforts, significant uncertainty may remain implying that inventories will still be
needed to ensure effective operations.
Models for optimizing inventory management decisions have been proposed, extended, and
applied for over 70 years.3 The models employ parameters describing holding costs, shortage
costs, replenishment delays, and probabilistic demand distributions for products specified at a
detailed SKU level. Classical inventory models use methods from statistics and applied
probability theory to compute safety stocks, replenishment points, and reorder quantities based
on these parameters. As such, they are very different in form from deterministic optimization
models, which broadly consider products, facilities, and transportation flows in analyzing resource
acquisition and allocation decisions. Inventory models involve relationships that are not easily
represented in optimization models.
Practitioners who develop supply chain models harbor an “open secret” about this modeling
incompatibility that is rarely revealed to the managers who are their clients. As a result, many
supply chain models developed to date are limited by one of two complementary deficiencies.
Some emphasize inventory decisions with insufficient detail about other supply chain decisions
such as those relating to facility location, manufacturing processes, transportation, and
warehousing. Conversely, other supply chain models emphasize these other decisions with
insufficient detail about inventory management.
In this chapter, we confront this modeling conflict and suggest approaches for resolving it by
integrating inventory decisions with other supply chain decisions. A guiding principle is
hierarchical planning. As shown in Figure 10.2, inventory planning as part of overall supply chain
management is divided into three categories:
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Strategic decision making involving long-term inventory deployment plans for
product families
Tactical decision making involving aggregate inventory plans for product families
Operational decision making involving detailed inventory management policies
for SKUs
Decision making at the three levels of planning should be linked to ensure short-, medium-, and
long-term profitability of the firm.
Classical inventory models are reviewed in Section 10.1. We explore modeling approaches in
Section 10.2 for incorporating inventory deployment decisions in strategic supply chain
optimization models. In Section 10.3, we examine an application of these approaches to
distribution network expansion planning in a retailing company. Aggregate inventory models for
tactical supply chain are examined in Section 10.4. A model for spare-parts inventory
management is reviewed in Section 10.5. Models for managing inventories in manufacturing
supply chains are presented in Section 10.6. The chapter concludes with final thoughts in Section
10.7.