09.12.2022 Views

Operations and Supply Chain Management The Core

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

350 OPERATIONS AND SUPPLY CHAIN MANAGEMENT

LO11–1 Explain

how inventory is

used and understand

what it costs.

UNDERSTANDING INVENTORY MANAGEMENT

The economic benefit from inventory reduction is evident from the following statistics:

The average cost of inventory in the United States is 30 to 35 percent of its value. For

example, if a firm carries an inventory of $20 million, it costs the firm more than $6 million

per year to keep it in stock. These costs are due mainly to obsolescence, insurance, and

opportunity costs. If the amount of inventory could be reduced to $10 million, for instance,

the firm would save over $3 million, which goes directly to the bottom line; that is, the savings

from reduced inventory results in increased profit.

This chapter and Chapter 9 present techniques designed to manage inventory in different

supply chain settings. In this chapter, the focus is on settings where the desire is

to maintain a stock of inventory that can be delivered to customers on demand. Recall in

Chapter 6 the concept of customer order decoupling point, which is a point where inventory

is positioned to allow processes or entities in the supply chain to operate independently.

For example, if a product is stocked at a retailer, the customer pulls the item from

the shelf and the manufacturer never sees a customer order. In this case, inventory acts as

a buffer to separate the customer from the manufacturing process. Selection of decoupling

points is a strategic decision that determines customer lead times and can greatly impact

inventory investment. The closer this point is to the customer, the quicker the customer can

be served.

The techniques described in this chapter are suited for managing the inventory at these

decoupling points. Typically, there is a trade-off where quicker response to customer

demand comes at the expense of greater inventory investment. This is because finished

goods inventory is more expensive than raw material inventory. In practice, the idea of a

single decoupling point in a supply chain is unrealistic. There may actually be multiple

points where buffering takes place.

Good examples of where the models described in this chapter are used include retail

stores, grocery stores, wholesale distributors, hospital suppliers, and suppliers of repair

parts needed to fix or maintain equipment quickly. Situations in which it is necessary to

have the item “in stock” are ideal candidates for the models described in this chapter. A

distinction that needs to be made with the models included in this chapter is whether this is

a one-time purchase, for example, for a seasonal item or for one used at a special event, or

whether the item will be stocked on an ongoing basis.

Exhibit 11.1 depicts different types of supply chain inventories that would exist in

a make-to-stock environment, typical of items directed at the consumer. In the upper

echelons of the supply chain, which are supply points closer to the customer, stock usually

is kept so that an item can be delivered quickly when a customer need occurs. Of

course, there are many exceptions, but in general this is the case. The raw materials and

manufacturing plant inventory held in the lower echelon potentially can be managed

in a special way to take advantage of the planning and synchronization needed to efficiently

operate this part of the supply chain. In this case, the models in this chapter are

most appropriate for the upper echelon inventories (retail and warehouse), and the lower

echelon should use the material requirements planning (MRP) technique described

in Chapter 9. The applicability of these models could be different for other environments

such as when we produce directly to customer order as in the case of an aircraft

manufacturer.

The techniques described here are most appropriate when demand is difficult to predict

with great precision. In these models, we characterize demand by using a probability

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!