Supply Chain Management

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Supply Chain Management

M. Eric Johnson

David F. Pyke

The Tuck School of BusinessDartmouth College

Hanover, NH 03755603 (646) 2136

July 27, 1999

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Introduction

Supply chain management (SCM) is the term used to describe the management of the flow of

materials, information, and funds across the entire supply chain, from suppliers to component producers

to final assemblers to distribution (warehouses and retailers), and ultimately to the consumer.1 In fact, it

often includes after-sales service and returns or recycling. Figure 1 is a schematic of a supply chain. In

contrast to multiechelon inventory management, which coordinates inventories at multiple locations,

SCM typically involves coordination of information and materials among multiple firms.

Supply chain management has generated much interest in recent years for a number of reasons.

Many managers now realize that actions taken by one member of the chain can influence the

profitability of all others in the chain.2 Firms are increasingly thinking in terms of competing as part of

a supply chain against other supply chains, rather than as a single firm against other individual firms.

Also, as firms successfully streamline their own operations, the next opportunity for improvement is

through better coordination with their suppliers and customers. The costs of poor coordination can be

extremely high. In the Italian pasta industry, consumer demand is quite steady throughout the year.

However, because of trade promotions, volume discounts, long lead times, full-truckload discounts, and

end-of-quarter sales incentives the orders seen at the manufacturers are highly variable (Hammond

(1994)). In fact, the variability increases in moving up the supply chain from consumer to grocery store

to distribution center to central warehouse to factory, a phenomenon that is often called the bullwhip

effect (see Figure 2 as an example). The bullwhip effect has been experienced by many students playing

the “Beer Distribution Game.” (Sterman (1989); Sterman (1992); Chen & Samroengraja (2000); Jacobs

(2000)) The costs of this variability are high -- inefficient use of production and warehouse resources,

1 Much of the material in this article is based on Chapter 12 of Silver, Pyke, & Peterson (1998) and Johnson & Pyke(2000a).

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high transportation costs, and high inventory costs, to name a few. Acer America, Inc. sacrificed $20

million in profits by paying $10 million for air freight to keep up with surging demand, and then paying

$10 million more later when that inventory became obsolete.3

It seems that integration, long the dream of management gurus, has finally been sinking

into the minds of western managers. Some would argue that managers have long been interested

in integration, but the lack of information technology made it impossible to implement a more

“systems-oriented” approach. Clearly industrial dynamics researchers dating back to the 1950’s

(Forrester (1958); Forrester (1961)) have maintained that supply chains should be viewed as an

integrated system. With the recent explosion of inexpensive information technology, it seems

only natural that business would become more supply chain focused. However, while

technology is clearly an enabler of integration, it alone can not explain the radical organizational

changes in both individual firms and whole industries. Changes in both technology and

management theory set the stage for integrated supply chain management. One reason for the

change in management theory is the power shift from manufacturers to retailers. Wal-Mart, for

instance, has forced many manufacturers to improve their management of inventories, and even

to manage inventories of their products at Wal-Mart.

While integration, information technology and retail power may be key catalysts in the

surge of interest surrounding supply chains, eBusiness is fueling even stronger excitement.

eBusiness facilitates the virtual supply chain, and as companies manage these virtual networks,

the importance of integration is magnified. Firms like Amazon.com are superb at managing the

2 Certain industries use other terms in place of SCM. For example, many grocery industry executives are pursuingefficient consumer response (ECR), the equivalent of just-in-time distribution or “continuous replenishment.” Weconsider initiatives such as ECR to be aspects of supply chain management.3 See Business Week (September 30, 1996), pg. 72. See also Towill & DelVecchio (1994); Berry, Naim, & Towill(1995); and Buzzell & Ortmeyer (1995).

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flow of information and funds, via the Internet and electronic funds transfer. Now, the challenge

is to efficiently manage the flow of products.

Some would argue that the language and metaphors are wrong. “Chains” evoke images of linear,

unchanging, and powerless. “Supply” feels pushy and reeks of mass production rather than mass

customization. Better names, like “demand networks” or “customer driven webs” have been proposed

by many a potential book author hoping to invent a new trend. Yet, for now, the name “supply chain”

seems to have stuck. And under any name, the future of supply chain management appears bright.

Component Producer (s)

Warehouse Warehouse

Retailoutlets

Customers

Outside supplier (s)

Landfill

Final Assembly

Figure 1: A Schematic of a Supply Chain

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Figure 2: An Illustration of the Bullwhip Effect

Grocery Store Order Size Units

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Distribution Center Order Size Units

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Central Warehouse Order Size Units

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Key Components of Supply Chain Management

Supply chain management is an enormous topic covering multiple disciplines and

employing many quantitative and qualitative tools. Within the last few years, several textbooks

on supply chain have arrived on the market providing both managerial overviews and detailed

technical treatments. For examples of managerial introductions to supply chain see Copacino

(1997), Fine (1998) and Handfield & Nichols (1998), and for logistics texts see Lambert, Stock,

Ellram, & Stockdale (1997) and Ballou (1998). For more technical, model-based treatments see

Silver et al. (1998) and Simchi-Levi, Kaminsky, & Simchi-Levi (1998). Tayur, Magazine, &

Ganeshan (1999) is an extensive collection of research papers while Johnson & Pyke (2000b) is

a collection papers on teaching supply chain management. Also, there are several casebooks that

give emphasis to global management issues including Taylor (1997), Flaherty (1996), and

Dornier, Ernst, Fender, & Kouvelis (1998). Introductory articles include Cooper, Lambert, &

Pagh (1997b), Davis (1993), Johnson (1998a), and Lee & Billington (1992).

To help order our discussion, we have divided supply chain management into twelve

areas.4 We identified these twelve areas from our own experience teaching and researching

supply chain management, from analysis of syllabi and research papers on supply chain, and

from our discussions with managers. Each area represents a supply chain issue facing the firm.

For any particular problem or issue, managers may apply analysis or decision support tools. For

each of the twelve areas, we provide a brief description of the basic content and refer the reader

to a few research papers that apply. We also mention likely Operations Research based tools that

may aid analysis and decision support. We do not provide an exhaustive review of the research

literature, but rather provide a few references to help the reader get started in an area. For a more

4 See Johnson & Pyke (2000a) for a list of teaching cases and popular press articles that fit within each area.

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detailed review of recent research and teaching in supply chain management see Ganeshan, Jack,

& Magazine (1999) and Johnson & Pyke (2000a) respectively.

The twelve categories we define are

• location

• transportation and logistics

• inventory and forecasting

• marketing and channel restructuring

• sourcing and supplier management

• information and electronic mediated environments

• product design and new product introduction

• service and after sales support

• reverse logistics and green issues

• outsourcing and strategic alliances

• metrics and incentives

• global issues.

Location pertains to both qualitative and quantitative aspects of facility location

decisions. This includes models of facility location, geographic information systems (GIS),

country differences, taxes and duties, transportation costs associated with certain locations, and

government incentives (Hammond & Kelly (1990)). Exchange rate issues fall in this category,

as do economies and diseconomies of scale and scope. Decisions at this level set the physical

structure of the supply chain and therefore establish constraints for more tactical decisions.

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Binary integer programming models play a role here, as do simple spreadsheet models and

qualitative analyses. There are many advanced texts specially dedicated to the modeling aspects

of location (Drezner (1996)) and most books on logistics also cover the subject. Simchi-Levi et

al. (1998) present a substantial treatment of GIS while Dornier et al. (1998) dedicate a chapter to

issues of taxes, duties, exchange rates, and other global location issues (Brush, Maritan, &

Karnani (1999)). Ballou & Masters (1999) examine several software products that provide

optimization tools for solving industrial location problems.

The transportation and logistics category encompasses all issues related to the flow of

goods through the supply chain, including transportation, warehousing, and material handling.

This category includes many of the current trends in transportation management including

vehicle routing (Bodin (1990), Gendreau, Laport, & Seguin (1996), and Anily & Bramel (1999)),

dynamic fleet management with global positioning systems, and merge-in-transit. Also included

are topics in warehousing and distribution such as cross docking (Kopczak, Lee, & Whang

(1995)) and materials handling technologies for sorting, storing, and retrieving products

(Johnson & Brandeau (1999) and Johnson (1998b)).

Because of globalization and the spread of outsourced logistics, this category has

received much attention in recent years. However, we will define a separate category to examine

issues specifically related to outsourcing and logistics alliances. Both deterministic (such as

linear programming and the traveling salesman problem) and stochastic optimization models

(stochastic routing and transportation models with queueing) often are used here, as are

spreadsheet models and qualitative analysis. Recent management literature has examined the

changes within the logistics functions of many firms as the result of functional integration (Greis

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& Kasarda (1997)) and the role of logistics in gaining competitive advantage (Fuller, O'Conor, &

Rawlinson (1993)).

Inventory and forecasting includes traditional inventory and forecasting models.

Inventory costs are some of the easiest to identify and reduce when attacking supply chain

problems. Simple stochastic inventory models can identify the potential cost savings from, for

example, sharing information with supply chain partners (Lee & Nahmias (1993)), but more

complex models are required to coordinate multiple locations. A few years ago, multiechelon

inventory theory captured most of the research in this area that would apply to supply chains.

However, in nearly every case, multiechelon inventory models assume a single decision-maker.

Supply chains, unfortunately, confront the problem of multiple firms, each with its own decision-

maker and objectives. Of course there are many full texts on the subject such as Silver et al.

(1998) and Graves, Rinnooy Kan, & Zipkin (1993)). Useful managerial articles focusing on

inventory and forecasting include Davis (1993) and Fisher, Hammond, Obermeyer, & Raman

(1994).

Clark & Scarf (1960) perform one of the earliest studies in serial systems with

probabilistic demand. They introduce the concept of an imputed penalty cost, wherein a shortage

at a higher echelon generates an additional cost. This cost enables us to decompose the

multiechelon system into a series of stages so that, assuming centralized control and the

availability of global information, the ordering policies can be optimized. Lee & Whang (1999a)

and Chen (1996) both propose performance measurement schemes for individual managers that

allow for decentralized control (so that each manager makes decisions independently), and in

certain instances, local information only. The result is a solution that achieves the same optimal

solution as if we assumed centralized control and global information.

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Marketing and channel restructuring includes fundamental thinking on supply chain

structure (Fisher (1997)) and covers the interface with marketing that emerges from having to

deal with downstream customers (Narus & Anderson (1996)). While the inventory category

addresses the quantitative side of these relationships, this category covers relationship

management, negotiations, and even the legal dimension. Most importantly, it examines the role

of channel management (Anderson, Day, & Rangan (1997)) and supply chain structure in light of

the well-studied phenomena of the bullwhip effect that was noted in the introduction.

The bullwhip effect has received enormous attention in the research literature. Many authors

have noted that central warehouses are designed to buffer the factory from variability in retail orders.

The inventory held in these warehouses should allow factories to smooth production while meeting

variable customer demand. However, empirical data suggests that exactly the opposite happens. (See

for example Blinder (1981), and Baganha & Cohen (1998).) Orders seen at the higher levels of the

supply chain exhibit more variability than those at levels closer to the customer. In other words, the

bullwhip effect is real. Typically causes include those noted in the introduction, as well as the fact that

retailers and distributors often over-react to shortages by ordering more than they need. Lee,

Padmanabhan, & Whang (1997) show how four rational factors help to create the bullwhip effect:

demand signal processing (if demand increases, firms order more in anticipation of further increases,

thereby communicating an artificially high level of demand); the rationing game (there is, or might be, a

shortage so a firm orders more than the actual forecast in the hope of receiving a larger share of the

items in short supply); order batching (fixed costs at one location lead to batching of orders); and

manufacturer price variations (which encourage bulk orders). The latter two factors generate large

orders that are followed by small orders, which implies increased variability at upstream locations.

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Some recent innovations, such as increased communication about consumer demand, via

electronic data interchange (EDI) and the Internet, and everyday low pricing5 (EDLP) (to

eliminate forward buying of bulk orders), can mitigate the bullwhip effect.6 In fact, the number

of firms ordering, and receiving orders, via EDI and the Internet is exploding. The information

available to supply chain partners, and the speed with which it is available, has the potential to

radically reduce inventories and increase customer service.7 Other initiatives can also mitigate

the bullwhip effect. For example, changes in pricing and trade promotions (Buzzell, Quelch, &

Salmon (1990)) and channel initiatives, such as vendor managed inventory (VMI), coordinated

forecasting and replenishment (CFAR), and continuous replenishment (Fites (1996), Verity

(1996), Waller, Johnson, & Davis (1999)), can significantly reduce demand variance. Vendor

Managed Inventory is one of the most widely discussed partnering initiatives for improving

multi-firm supply chain efficiency. Popularized in the late 1980s by Wal-Mart and Procter &

Gamble, VMI became one of the key programs in the grocery industry’s pursuit of “efficient

consumer response” and the garment industry’s “quick response.” Successful VMI initiatives

have been trumpeted by other companies in the United States, including Campbell Soup and

Johnson & Johnson, and by European firms like Barilla (the pasta manufacturer).

In a VMI partnership, the supplier—usually the manufacturer but sometimes a reseller or

distributor—makes the main inventory replenishment decisions for the consuming organization.

This means the supplier monitors the buyer’s inventory levels (physically or via electronic

5 See Bell, Ho, & Tang (1998a; Bell, Ho, & Tang (1998b) for a discussion of EDLP versus High-Low pricing. Theyshow with a simple model that High-Low pricers can charge a higher average price without risking the loss ofrational customers.6 In addition, Baganha & Cohen (1998) note that, if locations that are designed to buffer the factory from variabilityin retail orders follow the optimal policy, the variance can in fact be reduced. In particular, these locations shouldaccount for autocorrelation in the demand process; that is, if a retailer orders today, it is unlikely that she will orderin the next few days. See Bourland, Powell, & Pyke (1996); Srinivasan, Kekre, & Mukhopadhyay (1994), and Lee,So, & Tang (1999).

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messaging) and makes periodic resupply decisions regarding order quantities, shipping, and

timing. Transactions customarily initiated by the buyer (like purchase orders) are initiated by the

supplier instead. Indeed, the purchase order acknowledgment from the supplier may be the first

indication that a transaction is taking place; an advance shipping notice informs the buyer of

materials in transit. Thus the manufacturer is responsible for both its own inventory and the

inventory stored at is customers’ distribution centers (Figure 3).

Mfg-DC

Manufacturer

VMI-DC

VMI-DC

VMI-DC

VMI-DC

Retail

Retail

Retail

Retail

Retail

3rd-DC

Retail

Retail

Retail

Figure 3: Typical VMI implementation (adapted from Waller et al. (1999)).

Because many of these initiatives involve channel partnerships and distribution agreements, this

category also contains important information on pricing, along with anti-trust and other legal issues

7 See for example Moinzadeh & Aggarwal (1997) and Lee & Whang (1999b). Milgrom & Roberts (1988) note thatinventory and information are substitutes.

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(Train (1998)). These innovations require interfirm, and often intrafirm, cooperation and coordination

that can be difficult to achieve.

While marketing focuses downstream in the supply chain, sourcing and supplier

management looks upstream to suppliers. Make/buy decisions (Venkatesan (1992), Carroll

(1993), Christensen (1994), Quinn & Hilmer (1994), Kelley (1995), and Robertson & Langlois

(1995)) fall into this category, as does global sourcing (Little (1995) and Pyke (1994)). The

location category addresses the location of a firm’s own facilities, while this category pertains to

the location of the firm’s suppliers. Supplier relationship management falls into this category as

well (McMillan (1990) and Womack, Jones, & Roos (1991)). Some firms are putting part

specifications on the web so that dozens of suppliers can bid on jobs. GE, for instance, has

developed a trading process network that allows many more suppliers to bid than was possible

before. The automotive assemblers have developed a similar capability; and independent

Internet firms, such as Digital Market, are providing services focused on certain product

categories. Other firms are moving in the opposite direction by reducing the number of

suppliers, in some cases to a sole source (Helper & Sako (1995) and Cusumano & Takeishi

(1991)). Determining the number of suppliers and the best way to structure supplier

relationships is becoming an important topic in supply chains (Cohen & Agrawal (1996), Dyer

(1996), Magretta (1998), and Pyke (1998)).

Much of the research in this area makes use of game theory to understand supplier

relationships, contracts, and performance metrics. See, for instance, Cachon & Lariviere (1996);

Cachon (1997); and Tsay, Hahmias, & Agrawal (1999).

The information and electronic mediated environments category addresses long-standing

applications of information technology to reduce inventory (Woolley (1997)) and the rapidly

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expanding area of electronic commerce (Benjamin & Wigand (1997) and Schonfeld (1998)).

Often this subject may take a more systems orientation, examining the role of systems science

and information within a supply chain (Senge (1990)). Such a discussion naturally focuses

attention on integrative ERP software such as SAP (Whang, Gilland, & Lee (1995)), Baan and

Oracle, as well as supply chain offerings such as i2’s Rhythm and Peoplesoft’s Red Pepper. The

many supply chain changes wrought by electronic commerce are particularly interesting to

examine, including both the highly publicized retail channel changes (like Amazon.com) and the

more substantial business to business innovations (like the GE trading process network). It is

here that we interface most directly with colleagues in information technology and strategy,

which again creates opportunities for cross-functional integration (Lee & Whang (1999b)).

Product design and new product introduction deals with design issues for mass

customization, delayed differentiation, modularity and other issues for new product introduction.

With the increasing supply chain demands of product variety (Gilmore & Pine (1997)) and

customization (McCutcheon, Raturi, & Meredith (1994)), there is an increasing body of research

available. One of the most exciting applications of "supply chain thinking" is the increased use

of postponed product differentiation (Feitzinger & Lee (1997)). Traditionally, products destined

for world markets would be customized at the factory to suit local market tastes. While a

customized product is desirable, managing worldwide inventory is often a nightmare. Using

postponement the product is redesigned so that it can be customized for local tastes in the

distribution channel. The same generic product is produced at the factory and held throughout

the world (Figure 4). Thus if the French version selling well, but the German version is not,

German product can be quickly shipped to France and customized for the French market.

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Figure 4: Using postponement a product destined for both US and Europe markets isredesigned so that local content can be added to a common platform within distribution

(adapted from Johnson & Anderson (1999)).

Manufacturer4

3

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US Distribution

Europe Distribution

43

21

43

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Product Platform

Multiple SKUs -

Customers

Customers

at DC

Manufacturer

US Distribution

Europe Distribution

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Customers

Customers43

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For these problems, we find an interface with engineering and development, with clear

implications for product cost and inventory savings. Stochastic inventory models are often used

to identify some of the benefits of these initiatives (Lee, Billington, & Carter (1993)). Also

important are issues related to product design (Ulrich & Ellison (1999) and Robertson & Ulrich

(1998)), managing product variety (Fisher, Ramdas, & Ulrich (1999)) and managing new

product introduction and product rollover (Billington, Lee, & Tang (1998)).

The service and after sales support category addresses the critical, but often overlooked,

problem of providing service and service parts (Cohen & Lee (1990)). Some leading firms, such

as Saturn and Caterpillar, build their reputations on their ability in this area, and this capability

generates significant sales (Cohen, Zheng, & Agrawal (1997)). Stochastic inventory models for

slow-moving items fall into this category, and there are many papers on this topic related to

inventory management (Williams (1984); Cohen, Kleindorfer, & Lee (1986)) and forecasting

(Johnston & Boylan (1996)). While industry practice still shows much room for improvement

(Cohen et al. (1997)), several well-known firms have shown how spare parts can be managed

more effectively (Cohen, Kamesam, Kleindorfer, Lee, & Tekerian (1990); Cohen, Kleindorfer,

& Lee (1992); and Cohen, Zheng, & Wang (1999)).

Reverse logistics and green issues are emerging dimensions of supply chain management

(Marien (1998)). This area examines both environmental issues (Herzlinger (1994) and the

reverse logistics issues of product returns (Padmanabhan & Png (1995), Clendenin (1997), and

Rudi & Pyke (1998)). Because of legislation and consumer pressure, the growing importance of

these issues is evident to most managers. Managers are being compelled to consider the most

efficient and environmentally friendly way to deal with product recovery, and researchers have

begun significant effort in modeling these systems.

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The term “product recovery” encompasses the handling of all used and discarded products,

components and materials. Thierry, Salomon, Van Nunen, & Van Wassenhove (1995) note that product

recovery management attempts to recover as much economic value as possible, while reducing the total

amount of waste. They also provide a framework and a set of definitions that can help managers think

about the issues in an organized way (see Figure 5). These authors examine the differences among

various product recovery options including repair, refurbishing, remanufacturing, cannibalization, and

recycling. The whole process of manufacturing begins, of course, with product design. Today, firms

are beginning to consider design for the environment (DFE) and design for disassembly (DFD) in their

product development processes. Unfortunately, AT&T discovered that designing products for reuse can

result in more materials and complexity, thereby violating other environmental goals. (See Frankel

(1996), who also reports on product takeback and recycling initiatives in numerous countries.)

RawMaterials

PartsFabrication

Modules Subassembly

Product Assembly

Distribution Users

Recycle Cannibalize Remanufacture Refurbish Repair Reuse

Landfill

Figure 5: Product Recovery Options (adapted from Thierry et al. (1995)).

The analysis of the recovery situation is considerably more complicated than that of

consumables. Normally, in a recovery situation some items cannot be recovered, so the number of units

demanded is not balanced completely by the return of reusable units. Thus, in addition to recovered

units, a firm must also purchase some new units from time to time. Consequently, even at a single

location, there are five decision variables: (1) how often to review the stock status, (2) when to recover

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returned units, (3) how many to recover at a time, (4) when to order new units, and (5) how many to

order. When there are multiple locations, the firm must decide how many good units to deploy to a

central warehouse, and how many to deploy to each retailer or field stocking location.

With consumable items the lead time to the retailers is a transportation time from the warehouse

plus a random component, depending on whether the warehouse has stock. With recoverable items, the

lead time is the transportation time plus the time to recovery, if the warehouse does not have stock. So

in some cases the two systems can be treated in almost the same way. However, if the recovery facility

has limited capacity, or if the number of items in the system is small, the systems will differ

significantly. For example, if many items have failed and are now in recovery, they cannot be in the

field generating failures. Therefore, the demand rate at the warehouse will decline. In a consumable

system, it is usually assumed that the demand rate does not depend on how many items have been

consumed.

Most of the research in this area relevant to this article concerns products and packaging after

manufacturing has been completed. For example, a large U. S. chemical company gained significant

market share in water treatment chemicals by delivering its products in reusable containers. The

customers (hospitals and other large institutions, for example) need never touch the chemicals or deal

with the disposal of used containers. This problem has been addressed by Goh & Varaprasad (1986);

Kelle & Silver (1989); and Castillo & Cochran (1996), among others.

Some products that are not reused as is can be disassembled so that some of the parts can be used

in remanufactured products. Muckstadt & Isaac (1981) report on a model developed in connection with

a manufacturer of reprographic equipment. There is a single location with two types of inventory:

serviceable and repairable. Demands for serviceable units and returns of repairable units occur

probabilistically, specifically, according to independent Poisson processes with rates D and fD,

respectively (where f is a fraction). In addition, repairs are done on a continuous, first come-first served

basis (for example, at a local machine shop). Any demands for serviceable units, when none is

available, are backordered at a cost per unit short per unit time. Purchases of new stock from outside

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involve a known lead time. With respect to purchase decisions, a continuous review (s,Q) system is

used; specifically, when the inventory position drops to s or lower, a quantity Q is purchased.

Inderfurth (1997) extends the Muckstadt & Isaac model to a remanufacturing problem in which

there are two decisions each period: how many returned products to remanufacture (the remainder will

be disposed of), and how many new parts to procure. In this system, returned products arrive

probabilistically and are either remanufactured or thrown away. (In other words, there is no stock of

returned products.) Newly procured products are stored with remanufactured products in a finished

goods inventory that serves demand that arrives probabilistically. There are per-unit costs to procure,

remanufacture, and dispose, and holding costs are charged against ending inventory each period. For the

case of equal lead times to remanufacture and to procure, Inderfurth shows that the structure of the

optimal policy is based on two parameters, Lt and Ut, in each period t. To describe the policy, we need

some notation:

dt = the number of units to be disposed of in period t

pt = the number of units to procure in period t

IPt = the inventory position at the beginning of period t = stock on hand (which includes

products returned this period and finished goods inventory) + procurement orders outstanding +

remanufacturing orders outstanding – backordered demand

The optimal policy is then:

pt = Lt – IPt and dt = 0 for IPt < Lt;

pt = 0 and dt = 0 for Lt >= IPt <= Ut;

pt = 0 and dt = IPt – Ut for IPt > Ut

In words, if the inventory position is lower than the lower limit, Lt, order-up-to Lt and do not

dispose of any units. If the inventory position is higher than the upper limit, Ut, dispose “down to” Ut

and do not procure any units. Otherwise, do not buy or dispose. (Again, all returned units, not disposed

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of, are remanufactured.) Inderfurth points out that when one permits a stock of returned units waiting

for disposal or remanufacturing, or when the lead times to procure and remanufacture are different, the

policy is similar but more complex.

van der Laan, Dekker, Salomon, & Ridder (1996) propose a policy for a continuous review

version of this problem. Thierry et al. (1995) looks at the strategic issues related to product recovery.

See also Heyman (1977); Penev & de Ron (1996) who study the disassembly process, and van der Laan

et al. (1996), van der Laan, Salomon, & Dekker (1997); van der Laan, Salomon, & Dekker (1999);

Ferrer (1995); Richter (1996); Guide & Spencer (1997); and Taleb, Gupta, & Brennan (1997) who study

other aspects of the remanufacturing process. Other reverse logistics issues are also examined by Carter

& Ellram (1998). Fleischmann et al. (1997) provide a review of quantitative models for reverse

logistics.

Outsourcing and strategic alliances examines the supply chain impact of outsourcing

logistics services . With the rapid growth in third party logistics providers, there is a large and

expanding group of technologies and services to be examined. These include fascinating

initiatives such as supplier hubs managed by third parties. The rush to create strategic

relationships with logistics providers and the many well-published failures have raised questions

about the future of such relationships. (See Bowersox (1990), (1998).) In any case, outsourcing

continues to raise many interesting issues (Cooper, Ellram, Gardner, & Hanks (1997a)).

Metrics and incentives examines measurement and other organizational and economic

issues. This category includes both measurement within the supply chain (Meyer (1997)) and

industry benchmarking ((1994), (1997)). Because metrics are fundamental to business

management, there are many reading materials outside of the supply chain literature, including

accounting texts for instance. Several recent articles concentrate on the link between

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performance measurement and supply chain improvement (O'Laughlin (1997), Johnson & Davis

(1998)).

Finally, global issues examines how all of the above categories are affected when

companies operate in multiple countries. This category goes beyond country specific issues, to

encompass issues related to crossboarder distribution and sourcing (Kouvelis (1999)). For

example, currency exchange rates, duties & taxes, freight forwarding, customs issues,

government regulation, and country comparisons are all included. Note that the location

category, when applied in a global context, also addresses some of these issues. (Cohen &

Huchzermeier (1999)and Huchzermeier & Cohen (1996); Arntzen, Brown, Harrison, & Trafton

(1995).) As we mentioned earlier, there are several texts devoted to global management. Many

recent articles also examine challenges in specific regions of the world (for example, Asia -- Lee

& Kopczak (1997) or Europe -- Sharman (1997)).

Conclusion

Supply chain management is an exploding field, both in research and in practice. Major

international consulting firms have developed large practices in the supply chain field, and the

number of research papers in the field is growing rapidly. Our treatment covered twelve areas

often seen in supply chain research and practice. These areas appear to be somewhat disparate,

but they are all linked by the integrated nature of the problems at hand. Firms operate in global

environments, deal with multiple suppliers and customers, are required to manage inventories in

new and innovative ways, and are faced with possible channel restructuring. The field promises

to continue growing as the research advances and as firms continue to apply new knowledge in

their global networks. Finally, as the Internet changes fundamental assumptions about business,

22

firms operating in supply chains will be required to understand this new phenomenon and

respond accordingly.

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