WORKING PAPER 0208

From Supply Chain to Demand Chain:

The Role of Lead Time Reduction in Improving

Demand Chain Performance

Suzanne deTreville

And

Ari-Pekka Hameri

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From Supply Chain to Demand Chain: The Role of Lead Time Reduction in Improving Demand Chain

Performance

Authors: Suzanne de Treville1

Ari-Pekka Hameri

Ecole des Hautes Etudes Commerciales (HEC) Université de Lausanne

November 18, 2002

Submitted to Journal of Operations Management

1Corresponding author: Suzanne de Treville Ecole des Hautes Etudes Commerciales University of Lausanne BFSH1 Office 616 1015 Dorigny, Switzerland Telephone: +41 21 692 3448 e-mail: suzanne.detreville@hec.unil.ch

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From Supply Chain to Demand Chain: The Role of Lead Time Reduction in Improving Demand Chain

Performance

Abstract

In improving demand chain performance, is it better for parties in the chain to first

focus on lead time reduction, or first focus on improved information flow? Whereas supply

and demand chain management theory suggests that lead time reduction is a necessary

antecedent to successful information flow improvement, demand chain parties are often

observed in practice to begin with information flow improvement in spite of long lead times.

Parties are also observed to express uncertainty concerning how short lead times need to be if

information flow improvement efforts are to have a high probability of success. In this paper,

we propose a framework for prioritizing lead time reduction in a demand chain improvement

project, using a typology of demand chains to set specific improvement goals concerning both

lead time and information flow.

Key words: supply/demand chain management, lead time, logistics

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1.0 Introduction

A Nordic pulp and paper producer was experiencing difficulties in managing its

supply chain, which had become complex due to increasing customization of its products, and

due to the company's shipping production from its Nordic plants throughout continental

Europe. The lead time from customer order to delivery averaged six months, hence the

pipeline contained six months of inventory. In spite of the substantial inventory investment

and the long waiting times, service levels were unsatisfactory. Also, the long lead times were

hindering the company's ability to move into custom markets, where profit margins were

considerably higher than for standard products. Therefore, the company hired a well-known

consulting firm to help them improve the performance of their supply chain.

Although the company knew that the long lead times in the chain would need to be

reduced, management decided to begin the supply chain improvement project by improving

the flow of information through the chain through partnership with distributors, either through

acquisition or through strategic alliance. The information flow throughout the chain was to be

made more transparent through installation of an ERP-type software package. Management

believed that improving information flow first would be easier to implement than lead time

reduction, and that an improved information flow would facilitate lead time reduction.

Eighteen months and several hundreds of thousands of dollars later, it became clear to

management that the improvement project had failed. Inventories had continued to increase as

a percent of sales and attempts to increase control over the chain had led neither to inventory

nor to lead time reductions. The partnerships with the distributors and the information systems

installed were unsuccessful in improving the flow of either information or of physical goods.

To make matters worse, the management time invested in the project had caused the core

businesses to be neglected.

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Reacting to the problem, management decided to return to focusing on their core

businesses, and to solve the supply chain problem by distributing manufacturing rather than

attempting to improve the performance of their existing chain. The company acquired

production facilities in each major market, creating shorter and more manageable supply

chains. This solution solved the immediate problem of poor service levels and excessive

inventory investment, but limited the company's ability to improve competitiveness through

improved market mediation capabilities (Fisher, 1997) in their existing chain, rather than

making a strategic retreat into local production.

Supply chain management theory clearly specifies the limitations to improving

information flows when lead times are long (e.g., Fisher, 1997, 2002; Fisher & Raman, 1997;

Heikkilä, 2002; Lee, Padmanabhan & Whang, 1997; Mason-Jones & Towill, 1999; Perry,

Sohal & Rumpf, 1999). Fisher, Raman, and McClelland (2000: 118) described the plight of

such companies: "Many products today have such long lead times that retailers can't call for a

change in production--even if they have tracked early sales, have paid attention to product

testing, and know without a doubt that a change is warranted. As one merchant told us, 'We

do pay attention to our tests. The problem is we already own the product; the test merely

reveals that it will be a dog once it gets to the stores.'"

So, where is the problem here? Did the supply chain performance improvement

project fail because of a lack of understanding of theory, or is there a need to more clearly

articulate the relationship between the constructs of relative lead time and information flow

effectiveness?

Consider the supply chain described above. Should the producer have begun by

reducing lead times? If so, how much should lead times have been reduced before investing in

information flow? What would have been the best strategy for lead time reduction? What

approaches to information flow improvement were best? Does the choice of information flow

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improvement and lead time reduction approaches depend on the context, or are there certain

approaches that are always preferred? Note as well that managers making the decision about

whether to begin demand chain improvement efforts by reducing lead times or improving

information flow are likely to be steered toward the latter in a typical course in supply chain

management, since most of the classic cases taught in supply chain management courses

illustrate how to improve information flow in spite of long lead times (e.g., Sport Obermeyer

(Hammond & Raman, 1996) , Barilla Pasta (Hammond, 1994), and Hewlett-Packard DeskJet

(Kopczak & Lee, 2001)).

In our view, such confusion results from an insufficiently articulated theory

concerning the relationship between lead time reduction and improvements to information

flow in supply chains. Following Bacharach (1989), a theory is needed to tame the

complexities and richness of the supply chain management environment--which are

sufficiently complex that Choi, Dooley, and Rungtusanatham (2001; see also New, 1996)

suggested modeling supply chains as complex adaptive systems--so that we can capture and

understand the underlying relationships that lead to successful supply chain performance.

One approach to managing complexity of empirical data is to organize such data into a

typology (Bacharach, 1989). In this paper, we define a typology of supply chains according to

relative lead time and information flow efficiency, proposing that the position of the company

within the typology determines the approach to be followed in improving performance of the

chain. The typology specifies a set of different contexts, with the likelihood of success with a

given performance improvement tool depending on the context, following Bacharach's (1989)

statement that theory is bounded by context. Based on the typology, we suggest a number of

testable propositions specific to each context concerning the relationship between lead time,

information flow, and improvement strategies.

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2.0 Literature Review

2.1 Literature Concerning Supply Chains

Supply chain management has played a strategic role in operations management since

the Industrial Revolution, but it is only recently that the focus has shifted from satisfying the

need of the supplier for efficiency to satisfying the need of the customer for service. In the

early 1980s, just- in-time (JIT) production concepts caused a new awareness of the value of

market mediation: linking the decision to produce to evidence of downstream demand. Goods

in a typical JIT pull system are not made to order, but are replenished under the assumption

that demand is relatively stable. Because of the emphasis in JIT on minimizing demand

variability through techniques such as freezing the production schedule and heijunka (e.g.,

Monden, 1983), market mediation under JIT often consists as much of taming demand as of

adjusting production to meet demand. The objective within JIT is seldom to take full

advantage of the potential of market mediation. Fisher (1997) describes the unrecognized

potential for market mediation in automobile sales. Most automobiles are purchased from a

small selection that happens to be available at a dealer, rather than having the automobile built

to order. Nevertheless, JIT resulted in some initial movement toward including market

mediation in supply chain management rather than only considering efficient physical supply

of goods.

A second outcome of JIT was a change in attitudes toward suppliers, moving from

arms- length to partnership. In the early days of JIT, there was considerable emphasis on the

development of relationships between suppliers and customers, for example through

establishing close geographical proximity, or through increased communication. Moving from

arms- length relationships to some form of alliance was emphasized in most of the classic JIT

literature (e.g., Hall, 1983; Harmon, 1993; Hayes, Wheelwright & Clark, 1988; Schonberger,

1983; Suzaki, 1987). In the mid-1990s, however, authors such as Bensaou (1999) questioned

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the general recommendation emerging from JIT that partnership was the preferred form of

relationship with all suppliers. Bensaou (1999) described the move toward partnership with

suppliers as a leap of faith, demonstrating through an empirical study of supplier relationships

that the most effective level of closeness in such relationships was determined by the nature

of the product, market, and partner. Williams, Maull & Ellis (2002) evaluated supply chain

theory from a transaction cost economics viewpoint, noting that electronic commerce had the

greatest potential to reduce supply chain-related transaction costs when the trading

information to be exchanged was minimized: although gains could be realized from electronic

commerce for more complex transactions through systems involving partnership such as

common databases and system translators, such gains were less certain due to increased risk

of opportunism.

As interest in supply chain management increased during the late 1980s and 1990s,

researchers began to focus on the speed and quality of information flow through the chain,

with the Quick Response initiative in the apparel industry being one of the most visible efforts

(e.g. Abernathy, Dunlop, Hammond & Weil, 1999; Fisher & Raman, 1996).

Lee, Padmanabhan, and Whang (1997) applied Forrester's (1961) concept of industrial

dynamics to the flow of information in the supply chain, demonstrating that the variability of

information received by the supplier is substantially greater than the variability of demand

when (a) orders form the only information concerning demand that is transferred within the

chain; (b) orders are batched; (c) prices fluctuate; and (d) the customer reacts to concerns

about being placed on allocation by playing a rationing game. Lee et al. (1997: 548)

demonstrated that this "bullwhip effect," that is, the distortion of demand variance which is

amplified as it moves upstream, results from "strategic interactions among rational supply

chain members," in contradiction to Sterman's (1989) conclusion that the bullwhip effect

arises from irrationality or misinterpreted feedback. Lee et al. (1997) also demonstrated that

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the bullwhip effect becomes more severe as lead times increase, although some bullwhip

effect is present even with a lead time of zero under the conditions specified above. The

bullwhip effect theory appears to apply to all supply chains that attempt market mediation:

avoiding the conditions leading to the bullwhip effect is recommended for all supply chains.

Frohlich and Westbrook (2002) proposed a typology of supply (demand) chain

integration according to whether the producing company was linked upstream to suppliers or

downstream to customers, demonstrating that for manufacturing companies, upstream and

downstream integration combined resulted in more improvement in chain performance than

either upstream or downstream integration alone, although one-sided integration resulted in a

higher level of operational performance than no integration. Higher levels of integration

resulted in higher levels of performance (see also Frohlich & Westbrook, 2001). Frohlich and

Westbrook (2002) also emphasized that both supply and demand integration within the chain

have been greatly facilitated by the introduction of the Internet, which they credited with

eliminating many of the trade-offs previously inherent in improving information flow.

Rabinovich, Dresner and Evers (2002: 6) described implementation difficulties in

improving demand chain performance, noting that "...although there is substantial evidence

that learning about demand before undertaking production acts [as] a substitute for product

inventories, it is not understood what the most effective way is to achieve that substitution."

In their study, implementation of an enterprise-wide information system--hypothesized to

improve demand chain performance--led instead to an increase in inventory speculation

among demand chains surveyed. Rabinovich et al. (2002) concluded that improved demand

chain performance was more likely to result from operational initiatives such as lean

production that resulted in lead time reduction, than from direct investment in information

systems to improve information flow.

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Whereas most of the supply chain recommendations arising from JIT theory and from

an understanding of the bullwhip effect were expected to apply to all supply chains, Fisher

(1997) noted that the new supply chain management tools and concepts being widely

implemented did not appear to be resulting in improved supply chain performance, which he

attributed to lack of a framework for choosing the right type of supply chain for a given

context. He proposed that the choice of supply chain management approach should depend on

the nature of the demand for the product: the supply chain for functional products should

emphasize physical supply of goods, and the supply chain for innovative products should

emphasize market mediation. Whereas many aspects of demand for a given product--such as

product life cycle, the predictability of demand, the product variety, and lead times--were

noted by Fisher (1997) to be important, he proposed that it sufficed to know whether the

product was functional or innovative to determine the appropriate supply chain. Fisher (1997)

also noted that many products were shifting from being functiona l to being innovative as

companies attempted to improve competitiveness through product development.

Vollman, Cordon, and Heikkilä (2000) suggested that the term supply chain be

replaced by the term demand chain to emphasize the shift in emphasis from efficient supply to

meeting the needs of the customer. Combining Fisher's (1997) division of supply chains

according to whether product demand is functional or innovative with Vollmann et al.'s

(2000) suggested change of terminology from supply to demand chain management, we

propose the following definition:

Definition: A demand chain is supply chain that emphasizes the market mediation

function over its role of ensuring efficient physical supply of the product.

Corollary: A given supply chain can be decoupled into an upstream supply chain and

a downstream demand chain through a decoupling point (e.g., Naylor, Naim & Berry, 1999).

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Childerhouse, Aitken, and Towill (2002) developed an integrated framework for

"focused demand chains," incorporating a classification system for demand chains--which

they distinguish from supply chains--based on five variables: (a) duration and stage of product

life cycle, (b) time window or delivery lead time, (c) volume, (d) product variety, and (e)

variability of demand, which they refer to as DWV3, with the classification of product

demand determining the market mediation strategy--MRP, Kanban, postponement, or design

and build--to be followed for a given type of demand chain. The focused demand chain

framework was successfully implemented in a major UK lighting producer, supporting their

proposition that choice of demand chain strategy was facilitated through the DWV3

classification system.

Heikkilä (2002) studied attempts to improve information flow in six demand chains

within Nokia Networks. Each of these demand chains was having difficulties with market

mediation. In each case, Nokia offered a reduction in lead time from four months to ten days

in exchange for improved information concerning demand. Although according to Fisher's

(1997) framework, lead time reduction followed by market mediation should have been the

appropriate supply chain management approach in each of these cases, response to the

overtures made by Nokia ranged from enthusiastic to negative, and the outcomes of the

projects varied considerably. On the basis of these cases, Heikkilä (2002) concluded that

Fisher's (1997) proposition--that the choice of market mediation or physical efficiency for a

given chain depended essentially on the nature of demand--was too simple. Heikkilä (2002)

proposed that a variety of demand and supply chain structures were required to cover the

entire universe of customer needs and situations. In his model of demand chain management,

Heikkilä (2002) added constructs such as duration of relationship, trust, and customer

perceptions of the support received from the supplier to the nature of demand in determining

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whether a given chain should be more focused on efficiency or market mediation (customer

satisfaction).

In summary, interest in demand chain management began in the 1980s with JIT

production. Demand chain management techniques were initially assumed to be universally

applicable, but now are seen to apply to chains requiring market mediation more than efficient

delivery of goods. There is increasing evidence that tools for improving information flow in

the demand chain do not apply to all contexts, and that implementation of demand chain

improvements may be problematic.

2.2 Lead Time Reduction

Interest in lead time reduction was originally awakened by JIT production, even

though lead time reduction was considerably less emphasized in JIT than waste--especially

excess inventory--reduction (e.g. Blackburn, 1991; Hall, 1983; Monden, 1983; Schonberger,

1982; Suri, 1998; Suzaki, 1987; Womack, Jones & Roos, 1990). Whereas JIT is primarily

focused on repetitive manufacturing (Suri, 1998), Goldratt (1984) addressed lead time

reduction in a job shop environment, drawing attention to the impact of bottleneck resources

and lot sizing on lead times.

Results from lead time reductions from JIT and related initiatives led to identification

of a competitive strategy based solely on speed, referred to as "Time-Based Competition"

(TBC, e.g.; Blackburn, 1991; Holmström, 1995; Schmenner, 1988; Stalk, 1988; Stalk & Hout,

1990, Suri, 1994). Schmenner (2001) proposed that companies having emphasized flow,

implying a focus on speed and variability reduction, would outperform companies

emphasizing other goals.

Whereas much of the literature on lead time reduction was largely anecdotal and

exploratory, Hopp and Spearman (1996) compiled a set of the mathematical principles

determining lead time, which they referred to as "factory physics." Suri (1994, 1998)

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developed a manufacturing strategy entitled Quick Response Manufacturing1 (QRM) based

on the same mathematical principles described by Hopp and Spearman (1996). Factory

physics and QRM formalized the relationship of bottleneck utilization, lot sizes, and

variability to lead times. The underlying mathematical relationships had been well-known in

the field of queuing theory for many decades (Suri, Diehl, de Treville & Tomsicek, 1995), but

the work by Hopp and Spearman (1996) and Suri (1994, 1998) represented the first

comprehensive application of these principles to the general theory of operations

management. Eriksen and Suri (2001) described demand chain improvement at John Deere

Horicon works through use of supplier lead time as a key metric, and through working

together with suppliers to implement QRM princip les so that supplier lead times could be

reduced.

Although the set of mathematical principles driving lead time reduction are commonly

known and accepted among researchers in the field of operations management, such

knowledge does not appear to have been widely disseminated to practitioners (see also Suri

1994, 1998).

Lead time reduction is often described in the operations management literature as

arising from initiatives such as JIT/lean production or agility (e.g., Naylor et al., 1999) rather

than from identifying and reducing congestion at bottlenecks, reducing lot sizes, and moving

to a product layout, which facilitates managing these mathematical relationships (Suri, 1998).

Kourteros, Vonderembse, and Doll (1998) proposed that time-based manufacturing was

related to shop-floor employee involvement, setup time reduction, cellular manufacturing,

quality improvement efforts, preventive maintenance, dependable suppliers, and pull

production, but did not relate these constructs to the mathematical principles that drive lead

time.

1 The ressemblance in terminology between the supply chain strategy Quick Response and the manufacturing strategy Quick Response Manufacturing is coincidental.

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3.0 A Typology of Demand Chains

We propose that two primary constructs determine the context of a given demand

chain: the efficiency of information flow concerning demand, and the lead time relative to the

period of time during which demand can theoretically be observed, as stated earlier. We

define three levels of information flow in a given chain: (a) level IF I, in which information

concerning demand flows is transferred upstream in real time without distortion; (b) level IF

II, in which information concerning demand is transferred upstream, but with moderate delays

and distortions; and (c) level IF III, in which no effort is made to transfer demand

information. Relative lead time is similarly classified into three levels according to demand

information available when production begins. To facilitate operationalization of the construct

of relative lead time, we define two time periods: T1, representing the number of time periods

in advance that accurate information concerning demand is potentially available, and T2,

representing the number of time periods in advance that some information concerning demand

is potentially available, for example through observing early sales (e.g., Fisher & Raman,

1996). We define the three categories of relative lead time as follows: (a) category LT I, in

which the lead time is less than T1, making it feasible to begin production after completely

observing demand; (b) category LT II, in which the lead time is between T1 and T2, making it

feasible to observe some--but not all--demand prior to beginning production; and (c) category

LT III, in which the lead time is greater than T2, making it necessary to begin production

before observing any demand. Demand chains, then, can be classified by lead time

performance relative to the time horizon of observed demand, and by the efficiency of the

information flow through the chain with respect to speed of information flow and distortion of

information transferred. Demand chain strategy consists of choosing a desired position on the

demand chain matrix, and specifying a trajectory for achieving that position. The typology is

shown in Figure 1.

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[insert Figure 1 about here]

3.1 Classifications of Information Flow

At level IF III, the only information that the upstream members of the chain receive

concerning demand comes from the order itself, which creates a "bullwhip effect," and results

in substantial information distortion, especially when combined with price changes, order

batching, or rationing (Lee, Padmanabhan & Whang, 1997). Examples of a level III

information flow are seen in Campbell Soup's supply chain behavior prior to elimination of

price discounts (e.g., Fisher, 1997), and in the supply chain for Hewlett Packard's inkjet

printers prior to implementation of a postponement strategy (Kopczak & Lee, 2001).

At level II, the bullwhip effect is minimized, and there is some effort to transfer

demand information upstream in the chain. Tools used in establishing a level II information

flow range from simple--such as a JIT kanban-based pull system--to sophisticated, leading

Fisher, Raman, and McClelland (2000) to refer to them as "rocket science retailing."

Partnership and other forms of strategic alliance can also be used to achieve a level II

information flow.

A level I information flow typically requires an electronic link between demand chain

levels. The development of the Internet has greatly facilitated such a real time, undistorted

information flow (Frohlich & Westbrook, 2002). Examples of level I information flow can be

found outside of the Internet, however: consider the link between automobile assembly and

seat manufacturing at Toyota Motor Manufacturing (TMM), in which automobiles exiting the

paint line transmit a signal to the seat supplier so that the seat for that automobile can be

manufactured and delivered to TMM by the time the automobile reaches the point in the

assembly process where the seat is installed (Mishina, 1992). A level I information flow is

required for demand chains supplying a mass cus tomized product, such as the Levi's Personal

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Pair program (Abernathy et al., 1999), in which jeans were produced to customer order with a

lead time of two weeks, or a product that is built to order.

3.2 Classifications of Relative Lead Time

The second variable is the lead time relative to the time during which accurate

information concerning demand is available, whether or not such information is transferred to

the upstream parties of the chain. As mentioned previously, it is possible in many chains to

specify a decoupling point that separates an upstream supply chain from a downstream

demand chain. In such a case, the relevant lead time for that chain is the lead time from the

decoupling point to the customer.

Consider a demand chain that has a lead time between decoupling point and final

customer that is longer than the period of time during which some information concerning

demand is theoretically available. If the lead time is longer than the period of time during

which demand information is available, then production must be committed prior to observing

demand, hence transfer of demand information does not improve the performance of the

demand chain.

Proposition 1: The information flow performance cannot exceed the relative lead time

performance. For a demand chain in category LTi, IFj, i must be less than or equal to j.

3.3 Demand Chain Matrix Trajectories

Progress in demand chain integration usually takes place along one dimension at a

time: either improving relative lead time, or improving information flow, but not both

simultaneously. Moving along a single dimension at a time rather than attempting to move

along two dimensions simultaneously appears to be preferable for two reasons: (a) success is

more likely when demand chain participants focus on a single goal, rather than risking

confusion and distraction from concentrating on two challenging goals simultaneously, and

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(b) the tools required to achieve information flow depend on the level of relative lead time

performance. As an example, Nokia's efforts to improve demand chain performance began

with reducing lead times from four months to ten working days--achieving level LT I in some

cases, LT II in others--followed by improved communication of demand data (Heikkilä,

2002).

Proposition 2: Movement within the demand chain between positions occurs along

one dimension at a time--that is, either horizontally or vertically--rather than along the

diagonal.

Each move on the demand chain matrix is associated with a set of possible tools. The

suggested "toolbox" for each move is shown in Figure 2.

[insert Figure 2 about here]

Proposition 3: The appropriate tools to accomplish a given move on the demand chain

matrix are a function of the position on the matrix and the dimension along which the move is

planned to occur.

3.3.1 Starting Position: LT III

Consider a demand chain that is at level LT III. As proposed earlier, such a demand

chain is unable to achieve an information flow better than level IF III irrespective of what

investments are made in information technologies, partnerships, or other demand chain

management tools, hence the positions LT III/IF II and LT III/IF I are labelled as infeasible

on the typology matrix in Figure 1. The lack of communication between downstream and

upstream operations makes market mediation attempts in such demand chains particularly

vulnerable to the bullwhip effect. Successful market mediation is not possible in a demand

chain positioned at LT III/IF III, consistent with Fisher (1997; Fisher, Raman & McClelland,

2000). The only possible move in the demand chain matrix from the position LT III/IF III is

downward along the LT dimension, reducing the lead time from the decoupling point enough

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to permit the observation of some demand. Lead time reduction can be achieved either

through application of factory physics (e.g., reducing bottleneck utilization, lot sizes, and

variability), or through a reevaluation of the position of the decoupling point in the chain. If

lead time reduction from the decoupling point is infeasible, then the chain should cease

attempts to achieve market mediation, and should transform itself back into a traditional

supply chain, transferring management attention to efforts to increase demand for what has

been produced.

Proposition 4: Demand chains located at position LT III/IF III are not able to achieve

market mediation, hence traditional supply chains--focusing on efficient supply with no

efforts at market mediation--will outperform demand chains at this position.

3.3.2 Starting Position: LT II

Consider a demand chain at level LT II/IF III. This chain would theoretically be able

to observe some demand prior to beginning production, but has not taken the steps required to

eliminate bullwhip effect factors and make use of potentially observable data. The level LT

II/IF III is dominated strategically by both LT II/IF II and LT I/IF III: parties should either

invest in reducing lead times to improve the quality of demand information available, or

invest in information flow to make maximum use of available demand information. As stated

earlier, the best information flow performance available to this chain is IF II.

Which direction is best for the chain to take? In order to simplify the problem, let us

consider the situation in which reducing lead times to level LT I and improving information

flow to level IF II are equally feasible, represent the same level of difficulty, and have the

same cost. Let us define four total implementation costs, which include not only financial

costs, but also an estimation of the effort and risk involved (see Figure 1): X1 for a move

from LT II/IF III to LT I/IF III, X2 for a move from LT II/IF III to LT II/IF II, X3 for a move

from LT I, IF III to LT I/IF II, and X4 for a move from LT II/IF II to LT I/IF II. While an

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improvement in information flow might reduce lead times to some extent through an inverse

“planning loop” effect (Stalk, 1988), in most cases the improvement in information flow will

not result in a change in level from LT II to LT I. Therefore, we will make the assumption

that X1 = X4: the cost of reducing lead time from level LT II to LT I does not decline with an

improvement in information flow. Conversely, we will assume that X2 > X3: it is clear that

the availability of accurate demand data reduces the cost of improving information flow.

Consider the two possible trajectories between position LT II/IF III and LT I/IF II: the

chain can move through position LT I/IF III at a cost of X1 + X3, or through position LT II/IF

II at a cost of X2 + X4. Since X1 = X4 and X3 < X2, then X1 + X3 < X2 + X4: the trajectory

through LT I/IF III is expected to cost less than the trajectory through LT II/IF II.

Proposition 5: If the total cost of reducing lead times from LT II to LT I is

approximately the same as the total cost of improving information flow from IF III to IF II

given a lead time classification of LT II, then the trajectory LT II/IF III - LT I/IF III - LT I/IF

II will cost less than the trajectory LT II/IF III - LT II/IF II - LT I/IF II.

The supply chain management literature is rich with examples of chains moving from

level IF III to IF II with lead times at level LT II, including transmission of POS information

from retail stores to Barilla Pasta to permit smoothing of replenishment (Hammond, 1994);

and Sport Obermeyer's collection of early sales data (e.g., Fisher, Hammond, Obermeyer &

Raman, 1994)). These cases represent successful improvement of information flow. The

discussions of these cases, however, are as focused on implementation challenges as on

results.

The reasons for remaining at a lead time level of II vary. Barilla management was

concerned that lot size reductions would result in too low capacity utilization. Sport

Obermeyer had difficulties in reducing lead times because of producing overseas. Blocher,

Garrett, and Schmenner (1999) give an excellent example of a pharmaceutical firm limited in

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its ability to reduce lead times because the Federal Drug Administration approvals specified

the production lot size. Therefore, whether due to "mental models" (Senge, 1990) concerning

lot sizes and production efficiency (Fisher, 1997) or due to actual production constraints,

there are situations in which a demand chain will not be able to move to position LT I before

attempting to improve the information flow.

The cases evaluated by Heikkilä (2002) give some insight concerning the

improvement of information flow at a lead time level II. The lead time reduction from four

months to ten days carried out by Nokia brought one of the six cases to a level of LT I. In this

case, improving information flow to a level IF II was relatively easy and successful. In the

other five cases, the lead time reduction resulted in a level of LT II. Heikkilä (2002)

demonstrated that improving information flow in these cases required not only clear evidence

that such a change would be beneficial to all parties, but required substantial investment in

relationship development in order to build trust and commitment. Although Heikkilä (2002)

suggested that the constructs related to the quality of the relationship apply to information

flow improvements in all demand chains, we suggest that the dependence on relationship

quality is much higher in the context of longer lead times (see also Williams et al., 2002). In

relationships with a low value of these relationship-related constructs, the implication of

Heikkilä's research is that a level II information flow might not be achievable, hence reversion

to a traditional supply chain strategy might be preferred for such a chain.

Proposition 6: At a level II lead time, chain relationships exhibiting high levels of

constructs such as duration of relationship, trust, and customer perceptions of supplier support

will be more likely to succeed at improving information flow from level III to level II than

chains exhibiting low levels of these constructs, following Heikkilä (2002).

3.3.3 Starting Position: LT I

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Consider a demand chain that is in category LT I. If the members of this chain have

made no investment in transferring the available demand information upstream, then the

variability of orders will be higher than the variability of demand. The increased variability as

observed by the upstream members of the supply chain will result in increased inventory and

reduced demand chain service levels with respect to markdowns and stockouts. This

distortion of demand will increase still further if rationing, price promotions, or other sources

of order batching are used in the demand chain (Lee et al., 1997). Such a chain would be in

category LTI/IFIII. Moving to LT1/IFII requires nothing more than eliminating practices

encouraging order batching, and ensuring that the available demand information is transferred

upstream in a timely manner. Because manufacturing can begin after accurate demand

information is available, no complex forecasting systems are required. The famous Bennetton

case is an example of a technique that allowed a demand chain to move to LT1/IFII. The

postponement strategy followed by Bennetton in product design allowed lead times for dyed

garments to be short enough that production occurred in response to actual demand observed

at retail outlets (for a complete discussion of postponement, see van Hoek, 2001)+. Early

electronic data interchange (EDI) systems permitted the rapid transfer of demand information

upstream (Heskett & Signorelli, 1984). In general, demand chains at a level of LT I should

always strive to reach an information flow level of II or better.

Proposition 7: Demand chains at a level of LT I which have achieved an information

flow of level II will outperform those at a level IF III.

Consider a demand chain that is in position LT1/IFII, and is considering a move to

LT1/IFI. The position LT1/IF1 is required for mass customization. It is generally agreed that

the strategic and competitive possibilites inherent in a mass customization strategy are

tremendous. Also, many experts in the area of mass customization are cautioning that

attempting to implement a mass customization strategy in the presence of long lead times is

21

likely to lead to failure (e.g., Tu, Vonderembse & Ragu-Nathan, 2001). Long production lead

time is probably the primary reason why mass customization has been slow to gain

momentum. According to Frohlich and Westbrook’s (2002) claim that the Internet has created

a wide variety of new possibilities to improve the level of information flow through the

transfer of real-time demand information, we might expect to see a substantial increase in the

number of firms having achieved level LT I/IFI over the next several years.

Proposition 8: Demand chains at position LT I/IF I are more likely to succeed at mass

customization than demand chains in other positions on the demand chain matrix.

4.0 Conclusions

We began this paper with the story of a Nordic pulp and paper producer which had

failed to improve information flow in their demand chain. According to our typology, where

was this chain positioned? What would our theory have suggested concerning improving the

chain performance?

The producer's lead times were clearly at level LT III, therefore any efforts to improve

information flow so as to permit better market mediation were doomed to failure. The

producer had a choice of reducing lead times or reverting to traditional supply chain

management. Given that traditional supply chain management was inconsistent with the

producer's efforts to customize products, lead time reduction was a requirement for the

producer to accomplish its product customization strategy. Lead time reduction could have

been accomplished through applying factory physics and QRM principles to existing

operations, through establishment of a decoupling point in combination with a postponement

strategy, or through dedicating fast and flexible capacity to customized products.

Our experience has been that managers in many companies believe that reducing lead

times is difficult and expensive, and that information systems will make lead time reduction

easier. We have watched many companies fail. We have also watched companies begin with

22

efforts to improve information flow, recognize that little improvement was taking place, and

revert to lead time reduction, ending up with a successful demand chain improvement.

Our typology has allowed us to establish a set of contexts for demand chain that

permit us to address questions of how demand chain improvement should take place in a

given chain--that is, what tools are appropriate, why a given tool is likely to succeed in a

given context, and when parties in the chain should move from lead time reduction to

information flow improvement (Bacharach, 1989). We have argued that demand chain theory

is bounded by context: propositions that apply to one position in the typology do not

necessarily apply to other contexts. Our goal in writing this paper is to organize what we

know about improving information flow and reducing lead times into a theory of demand

chain improvement so that we can better make sense out of the tremendously diverse and

complex set of cases that have emerged from the field (Bacharach, 1989), and so that we can

better communicate to managers the body of knowledge that we have amassed concerning

how to improve the performance of a given supply or demand chain. As Lewin (1945: 129)

said, "Nothing is as practical as a good theory."

23

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Figure 1. A typology for demand chain management.

Rel

ativ

e Le

ad T

ime

Information Flow Effectiveness

I

II

III

III III

Infeasible

Infeasible

Unmanageable asa demand chain

Manage as traditionalsupply chain

Permits masscustomizationand build to order

« Rocket ScienceRetailing » (Fisher,2000)

Uncompetitive

UncompetitiveAcceptableperformance

X1

X2X3

X4

Infeasible

29

Figure 2. A suggested toolbox for demand chain performance

improvement.

III

II

I

III II I

LT III/IF III to LT II/IF III or LT I/IF III:LT II/IF II to LT 1/ IFII:Lot size and set-up time reductionBottleneck utilization, i.e. matching production plan to capacityProduct layout to support flowRevise production allocation policiesReducing variability by minimising rework, scrap and downtimePostponement according to introduction of product variabilityPositioning of decoupling pointInventory and production control (e.g. kanban)Mathematical modeling toolsImproved equipment and technologyProduct design, i.e. modular products and design for manufacturing and assembly

LT II/IF III to LT II/IF II:Partnership or other form of strategic alliance Tools and systems to collect, analyze and share of dataForecasting

LT I/IF II to LT I/IF I:Internet-based systemsClose partnerships based on computer integrated control

LT I/IF III to LT I/IF II:Eliminate bullwhip factorsSimple and transparent systems to communicate demand information (e.g. EDI, POS)Vendor managed inventories