Investigation of Information Availability

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1. INDIVIDUAL AND ENVIRONMENTAL IMPACTS ON SUPPLY CHAIN INVENTORY MANAGEMENT: AN

EXPERIMENTAL INVESTIGATION OF INFORMATION AVAILABILITY AND PROCEDURAL

RATIONALITY................................................................................................................................................. 1

Bibliography...................................................................................................................................................... 20

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INDIVIDUAL AND ENVIRONMENTAL IMPACTS ON SUPPLY CHAIN INVENTORY MANAGEMENT

AN EXPERIMENTAL INVESTIGATION OF INFORMATION AVAILABILITY AND PROCEDURAL

RATIONALITY

Author Haines, Russell; Hough, Jill R

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Abstract

Ordering inefficiencies can lead to considerable problems for supply chains: high inventories, poor

customer service, and lost revenues. One of the most striking results of ordering inefficiency is the "bullwhip

effect," where small variations in demand at the retail level produce increasing levels of order variability further

up the supply chain (Forrester 1958; Lee, Padmanabhan, and Whang 1997; Sterman 1989). The bullwhip effect

also has been observed in orders for Hewlett-Packard ink jet printers (Davis 1993), in orders for Campbell's

condensed soup (Fisher 1997), and in orders received by semiconductor manufacturers (Lee 2004). Managing

factory utilization becomes a major challenge when orders vary, requiring supply chain members to hold more

items in inventory (Goldsby, Griffis, and Roath 2006). Using a laboratory experiment, the authors sought to

determine how the availability of information about consumer demand and information about unfilled orders

interact with one aspect of individual differences in decision-making: procedural rationality.

Full text INTRODUCTION

Ordering inefficiencies can lead to considerable problems for supply chains: high inventories, poor customer

service, and lost revenues. One of the most striking results of ordering inefficiency is the "bullwhip effect," where

small variations in demand at the retail level produce increasing levels of order variability further up the supply

chain (Forrester 1958; Lee, Padmanabhan, and Whang 1997; Sterman 1989). For example, orders for

disposable diapers that Procter and Gamble's factory receives from its distributors rise and fall sharply over

time, even though the number of children that need diapers and the amount of diapers that they use remains

relatively consistent from month to month (Lee, Padmanabhan, and Whang 1997). The bullwhip effect also has

been observed in orders for Hewlett-Packard ink jet printers (Davis 1993), in orders for Campbell's condensed

soup (Fisher 1997), and in orders received by semiconductor manufacturers (Lee 2004). Managing factory

utilization becomes a major challenge when orders vary, requiring supply chain members to hold more items in

inventory (Goldsby, Griffis, and Roath 2006).

The managers making these orders are characterized as lacking complete understanding of their supply chains;

i.e., having no clear, analytical method for calculating how much inventory they need, and relying on a

combination of experience and intuition against the "vagaries" of their supply chain (Davis 1993). Decisions of

supply chain managers can have profound effects on the performance of a firm or industry. For example, Cisco

wrote off US $2.5 billion in surplus raw materials in 2001 because its managers were unable to cut off supplies

in the face of slowing demand (Narayanan and Raman 2004).

Sterman (1989) and Senge (1990) suggest that ordering inefficiencies are a consequence of "misperceptions of

feedback." They propose that supply chain managers create the bullwhip effect because they do not account for

the impact that their own actions (i.e., their orders) have on their environment (supply chain). Sterman notes

that while many participants in supply chain experiments ask for more accurate consumer demand estimates to

improve their decision-making, their poor performance is largely due to a failure to account for orders they have

made but have not been delivered.

On the other hand, Lee and Padmanabhan (1997) suggest that ordering inefficiencies occur becausedecisionmakers account for available information in their environment. They suggest that managers use a

rational ordering technique based on the orders of their closest downstream partner. If the downstream orders

do not correspond to consumer demand, the upstream member is more likely to make an inefficient order. For

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example, if a retailer orders more diapers than normal so that they can create an in-store display, their

wholesaler may interpret the increased order as a change in consumer demand and order even more diapers

from their distributor. The distributor may similarly overreact and order too many diapers from the factory. When

the retailer changes their in-store display back to normal, their orders may decrease to zero while the extra

inventory is sold. Because the upstream members use the downstream member's demand as their signal

instead of information about actual consumer demand, the upstream members create inefficiencies like the

bullwhip effect. This means that ordering inefficiencies can occur even when decision-makers account for the

supply line.

If supply chain managers applied the same mathematical techniques that are used in computer-simulated

supply chains, additional information about consumer demand and/or undelivered orders would inevitably lead

to more optimal decisions (Chen 1999; Chen et al. 2000; Lee, So, and Tang 2000; Riddalls and Benne« 2002).

However, supply chain managers are not rational to the extent that supply chain researchers sometimes

assume. Sterman (1989) reports a significant amount of variation in the ordering techniques among participants

in supply chain experiments, with some diminishing, rather than adding, to the bullwhip effect - even though

information sharing was specifically prohibited in his experiments. There is also evidence of order variation even

when supply chain decision-makers are told an "optimal" ordering technique (Croson et al. 2005). Therefore, we

suggest that individual differences in decision-making may account for much of the order variation in supply

chain settings and bear significant responsibility for supply chain inefficiencies.

Using a laboratory experiment, we sought to determine how the availability of information about consumer

demand and information about unfilled orders interact with one aspect of individual differences in decision-

making: procedural rationality. Procedural rationality is the "extent to which the decision process involves the

collection of information relevant to the decision and the reliance upon analysis of this information in making the

choice" (Dean and Sharfman 1993, p. 1071).

THEORETICAL FOUNDATION

In order to make effective ordering decisions, a supply chain decision-maker must consider: (1) informationabout consumer demand; and (2) information about orders in the supply line that have not been delivered.

Providing each member of the supply chain with complete information about customer demand is the most

frequent proposal for reducing ordering inefficiencies (Chen et al. 2000; Lee, So, and Tang 2000; Simchi-Levi,

Kaminsky, and Simchi-Levi 1999; Van Ackere, Larsen, and Morecroft 1993). Some even advocate that one

member of the supply chain perform all of the demand forecasting and make all of the orders (Lee and

Padmanabhan 1997; Van Landeghem and Vanmaele 2002). Information technology (IT) practitioners also

advise providing immediate and complete access to consumer demand through extranets (Bacheldor 2003,

2004). The potential for customer demand information to decrease the bullwhip effect was confirmed in two

studies - one in which the distribution of demand was uniform and known (Croson and Donohue 2003), and onein which the distribution of demand followed a stepup function, but was unknown (Steckel, Gupta, and Banerji

2004).

As mentioned earlier, Sterman (1989) proposed that poor performance was related to decision-makers' failure

to account for orders that have been made but have not yet been delivered. He noted: "Even a perfect [demand]

forecast will not prevent a manager who ignores the supply line from overordering" (p. 336). This finding is

echoed in recent studies. Riddalls and Bennett's (2002) mathematical models suggest that continuous variability

in ordering would be eliminated if the person placing orders fully accounted for undelivered inventory. Likewise,

Chen (1999) suggests that accounting for the complete supply line will lead to better supply chain performance.

One recent study suggests that the bullwhip effect is reduced when the inventory position of all supply chain

members is available in graphical form (Croson and Donohue 2006).

These explanations regarding the availability of customer demand and supply line information assume that

decision-making is deterministic or mechanistically controlled by decision support systems (Silver 1991), or




executed by fully-rational decision-makers. Such an approach ignores the fact that bounded rational humans

may control the decision-making process in at least one segment of a supply chain or make adjustments to the

recommendations of IT systems. As we noted earlier, research participants have exhibited tremendous

variability in ordering behavior, regardless of the available information or instructions regarding their ordering

technique (Croson and Donohue 2003, 2006; Steckel, Gupta, and Banerji 2004; Sterman 1989; Wu and Katok

2006). Thus, we suggest that the extent to which managers perceive, acquire, interpret, and use information in

the decision-making process, must be considered alongside more deterministic approaches. We approach

supply chain decision-making from a perceptual perspective, with decision outcomes (good or bad) depending

on a person's decision-making processes. These processes occur in a specific organizational and

environmental context. Similar models have been proposed for strategic change (Rajagopalan and Spreitzer

1997) and strategic decision-making (Rajagopalan et al. 1998). However, to our knowledge, such models have

not been specifically applied to decision-making within a supply chain.

Because supply chain decision-makers are embedded in organizations, the processes they use for making

decisions are influenced and constrained by their environment and the organization in which they work

(Boulding 1956). For example, a supply chain decision-maker may not be able to incorporate information about

undelivered orders in their supply line because that information is simply not available due to the way their firm's

systems were designed. This "linear view of decision-making" (Chaffee 1985) is represented by the three solid

lines in Figure 1 that move from environmental and organizational contexts, through the decision process,

leading to an outcome. The linear view is reflected in Sterman's (1989) observation that supply chain decision-

makers attribute outcomes to external events rather than their own actions. However, as Sterman noted, the

decision-makers' interaction with their environment (i.e., their ordering behavior) actually determined the

performance of the supply chain. Therefore, behavior is better explained by the interaction of individual

decisions and the contextual environment. In other words, decision-makers incorporate feedback from a given

outcome back into the decision process, as represented by the bold line moving from right to left in Figure 1 .

This view has been labeled an adaptive or learning view of decision-making (Chaffee 1985; Rajagopalan andSpreitzer 1997). For example, a supply chain decision-maker may learn over time to remember which orders

have not been filled and be able to at least partially account for their supply line even though information about

undelivered orders is not explicitly provided.

Even in structured settings like supply chains, the actions of decision-makers are difficult to connect to internaldecision-making processes (Langley et al. 1995). For example, Sterman (1989) attempted to determine the

decision rule that supply chain decision-makers used when making orders and found that an (s, S) ordering

scheme based on filled orders, discrepancies between desired and actual stock, and discrepancies between




desired and actual supply line, provided good overall fit for the data (mean r2= 0.71). Croson and Donohue

(2003) reported that their subjects used a similar ordering technique (r2 = 0.66). However, Sterman reported

that his individual decision-makers had a significant amount of variation in their ordering techniques (r2 ranged

from 0.98 to 0.10). This implies that (1) the fit of a given ordering decision rule does not fully describe the supply

chain decision-making process, and/or (2) the decision-making process varies over time as decision-makers

receive feedback about their performance.

Complex systems, such as those found in supply chains (Diehl and Sterman 1995), are affected by the

perceptual nature of their participants - where information is given meaning in an ever-changing context.

Decisionmaking is re-"humanized" (Langley et al. 1 995) by an interpretative lens (Chaffee 1 985; Rajagopalan

and Spreitzer 1997), which highlights the role of managerial perceptions in both determining the decision-

making process to be followed and the degree to which outcome feedback is incorporated into the decision-

making process. The dotted lines in Figure 1 highlight the central role that decision-maker perceptions play in

the decision-making process.

Decision-making is an !inobservable process (Hambrick and Mason 1984; Langley et al. 1995) especially with

respect to the extent a decision-maker uses or analyzes specific items of data. In the absence of brain scans,

researchers can gain insight into decision processes by asking the decision-maker about the process they used

in making a particular decision. Naturally, the elapsed time between a decision and the questioning should be

held to a minimum in order to increase recall (Huber and Power 1985). Since the "generic stock management

control problem" (Sterman 1989), such as exhibited by a supply chain, can be mathematically optimized,

decision-makers would optimally use rational, rather than intuitive, decision processes.

Again, procedural rationality is the "extent to which the decision process involves the collection of information

relevant to the decision and the reliance upon analysis of this information in making the choice" (Dean and

Sharfman 1993, p. 1071). Decision-makers engaging in less rational decision processes are more likely to make

decisions based on hunches or simple rules of thumb (Tversky and Kahneman 1981). Inventory ordering rules

are based on a relatively small set of information such as customer orders, current inventory levels, supplier lead times, and desired inventory levels. Therefore, the more decision-makers feel that they are able to access

and analyze such information, the less the decision-maker should contribute to overall supply chain ordering

variability. In turn, decreased ordering variability improves supply chain performance by reducing inventory

holding and out-of-stock costs.

HI: Lower levels of inventory management costs will be incurred as the procedural rationality of decisionmakers

increases.

Information gathering is a critical component of procedural rationality. Indeed, research indicates that firm

performance increases when information is gathered more frequently (Daft, Sormunen, and Parks 1988). Yet, in

highly uncertain or dynamic environments, information gathering may be reduced when managers believe thatuseful information is indeterminant (Becker 2001) or not accessible (May, Stewart, and Sweo 2000). Thus, as

shown in Figure 1, managerial perceptions regarding environmental conditions will affect the procedural

rationality of the decision process. Characterized by their "dynamic complexity" (Diehl and Sterman 1995),

supply chains may overwhelm the information processing abilities of decision-makers. Thus, decision-makers in

dynamic environments would collect less data, leaving less information to be analyzed. This implies an

increased likelihood of relying on less rational and more intuitive decision-making processes.

H2: Higher levels of inventory management costs will be incurred when supply chain decision-makers feel that

their environment changes rapidly.

Information about the inventory position of other members in the supply chain appears to have a straightforward

and beneficial effect on supply chain performance (Croson and Donohue 2006; Riddals and Bennett 2002;

Steckel, Gupta, and Banerji 2004). As Sterman (1989) noted, poor performance seems to depend most on the

degree to which supply chain decision-makers account for undelivered orders. Likewise, Croson and Donohue




(2006) found that availability of inventory information about other supply chain members, along with specific

instructions for obtaining it, increased performance. We suggest that simple availability of inventory information

does not have a direct impact on performance. Rather, the relationship between information availability and

performance is mediated by human decision-making processes. In essence, we suggest that in a neutral

environment, where information is simply available (without additional instructions), decision-makers using

higher levels of procedural rationality will be able to achieve lower inventory management costs than those

using more intuitive processes. Thus, we hypothesize:

H3: The availability of information regarding inventory levels within the supply chain in combination with higher

procedural rationality will decrease inventory management costs relative to the level of costs incurred when

information is absent and/or procedural rationality is lower.

The proposition that additional information can hurt, rather than help, decision-making (Steckel, Gupta, and

Banerji 2004) implicitly relies on the notion that human decision-making processes intervene in the link between

information availability and decision outcomes (Chaffee 1985). Though information affects performance, there is

a tipping point within a decision-maker with respect to additional information. Until the tipping point is reached,

additional information improves decision-making. Beyond the tipping point, however, additional information

overwhelms individual processing capacity and may have a negative effect on the outcome. This was

demonstrated in an experimental supply chain setting by Steckel, Gupta, and Banerji (2004), who found that

consumer demand information hurt, rather than helped, inventory management performance in the most

disruptive contexts. Thus, the availability of information in combination with individual decision-making

processes may determine decision outcomes.

We suggest that as a decision-maker in a supply chain is further removed from the consumer, the derived

demand pattern he/she imputes from observing the orders of a downstream partner may bear less and less

resemblance to actual consumer demand. However, the ordering efficiency of a supply chain member above

the level of the retailer depends on their response to their downstream customer's orders, not their response to

consumer demand. For example, a wholesaler's orders to a distributor are based on the wholesaler'srequirements, and only indirectly on consumer demand. This means that a wholesaler is poorly served by a

distributor who second guesses the wholesaler's order and ships to meet consumer demand instead (Van

Ackere, Larsen, and Morecroft 1993). Thus, we suggest that the usefulness of consumer demand information

decreases the further one's position in the supply chain is away from the consumers. We hypothesize:

H4. Higher levels of inventory management costs will be incurred when consumer demand information is

available and the procedural rationality of decision-makers increases. The effect will be more evident as

distance from the consumer increases.

METHODS

We examined supply chain performance in a laboratory experiment using a web-based simulation of a fourlevelsupply chain (i.e., retailer, wholesaler, distributor, and factory), which was modeled after the original Beer Game

(Sterman 1989). Our on-line, multi-player version of the beer game helps mitigate problems with incomplete or

erroneous information from the participants in the original board game (Sterman 1989), while retaining the

behavioral aspects that are lost in single player computerized games (Simchi-Levi, Kaminsky, and Simchi-Levi

1999). No references were made to any specific industries - the exercise was called the "Distribution System

Simulation" to mitigate any potential environmental framing effects regarding inherent demand variability. We

conducted sessions using participants recruited from upper-level, required business classes at a private

Midwestern university and a public Western university. In spite of differences from real world supply chain

decision-making (see the Limitations section), a simulated supply chain environment was an appropriate means

to examine the effects of individual perceptions on decision-making performance in a controlled environment

(Greenberg and Eskew 1993). Debriefmgs with the participants suggested that they took the task seriously and

were disappointed when they or their supply chains performed poorly.




Since information availability treatments were embedded in the application and did not involve special training,

all sessions received the same instructions, and treatments were randomly assigned to supply chains within

sessions. This procedure improves internal validity because it increases the probability that the treatments

influenced the use of the data in the decision-making process, rather than the treatments potentially being

confounded with specific instructions regarding the availability of certain types of data.

Experimental Procedures

After being directed to the appropriate website, participants completed a demographic questionnaire. When all

of the students had completed the questionnaire, an instruction screen appeared and was read aloud by the

experiment administrator. The instructions explained (a) the structure of the supply chain, (b) the objective of

meeting downstream demand while minimizing backlog plus carrying costs for the supply chain as a whole, (c)

the general layout of the display with the supply chain shown on the left-hand side and the tracking sheet on the

righthand side of the screen, and (d) the rules of the simulation. Rules included such items as: communication

of any type between participants was prohibited; time would progress to the next period after all members in a

supply chain entered a non-negative order for the current period; unfilled orders would be added to backlog; and

factories could assume infinite capacity. The instructions did not include any indication of potential demand.

Next, the computer randomly assigned each participant to a supply chain and one of four positions in the supply

chain. When the session size was not a multiple of four, computer "managers" filled the remaining positions in

one supply chain; these chains were not included in the analysis.

Once assigned to a supply chain, participants ordered four cases of product for each of three weeks as part of a

scripted familiarizing period. This ensured that participants were familiar with both the order process and the

calculation of inventory and backlog costs. Beginning with week four, participants were allowed to order any

nonnegative whole number of cases and complete the simulation as a supply chain. Participants were told the

simulation would run for 52 weeks, but the experiment halted after the 36* week to prevent endplay. All supply

chains faced the same, step up consumer demand beginning with four cases and increasing to six cases in

week five. All order and inventory quantities were whole cases. The participants filled out the proceduralrationality and perceived change questionnaires immediately after finishing the simulation.

Sample

Complete data were available for 53 supply chains. Participants averaged 22.3 years of age, were 57.5 % male

and had 1 .4 years of full-time work experience. Tests indicated no statistically significant differences between

treatments for age, gender, work experience, procedural rationality or perceived change. This suggests that the

groups were equivalent in terms of demographics, experience, and cognition (i.e., rational decision-making

tendencies and perceptions of change). Thus, observed differences in performance are likely to be a result of

the treatments and individual differences in procedural rationality.

Variables and MeasuresIndependent Variables

We were focused on examining individual-level causes of ordering inefficiency in the presence of additional

information. The independent variables were the information availability treatments, procedural rationality, and

perceived environmental change. We used four information treatments: consumer demand; order backlog;

consumer demand plus order backlog; and neither consumer demand nor order backlog. Figure 2 illustrates

how these treatments appeared on participant displays. Since different instructions for the different treatments

might have indicated to participants that the information was useful in making ordering decisions and biased

their behavior and responses, all participants received the same, scripted instructions.

Information Treatment: Consumer demand indicates that each person in the supply chain above the retailer

position had a column headed "Reported Consumer Demand" on the right side of their on-screen record sheet

(Figure 2). This column displayed the consumer orders received by the retailer each week. Although consumer

demand information is available by observing the movement of materials in and out of the retailer position,




some decision-makers do not seem to make this connection and attribute poor performance to large oscillations

in perceived demand (Sterman 1989). Furthermore, actual consumer demand is only discernable by upstream

supply chain members when the retailer is able to fill consumer demand from their inventory each period. As in

real world supply chains, when consumer demand exceeds retail inventories, actual demand may never be

known.

Information Treatment: Order backlog displayed information on the quantity that each member of the supply

chain was backlogged when the member had a net inventory of less than zero (Figure 2). In the absence of this

information, computing the backlog of the upstream partner requires a decision-maker to total the orders they

have placed, and then determine how much ofthat total remains undelivered.Procedural rationality is the use of information for the purpose of selecting a sensible alternative in the pursuit of

one's goals (Dean and Sharfman 1993; Fredrickson 1984). After completing the simulation, subjects answered

Dean and Sharfman's (1996) five-question procedural rationality scale, which was adapted to the decision-

maker level of analysis (i.e., "your ordering decision" rather than "the group's decision"). The questions

assessed the extent to which the person felt they used rational decision-making procedures (a = 0.73). The

items are given in Appendix A.

Perceived change measures the extent to which the external environment is perceived to stay the same over

time. After completing the simulation, subjects answered the following question adapted from Boyd and FuIk

(1996): "Change means the extent to which things change over time in your company's external environment. A

low rate of change means things stay about the same from period to period and a high rate of change means

things change quickly from period to period. How would you rate the overall level of change in your

environment?"

Dependent Variable

Supply chain performance was measured using total cost, which participants were instructed to keep as low as

possible. Consistent with Sterman's (1989) Beer Game, each decision-maker in the supply chain was charged

US $ 0.50 for each case in inventory at the end of a period, and US $ 1.00 for each case of undelivered orders

(backlog). The higher cost for backlogs encourages participants to quickly fill orders and meet demand (i.e., it is

the cost of not losing a sale or customer). Total cost is the sum of the costs at all four positions in the supply

chain. To adjust for the skew in cost data, the logarithm of total costs was used in the statistical analyses.

RESULTS

Table 1 provides means, standard deviations and correlation coefficients for the dependent variables and the




continuous independent variables. Table 2 shows the means and standard deviations of total cost for supply

chains by treatment. The performance of supply chains with consumer demand or backlog information was

better, on average, than those without additional information, which provides a simple manipulation check for

our treatments. However, the difference between the four cells was not statistically significant (F = 0.80; df 3,

49; p = 0.50), providing no support, as expected, for the idea that supply decision-makers will behave in a fully

rational manner in the presence of consumer demand and/or supply line inventory information.

Table 3 summarizes regression results for inventory management costs as predicted by the information

treatments, the procedural rationality of each member, the level of change perceived by each member and the

interaction of these variables. A regression with only the information availability treatments is not significant

(Model Ia; F = 0.35; df 3, 49; p = 0.79), which fails to support, as expected, the fully rational assumptions

underlying propositions that simply enriching the decision-making environment will lead to better performance.

Adding the procedural rationality of each supply chain member and the supply chain member's perceived

change in their environment still does not produce a significant model (Model Ib: F = 1.44; df 11, 49; ? = 0.19),

which fails to support HI and HI. It is not until the interactions of treatments and procedural rationality are added

that the regression model is significant (Model Ic: F = 2.18; df 23, 29; ? = 0.02; R2 = 0.63), suggesting that

individual differences in decision-making interact with the information available in the environment to affectsupply chain performance. This provides preliminary support for H3.

The significant interactions suggest that, compared with the control condition, supply chain performance




improved (i.e., costs decreased) when consumer demand information was available and wholesalers reported

high procedural rationality (p = 0.07). Similarly, compared to the control condition, supply chain performance

improved when backlog information was available and retailers or distributors reported high procedural

rationality (p = 0.01 and p = 0.03, respectively), which supports H3. This suggests that an interrelationship of

information availability and individual decision-making impacts supply chain performance. Only the factory's

perceived change was significantly associated with reduced performance (p <0.001), providing limited support

for H2.

Figure 3 graphically illustrates the interaction between decision-makers' rationality and information availability

on supply chain performance. Results of a simple slope analysis (Aiken and West 1991) are summarized in the

figure by using solid lines to indicate that a relationship was significantly different from zero (p <0.05 for

lightweight solid lines and p <0.10 for heavy-weight solid lines). Dashed lines represented relationships that

were not significantly different from zero (p >0.10).

Under the control condition (Figure 3, panel 1) or when consumer demand information was available (Figure 3,

panel 2), higher retailer procedural rationality led to significantly worse supply chain performance, while higher

rationality at the other positions did not significantly affect supply chain performance. When backlog information

was available (Figure 3, panel 3), higher retailer and distributor procedural rationality significantly improved

supply chain performance, while higher rationality at the other positions did not significantly affect performance.

When both consumer demand and backlog information were available (Figure 3, panel 4), wholesaler rationality

led to worse supply chain performance, while higher rationality at the other positions did not significantly affect

performance. Table 4 summarizes the results.

DISCUSSION

When considering the overall objective of minimizing total costs in the supply chain, information availability was

not effective by itself, nor was the procedural rationality of the decision-makers. Rather, the interaction of

information availability and the procedural rationality of the decision-makers acting as retailers, wholesalers, and

distributors in supply chains influenced overall supply chain performance, as described in the followingparagraphs. Thus, we suggest that the perceptions of supply chain decision-makers intervene in the relationship

between external factors, decision-making processes, and outcomes. Of the three decision-makers, the

procedural rationality of decision-makers in the retailer position had the most influence on supply chain

performance.




We found that higher procedural rationality by decision-makers in the retailer position led to worse performance

for supply chains when the supply chain had no extra information. While this may seem counterintuitive, it

supports Sterman's (1989) contention that decision-makers misunderstand their role in the supply chain and

make decisions that they believe are rational, yet the decision-maker often fails to account for important

information thereby adding ordering variability to the detriment of upstream supply chain members. When

backlog information was available, supply chain performance improved as the retailer's made use of the

additional information. The retailer's decisionmaking appears to be similar to the process described by Senge

(1990), who described the retailer in the traditional beer game as someone that is rational in every way except

for a failure to account for upstream backlogs. When upstream backlog numbers are shown, it is easier for more

procedurally rational retailers to account for upstream backlogs, and performance for the rest of the supply

chain improves. Higher levels of procedural rationality also allowed distributors with backlog information to have

a positive effect on the supply chain costs.




Given that the retailer was closest to actual consumer demand, it was somewhat surprising to find that the

availability of consumer demand information to all members of a supply chain had a negative effect on supply

chain performance when retailers had higher procedural rationality. ' Before discussing this further, we

emphasize that the supply chain's having consumer demand should not have significantly affected the retailer's

ordering behavior because all retailers were exposed to consumer demand (referencing Figure 2, the retailer's

record sheet had "Consumer Ordered" in the fourth column for all treatment conditions - none saw a "Reported

Consumer Demand" column because the information would have been redundant). This suggests that the

behavior of the retailer (and perhaps others as order variation moved up the supply chain) conflicted in some

way with the displayed consumer demand information, such that it led upstream members of the supply chain to

make poor decisions.




Thus, we suggest that consumer demand information can hurt, rather than help, decision-making. In

mathematically-simulated contexts, consumer demand information has a straightforward and beneficial effect on

supply chain performance (van Ackere, Larsen, and Morecroft 1993). However, our results suggest that when

this information is provided to human supply chain decision-makers, they may react to the combination of

consumer demand information and the orders of others in ways that decrease performance. In our supply

chains, as procedural rationality increased in the consumer demand information condition, only decision-makers

in the wholesaler position improved supply chain performance - all other positions decreased performance

(although only the retailer's effect was statistically different than zero). We suggest that wholesaler decision-

making improves from knowing consumer demand information, while those further up the chain are so removed

from consumer demand that they may be hindered by seeing the additional information. Similarly, we suggest

that members further upstream may be better off knowing the demand faced by their downstream partner (e.g.,

distributors would benefit from knowing the orders that the wholesaler received from the retailer). Thus, Steckel,

Gupta, and Banerji's (2004) finding of a significant overall benefit of consumer demand information when

consumer demand followed a step-up function may have been partially an artifact of having three- rather than

four-level supply chains.

The usefulness of consumer demand information in real world supply chains may similarly depend on "distance"

from the consumer. For example, the DRAM semiconductor market suffers from the bullwhip effect

(ChannelTimes 2006) in spite of publicly available consumer demand information about devices that use DRAM

(e.g., computers, MP3 players, cell phones, digital cameras). On the other hand, the processor semiconductor

industry has similarly available consumer demand information, but fewer suppliers for a given part (Gruber

2000), and relatively little bullwhip effect when compared with the DRAM market. We speculate that because

DRAM suppliers are prohibited by antitrust laws from discussing the implications of changes in consumer

demand with one another (Smith 2004), each company has to impute separately how changes in consumer

demand will affect demand for their DRAM from the consumer electronics manufacturers that purchase from

them. Such discussions are ostensibly allowed in the processor market (Parloff 2006), meaning that processor companies can make reasonably accurate demand projections (Hopman 2007).

Backlog information appears to have a much more straightforward and beneficial effect on decision-making than

the availability of demand information. For decision-makers in the retailer, distributor, and factory positions with

higher procedural rationality, available backlog information led to improved supply chain performance

(statistically significant for retailers and distributors). Referring to Sterman's (1989) note that decision-makers

seek better estimates of consumer demand, we speculate that more rational wholesalers may have been

preoccupied with observing the behavior of their retailer in an attempt to better predict consumer demand and

ignored backlog information that upstream members of their supply chain expected them to incorporate in their

decision-making processes. Being more removed from the consumer, more rational distributors with access tobacklog information appear to have attended more to the information that most directly affected them. Thus, we

suggest that as procedural rationality increases, decision-makers will be more likely to account for their supply

line when information for computing the supply line is available. At the factory, the perceived amount of

environmental change rather than their degree of procedural rationality was a better predictor of supply chain

performance. In particular, when the factory perceived high levels of environmental change, order variability of

the downstream members had already been amplified and negative effects on the system could not be

mitigated.

Overall, procedural rationality and/or information on its own had no statistically significant effect on

performance; rather, information usefulness is position-specific. The significance of the interaction of information

availability with the procedural rationality of decision-makers suggests that a lack of information may indeed be

an underlying driver of poor performance for supply chains. However, the ability to improve supply chain

performance with information hinges on position in the supply chain, plus the degree to which a decision-maker




was procedurally rational, and implies an entangled relationship between structural and perceptual causes of

ordering inefficiencies.

Limitations

Although the participants in this supply chain exercise had studied supply chains and decision rules as part of

their curriculum, they were not seasoned supply chain professionals. Furthermore, the limitations of the beer

game's supply chain structure as compared to supply chains in the real world have been noted (Simchi-Levi,

Kaminsky, and Simchi-Levi 1999; Steckel, Gupta, and Banerji 2004; Sterman 1989). The controlled decision-

making environment of our supply chain exercise allowed us to examine the decision-making processes of a

large number of decisionmakers under similar conditions, but the trade-off is that our setting was dissimilar from

the environment of a real world supply chain decision-maker in that(a) few real world supply chain members

have only one customer, and few have only one supplier,(b) supply chain decision-makers generally make few

decisions about a given supply chain over the course of a day (rather than 36 decisions), and (c) supply chain

managers vary in the amount of information they have available when making an ordering decision, just as firms

vary in their implementations of information technology.

We tested hypotheses that procedurally rational decision processes intervene and interact with information

availability in affecting supply chain performance, which meant that a controlled laboratory experiment with

adequately involved participants was appropriate (Greenberg and Eskew 1993). Noting that our theoretical

framing treats supply chain decision-making as a situation in which decision-makers adapt their decision

process over time in response to the perceptions of outcome feedback and their context, our results are likely to

apply most strongly with less experienced supply chain members or more experienced supply chain members

that change settings (e.g., changing product lines, moving from a retailer to a wholesaler role, or unpredicted

changes in demand). Similarly, we speculate that changing the environmental or organization context of an

experienced supply chain manager (e.g., implementing a new ordering system or adding new customers)

entails adaptation of their decision-making processes. This implies that contextual changes could lead to

positive or negative changes in procedural rationality.CONCLUSIONS

Implications for Researchers

The results of this study extend an adaptive, perceptual model of decision-making to a supply chain context. In

contrast to the propositions of van Ackere, Larsen, and Morecroft (1993) and Lee and Padmanabhan (1997),

and the experimental results reported by Croson and Donohue (2002), these results suggest that ordering

inefficiencies are largely due to a combination of information availability and the degree to which the decision-

maker used this information in a procedurally rational decision process. Providing information that ordinarily

would not be available (e.g., consumer demand) or information that would be difficult to produce (e.g., upstream

backlog), did not significantly affect decision-making performance by itself.Performance variability based on environment context, organization context, and decision-makers' procedural

rationality suggests that further examination of individuals' decision processes in simulated supply chains is

needed for improving supply chain decision-making and reducing the bullwhip effect. Important contextual

factors that may interact with procedural rationality in influencing decision-making performance include lead

time, variability in lead time, consumer demand variability, the time available to make order decisions, etc.

The results suggest that the retailer's decision-making was the principal determinant of supply chain

performance. When retailers had higher procedural rationality, supply chains with backlog information had

better performance, while supply chains with consumer demand information or no additional information had

worse performance. Senge (1990) offers a theoretical explanation for how a procedurally rational retailer would

lead to ordering inefficiencies when they do not account for their supply line. Even with a relatively small

increase in demand, a retailer that does not account for their supply line will make high orders when they run

out of stock, then increasingly higher orders as they remain out of stock while the orders are being shipped. In




this situation, the retailer's orders bear little relationship to the underlying consumer demand, which increased

slightly, but remains relatively flat. Future research should explore the role of procedurally rational retailer

ordering behavior in other contexts (i.e., other demand distributions, different lead times, etc.) to examine the

degree to which the overall effects on their supply chains are due to incorrect demand imputing by other supply

chain members (i.e., misinterpreting their downstream partner's order variability as consumer demand

variability). Similarly, examining why more procedurally rational wholesalers negatively affect performance when

they have access to backlog information may offer more insight on the degree to which the quality of a supply

chain member's ordering decisions is more important to performance than the ability of the other supply chain

members to predict what that member will order.

We also found that perceptions of environmental change at the factory level in our supply chains had a negative

effect on supply chain costs. We suggested that high levels of change would curtail information gathering and

thereby increase costs, but noted that decision-makers at the factory level may have been dealt an impossible

hand and are simply noting the already poor performance of their supply chain. Future research should examine

the extent to which perceptions of environmental change curtail information gathering.

Implications for Practitioners

From a practical standpoint, a retailer that keeps a relatively steady hand on their ordering in the face of the

jump in consumer demand or upstream backlogs would inevitably lead to better performance for the supply

chain as a whole. At higher levels in the supply chain, procedural rationality impacted supply chain performance

under fewer treatment conditions. Thus, decision-makers at higher positions in supply chains may be dealt a

difficult hand. In particular, ordering variability introduced by the retailer (and added at other positions) can't

always be overcome by procedural rationality to a sufficient degree to improve supply chain performance, which

could imply that decisionmakers under such conditions don't believe that increasing rational decision-making

processes will have a positive impact. For factories in our supply chains, perceptions of volatility in their

environment, and not the extent of their procedural rationality, affected overall performance of their supply

chain. In real-world supply chains, the behavior of downstream firms is similarly critical to the success of anupstream firm (Narayanan and Raman 2004). This provides further support for the alignment of manufacturer-

retailer incentives (Lee 2004; Narayanan and Raman 2004) and the avoidance of seasonal price promotions to

retailers (Fisher 1997).

This study demonstrates that the ability of a decision-maker to improve supply chain performance depends

upon (1) their position in the supply chain (environmental context), (2) the availability of information to them and

other supply chain members (organization context), and (3) the decision-maker's procedural rationality

(perceptions about their decision process). Although the structure of a supply chain is part of the environmental

context and beyond the control of most decision-makers, it would be simplistic to suggest that supply chains are

stacked against making quality decisions. Indeed, some decision-makers dampen the bullwhip effect andreduce the costs of their upstream partners. We noted in the introduction that decision-makers often lack full

understanding of their supply chains. Applying an adaptive model of decision-making, we speculate that

understanding increases when decision-makers have relevant information available for use in a rational

decision process, and that understanding decreases when relevant information is not available. Thus, it appears

beneficial to display supply chain information in a way that would most likely lead to its use when making

ordering decisions (Wu and Katok 2006). Specific training about the value and availability of upstream and/or

downstream information when making orders seems to make information usage more likely (Croson and

Donohue 2006; Steckel, Gupta, and Banerji 2004), so training about how to use the information should further

increase the likelihood that decision-makers would engage in a rational decision-making process. It may also

benefit supply chain decision-makers to discuss the implications of various items of information with each other;

rather than simply providing the information to others as was done in our experiment.

Sidebar




Some strategies for mitigating ordering inefficiencies in supply chains advise sharing information among

decision-makers. However, there has been little consideration of how individual perceptions intervene in the use

of available information in decision-making processes. This article reports the results of an experiment in which

participants were instructed to minimize inventory holding and backlog costs for their supply chains as a whole.

The analysis suggests that additional information affects supply chain inventory management costs only when

rational decision-making processes are followed. Decreased costs are observed when rational decision-making

is applied with backlog information. In contrast, increased costs are observed when consumer demand

information is available.

Key Words: Bullwhip effect; Individual perceptions; Information availability; Inventory management; Laboratory

experiment

Footnote

1 As requested by a reviewer, we examined the possibility that this result was due to outliers in the data or

some failure of the randomization procedures. The 53 observations of log(teamcost) could not be rejected as

being obtained from a normal distribution; yet, the stem and leaf plot showed one potential outlier associated

with the control condition. Removing this observation produced the same overall conclusions. In addition, no

significant individual differences were found between treatment or position assignment. Thus, we conclude that

our results were not driven by outliers or any bias in our randomization process.

References

REFERENCES

Aiken, Leona S . and Stephen G. West (1991), Multiple Regression: Testing and Interpreting Interactions,

Newbury Park, CA: Sage Publications, Inc.

Bacheldor, Beth (2003), "Steady Supply," InformationWeek, Vol. 965, pp. 35-42.

Bacheldor, Beth (2004), "Supply-Chain Economics," InformationWeek, Vol. 979. pp. 32-42.

Becker, Markus C. (2001), "Managing Dispersed Knowledge: Organizational Problems, Managerial Strategies,

and their Effectiveness," Journal of Management Studies, Vol. 38, No. 7, pp. 1037-1051.Boulding, Kenneth E. (1956), "General Systems Theory - The Skeleton of Science," Management Science, Vol.

2, No. 3, pp. 197-208.

Boyd, Brian K. and Janet FuIk (1996), "Executive Scanning and Perceived Uncertainty: A Multidimensional

Model," Journal of Management, Vol. 22, No. l,pp. 1-21.

Chaffee, Ellen Earle (1985), "Three Models of Strategy," Academy of Management Review, Vol. 10, No. 1, pp.

8998.

Channel Times (Sept 26, 2006), "Hynix Educates Partners on RAM Trends;" Accessed on June 9, 2009 from

ChannelTimes website: http://www.channeltimes.com/India/News/Hynix_Educates Partners on_RAM_Trends/55

1 -76 1 27-744.htm .Chen, Fangruo (1999), "Decentralized Supply Chains Subject to Information Delays," Management Science,

Vol. 45,No. 8, pp. 1076-1090.

Chen, Frank, Zvi Drezner, Jennifer K. Ryan, and David Simchi-Levi (2000), "Quantifying the Bullwhip Effect in a

Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, Vol. 46,

No. 3, pp. 436-443.

Croson, Rachel and Karen Donohue (2006), "Behavioral Causes of the Bullwhip Effect and the Observed Value

of Inventory Information," Management Science, Vol. 52, No. 3, pp. 323-336.

Croson, Rachel and Karen Donohue (2002), "Experimental Economics and Supply-Chain Management,"

Interfaces, Vol. 32, No. 5, pp. 74-82.

Croson, Rachel and Karen Donohue (2003), "Impact of POS Data Sharing on Supply Chain Management: An

Experimental Study," Production &Operations Management, Vol. 12, No. 1, pp. 1-11.

Croson, Rachel, Karen Donohue, Elena Katok, and John D. Sterman (2005), "Order Stability in Supply Chains:




Coordination Risk and the Role of Coordination Stock," Working Paper; Available on-line:

http://web.mit.edu/jsterman/www/Order_stability.html. Accessed 12/02/2005 .

Daft, Richard L., Juhani Sormunen, and Don Parks (1988), "Chief Executive Scanning, Environmental

Characteristics, and Company Performance: An Empirical Study," Strategic Management Journal, Vol. 9, No. 2,

pp. 123-139.

Davis, Tom (1993), "Effective Supply Chain Management," Sloan Management Review, Vol. 34, No. 4, pp. 35-

46.

Dean, James W., Jr. and Mark P. Sharfman (1996), "Does Decision Process Matter? A Study of Strategic

DecisionMaking Effectiveness," Academy of Management Journal, Vol. 39, No. 2, pp. 368-396.

Dean, James W., Jr. and Mark P. Sharfman (1993), "The Relationship between Procedural Rationality and

Political Behavior in Strategic Decision-making," Decision Sciences, Vol. 24, No. 6, pp. 1069-1083.

Diehl, Ernst and John D. Sterman (1995), "Effects of Feedback Complexity on Dynamic Decision-making,"

Organizational Behavior &Human Decision Processes, Vol. 62, No. 2, pp. 198-215.

Fisher, Marshall L. (1997), "What is the Right Supply Chain for your Product?" Harvard Business Review, Vol.

75, No. 2, pp. 105-116.

Forrester, Jay Wright (1958), "Industrial Dynamics," Harvard Business Review, Vol. 36, No. 4, pp. 37-66.

Fredrickson, James W. (1984), "The Comprehensiveness of Strategic Decision Processes: Extensions,

Observations, Future Directions," Academy of Management Journal, Vol. 27, No. 3, pp. 445-466.

Goldsby, Thomas J., Stanley E. Griffis, and Anthony S. Roam (2006), "Modeling Lean, Agile, and League

Supply Chain Strategies," Journal of Business Logistics, Vol. 27, No. 1, pp. 57-80.

Greenberg, Jerald and Don E. Eskew (1993), "The Role of Role Playing in Organizational Research," Journal of

Management, Vol. 19, No. 2, pp. 221-241.

Gruber, Harold (2000), "The Evolution of Market Structure in Semiconductors: The Role of Product Standards,"

Research Policy, Vol. 29, No. 6, pp.725-740.

Hambrick, Donald C. and Phyllis Mason (1984), "Upper Echelons: The Organization as a Reflection of its TopManagers," Academy of Management Journal, Vol. 9, No. 2, pp. 193-206.

Hopman, Jay (2007), "Using Forecasting Markets to Manage Demand Risk;" Accessed on June 9, 2009 from

Intel website: http://www.intel.com/technology/itj/2007/vlli2/4-forecasting/3-demand.htm .

Huber, George P. and Daniel J. Power (1985), "Retrospective Reports of Strategic-Level Managers: Guidelines

for Increasing their Accuracy," Strategic Management Journal, Vol. 6, No. 2, pp. 171-180.

Langley, Ann, Henry Mintzberg, Patricia Pitcher, Elizabeth Posada, and Jan Saint-Macary (1995), "Opening Up

Decision-making: The View from the Black Stool," Organization Science, Vol. 6, No. 3, pp. 260-279.

Lee, Hau L. (2004), "The Triple-? Supply Chain," Harvard Business Review, Vol. 82, No. 10, pp. 102-1 12.

Lee, Hau L. and V. Padmanabhan (1997), "Information Distortion in a Supply Chain: The Bullwhip Effect,"Management Science, Vol. 43, No. 4, pp. 546-558.

Lee, Hau L., V. Padmanabhan, and Seungjin Whang (1997), "The Bullwhip Effect in Supply Chains," Sloan

Management Review, Vol. 38, No. 3, pp. 93-102.

Lee, Hau L., Kut C. So, and Christopher S. Tang (2000), "The Value of Information Sharing in a Two-Level

Supply Chain," Management Science, Vol. 46, No. 5, pp. 626-643.

May, Ruth C., Wayne H. Stewart, Jr., and Robert Sweo (2000), "Environmental Scanning Behavior in a

Transitional Economy: Evidence from Russia," Academy of Management Journal, Vol. 43, No. 3, pp. 403-427.

Narayanan, V.G. and Ananth Raman (2004), "Aligning Incentives in Supply Chains," Harvard Business Review,

Vol. 82, No. 11, pp. 94-102.

Parloff, Roger (November 16, 2006), "Intel's Worst Nightmare;" Accessed on June 9, 2009 from Fortune

Magazine website: http://money.cnn. com/magazines/fortune/fortune_archive/2006/08/21/8383598/index.htm .

Rajagopalan, Nandini, Abdul Rasheed, Deepak K Datta, and Gretchen M. Spreitzer (1998), "A Multi-Theoretic




Model of Strategic Decision-making Processes," in Vassilis Papadakis and Patrick Barwise, eds., Strategic

Decisions, Norwell, MA: Kluwer Academic, pp. 229-249.

Rajagopalan, Nandini and Gretchen M. Spreitzer (1997), "Toward a Theory of Strategic Change: A Multi-Lens

Perspective and Intregrative Framework," Academy of Management Review, Vol. 22, No. 1, pp. 48-79.

Riddalls, C. E. and S. Bennett (2002), "The Stability of Supply Chains," International Journal of Production

Research, Vol. 40, No. 2, pp. 459-475.

Senge, Peter M. ( 1 990), The Fifth Discipline: The Art And Practice of the Learning Organization, New York:

Doubleday/Currency.

Silver, Mark ( 1 99 1 ), Systems That Support Decision-makers: Description and Analysis, New York: John

Wiley &Sons.

Simchi-Levi, David, Philip Kaminsky, and Edith Simchi-Levi (1999), Designing and Managing the Supply Chain:

Concepts, Strategies and Case Studies, New York: Irwin/McGraw-Hill.

Smith, Tony (February 27, 2004), "Memory Makers Hit by Price-fixing Claims;" Accessed on June 9, 2009 from

The Register website: http://www.theregister.co.uk/20Q4/02/27/memory makers hit by jricefixing.

Steckel, Joel H., Sunil Gupta, and Anirvan Banerji (2004), "Supply Chain Decision-making: Will Shorter Cycle

Times and Shared Point-of-sale Information Necessarily Help?" Management Science, Vol. 50, No. 4, pp. 458-

464.

Sterman, John D. (1989), "Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic

Decisionmaking Experiment," Management Science, Vol. 35, No. 3, pp. 321-339.

Tversky, Amos and Daniel Kahneman (1981), "The Framing of Decisions and the Psychology of Choice,"

Science, Vol. 211, No. 4481, pp. 453-458.

Van Ackere, Ann, Erik R. Larsen, and J. D. W. Morecroft (1993), "Systems Thinking and Business Process

Redesign: An Application to the Beer Game," European Management Journal, Vol. 11, No. 4, pp. 412-423.

Van Landeghem, Hendrik and Hendrik Vanmaele (2002), "Robust Planning: A New Paradigm for Demand

Chain Planning," Journal of Operations Management, Vol. 20, No. 6, pp. 769-783.Wu, Diana Yan and Elena Katok (2006), "Learning, Communication, and the Bullwhip Effect," Journal of

Operations Management, Vol. 24, No. 6, pp. 839-850.

AuthorAffiliation

by

Russell Haines

Old Dominion University

Jill R. Hough

University of Tulsa

andDouglas Haines

University of Idaho

AuthorAffiliation

ABOUT THE AUTHORS

Russell Haines (Ph.D. University of Houston) is an Assistant Professor of Information Technology at Old

Dominion University. His research focuses on the impact of information technology on individual and group

decision-making, and has been published in Journal of Business Ethics, European Journal of Information

Systems, and Information &Management, among others. Prior to entering academe, he worked as a manager in

the discount retail and convenience store industries, as an owner of a quick service restaurant, and as a system

developer.

Jill R. Hough (Ph.D. Oklahoma State University) is an Associate Professor of Management in the Collins

College of Business at the University of Tulsa. Her research on strategic decision-making, business segment




performance, and management history has been published in the Strategic Management Journal, Journal of

Management Studies, Journal of Management, and others. Prior to entering academe, Jill held positions as a

Manufacturing Engineer at a General Motors assembly plant, Manager of Computer Operations for a nonprofit

organization, Total Quality Coordinator and Software Quality Manager at an engineering design and

development firm, and Management Consultant.

Douglas C. Haines (Ph.D. University of Oregon) is an Associate Professor of Marketing and Department Chair

of the Department of Business in the College of Business and Economics at the University of Idaho. He has

published in Academy of Marketing Studies Journal, Journal of Business Ethics, International Business

&Economics Journal, Journal of the International Academy for Case Studies, Journal of College Teaching and

Learning, and Journal of Accounting and Financial Research. He worked for 15 years in various positions at the

H. J. Heinz Company, including Vice President of the Weight Watchers Foods Division of Heinz USA.

Contact author: Russell Haines; E-mail: [email protected]

Appendix

APPENDIX A

PROCEDURAL RATIONALITY ITEMS

1. How extensively did you look for information in making your ordering decision? (1 = Not at all - 7 =

Extensively)

2. How extensively did you analyze relevant information before making your ordering decision? (1 = Not at all -

7 = Extensively)

3. How important were quantitative (mathematical) analytic techniques in making your ordering decision? (1 =

Not at all Important - 7 = Very Important)

4. How would you describe the process that had the most influence on your ordering decision? (1 = Mostly

Analytical - 7 = Mostly Intuitive, reverse scored)

5. In general how effective were you in focusing your attention on crucial information and ignoring irrelevant

information? (1 = Not at all Effective - 7 = Very Effective)

Subject Studies; Supply chain management; Decision making; Environmental impact; Demand;

Classification

5160: Transportation management; 1540: Pollution control; 9130: Experiment/theoretical

treatment

Publication title Journal of Business Logistics

Volume 31

Issue 2

Pages

111-X

Number of pages 19

Publication year 2010

Publication date 2010

Year 2010

Publisher Blackwell Publishing Ltd., Council of Logistics Management

Place of publication Hoboken

Country of publication United Kingdom

Publication subject Business And Economics




ISSN

07353766

Source type Scholarly Journals

Language of publication English

Document type Feature

Document feature Diagrams Tables Graphs References

ProQuest document ID 805375562

Document URL http://search.proquest.com/docview/805375562?accountid=32031

Copyright Copyright Council of Logistics Management 2010

Last updated 2014-04-21

Database ABI/INFORM Research






Bibliography

Citation style: APA 6th - American Psychological Association, 6th Edition

Haines, R., & Jill, R. H. (2010). INDIVIDUAL AND ENVIRONMENTAL IMPACTS ON SUPPLY CHAIN

INVENTORY MANAGEMENT: AN EXPERIMENTAL INVESTIGATION OF INFORMATION AVAILABILITY AND

PROCEDURAL RATIONALITY. Journal of Business Logistics, 31(2), 111-X. Retrieved from


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