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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
ProQuest document link
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
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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
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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
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(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.
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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,
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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
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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
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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.
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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.
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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.
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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
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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
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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
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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.
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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
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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: RHaines@odu.edu
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
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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
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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
http://search.proquest.com/docview/805375562?accountid=32031
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