High inventories, poor customer services and less revenue are the problems due to
ordering inefficiencies. 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;
Sterman, 1989; and Lee et al., 1997a and 1997b). For example, the number of orders
that Procter and Gamble’s factory receives for disposable diapers from its distributors
may rise and fall, 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 et al., 1997a and 1997b). Bullwhip effect was also observed in orders for Hewlett-Packard
inkjet printers (Davis, 1993), Campbell’s condensed soup (Fisher, 1997), and 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 et al., 2006).
Managers making these orders lack complete understanding of their supply chains,
i.e., have no clear analytical method for calculating how much inventory they need,
and rely 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 $2.5
bn 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 et al. (1997a and 1997b) suggest that ordering inefficiencies
occur because decision makers 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 the consumer demand, the upstream member is more likely to make an inefficient
order. For 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 et al. 2000; and Riddalls and Bennett, 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).
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