This article is an extract from Performance, Volume 5, Issue 4, November 2013. The full journal is available at
ey.com/performance
Reducing human
error in revenue
management
decision-making
Recent revenue management (RM) research
has demonstrated what appear to be
systematic deviations from optimization
models of decision-making. It turns out that
decision complexity exacerbates specific
elements of biased RM decision-making.
There are ways, however, in which managers
can use this information to improve the
quality of RM decision-making in their
companies. More fundamentally, companies
can learn from these predictable patterns to
design programs that minimize RM decisionmaking error.
02
Volume 5 │ Issue 4
This article is an extract from Performance, Volume 5, Issue 4, November 2013. The full journal is available at
ey.com/performance
Authors
Elliot Bendoly Associate Professor
Goizueta Business School
Emory University, US
Michael Alan Sacks Associate Professor
Goizueta Business School
Emory University, US
03
This article is an extract from Performance, Volume 5, Issue 4, November 2013. The full journal is available at
ey.com/performance
Reducing human error in revenue management decision-making
RM is the science of
maximizing the sales
and price per unit
of inventory.
H
ave you noticed recently that
airline flights almost always
fill to capacity? If you’re a
seasoned traveler, you may
have also observed that full
flights are more common
today compared with even only a few years
ago. The reason: sophisticated RM systems
that maximize the yield for seat sales.
RM is the science of maximizing the sales
and price per unit of inventory. The airlines
industry has developed highly sophisticated
dynamic computer models for its pricing
structure. These models make real-time
pricing decisions based on the number of
seats available at the time of purchase,
amount of time remaining prior to the
flight, and past purchase history for that
specific flight. With a significant amount of
meaningful data available, these systems
optimize revenue and seat capacity, largely
eliminating the role of human judgment in
the process.
However, the airline industry is an
extreme case where all such RM decisions
can be made via optimization models. For
most other industries, a mix of computer
models and human judgment are required
for RM decision-making. The hotel industry
serves as an ideal example. RM packages
designed for the hotel industry allow for
real-time monitoring of capacity utilization,
integration with forecasts, and advanced
analytics. However, individual RM judgment
is still viewed as essential to appropriately
manage the nuances confronted in realworld RM settings. For example, complex
large group bookings, last-minute
purchases, cancelations, weather-related
changes and trip adjustments all complicate
04
the RM system. Hence, the effectiveness of
many of the applications of these systems,
and their associated operating policies, is
still very much human driven.
Predictable human errors in
RM judgment
Given the central role of human decisionmaking in RM decisions, one might question
the extent to which people make optimal
versus suboptimal RM decisions. A wealth
of research shows that human beings
make common, predictable errors in
general decision-making. However, little is
known about decision-making errors in RM
judgment specifically, and how to mitigate
them moving forward. Just how good are
people at making challenging RM decisions?
It turns out that the answer is complex, yet
understanding decision-making errors in
RM can make a big difference in optimizing
RM outcomes.
A recent study by Bendoly1 sheds light
on this very question. The author sought
to measure the extent to which human
decision-making in an RM context differs
from optimal levels, then study the reasons
for such decision errors. The author
carefully designed a controlled experiment
to test pricing decisions across variations of
resource capacity and time urgency.
In the experiment, a revenue manager
had S total units of capacity available for
allocation to clients over a time frame of T
discrete periods. The goal of the experiment
was to maximize total revenue by selling as
much of S as possible, and at the highest
possible price levels, prior to the end of
the total time T available (think of a hotel
manager selling rooms for a specific date in
Volume 5 │ Issue 4
the future). The experiment began at time
T=40 (the full amount of time) then slowly
advanced until T=0 (no time remaining),
with subjects charged to sell five units
at the highest possible levels of revenue.
Subjects would receive computerized bids
along the way, which they had to either
accept or reject. The actual decision faced
in each period is, therefore, whether to
allocate the requested capacity, thus
generating an associated level of revenue,
or reject the request, assumedly with the
hope of allocating that capacity to a higherpaying customer later (with the associated
risk that such an opportunity might
never surface).
To address how complexity affects
optimal RM decision-making, three
experimental contexts were designed (see
Figure 1). Experiment A was the most
straightforward and experiment C was the
most complex. Thus, the goal was to assess
human decision-making on RM across
three levels of increasing complexity, then
compare the results to the same decisions
made by optimization models.
The final twist in the experiment is
perhaps the most intriguing. The author
used the latest theories within behavioral
operations to assess how, and why,
subjects make distinct types of decisionmaking errors, specifically looking at the
relationship between motivational levels
and performance. The first motivational
dynamic might be categorized as
indifference, in which a lack of sufficient
1. E. Bendoly, “Linking task conditions to physiology and
judgment errors in RM systems.” Production and Operations
Management 20(6), pp. 860-876, 2011.
This article is an extract from Performance, Volume 5, Issue 4, November 2013. The full journal is available at
ey.com/performance
With too little
challenge and plenty
of time, people are
more likely to reject
offers they otherwise
should accept.
challenge in a task results in a low state
of motivation for the individual worker.
Typically, such a lack of motivation is
ultimately associated with lower measures
of objective performance in a task. The
second dynamic revolves around increasing
the level of challenge associated with a
task by a sufficient, but not an excessive,
amount. Challenging, yet attainable, tasks
can raise a decision-maker’s motivational
level and increase attentiveness and arousal
regarding the task, generally resulting
in enhanced performance. The qualifier
‘‘not an excessive amount’’ is critical here,
as excessive challenge raises the third
well as their impact on performance. The
number of blocks simultaneously managed
by an RM is, of course, only one possible
contributor to the level of challenge
associated with a task. Also ostensibly
relevant to the management of these tasks,
is the comparison of time remaining to
capacity yet to sell. For example, it may
be that individuals faced with seemingly
high levels of capacity still unallocated
close to the deadline may feel particularly
challenged. In an attempt to fill this
capacity and, hence, avoid non-occupancy,
individuals might operate with lower than
normatively modeled price thresholds.
dynamic: distraction, stress and a general
reduction in motivation driven by a kind
of hopelessness from perceptions of
being overwhelmed by unattainable work
expectations. Together, these findings
imply an inverted-U relationship between
challenge and performance outcomes
(see Figure 2).
Because the concurrent management of
multiple blocks of capacity (in experiments
B and C) can generally be viewed as more
challenging than the management of a
single block (in experiment A), one would
expect to see differences in the dynamics
of arousal, indifference and stress, as
Figure 1. Assessing human decision-making on RM across three experiments of increasing complexity
Experiment
A
B
C
Units of capacity available
for sale by revenue manager
One block of
five units
Two independent
blocks of five units
Four independent
blocks of five units
Example
One hotel with
five rooms
Two hotels in different cities
with five rooms
Four hotels in different cities
with five rooms
City 1
City 1
City 3
City 1
City 2
City 2
City 4
Source: Authors’ own.
05
This article is an extract from Performance, Volume 5, Issue 4, November 2013. The full journal is available at
ey.com/performance
Reducing human error in revenue management decision-making
Given the central
role of human
decision-making in
RM decisions, one
might question the
extent to which
people make optimal
versus suboptimal
RM decisions.
Alternatively, those with limited capacity
remaining near the deadline may feel that
earlier efforts to use ambitious thresholds
yielded the result desired, and are sufficient
in securing their net performance over the
horizon considered. They may, in turn, be
less likely to seek out ambitious revenue
levels for the remaining capacity, either by
virtue of a fundamental lack of motivation
(indifference) or the view that pursuing
ambitious revenues on such capacity is
simply less realistic (overwhelming), given
their own assumptions regarding the pool
of demand. The result that follows might
be a tendency to use less than normatively
modeled thresholds at these low capacity
levels, irrespective of the time remaining.
The overall expectation was that low
levels of time remaining, coupled with
high levels of capacity yet to allocate,
should be particularly associated with
feelings of stress related to overwhelming
workload. Additionally, such effects should
be particularly relevant in increasingly
complex contexts (experiments B and
C) where multiple blocks of capacity are
managed concurrently. These compounded
issues, if in fact serving to generate stress
rather than simply arousal, are expected
to be associated with increases in ‘‘accept’’
errors (agreeing to bids priced at lower
than necessary rates) more so than ‘‘reject’’
errors (taking a pass on bids that otherwise
should be accepted). That is, people will
accept bids that they should objectively
reject due to the aforementioned biases.
Conversely, with high amounts of time
remaining and no imminent decision
necessary, people may be more likely to
make reject errors due to low motivation
06
caused by under-stimulation. This would be
most likely in the least complex situations
(experiment A) as compared to more
complex scenarios (experiments B and C).
Measuring motivational state
The only way to determine whether certain
types of deviations from anticipated RM
(i.e., certain errors) are being driven by
either a lack of arousal or a sense of being
overwhelmed, is to simultaneously study
hypothetical markers of such emotional
reactions more directly. Fortunately,
objectively observable physiological
markers of emotional and cognitive states,
such as arousal (or lack thereof in the case
of indifference) and stress, exist. Markers
that have been studied in the past, range
from neuroelectrical activity in the brain,
as measurable by electrodes, or functional
magnetic resonance imaging to heart
rate variance. Physiological responses
that require less obtrusive means for
measurement, such as those measurable
through the video-monitoring of the eye,
have also proved useful and increasingly
cost-effective.
Eye tracking offers the advantage of
allowing the simultaneous observation of
multiple potentially idiosyncratic markers
(e.g., pupil size, blink rate, x-y fixation).
Pupil size has consistently been shown to
increase upon heightened levels of mental
workload and arousal. A typical range of
pupil diameters for humans is 2mm–8mm,
with relative variations from an individual’s
relaxed state (i.e., when not engaged with
work) typically used to denote increases
in arousal. In contrast to the observed
linkage between pupil size and arousal,
Volume 5 │ Issue 4
blink frequency is largely thought to reflect
negative mood states, such as nervousness,
stress and feeling overwhelmed by a task.
An often-cited reference for this marker is
“the Nixon effect,” referring to the former
president’s increased blink frequency (50
times per minute) during a discussion of his
removal from office. Conversely, blink rate
is thought to slow down when individuals
engage in successful and comfortable
problem-solving endeavors. Thus, the
author utilized pupil size to measure arousal
and motivation, and blink frequency to
measure stress and anxiety.
This article is an extract from Performance, Volume 5, Issue 4, November 2013. The full journal is available at
ey.com/performance
The result
In the single-block experiment (A), reject
errors were more commonly made with
high levels of capacity and time remaining
as compared to the optimization model. In
other words, subjects took a pass on bids
that they otherwise should have accepted
when they had plenty of time and capacity
remaining. Conversely, accept errors were
more commonly made with low amounts
of time remaining and resources yet to
allocate. In this case, subjects settled for
lower offers than they needed to accept.
The next step was to assess the extent
to which motivational levels drove these
behaviors. As predicted, pupil-dilation levels
(indicative of arousal levels) rose as capacity
decreased and as the number of blocks
increased. In contrast, blink rates (measures
of stress and discomfort) increased with
higher levels of remaining capacity,
combined with lower levels of time left.
This shows that the low levels of arousal at
the early stages of the experiment (where
high levels of time and capacity remain)
are associated with reject errors, and
higher levels of stress in the latter stages
(where time is limited and capacity remains)
contribute to the accept errors.
In experiments with multiple concurrent
blocks to manage (B and C), reject errors
appeared much less common, or at least
smaller in magnitude on average. Pupil
dilation rates suggest that the greater
complexity in managing multiple blocks
appears to mitigate the lower levels of
engagement. In contrast, accept errors
appear much more common in the
more complex experiments (B and C),
when compared with those observed in
experiment A. Thus, in the comparatively
more complex context, subjects were less
likely to reject offers they should accept but
more likely to accept suboptimal offers they
should reject.
The impact of time remaining and
capacity appear to have the greatest
impact on blink rate in experiment C, and,
conversely, to be significantly reduced
in experiment A. In experiments B and
C, blink rate seems consistently on the
rise, with decreasing time and increasing
capacity levels. Interestingly, the point of
this inversion seems to map to the point
at which individuals appear to shift from
a tendency to commit reject errors to a
tendency toward accept errors.
The findings of this study shed important
light on the biases that people may have
in making RM decisions. With too little
challenge and plenty of time, people are
more likely to reject offers they otherwise
should accept. Conversely, overwhelmed
by limited time and capacity yet to allocate,
people are more likely to accept offers
they otherwise should reject. This effect is
07
This article is an extract from Performance, Volume 5, Issue 4, November 2013. The full journal is available at
ey.com/performance
Reducing human error in revenue management decision-making
Knowing the types of
errors that take place
in RM decisions, and
why they occur, helps
to build actionable
tools for preventing
and overcoming
such biases.
magnified in conditions of complexity. In
especially complex RM decisions, people are
less likely to be bored by any decision, thus
less reject errors take place. The concern
in complex scenarios is higher rates of
accept errors, where the combination of
complexity and time scarcity with resources
yet to allocate adds significant levels of
stress. Knowing the types of errors that
take place in RM decisions, and why they
occur, helps to build actionable tools for
preventing and overcoming such biases.
stressful, one solution may be simply to
artificially lower the apparent amount
of capacity that needs filling. Such an
adjustment might involve a reallocation
of some of that capacity task to another
revenue manager or an individual cross-
trained sufficiently to deal with it, or even
to a more automated, if imperfect, artificial
intelligence (AI) mechanism. Alternately,
portions of capacity might be held in buffer,
beyond the purview of RMs, until other
capacity units are allocated, and the threat
Figure 2. Motivation and performance: inverted-U relationship between
challenge and performance outcomes
Implications for practice
08
Negative
stress
effects
Cognitive
limit?
Blink rate
Arousal
Pupil dilation
Indifference
Visual engagement
Level of challenge
Level of challenge
l
usa
Aro
Performance
As a starting point, managers should be
aware of the conditions under which RM
decision-makers will make suboptimal
decisions, reject or accept errors
specifically. By doing so, they can begin
to monitor for the root causes of such
behaviors and reduce their frequency.
The onset of stress due to excessively
challenging work (to the detriment of
performance) may be dealt with simply
through creating mechanisms to bolster
self-confidence commensurate with
difficulty level. A broad approach to
achieving this state is suggested in the
form of increased training and resource
availability to bolster awarenessRecommendations
of these
important dynamics. If successful, these
Business culture
programs can reduce the negative effects
check
of stress, and consequently reduce costly
Cost optimization
accept errors.
check
Aside from these general tactics,
Growth strategy
check
however, the present work suggests that
certain more nuanced approaches to
managing workload might be applied. For
example, if the amount of capacity to be
filled in a limited amount of time appears
Few errors
Errors of some kind are made
Level of challenge
Source: Authors’ own.
Volume 5 │ Issue 4
Str
ess
Data validit
This article is an extract from Performance, Volume 5, Issue 4, November 2013. The full journal is available at
ey.com/performance
of stress-based complications in judgment
are mitigated. By selectively reducing
capacity, RM decision-makers will feel less
stress and thus have a reduced tendency to
make accept errors.
Having access to accurate measures
of work arousal (through pupil dilation
measures as in this study) and stress
(through blink frequency) could deliver
a huge impact in real-time adjustments
to work conditions. On first thought, it is
difficult to imagine a typical workplace
adopting the type of software necessary
for such measurements today. However, it
is conceivable that at some point certain
workers themselves may be willing to have
stress levels monitored with an interest
in allowing management to take action
to reduce it. If acceptable, such statemanipulations might be applied more
effectively. Based on the views of revenue
managers participating in a related followup study, this manipulation tactic appears
to be a tenable prospect.
Finally, we can use the above tools to
help pinpoint thresholds where stress levels
affect the quality of RM decisions across
specific industries. The challenges of RM
decisions within the airline industry, for
example, may be distinct to those within the
bed mattress industry. Examining the role
of psychological motivation in RM decisions
within industries can help solidify measures
specific to that industry. From there,
interventions can be designed specific to
employee needs within industries and even
within unique firms. These efforts can help
maximize human decision-making in an RM
context and improve working conditions
at the same time, a win for companies and
their employees. 
These efforts can
help maximize human
decision-making in
an RM context and
improve working
conditions at the
same time, a win
for companies and
their employees.
09