Two cases on behavioral operations management research
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Behavioral operations management –
two studies
Andreas Größler
Radboud University Nijmegen, the Netherlands
Behavioral OM: Trying a definition…
Operations Management (OM) is concerned with the design and
management of transformation processes in organisations, in order
to achieve (societal and/or economic) value.
Behavioral Operations Management (BOM) is a multi-disciplinary
branch of OM that explicitly considers the effects of human
behaviour and cognition on the transformation processes, which
are influence by individual biases, social preferences, and cultural
norms.
(cf. Loch&Wu, 2007)
Various special issues (JOM, POM, MSOM), academic conferences,
sub-groups in academic associations, ...
Nijmegen
2000 years old (lat. noviomagus)
Today ca. 170,000 inhabitants
Radboud University Nijmegen
• Seven faculties (including university hospital): 19,000 students
• Nijmegen School of Management: 3,100 students
European Master in System Dynamics
Bergen
Lisbon
Nijmegen
Palermo
Erasmus Mundus
Label of Excellence of
European Commission
Basis
kart
e: dig
itale
-euro
pakart
e.d
e
European Master in System Dynamics:
Overall programme
Intelligence, Knowledge, Personality, and
Interests – Determinants of Individual
Inventory Management Performance?
Jürgen Strohhecker and Andreas Größler (2013). Intelligence, Personality,
Interest and Knowledge – The effect of personal traits on inventory management
performance, International Journal of Production Economics, 142(3), 37–50.
Inventory management issues are commonplace in daily life
and in business.
Spiegel.de n24de
There are three bodies of literature dealing with the issue of
inventory management failures:
1. Normative approaches, operational research (for reviews cf., Williams and
Tokar, 2008; Gino and Pisano, 2008)
2. Psychological research on complex problem solving (e.g., Dörner, 1980;
Sternberg and Frensch, 1991; Brehmer, 1992; Brehmer and Dörner, 1993;
Ackerman and Kanfer, 1993; Dörner et al., 1994; Frensch and Funke,
1995; Dörner, 1996; Wittmann and Hattrup, 2004)
3. System dynamics research on stock management behaviour (e.g.,
Sterman,1989; Booth Sweeny and Sterman, 2000; Ossimitz, 2002;
Sterman and Booth Sweeny, 2002; Croson and Donohue, 2003, 2006;
Pala and Vennix, 2005; Cronin et al., 2009; Sterman, 2010)
In operational research, optimal solutions to inventory
management problems are sought.
Search.com
Inventoryops.com
In psychology, complex problem solving deals with the
behaviour of people in simulated situations.
Dörner
In system dynamics, experiments show the difficulty of
people to understand/to control stocks and flows.
The research gap lies in the intersection of the three
literatures.
OR – domain
CPS – person factors
SD – experimental
design
Why is stock management relevant? A more substantial
example…
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w.n
ationalg
eogra
phic
.com
Ackerman‘s (1996) PPIK theory has been tested against
performance in an inventory management task.
Pugepo: Inventory management for a pump producer as
experimental task.
Experimental design and participants:
• N = 72 participants, 3rd year bachelor students from German business
school; elective course “Operations Management”, autumn 2010
• Tests: - Intelligence: BIS at start of study
- Knowledge: average of marks for related courses from studies
- Personality: NEO FFI at start of experiment
- Interests: AIST-R at start of experiment
- Performance: PUGEPO simulation as last part of experiment, total accumulated costs
as measure
• Financial incentive: max. 9.55 €, average achieved 4.55 €
About 50% of participants fail to beat simple benchmark
strategies.
replicating incoming orders
keeping initial orders
Intelligence and knowledge have a clear effect on performance;
entrepreneurial interests have an adverse effect. A B C D E
Constant -.005 .028 .004 .001 .005
(.111) (.117) (.112) (.108) (.106)
BIS-AI-S -.363*** -.326*** -.383*** -.335***
(.111) .109 (.118) (.110)
WIWI -.286*** -.234** -.161 -.244**
(.122) (.118) (.131) (.115)
AIST-R practical and technical -.084
(.142)
AIST-R intellectual and investigative -.170
(.140)
AIST-R artistic and linguistic .062
(.137)
AIST-R social -.052
(.137)
AIST-R entrepreneurial .313** .219**
(.153) (.107)
AIST-R organizational and administrational -.016
(.123)
NEO-FFI neuroticism -.105
(.132)
NEO-FFI extraversion -.167
(.149)
NEO-FFI openness to experience .159
(.137)
NEO-FFI agreeableness .200
(.143)
NEO-FFI conscientiousness -.057
(.126)
R-squared .132 .082 .186 .330 .233
Adjusted R-squared .120 .069 .162 .180 .199
No. observations 72 72 72 72 72
Intelligence and knowledge highly relevant; little evidence
for interest and personality factors.
• One significant interest dimension: entrepreneurial interests (negative!)
• Variance explained is limited
• Number of participants needs to be increased
• Other forms of statistical analysis (e.g. structural equation modelling) would
allow investigating inter-construct relationships
• Influence of task complexity/ demand uncertainty on performance?
Tangible Stock/Flow Experiments −
Addressing Issues of Naturalistic
Decision Making
Jürgen Strohhecker and Andreas Größler (under review). Closer than expected −
(Missing) Differences between Tangible and Abstract Stock-Flow Task Performance,
Simulation&Gaming.
Human shortcomings in dynamic decision making
performance
Dynamic stock management largely fails!
Ample evidence from System Dynamics and psychological dynamic decision
making research:
Edwards 1962, Dörner 1980, Sterman 1989, Brehmer 1992, Dörner
1996, Paich & Sterman 1993, Moxnes 1998, Wittman & Hattrup 2004,
Moxnes & Jensen 2009
Classic example: controlling temperature (Reichert & Dörner 1988)
Period
Tem
pera
ture
in °
C
12.07.2013 .
Human deficits in understanding of accumulation (UoA)
processes
Average UoA (= understanding of stock/flow relations) is poor! Ample evidence from System Dynamics research:
Booth Sweeney and Sterman 2000, Ossimitz 2002, Sterman and Booth Sweeney 2002, 2007, Cronin and Gonzales 2007, Strohhecker 2009, Sterman 2010
Classic example: Bath tub paper/pencil task
Consider the bathtub shown below. Water flows in at a certain rate
and exits through the drain at another rate:
0
25
50
75
100
0 2 4 6 8 10 12 14 16
Flo
ws (
Litre
s p
er
Min
ute
)
Inflow
Outflow
0
50
100
150
200
0 2 4 6 8 10 12 14 16
Time (Minutes)
Wate
r in
Bath
tub
(Litre
s)
57.9 % correct
(Booth Sweeney & Sterman, 2000)
12.07.2013 .
Criticism from naturalistic decision making research
• “After all, people do manage to fill and drain their bathtubs… “ (Booth Sweeney & Sterman, 2000, p. 280)
• Proponents:
Zsambok & Klein, 1997, Lipshitz et al. 2001, Klein 2008, Lipshitz et al. 2006
• Main argument: Failures of humans to deal adequately with dynamic
complexity are apparent.
• However, these failures do not result from erroneous thinking but are artifacts
of the experimental method employed
• Our research objective: shedding light on two assumptions in this debate:
1. people are good decision makers in naturalistic situations when it comes to
stock/flow tasks and
2. performance in naturalistic and in more abstract tasks is related.
12.07.2013 .
Proposition 1:
Participants are able to achieve good performance (that is, a minor deviation
from the target in a reasonable short time span) when conducting a tangible
stock/flow experiment.
Proposition 2:
Participants are able to perform better in a tangible stock/flow experiment
than
a) in a similar simulator based experiment;
b) in paper-pencil stock/flow tasks.
Proposition 3:
Participants performing well in the tangible stock/flow task will also show a
good understanding of accumulation in a paper-pencil test and vice versa.
Our propositions
.
Research method
• Non-experimental (correlational/observational) with two observations:
1. Paper and pencil stock/flow inventory
2. Physical funnel and glass test
• Laboratory (controlled environment)
• 4th semester bachelor students
• Financial incentive of up to 20 € (linked to the performance in both tasks)
• Proposition 2a: comparison with results from literature
Instructions
Please look carefully at the following experiment setup:
Target
filling level
Beaker
Funnel
d = 3.0 mm
It is your task to pour the red liquid from the flask into the funnel so that it flows in the beaker
positioned below. You must by all means avoid that the liquid brims over the top of the funnel.
You are allowed two runs. Please achieve the following targets as best as you can:
1. Minimize the variance between the target filling level marked on the beaker and the
actual filling level (measured in milliliter)!
2. Minimize the filling time, which is measured as the time span between lifting the flask
from the table and posing it back onto the table!
For your participation in this experiment you are rewarded depending on the achievement of
these targets: The less the volume variance and the less time you need, the higher is the
monetary reward. It becomes zero if the liquid brims over the top of the funnel. The reward per
run is calculated exactly as follows (negative amounts are set equal to zero):
s
€2,0s5sTime Fillingml VarianceVolume
ml
€2,0€5wardRe
Propositions and results…
Proposition Measurement Result
Tangible SF
performance is good
Volume and time
deviation from
target/benchmark
Participants
significantly miss
target/benchmark
Tangible SF
performance is better
than abstract
Percentage deviation
from goals in tangible
task vs. in abstract
tasks
Participants do better
in abstract task than
in tangible task
Tangible and abstract
SF performance are
related
Correlation of
tangible and abstract
performance
measures
Participants’
performances are not
correlated
12.07.2013 .
Conclusions
• Naturalistic decision making proponents: people perform better in natural
situations
• Our goal: empirical investigation of this assumption
• Our (preliminary) result: Not one single of our propositions is supported by
empirical evidence
• Implication: the assumption of increased performance in natural decision
making situations does not hold for stock/flow tasks with a delay
• Two possible explanations:
1. No heuristic available “that makes us smart” regarding accumulation
processes involving a delay
2. Method shortcomings: although tangible still an artificial task in a
laboratory setting is used
Open issues
1. Classification of situations (according to structure or context?)
2. Giving information/cues in a way to support good decisions
3. Provide tools to acquire heuristics in new situations and to
support deliberate decision making
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