Psychological determinants of inventory management performance

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Presentation at the Resorg research seminar, Radboud University Nijmegen, 13th October 2011

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Psychological determinants of

inventory management performance

Dr. Andreas Größler

Nijmegen School of Management

Business Administration – Methodology

E-mail: a.groessler@fm.ru.nl

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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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

keep initial orders

Intelligence and knowledge have a clear effect on

performance; entrepreneurial spirit has 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 spirit (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?