An exploratory study of risk aversion in supply chain dynamics via human experiment and agent-based simulation

Abstract

The literature on the impact of risk aversion on supply chains (SCs) is relatively limited and, in particular, there is a dearth of theory and a lack of empirical evidence concerning: (1) the impact of individual risk aversion on the generation and dynamics of the order policy (e.g. order patterns and inventory holding costs); (2) the impact of several combinations of risk-averse members in each stage of a multi-echelon SC. We explore these gaps through a multi-method approach (i.e. human experiments and agent-based simulation), thus using both empirical and simulated data. Specifically, based on results from a human experiment, we develop the conjecture that risk aversion is positively correlated to the desired stock level and consequently to the safety stock factor of inventory order policies. Building on this finding, we perform a simulation study to infer the impact of individual risk aversion in a multi-echelon SC. Results show that alternative compositions of the SC in terms of risk aversion levels of the echelons significantly influence inventory holdings and SC dynamics. The study implies that a company facing problems of high inventory days-on-hand should favour low-risk aversion managers, as instrumental to lowering stock and improving net working capital.

Keywords:

• risk aversion
• behavioural operations
• human experiment
• supply chain dynamics
• multi agent-based simulation

Acknowledgements

We are grateful to the anonymous referees and the editorial team as their suggestions allowed us to improve earlier versions of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Carmela Di Mauro http://orcid.org/0000-0001-7317-945X

Alessandro Ancarani http://orcid.org/0000-0001-8504-0332

Additional information

Funding

This research was supported by the Italian Ministry of University and Research (Rita Levi Montalcini fellowship), by the University of Seville (V PPIT-US), and by the Spanish Ministry of Science and Innovation, under the project PROMISE with reference DPI201680750P.