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<p>This research deals with a phenomenon well-known in socio-economic studies and referred to as the Agency Theory: the principal-agent problem. The agent is designated to act on behalf of the company owner, i.e., the principal, in the domain of supply chain management in the face of a supplier’s default uncertainty. Each of the players is gain-oriented, but their objective functions and the degree of risk-bearing may significantly differ, leading to an agent’s decisions not being optimal in terms of the principal’s outcome. This article proposes a multi-period supply chain simulation model that can be used to perform a supply chain optimization and comparison between the agent and the principal. The proposed framework allows for differentiating the model parametrization depending on the industry, in particular the consequences of the inability to deliver the end product, costs of keeping safety stocks, or the uncertainty regarding the suppliers’ delivery failure. As players’ objective functions, we consider expected profit-based indicators and measures taking the inter-period variance into account. We find that the agent’s actions may diverge from the principal’s optimum if the agent’s incentive system is not selected correctly. We also propose a solution that unifies the goals of the players. The recent COVID-19 pandemic amplifies the importance of such research. Many companies had to limit production capacities due to global lockdowns and, per the JIT production strategy, the prior safety stock levels were low.</p>
</abstract>

This research deals with a phenomenon well-known in socio-economic studies and referred to as the Agency Theory: the principal-agent problem. The agent is designated to act on behalf of the company owner, i.e., the principal, in the domain of supply chain management in the face of a supplier’s default uncertainty. Each of the players is gain-oriented, but their objective functions and the degree of risk-bearing may significantly differ, leading to an agent’s decisions not being optimal in terms of the principal’s outcome. This article proposes a multi-period supply chain simulation model that can be used to perform a supply chain optimization and comparison between the agent and the principal. The proposed framework allows for differentiating the model parametrization depending on the industry, in particular the consequences of the inability to deliver the end product, costs of keeping safety stocks, or the uncertainty regarding the suppliers’ delivery failure. As players’ objective functions, we consider expected profit-based indicators and measures taking the inter-period variance into account. We find that the agent’s actions may diverge from the principal’s optimum if the agent’s incentive system is not selected correctly. We also propose a solution that unifies the goals of the players. The recent COVID-19 pandemic amplifies the importance of such research. Many companies had to limit production capacities due to global lockdowns and, per the JIT production strategy, the prior safety stock levels were low.

The principal-agent problem in supply chain management – the simulation based framework

Control and Cybernetics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This research deals with a phenomenon well-known in socio-economic studies and referred to as the Agency Theory: the principal-agent problem. The agent is...

The principal-agent problem in supply chain management – the simulation based framework

Published Online: Jun 23, 2022

Page range: 195 - 221

Received: Dec 01, 2020

Accepted: Mar 01, 2021

DOI: https://doi.org/10.2478/candc-2021-0011

Keywords
supply chain, quantitative models for decision-making, simulation framework, agency problem, principal-agent problem, ESOP

© 2021 Kinga Siuta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The principal-agent problem in supply chain management – the simulation based framework

Published Online: Jun 23, 2022

Page range: 195 - 221

Received: Dec 01, 2020

Accepted: Mar 01, 2021

DOI: https://doi.org/10.2478/candc-2021-0011

Keywordssupply chain, quantitative models for decision-making, simulation framework, agency problem, principal-agent problem, ESOP

© 2021 Kinga Siuta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
supply chain, quantitative models for decision-making, simulation framework, agency problem, principal-agent problem, ESOP