Assessment model of risk tolerability level of perishable agricultural products transportation

Authors

DOI:

https://doi.org/10.31548/machenergy2020.02.059

Keywords:

agricultural products, supply chains, risk, metrics, transportation.

Abstract

The level of risk tolerability for agricultural supply chains due to its characteristics, primarily related to limited shelf life, is a complex indicator that reflects the likelihood of an occurrence and the severity of an adverse event (risk event).

At that, a significant number of external and internal environmental factors expressed in the aggregate of certain indicators have the impact on the fact of the occurrence of a risk event. The significance of these indicators, as well as the vector of their power of influence, is unique to each individual factor. However, transport factors indicators influencing the riskiness of supply chains have the greatest influence and some uniqueness in logistics.

The article proposes the system of factors typified by indicators reflecting the influence of the internal and external environment on the risk level of the transportation process of perishable agricultural products with fuzzy multiple approach being one of the most suitable for the development of the assessment model of logistical risks level in the process of perishable agricultural products transportation.

On the basis of the defined system of indicators, a fuzzy-multiple assessment model of the proposed logistics risk groups tolerability has been developed. In accordance with the defined approach, it is proposed, first of all, to assess the level of tolerability of all possible transportation scenarios implementation accepted apart from economic indicators.

A scenario that does not meet the regulatory norms of the logistical risk tolerability level should be automatically excluded from the list of potential for implementation regardless of its level of economic attractiveness.

References

Bandaly D., Satir A., Shanker L. (2014). Integrated Supply Chain Risk Management via Operational Methods and Financial Instruments, International Journal of Production Research 52 (7): 2007-2025. https://doi.org/10.1080/00207543.2013.844376

Chen J, Sohal A. S., Prajogo D. I. (2016). Supply risk mitigation: a multi-theoretical perspective. Production Planning & Control The Management of Operations Volume 27, Issue 10. 853-863. https://doi.org/10.1080/09537287.2016.1147620

Nooraie S. V., Parast M.M. (2015). A Multi-Objective Approach to Supply Chain Risk Management: Integrating Visibility with Supply and Demand Risk", International Journal of Production Economics 161: 192-200. https://doi.org/10.1016/j.ijpe.2014.12.024

Nyaga, G. N., Lynch, D. F., Marshall, D., & Ambrose, E. (2013). Power Asymmetry, Adaptation and Collaboration in Dyadic Relationships Involving a Powerful Partner. Journal of Supply Chain Management, 49(3), 42-65. https://doi.org/10.1111/jscm.12011

Oracle (2013). Managing the Value Chain in Turbulent Times. Dynamic Markets Limited. Independent Market Research Report Commissioned by Oracle.

Tang O. Musa S. N. (2011). Identifying Risk Issues and Research Advancements in Supply Chain Risk Management. International Journal of Production Economics 133 (1): 25-34.

https://doi.org/10.1016/j.ijpe.2010.06.013

Vilko J., Ritala P., Edelmann J. (2014). On Uncertainty in Supply Chain Risk Management. The International Journal of Logistics Management 25 (1): 3-19.

https://doi.org/10.1108/IJLM-10-2012-0126

Wildgoose, N., P. Brennan, and S. Thompson. (2012). Understanding Your Supply Chain to Reduce the Risk of Supply Chain Disruption. Journal of Business Continuity & Emergency Planning 6 (1): 55-67.

Downloads

Published

2020-07-05

Issue

Section

Статті