DOI: https://doi.org/10.15368/theses.2019.140
Available at: https://digitalcommons.calpoly.edu/theses/2528
Date of Award
6-2019
Degree Name
MS in Industrial Engineering
Department/Program
Industrial and Manufacturing Engineering
College
College of Engineering
Advisor
Reza Pouraghabagher
Advisor Department
Industrial and Manufacturing Engineering
Advisor College
College of Engineering
Abstract
The increasing trend in frequency of natural disasters in tandem with globalization of business makes the agricultural supply chain significantly vulnerable to disruption. This thesis presents a pragmatic approach for creating a Business Continuity Model that can notify supply chain planners when there is an increase in risk of agriculture supply chain disruption due to natural disasters. The methodology presented in this thesis applied big data analytics and machine learning algorithms along with agriculture product related exponential decay function to create a regionalized composite risk score, that incorporated both direct and indirect risk associated with the Agriculture Fresh Supply Chain. This model will aid supply chain planners in creating and implementing contingency plans, at the right time per given food production location. This risk score can help food manufacturing organizations to have a Business Continuity Plan that alleviate agriculture business supply chain interruptions. An example application of this model is illustrated with a melon packaging industry.
Included in
Industrial Engineering Commons, Industrial Technology Commons, Operational Research Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons