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Publication
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
In the context of a growing world population, the fundamental need for foodstuff makes a sustainable management of the food supply an imperative. Sustainability issues in the food supply chain range from responsible use of limited resources, safe and secure food supply, minimizing food waste, while maintaining economic interests of each player. A novel ap- proach is presented integrating Processing Time, Cost and Operational Risk (PTCORk1) in one quantitative model. It allows for designing Pareto optimal monitoring systems. In a com- parative study, exact and (meta) heuristic methods are compared for best performance. The study with real-world data from an international food company is based on a common and widely consumed foodstuff with typical sustainable food supply chain challenges. Results are thus generalizable to other value chains in the food industry. It provides novel understanding of the seeming contradiction between Processing Time & Cost (PTC) and Operational Risk (ORk). On the system level, the efficient frontier reveals a quantified non-linear relationship. Within the monitoring system the relative contribution of each monitoring activity to the objective function shows a saturation effect enabling practicioners to identify critical monitor- ing activities and engage in step-by-step optimization. Evidence is presented that favorable solutions reveal potential for equidistribution of operational risks and time- and cost-efficient risk allocation. The advantages of a multi-objective approach are compared to either single objective approach. Away from conflicting formulations of minimal operational risk, process- ing time and cost, the approach fosters a more differentiated and quantifiable understanding of the relationship of processing time, cost and operational risk enabeling decision makes to formulate advantageous trade-offs. Optimized scenarios are tested for robustness and potential multiplicatory effects by numerical simulation of a connected dynamic supply chain environment. The approach provides valuable insights for practitioners and enables specific actions for a sustainable and competitive food supply.