The objective of inventory management models is to determine efficient policies for managing the trade-off between customer satisfaction and the cost of goods. This chapter presents a methodology that uses the Monte Carlo Method (MCM) to estimate the behavior of a raw material supply model, considering uncertain variables such as demand, prices, and exchange rates. In order to show how to use this methodology, we analyze the case of a Colombian company in the aluminum industry. This company imports aluminum sheets from China. In this case, we analyze the financial impact of the raw material supply contract proposed by the Chinese supplier. The model considers different supply scenarios for the raw material. We calculate robust indicators such as Value at Risk (VaR), the Conditional Value at Risk (CVaR) and the probability of success for each scenario analyzed. Finally, we conduct a sensitivity analysis with respect to the sales price to validate the proposed models and solution approaches. The results show that considering risk metrics to evaluate the impact of endogenous factors over the supply process is a useful approach to improve decision-making related to this process and also can help to ensure the profitability of the company.
This research is supported by Universidad del Valle under the research project “Identification and Measurement of Financial and Operational Risk in Supply Chains – CI-2813.” The authors acknowledge the support of the School of Industrial Engineering and Universidad del Valle and MSc. Osman Camilo Soto Cardona for his valuable help improving this chapter.
Diego Fernando, M.-D., Leonardo, R.-C. and Stephanía, M.-L. (2018), "Financial Risk Measurement in a Model of Supply of Raw Materials", Yoshizaki, H.T.Y., Velázquez Martínez, J.C. and Argueta, C.M. (Ed.) Supply Chain Management and Logistics in Latin America, Emerald Publishing Limited, Bingley, pp. 171-181. https://doi.org/10.1108/978-1-78756-803-720181011
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