A Maximal Profit Supply Chain Design: A Biopesticide Production-Distribution Case Study

Abstract

Context: This article shows the design of a supply chain for a company that will be located in the department of Sucre, Colombia. This company will produce two biopesticides that will be used to fight the Burkholderia glumae bacterium, which causes white panicle blight in rice crops. The first is derived from vegetal extracts, and the second is based on endophytic bacteria, both of proven use in attacking the crop bacterial disease. Method: A modeling of the price and demand parameters is developed using information obtained from the databases of the DANE, FiBL, and Cotrisa institutions. Then, linear and mixed integer programming is used to decide between alternative markets that make up a maximum-profit international supply chain. For each biopesticide, thirteen scenarios subject to variations in price, demand, and installed capacity were considered. Results: The vegetable biopesticide should be prioritized for commercialization in Colombia and China, since the utility obtained from such markets exceeds that of the bacterial one by 47 % in favorable scenarios. Only in worst-case scenarios, the profits of the bacterial biopesticide exceed those of the vegetable one by 11 %. Beyond a mere decision, the authors provide a casuistic heuristic for making decisions under uncertainty. Conclusions: The decision-making process in this study can be seen as a prospective analysis of the most probable scenarios and the more conservative or riskier bets made by project investors. The contributions include the use of linear optimization for profit maximization in new contexts, such as investment decisions in foreign trade chains of new biopesticide products, as well as novel associations of such optimization models with forecasts and regressions to increase the scope of the products.