Application of Risk Solver Platform to Improve Soybean Crushing Profitability
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AJCS 13(09):1533-1539 (2019) ISSN:1835-2707 doi: 10.21475/ajcs.19.13.09.p1829 Application of risk solver platform to improve soybean crushing profitability Geovano Ceratti1*, Jéfferson de Oliveira Costa2, Keith Harris1 1Kansas State University - College of Agriculture, Department of Agricultural Economics, Manhattan, KS 66506 785- 532-6011, United States 2University of São Paulo - USP, College of Agriculture “Luiz de Queiroz” - ESALQ, Biosystems Engineering Department, 11 Pádua Dias Avenue Piracicaba, 13.418-900, Brazil *Corresponding author: [email protected] Abstract The growing number of participants in the grain market shows just how globalized this sector is in Brazil nowadays. It is difficult to note how mergers and acquisitions between local companies as major tradings and funds have been acquiring part or even the totality of Brazilian companies, like the soybean crushing company “XYZ” that acquired some assets such as crushing plants in the state of Mato Grosso, located on the center-west region of Brazil and access to 2 port terminals to export the byproducts. This work contains data from January of 2014 to September of 2017 including prices of soybean and by products, freight rates, basis at the ports and operational costs. This information comes from different sources including government and private agencies and Thomson Reuters Eikon software. This information was used to build a model in Microsoft Excel and by using the Risk Solver Platform, a powerful statistic calculator tool, to find a solution to improve “XYZ” profitability. The results of the model suggest changes in the origin of grains, reducing the volume in some locations and increasing the volume originated in other locations. After running 2 different scenarios, “XYZ” profits was increased by 1.24 million of US dollars. Keywords: Exports; Glycine max L.; logistics; origination. Abbreviations: FETHAB_State Transport and Housing Fund, FOB_Free on board, FUNRURAL_Rural Workers Assistance Fund, IAC_Instituto Agronomico de Campinas, ICMS_Circulation of Goods and Services Tax, IMEA_Instituto Matogrossense de Economia Agropecuária, SEFAZ- MT_Treasury Department of the State of Mato Grosso, USA_United States, USDA_United States Department of Agriculture. Introduction The soybean is a native plant of China, its scientific name is (USDA, 2018). In terms of exports, Brazil is currently the Glycine max and it is part of the family Fabaceae, as well as world's leading exporter for the crop 2017/2018, with 70.50 peas, beans and lentils. It is used for human food in the form million metric tons, followed by the United States, which is of edible oil, soy sauce, soy milk, soy protein, whole grain expected to export 56.20 million tons. The harvest survey consumption and mostly in animal feed as an ingredient that released in March of 2018 by Companhia Nacional de makes up the ration (Hymowitz, 1970; EMBRAPA, 2004). Abastecimento the Brazilian National Supply Company, This oilseed is rich in protein and in minerals such as indicates an area of 35.04 million hectares sown soybean in potassium, calcium, magnesium, phosphorus, copper and Brazil for the 2017/2018 crop. The average yield of 3.22 metric zinc, B vitamins such as riboflavin and niacin and also in tons per hectare and a total production of 113 million tons. vitamin C (boric acid), among others (Olaofe et al., 1994; The three-main oilseed producing states are Mato Grosso Buranello, 2011). In Brazil, the soybean was introduced in with 30.86 million tons, Paraná with 18.52 million tons and Rio the state of Bahia in the year 1882, but without much Grande do Sul with 17.54 million ton (CONAB, 2018). The success. In 1892, it was cultivated by Instituto Agronomico importance of soybean to the Brazilian economy should be de Campinas (IAC). In 1914, the crop was introduced in the underscored by the generation of jobs and income for state of Rio Grande do Sul, where it presented more thousands of rural producers and workers from various consistent evolution (Camara, 2001; Alves et al., 2003). sectors, including the input-producing industry such as According to the United States Department of Agriculture fertilizers, pesticides and machinery, the financial sector (USDA) supply and demand report, released in March of responsible for rural credit, logistics sector responsible for 2018, Brazil is the world's second largest soybean producer storage, transportation and port operations, as well as for with 113.00 million metric tons produced in the 2017/2018 the processing industry that transforms soybean into bran cycle, just behind only the United States (USA) which in the and oil. The president of the Federação da Agricultura e same period reached a production of 119.53 million tons Pecuária de Mato Grosso do Sul (FAMASUL) quantified in July 1533 2013 that 37% of jobs in Brazil have a direct or indirect link multiplied by the cost (in the case of inputs and crushing with agribusiness (Scot, 2014). The commercialization of costs) or revenue (sale of byproducts), XYZ obtained a total soybean occurs through cooperatives, grain merchants, profit of BRL 60,893,000.00 which is approximately $19.77 chemical/fertilizer input companies, grain brokers, industry million considering the currency average rate along the and merchandisers. The risk involved in the period of study. The Lucas do Rio Verde crushing facility was commercialization of the production explains the three supplied with 100,000 tons originating locally, 250,000 tons possible commercialization situations: 1) Make a forward of soybean from Sorriso, and 150,000 tons of Campo Novo cash sale, where the flat price is fixed today with a buyer, for do Parecis. The model indicates that all the volume of future delivery of the merchandise; 2) To hedge the price of soybean meal and degummed oil produced in Lucas do Rio the merchandise by selling a future contract; 3) Do absolutely Verde should be exported through the port of Barcarena. nothing, acting as speculator, hoping that prices will not fall at Meanwhile, the Primavera do Leste facility was supplied the moment of sale (Marques et al., 2006; Oliveira and Alvim, with 100 thousand tons originated locally, 200 thousand 2017). On one hand, the impressive production and exports tons of soybean from Rondonópolis, 200 thousand tons from of this oilseed and its byproducts, it is possible to perceive the Canarana. The model indicates that all the volume of relevance and attractiveness of Brazil in the global market, soybean meal and degummed oil produced in Primavera do which justifies the entry of new companies, mainly Leste must be exported by the port of Paranaguá. Both multinationals, in the soybean origination, processing and soybean crushing facilities operated with total capacity and export market. On the other hand, the fact that many there was no export of soybeans without being companies exit the market due to high competitiveness industrialized. Table 1 shows the results of the first scenario resulting in, insignificant or even negative margins, bad including the flow between nodes. Columns “From” and operational and financial practices, difficulties in adapting to “To” identify the origin and the destination of the soybean the current market conditions of the commercialization and byproducts. The “Cash Flow” column representing the complex. Additionally, the lack of knowledge and analytical cost of inputs delivered to de crushing facilities, the crushing tools that help identify logistics bottlenecks has an impact on costs and the revenue obtained by selling soymeal and the industry’s performance (Spera et al., 2014; Reis and Leal, soyoil. All possible flows from the locations where XYZ 2015; Oliveira et al., 2016; Forssell and Lankoski, 2018). originates soybeans to the soybean crushing plants or ports Logistics bottlenecks can be noted in the Para’s state where are considered. there are 65 kilometers of dirty road between Novo The Table 2 shows the identification of each origination Progresso and Moraes de Almeida, two municipalities spot, crushing plant and destination port on the column located on the way where soybean are transported from “Nodes”, the name of each facility on the column the state of Mato Grosso to the ports located in the “Location”, the “Supply/Demand” column identifying the northern Brazil. Freight prices reach twice the price availability of goods in the origination points and also the considering the same distances in other Brazilian States. export capacity in each of the ports and the column “Flow” The transportation problem is so serious that in 2017 trucks showing that 100% of the available volume is being used on got stuck in the road because of heavy rains during the XYZ operation. When the Risk Solver model calculates the harvest season and there was a 20 km traffic congestion. best strategy for the soybean crushing enterprise, it has a The Brazilian army was called upon to supply water, food feature called sensitivity analysis that allows the board of and control the traffic jam (Lopes et al., 2017; Teixeira, directors to make a decision. The XYZ decision makers can 2018). “XYZ” is a soy crusher, subsidiary of a Japanese also realize the impact of unnecessary or excessive corporation that just acquired crushing facilities in Mato transportation. In regards to the origination in Sorriso for Grosso State and is trying to understand the market and example, where the soybean to be processed travel 66 km improve the profitability in the short-term. Their operation to the factory in Lucas do Rio Verde and then again takes has six origination regions and they are able to export this route to the port of Barcarena. At the moment that the soymeal and soy oil through two ports. The objective of commercial team decides where to originate, they must pay this thesis is to provide answers for questions that will help close attention to the details of local availability of raw “XYZ” board of directors better understand the soybean material, existing competition, and seasonality of marketing market in Mato Grosso/Brazil so they might make in order to avoid paying twice the freight because of the raw informed decisions to improve the company`s material coming and going back on the same route.