MODELLING AND OPTIMIZING THE TACTICAL PLANNING OF THE CORN SUPPLY CHAIN CONSIDERING DOMESTIC AND EXPORT MARKETS

Marins, Lara; Morabito, Reinaldo; Rocco, Cleber

doi:10.1590/0101-7438.2023.043.00273457

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ARTICLES • Pesqui. Oper. 44 • 2024 • https://doi.org/10.1590/0101-7438.2023.043.00273457 copy

MODELLING AND OPTIMIZING THE TACTICAL PLANNING OF THE CORN SUPPLY CHAIN CONSIDERING DOMESTIC AND EXPORT MARKETS

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

In this study, optimization models for the production and logistics tactical planning of the corn supply chain are presented and several outputs are explored. They consider the domestic market for animal consumption, ethanol production, and the international market. They are mainly oriented to support policies and decisions of public agents who have some interference in the supply chain. To evaluate the consistency and effectiveness of the models, they were applied to solve realistic problem instances of the Brazilian corn supply chain considering different scenarios and using data collected from several sources. The outcomes demonstrated that models can produce analysis considering aspects that have not been fully addressed in the literature before, such as some opportunities to use different modes of transport for corn distribution, to contract extra capacity for stocking and the impacts of some state taxes on its production and logistic systems. Other important aspects explored in the scenario analysis are the identification of capacity bottlenecks and investment opportunities. The conclusions and insights are converted into practical interpretations to support relevant supply chain decisions.

Keywords:
production tactical planning; logistic planning; supply chain management; optimization model; corn agroindustry

Author	Problem	Optimization approach
Zuo, Kuo, and McRoberts (1991ZUO M, KUO W & MCROBERTS KL. 1991. Application of Mathematical Programming to a Large-Scale Agricultural Production and Distribution System. The Journal of the Operational Research Society, 42(8): 639. Available at: https://doi.org/10.2307/2583783. https://doi.org/10.2307/2583783... )	Production planning of a seed corn company and cost reduction.	Linear optimization and mixed integer linear optimization combined by the heuristic method.
Jones et al. (2003JONES PC, KEGLER G, LOWE TJ & TRAUB RD. 2003. Managing the Seed-Corn Supply Chain at Syngenta. Interfaces, 33(1): 80-90. Available at: http://www.jstor.org/stable/20141224. http://www.jstor.org/stable/20141224... )	Modeling Syngenta’s two-stage corn seed plan to maximize expected gross margin.	Linear optimization using discrete approximations for demand and throughput distributions.
Johnston and Singh (2004JOHNSTON DB & SINGH V. 2004. Enzymatic milling of corn: Optimization of soaking, grinding, and enzyme incubation steps. Cereal Chemistry, 81(5): 626-32. Available at: https://doi.org/10.1094/CCHEM.2004.81.5.626. https://doi.org/10.1094/CCHEM.2004.81.5.... )	Minimization of the quantity of enzymes compared to conventional milling.	Optimization of the first grinding, immersion time and enzyme incubation time.
Junqueira and Morabito (2006JUNQUEIRA RAR & MORABITO R. 2006. Um modelo de otimização linear para o planejamento agregado da produção e logística de sementes de milho. Production, 16(3): 510-25. Available at: https://doi.org/10.1590/S0103-65132006000300012. https://doi.org/10.1590/S0103-6513200600... )	Minimization of the total cost of production and logistics, including transportation, corn seed processing and ICMS costs.	Linear optimization model.
Junqueira and Morabito (2008JUNQUEIRA RAR. 2008. Planejamento otimizado da produção e logística de empresas produtoras de sementes de milho: um estudo de caso. Gestão & Produção, 15(2): 367-80. Available at: https://doi.org/10.1590/S0104-530X2008000200012. https://doi.org/10.1590/S0104-530X200800... )	Optimization in aggregate crop planning in a corn seed company.	Application of a linear optimization model in a case study.
Junqueira and Morabito (2012JUNQUEIRA RAR & MORABITO R. 2012. Production and logistics planning considering circulation taxes in a multi-plant seed corn company. Computers and Electronics in Agriculture, 84(junho): 100-110. Available at: https://doi.org/10.1016/j.compag.2012.02.019. https://doi.org/10.1016/j.compag.2012.02... )	Tactical planning of production, storage, and transport of corn seeds in an entire crop.	Application of a linear optimization model to support decisions of a Brazilian company in the corn seed sector.
Soto et al. (2013SOTO MM, MONROY CR, ARAUJO MG & DÍAZ AM. 2013. System dynamics in the simulation of the effect of knowledge management on the supply chain of corn agroindustry (Zea mays L.). Revista Técnica de la Facultad de Ingeniería Universidad del Zulia, 36(1): 80-90. Available at: https://ve.scielo.org/scielo.php?script=sci_abstract&pid=S0254-07702013000100011&lng=en&nrm=iso. https://ve.scielo.org/scielo.php?script=... )	Knowledge management in the supply chain in the corn industry.	Modeling in information technology that enables simulation.
Oliveira and Silveira (2014OLIVEIRA ALRD & SILVEIRA JM. 2014. Restructuring of the Corn Supply Chain in Brazil: Facing the Challenges in Logistics or Regulation of Biotechnology. International Food and Agribusiness Management Review, 16(4): 1-24.)	Measurement of how biotechnology regulatory issues directly affect infrastructure logistics.	Partial equilibrium model as a mixed complementarity problem and application of simulation.
Busato et al. (2019BUSATO P, SOPEGNO A, PAMPURO N, SARTORI L & BERRUTO R. 2019. Optimisation tool for logistics operations in silage production. Biosystems Engineering, 180(abril): 146-60. Available at: https://doi.org/10.1016/J.BIOSYSTEMSENG.2019.01.008. https://doi.org/10.1016/J.BIOSYSTEMSENG.... )	Decision support system to minimize the total operating cost of a production system under time constraints.	Combination of a simulation model and an optimization model based on linear optimization.

Objective function cost parcels	Baseline	Scenario 1	Difference between absolute values*
Planting and harvesting crops	58.64%	57.42%	-5.65%
Transport for external customer	20.94%	21.55%	-0.85%
Transport for internal customer	17.10%	17.61%	-0.79%
Grain drying	2.28%	2.36%	0.00%
Extra capacity contracting	1.01%	1.04%	-1.26%
Grain import	0.03%	0.03%	-0.51%
Total	100.00%	100.00%	-3.64%

	Baseline	Scenario 8	Difference between absolute values
Billing installments of the objective function (1.2)
External demand	59.57%	69.67%	55.43%
Animal nutrition demand	40.41%	30.32%	-0.30%
Ethanol demand	0.02%	0.02%	0.000%
Objective function costs (1.2)
Planting and harvesting crops	61.10%	60.83%	27.57%
Transport for external customer	22.06%	25.00%	45.26%
Transport for internal customer	13.41%	10.85%	3.74%
Grain drying	2.41%	2.33%	23.77%
Extra capacity contracting	1.04%	0.99%	22.49%
Grain import	0.00%	0.00%	0.00%
Total	100.00%	100.00%	57.89%

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