Planning Agro-Industrial Logistics with Simulation

Planning Agro-Industrial Logistics with Simulation

Problem

Kernel is the world-leading manufacturer and exporter of sunflower oil and provider of agricultural products from the Black Sea basin across world markets. The company owns 550,000 hectares of land and more than 40 grain elevators, with a total storage volume of up to 2.8 million tons.

One of the company's regular challenges is the planning, harvesting, and transportation of agro-products. Planning requires considering a large amount of input data that can influence the performance of the entire supply chain, including:

The company needed to meet this annual challenge without additional financial investments and to forecast supply chain behavior with a changing number of equipment. They decided to analyze logistics operations and perform logistics network optimization in a risk-free environment to avoid extra costs. They commissioned research by the Business Logic consulting company. The consultants created a digital logistics optimization model of the company’s supply chain network using AnyLogic simulation.

Solution

Logistics optimization model screenshot

Logistics optimization model screenshot
(click to enlarge)

The logistics operations optimization model, developed by Business Logic consultants, reflected Kernel’s supply chain, including the processes of harvest transportation from fields to elevators, its treatment and storage in elevators, transportation from elevators to ports, and its shipment inside ports.

The consultants applied several methods to develop the logistics operations optimization model. The supply chain components were represented as agents, while production processes in elevators and ports were simulated with the help of discrete-event modeling. The model also reflected the interaction between various hubs and equipment in elevators and ports, including:

The developed logistics network optimization solution allows users to simulate supply chain operations and also conduct experiments with its components to forecast how various circumstances will affect network performance. In the model, users can adjust equipment characteristics, speed of harvest drying process, location and quantity of elevators, different transportation strategies, and specify various characteristics of the harvested products, like its moistness.

The model is also useful for:

Result

The developed logistics operations optimization model allows Kernel specialists to:

At the end of the simulation, several reports are generated. These reports contain data on storage space turnover, elevators’ equipment, vehicles’ occupancy, and other indicators required for making decisions on supply chain configuration and for scheduling daily transportation operations.

The logistics optimization model is a useful decision support tool for planning supply chain seasonal operations. The tool also allows users to plan product distribution weekly, monthly, and annually.

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