The transformation of Agribusiness: solving challenges with data
Data usage in agribusiness is essential for optimizing complex operations, such as logistics between the field and the mill. By analyzing variables like distances, cycle times, and machinery maintenance, businesses can make more effective decisions, overcoming the limitations of traditional spreadsheets. The emphasis should be on solving problems through data, ensuring efficiency and productivity.
Willian Fernandes
In consultancy, years of experience in Big-Data and Business Intelligence projects, with Ihara, Long-Ping, Nutrien, Agrichem, Minerva Foods and Honda as the main clients.
In Business Development, advisory work on various technology and digitalization projects, with a focus on improving client performance through data analysis, governance and processes.
Projects:
Tereos DoT (Database of Truth): centralization of industrial and agricultural data in a single source for visibility of plant maintenance and operations;
Agrichem (Nutrien): mapping commercial area indicators to create a data warehouse and Power BI dashboards;
Ihara Data lake: mapping of more than 136 indicators and data modeling of the commercial, marketing, logistics and operations areas for centralization in an AWS repository;
brMalls: 360º Assessment Data Advisor in the areas of sales, marketing, finance and mall operations, looking for gaps and proposing improvement actions.
Contact:
willian.fernandes@staufen.com.br
* By Willian Fernandes
Project manager at Staufen
Analytics and Agribusiness
Agribusiness, a cornerstone of the global economy, is undergoing a profound transformation driven by the adoption of data and analytics. The true value of these technologies, however, lies not in the tools themselves but in their ability to solve practical challenges and improve processes. From optimizing field operations to supporting strategic decision-making, the application of data in agribusiness is delivering significant gains in efficiency, cost reduction, and productivity.
Instead of focusing on specific tools like analytics software or artificial intelligence platforms, the priority should be understanding how data can help tackle the sector’s key challenges. With the digitalization of agricultural operations, vast amounts of data are collected daily from onboard computers, weather stations, and harvesting machines. However, only well-processed and insightful data can effectively influence decision-making. This process involves collecting, cleaning, analyzing, and applying data from diverse sources, such as climate conditions, soil properties, machinery performance, and financial metrics.
Success stories in agribusiness include improved crop planning, optimized resource usage, and the ability to predict machinery maintenance issues. The secret lies in the straightforward application of analytics focused on real-world problem-solving, rather than on the complexity of the tools. In this way, digitalization becomes a means to an end—achieving tangible results that benefit both production and sustainability in the field.
The real advantage of data analysis lies in empowering agronomists, managers, and operators to make well-informed decisions by seamlessly integrating operational and financial data. By focusing on problem-solving and leveraging relevant data, agribusiness can unlock its full potential, regardless of the specific technology utilized.
Data Analytics in Action: A Case Study in Sugarcane Logistics
In the sugar-energy sector, logistics between the field and the mill plays a critical role in operational efficiency and profitability. A practical example demonstrating the importance of data in this process involves two sugarcane harvesting fronts, both equipped with the same number of harvesters, tractors, and trucks but situated at different distances from the mill.
The first harvesting front is located 10 km from the mill, while the second is 60 km away. Despite identical operational setups, the longer distance of the second front poses greater logistical challenges, such as extended travel times for trucks hauling sugarcane to the mill and returning for additional loads. This disparity demands meticulous planning to ensure that the mill receives a steady supply of sugarcane, avoiding interruptions in production.
Now, imagine that the second harvesting front is moved even farther to 80 km from the mill. This increased distance lengthens truck cycle times, disrupting the continuous flow of sugarcane to the mill. To address this logistical bottleneck, more trucks would be required to maintain a stable supply of sugarcane. Otherwise, the mill’s processing capabilities may be compromised, resulting in downtime and reduced operational efficiency.
Additionally, if one of the harvesters in either front experiences extended maintenance time, leading to reduced harvesting output, the logistics will be directly affected. Less sugarcane will be harvested and transported to the mill, further straining operations. In such scenarios, the interplay of factors like mill distance, truck availability, and machine maintenance time creates a complex planning challenge.
Optimizing Logistics Through Data-Driven Insights: Overcoming Complexity for Greater Efficiency
Analyzing these interconnected variables—distance, truck cycle times, harvester maintenance, and field productivity—quickly exceeds the capabilities of traditional spreadsheet-based management. Analytics tools and data-driven optimization models become indispensable for simulating scenarios, anticipating bottlenecks, and making decisions that enhance transport efficiency while minimizing operational costs.
By integrating real-time data from the field, maintenance operations, and logistics, mills and agricultural operations can allocate resources more effectively, ensuring a consistent sugarcane supply even under complex conditions.
Focusing on Solutions, Not Just Tools
Agribusiness logistics, particularly in the sugar-energy sector, involves a network of interdependent variables that, if analyzed in isolation, can lead to suboptimal decisions. In the example of sugarcane harvesting fronts, factors like distance to the mill, truck availability, and harvester maintenance times directly impact operational efficiency. As these variables grow more complex, traditional spreadsheets prove insufficient, necessitating analytics tools to optimize decision-making and foresee bottlenecks.
The key to success lies not in the sophistication of the tools but in solving the right problems using the available data. Managers and operators play a strategic role in this process, translating data into actionable insights in the field. The ability to integrate data from diverse sources and transform it into precise, actionable decisions allows agricultural operations to become more efficient and resilient to unforeseen challenges.
The primary focus should always be on solving practical problems, using data intelligently to optimize resources, streamline logistics, and ensure that sugarcane reaches the mill on time and in the required quantity. Ultimately, the ability to turn data into insights that drive impactful decisions is more critical than the specific technology used, leading to tangible improvements in agribusiness operations and outcomes.
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