Method for monitoring and optimizing raw material flows for animal feed

Abstract

Method (500) for monitoring and optimizing the distribution of raw materials (RM) for animal feed in livestock farms, implemented by computer via database servers (110, 120), comprising: a step (510) of collecting input data on the availability of RM, the nutritional needs of animals, and local economic data; a step (520) of processing the collected data, with automatic extraction and verification; a step (530) of integrating physiological data and nutritional recommendations; a step (540) of optimizing RM flows by an algorithm taking into account economic, territorial and nutritional constraints; a step (550) of monitoring the consumption of raw materials; a step (560) of updating the databases; a step (570) of calculating sustainability indicators; and a step (580) of reporting the results via an online platform.Figure for the abbreviation: figure 1.

Description

Title of the invention: Method for monitoring and optimizing the flow of raw materials for animal feed. Technical field

[0001] The present invention belongs to the general field of agriculture, in particular the distribution of raw materials for animal feed. It relates more particularly to a method for monitoring and optimizing the flow of raw materials for animal feed.

[0002] The invention finds a direct application in animal production, in particular livestock farming. State of the art

[0003] The animal feed industry plays a crucial role in the global agricultural economy, providing essential nutrients for livestock and other farm animals. Historically, managing the flow of raw materials for animal feed has been a major challenge, requiring complex coordination between suppliers, producers, and consumers. Over the decades, several approaches have been developed to improve this management, including the use of standardized feed formulas, computerized animal ration formulation tools, and crop sampling and forecasting techniques.

[0004] Computer-aided animal feed management systems, such as TMR Tracker, FeedWatch, and Ration Manager (registered trademarks), are commonly used to track and manage animal feed. These systems allow for the planning of feed rations, monitoring of consumption, and the generation of performance reports. While these systems offer tracking and planning tools, they often lack the flexibility to adapt quickly to different regions, fluctuations in commodity prices, and variations in the specific nutritional needs of each livestock sector and species. Furthermore, these tools are highly specialized and focus on a very small scale, such as a herd on a farm or a specific type of animal within that herd.

[0005] Crop and supply forecasting models, using historical and meteorological data to estimate the future availability of raw materials, are also used. Software such as Cropio and Granular provides valuable estimates, but it does not take into account the specific nutritional needs of animals, nor economic constraints, which can lead to inconsistencies in resource production compared to their uses.

[0006] Standardized feed formulas based on general nutritional recommendations are commonly used for animal feed. Companies such as Nutreco and Cargill offer standardized feed formulation solutions. These formulas are suitable for animal feed but are dependent on the supplies of feed manufacturing plants and provide no information on the geographical extent of resource consumption.

[0007] Tracking devices such as RFID sensors and video surveillance systems are used to monitor animal consumption in real time. These solutions provide accurate consumption data, but generally do not integrate this data into an overall optimization framework, which limits their usefulness for the economic and nutritional management of raw material flows.

[0008] Current solutions have several major limitations that hinder the efficient optimization of raw material flows for animal feed. Current systems often lack the flexibility to quickly adjust feed rations in response to fluctuations in raw material prices and the specific nutritional needs of animals. Furthermore, they are not designed to accommodate an approach different from that of the farm, thus limiting the service provided to technical livestock farming advice.

[0009] Many current solutions do not sufficiently take into account local economic constraints, such as territorial variations in raw material costs. This limits their ability to optimize costs while meeting nutritional needs. Consumption monitoring and crop forecasting systems generate data, but this data often remains fragmented and is not integrated into a comprehensive optimization framework. This limits farmers' ability to make informed and optimized decisions.

[0010] Updating the databases required for current systems can be complex and costly, often requiring tenders for the acquisition of new data. This slows down the adaptation of systems to new conditions and market developments.

[0011] To illustrate recent developments in the optimization of raw material flows, we can cite the article “Harpet, Cyrille & Gully, Emilie. (2015). Industrial and territorial ecology: what decision-making tools? From flow analysis to the integrated approach. Waste, Science and Technology. No. 63 - March 2013. 10.4267 / waste-science-technology.2580.” which describes a systemic and integrated approach aimed at managing the flows of materials and energy resulting from all the Human activities, whether in industrial systems or urban, rural, or industrialized territories, are subject to this article. According to this article, the systematic search for pathways and channels for optimizing and valorizing by-products and waste from these activities is often hampered by the complexity and diversity of the flows and their quantities. For about ten years, computer tools have been developed to comprehensively inventory residual materials and, more generally, all material and energy flows. These tools record these flows in databases. This article explores whether new generations of digital tools designed for industrial and territorial ecology could facilitate the search for synergies between organizations (businesses, industries, services) to valorize these resources and residual materials.This article also presents an overview of existing material-energy flow analysis (MEA) tools, highlighting their advantages and limitations. It also explores the possibilities for designing more integrated and efficient tools to identify synergies between activities.

[0012] On the other hand, US2011010154 describes a system for optimizing animal production by generating an animal feed formulation based on the utilization of nutrients received by an animal. The system includes a simulation engine configured to generate a set of animal requirements based on the characteristics of an animal and to generate an input for an animal feed formulation based at least in part on projected nutrient utilization for the animal and the set of animal requirements. The system also includes a formulation engine configured to receive the animal feed formulation and generate an optimized animal feed formulation based on the animal feed formulation.

[0013] The solutions presented, related to raw material flows in animal feed, have significant limitations in terms of flexibility, integration of economic constraints, and data management. It is therefore essential to have new approaches that allow for dynamic and comprehensive optimization, integrating economic, territorial, and nutritional constraints to improve the efficiency and profitability of animal feed. Summary of the invention

[0014] The present invention aims to overcome all or part of the drawbacks of the prior art described above, by providing an innovative tool for monitoring and optimizing the flow of raw materials for animal feed. This tool makes it possible to automatically collect, process, and optimize essential data related to the nutritional needs of animals, the availability of raw materials, and economic and territorial constraints.

[0015] One of the objectives of the invention is to provide a systematic and efficient method for managing raw material flows, thereby reducing costs and improving the economic and environmental sustainability of livestock farms.

[0016] To this end, the present invention relates to a method for monitoring and optimizing the distribution of raw materials for animal feed, in various areas ranging from one or more farms to a territorial scale (national, supranational), said method being implemented by computer, via database servers, and comprising: • an input data collection step including raw material availability data, animal nutritional requirements data, and local economic data; • a data processing stage, implementing automatic data extraction and verification to ensure consistency and reliability; • a step of integrating physiological data from animals and nutritional recommendations, based on animal nutrition models; • a raw material flow optimization step implementing an optimization algorithm that takes into account economic, territorial and nutritional constraints; • a step of monitoring the consumption of raw materials by animals to identify consumption trends specific to each species and / or farm; • a step of updating the databases, said databases including an input database and an output database linked by the optimization algorithm; • a step involving the calculation of economic and environmental sustainability indicators from the processed and optimized data; and • a stage of presenting the results with secure access to the information.

[0017] By integrating animal nutrition models and specific nutritional recommendations, the invention makes it possible to optimally adjust feed rations according to the animals' actual needs and the territorial availability of raw materials. Furthermore, the use of an advanced optimization algorithm ensures increased flexibility and responsiveness to variations in raw material prices and the nutritional needs of different animal species.

[0018] The invention also provides precise monitoring of raw material consumption by animals, thus facilitating the surveillance of consumption trends specific to each species and / or farm. The data is secure and regularly updated in databases hosted on cloud servers, ensuring its availability and integrity. Furthermore, the calculation of economic and environmental sustainability indicators allows for continuous assessment of the impacts of animal feeding practices.

[0019] Finally, the presentation of results, particularly via an interactive online platform, allows users to visualize the data in the form of dynamic dashboards, thus providing a powerful tool for informed decision-making. In short, this invention aims to improve the efficiency, profitability, and sustainability of animal feed through optimized and integrated management of raw material flows, thereby improving animal production and livestock quality.

[0020] According to an advantageous aspect of the invention, the input data collection step also includes the collection of data on weather conditions and crop forecasts, this data being used to dynamically adjust the availability of raw materials according to climatic variations and agricultural production cycles.

[0021] This allows for more precise and responsive planning of the supply of raw materials for animal feed.

[0022] According to an advantageous aspect of the invention, the data processing step includes a raw data reconciliation phase, in which the collected data is checked and adjusted to ensure its consistency and reliability. This phase includes the use of anomaly detection and automatic correction algorithms, making it possible to identify and rectify errors or inconsistencies in the data, thus guaranteeing an accurate and reliable database for subsequent steps of the process.

[0023] According to an advantageous aspect of the invention, the optimization algorithm implemented in the optimization step is configured to minimize supply costs while maximizing the satisfaction of the specific nutritional needs of the animals and respecting environmental constraints.

[0024] This algorithm takes into account variations in raw material prices, territorial availability, as well as nutritional recommendations for each animal species, in order to generate optimized feeding solutions.

[0025] According to an advantageous aspect of the invention, the step of monitoring the consumption of raw materials by animals uses sensors, in particular RFID, and monitoring systems to collect accurate real-time data on the individual consumption of each animal.

[0026] This data is then integrated into the database to provide a detailed and up-to-date picture of consumption habits, thus allowing food rations to be adjusted precisely and in a personalized manner.

[0027] According to an advantageous aspect of the invention, the database update step includes the use of a data management system that ensures data security and guarantees non-redundancy. This system includes mechanisms for regular backups, data encryption, and replication across multiple cloud servers.

[0028] This ensures the continuous availability and integrity of information, even in the event of hardware failure or cyberattack.

[0029] According to an advantageous aspect of the invention, the step of calculating economic and environmental sustainability indicators includes the calculation of indicators such as greenhouse gas emissions generated by livestock farming activities, water use for raw material production, and land use for crops intended for animal feed. These indicators are calculated from the collected and optimized data.

[0030] This makes it possible to provide quantitative metrics to assess the environmental impact of feeding practices.

[0031] According to an advantageous aspect of the invention, the results reporting step is carried out via an online platform allowing the visualization of data in the form of interactive dashboards, including dynamic graphs, diagrams and geographical maps.

[0032] These dashboards allow users to visualize and analyze raw material flows, consumption by animal species, and sustainability indicators, thus providing a powerful tool for informed decision-making.

[0033] According to an advantageous aspect of the invention, the optimization algorithm is configured to take into account the technical specifications of the raw materials, such as their dry matter content, nutritional value, and digestibility by different animal species. This algorithm adjusts the feed rations according to these technical specifications to ensure that the animals' nutritional needs are optimally met, while minimizing costs and environmental impacts.

[0034] The invention also relates to: • a computer program product downloadable from a communication network and / or stored on a microprocessor-readable medium and executable by a microprocessor, comprising program code instructions for the execution of a process as described; and • a terminal-readable and non-transient storage medium, storing a computer program comprising a set of instructions executable by a computer or processor to implement this process.

[0035] The fundamental concepts of the invention having been set out above in their most elementary form, other details and features will become clearer from the reading of the following description and with regard to the attached drawings, giving by way of non-limiting example an embodiment of a method for monitoring and optimizing the flow of raw materials for animal feed, in accordance with the principles of the invention. Presentation of the drawings

[0036] The figures are given for illustrative purposes only to facilitate a better understanding of the invention without limiting its scope. The various elements may be represented schematically and are not necessarily to scale. Throughout the figures, identical or equivalent elements are identified by the same numerical reference.

[0037] It is thus illustrated in:

[0038] [Fig-1]: a flowchart of the main steps of a monitoring and optimization of raw material flows for animal feed according to the invention;

[0039] [Fig.2]: an overall architecture of an information and communication system for the implementation of the process according to the invention;

[0040] [Fig.3]: a simplified diagram of optimized raw material flows involving different farms. Detailed description of implementation methods

[0041] It should be noted that certain technical elements well known to those skilled in the art are recalled here to avoid any insufficiency or ambiguity in the understanding of the present invention.

[0042] The embodiment described below refers to a method for monitoring and optimizing the distribution of raw materials for animal feed, primarily intended for the informed management of raw materials on farms and in rural areas. This non-limiting example is given for a better understanding of the invention and does not preclude adapting the method to other related applications requiring improved management of any resources.

[0043] In this description, and unless otherwise indicated, the following definitions shall apply: • A “model” refers to an abstract concept based on data and having parameters, which is characterized by an output of elements of interest obtained from input data.

[0044] All other technical and scientific terms used have the same meanings as those commonly accepted in the technical field of the invention.

[0045] Also, the terms “collect,” “process,” “integrate,” “calculate,” “track,” “generate,” and their derived forms, or more broadly, “executable operation” within the meaning of the invention, refer to an action performed by a device or processor unless the context indicates otherwise. In this respect, operations relate to actions and / or processes of a data processing system, for example, a cloud computing system or a standalone electronic computing device, which manipulates and transforms data represented as physical (electronic) quantities in the memories of the computing system or other information storage, transmission, or display devices. These operations may be based on applications or software.

[0046] Fig. 1 represents the main steps of a process 500 for monitoring and optimizing the distribution of raw materials for animal feed in one or more farms, said process being implemented by computer, via database servers.

[0047] The process 500 comprises: • a 510 input data collection step including raw material availability data, animal nutritional requirements data, and local economic data; • a step 520 of processing the collected data, implementing automatic data extraction and verification to ensure its consistency and reliability; • a step 530 of integration of physiological data from animals and nutritional recommendations, based on animal nutrition models; • a step 540 of raw material flow optimization implementing an optimization algorithm taking into account economic, territorial and nutritional constraints; • a step 550 of monitoring the consumption of raw materials by animals to know consumption trends specific to each animal species and / or farm; • a step 560 of updating the databases, said databases including an input database and an output database linked by the optimization algorithm; • a step 570 of calculating economic and environmental sustainability indicators from the processed and optimized data; and • a step 580 of reporting the results, notably via an online platform, allowing secure access to information.

[0048] The first step 510 of the process consists of collecting input data, including raw material availability data, animal nutritional requirements data, and local economic data. For example, raw material availability data may include information on current stocks, harvest forecasts based on weather data, and supply logistics. This data is often collected from various sources such as public databases, raw material supplier reports, and farm management systems.

[0049] The nutritional needs of animals are determined according to standardized nutritional recommendations for each animal species and growth phase, often provided by specialized organizations such as INRAE ​​(National Research Institute for Agriculture, Food and the Environment) or animal nutrition research institutes.

[0050] Local economic data includes raw material prices on local markets, transportation costs, and applicable subsidies or taxes. Furthermore, contextual data such as weather conditions and harvest forecasts are integrated to dynamically adjust raw material availability based on climatic variations and agricultural production cycles, thereby enabling more precise and responsive planning of raw material supply for animal feed.

[0051] The next step 520 is the processing of the collected data, implementing automatic data extraction and verification to ensure its consistency and reliability. This step uses data processing algorithms to clean and reconcile information from different sources. For example, data normalization techniques are applied to unify the formats and units of measurement of the collected data.

[0052] In addition, anomaly detection algorithms are used to identify and correct potential errors in the data, such as outliers or logical inconsistencies.

[0053] Once processed, the data is stored in a centralized, secure and redundant relational database, ensuring its availability for subsequent steps of the process.

[0054] Reconciliation of raw data ensures a correspondence between the data collected and the realities of raw material flows, thus guaranteeing an accurate and reliable database for subsequent steps.

[0055] Step 530 of integrating physiological data from animals and nutritional recommendations, based on animal nutrition models, is crucial for adapting feed rations to the specific needs of animals.

[0056] For example, the animal nutrition models used may include simulations based on the animals' age, weight, breed, and health status, thus providing precise and personalized nutritional recommendations. These models take into account energy, protein, vitamin, and mineral requirements, and adjust feed rations accordingly.

[0057] This makes it possible to model raw material flows and the nutritional needs of animals, taking into account the differential competitiveness of raw materials depending on their availability and location. These models make it possible to simulate and optimize feed rations according to the specific characteristics of each farm, thus guaranteeing a balanced diet adapted to the animals' needs.

[0058] Next, step 540, which optimizes raw material flows, implements an optimization algorithm that takes into account economic, territorial, and nutritional constraints. This algorithm is configured to minimize supply costs while maximizing the satisfaction of the animals' nutritional needs. It considers variations in raw material prices, territorial availability, and the specific nutritional recommendations for each animal species.

[0059] For example, in a context where soybean prices fluctuate due to seasonality, the algorithm can adjust rations to include more sunflower or rapeseed, depending on their availability and relative cost, while ensuring a balanced nutritional intake.

[0060] Tools such as FICO Xpress (registered trademark) can be used for this optimization, making it possible to efficiently solve complex linear and nonlinear programming problems. The algorithm also takes into account the technical specifications of the raw materials, such as their dry matter content, nutritional value, and digestibility by different animal species, thus adjusting the feed rations according to these technical parameters to ensure that the animals' nutritional needs are optimally met.

[0061] Step 550 of monitoring raw material consumption by animals consists of understanding and adjusting consumption trends specific to each species and / or farm. This monitoring can use RFID sensors, video surveillance systems, or any suitable sensors to collect accurate, real-time data on the individual consumption of each animal.

[0062] This data is then integrated into the central database, providing a detailed and up-to-date picture of consumption patterns. For example, if a sensor detects that a particular group of cows is consuming more corn than expected, the rations can be adjusted accordingly to avoid nutritional deficiencies or excesses. The consumption data is then used to precisely and individually adjust the feed rations, thus ensuring optimal nutrition for each animal.

[0063] Step 560 of the database update includes the use of a data management system ensuring data security and redundancy. The data is regularly backed up and encrypted, with copies replicated on multiple cloud servers to guarantee continuous availability and data integrity, even in the event of hardware failure or a cyberattack.

[0064] Automated backup procedures and regular data recovery tests are in place to ensure that information can be restored quickly and completely if needed.

[0065] The data management system also includes security mechanisms such as multi-factor authentication and access control, ensuring that only authorized persons can access or modify data, thus ensuring the confidentiality and security of sensitive information.

[0066] Step 570 of calculating economic and environmental sustainability indicators consists of evaluating the performance of animal feeding practices.

[0067] The indicators produced include greenhouse gas emissions generated by livestock farming activities, water use for the production of raw materials, and land use for crops intended for animal feed.

[0068] For example, carbon footprint calculations can be performed for each batch of raw materials used, making it possible to quantify the environmental impact of food choices. Similarly, life cycle assessments (LCAs) can be integrated to evaluate environmental impacts throughout the raw material production and distribution chain. These indicators make it possible to identify the most sustainable practices and adjust sourcing strategies to minimize environmental impact while maximizing economic and nutritional benefits.

[0069] Finally, step 580, which involves presenting the results, can be done via an online platform or by sending a simple XLS file, and allows users to view the data in the form of interactive dashboards. These dashboards include dynamic graphs, charts, and geographic maps, providing a clear and intuitive visualization of raw material flows, consumption by animal species, and sustainability indicators.

[0070] For example, a farmer can use this platform to compare the nutritional and economic performance of different feed formulations, or to identify opportunities for improvement in terms of environmental sustainability.

[0071] Access rights management ensures that only authorized individuals can view or modify data, thereby guaranteeing the confidentiality and security of sensitive information. Users can also configure customized alerts and automated reports to receive regular updates on raw material flow performance and sustainability indicators, facilitating informed and proactive decision-making.

[0072] The present invention thus proposes an integrated and automated process for optimizing the management of raw material flows in animal feed, in order to improve animal production and livestock farming, using advanced algorithms and sophisticated nutrition models that have never been exploited in this way in the prior art.

[0073] This process is implemented using an information system consisting of databases and a processing platform based on optimization algorithms and deployed on the cloud.

[0074] Figure 2 illustrates the architecture of such a system for monitoring and optimizing the distribution of raw materials for animal feed. This architecture is based on the interconnection of different databases, processing modules and user interfaces, deployed on a cloud computing infrastructure.

[0075] An input database 110 receives and stores the data collected during step 510 of input data collection. This data includes information on the availability of raw materials, the nutritional requirements of the animals, the costs of raw materials, and territorial specificities. The input database 110 is linked to a central module deployed on a cloud server 200, where the optimization algorithm resides.

[0076] The optimization algorithm, deployed on cloud 200, comprises several computational sub-modules, including extraction, processing, and prediction. These sub-modules are responsible for analyzing and transforming raw data into usable information. Data extraction allows the retrieval of Relevant information is stored in the input database. Data processing involves verifying and normalizing the data to ensure its consistency and reliability. Prediction uses animal nutrition models and, optionally, machine learning algorithms to anticipate future needs and adjust feed rations accordingly.

[0077] The optimization algorithm execution module applies the calculations necessary to determine optimal feed rations, taking into account economic, territorial, and nutritional constraints. The results of these calculations are then stored in an output database 120. This database contains data on optimized feed rations, ready for implementation on farms.

[0078] The optimization results are delivered via a variety of user terminals, including web interfaces, mobile applications, and Excel spreadsheets (XLS). These terminals allow users to access the optimized data securely, facilitating the visualization of results and decision-making.

[0079] Figure 3 illustrates schematic examples of optimized raw material flows within a territory, particularly a national one, highlighting how optimization is achieved based on supplier proximity, volume requirements, and regional specificities. The map of France is divided into several zones representing raw material crops, production plants, and livestock farms.

[0080] Raw material crops, indicated by Cl, C2, C3, and C4, represent areas where various raw materials such as wheat, corn, soybeans and other cereals are grown.

[0081] The production plants, represented by U1, U2, U3 and U4, transform these raw materials into ready-to-eat animal feed or various products usable in human food, industry and animal feed. For example, these plants can produce soybean meal, wheat pellets, etc.

[0082] The livestock farms, represented by El, E2, E3 and E4, are the places where animals consume these processed raw materials. For example, El could be a cattle farm, E2 a pig farm and E3 a sheep farm.

[0083] The flows of raw materials between these points are represented in two distinct ways. Solid arrows indicate optimized flows of raw materials, favoring proximity to minimize transport costs and maximize logistical efficiency. Dashed arrows indicate non-optimized flows, where raw materials must travel longer distances, resulting in higher costs. additional costs and potential inefficiencies. Suboptimal flows can result from several factors such as limited seasonal availability of raw materials in a specific region, production capacity constraints of local factories, or specific nutritional needs that cannot be fully met by locally available raw materials.

[0084] The economic optimization process uses several criteria to promote optimized flows. First, geographical proximity is favored: raw materials are preferentially transported to the nearest factories and farms to minimize transport costs. For example, a corn crop at Cl will be sent first to factory U1 and farms El, which are geographically close.

[0085] The system also assesses the specific nutritional needs of each farm in terms of the required raw material volumes. For example, if farm E2 needs large quantities of soybean meal, the algorithm will ensure that the factory U2, located near the farm, is optimally supplied to meet this demand.

[0086] The algorithm also takes into account territorial specificities, such as the seasonal availability of crops, climatic conditions, and local agricultural policies. For example, crops in C3 will be planned according to the best harvest periods and favorable local conditions.

[0087] For example, the system can determine which structure, in which region, will prioritize the collection of which raw material. In cases where several farms require the same raw material, the algorithm decides on the optimal allocation in terms of volume and sequence.

[0088] The non-optimized flows, represented by dashed arrows, show scenarios where raw materials must travel longer distances to reach factories or farms. These flows are not optimized due to various constraints. For example, if a specific region experiences a poor corn harvest, farms in that region will have to import corn from a more distant region, resulting in higher transportation costs and additional delays.

[0089] The optimization of flows is therefore carried out by taking into account not only costs and distances, but also the production capacities of the factories and the specific needs of the farms, thus ensuring an efficient use of available resources while minimizing losses and inefficiencies.

[0090] The examples and variations described show how this process can be adapted to various conditions and constraints, ensuring maximum efficiency, increased profitability, and improved sustainability of livestock farming practices. This process thus meets the current challenges of the animal feed industry, offering Innovative solutions to improve resource management and reduce environmental impacts. Through comprehensive data collection, rigorous processing, intelligent optimization, and intuitive visualization of results, this invention enables farmers and managers to make informed, proactive, and sustainable decisions, thus transforming the way raw materials are managed and used in animal feed.

Claims

Demands

1. A method (500) for monitoring and optimizing the distribution of raw materials (RM) for animal feed in one or more farms (E1, E2, E3, E4), said method being implemented by computer, via database servers (110, 120), and comprising: a step (510) of collecting input data including raw material availability data, animal nutritional requirements data, and local economic data; a step (520) of processing the collected data, implementing automatic data extraction and verification to ensure consistency and reliability; a step (530) of integrating physiological data of the animals and nutritional recommendations, based on animal nutrition models;a step (540) of optimizing raw material flows implementing an optimization algorithm that takes into account economic, territorial and nutritional constraints; a step (550) of monitoring raw material consumption by animals to identify consumption trends specific to each species and / or farm; a step (560) of updating the databases, said databases including an input database (110) and an output database (120) linked by the optimization algorithm; a step (570) of calculating economic and environmental sustainability indicators from the processed and optimized data; and a step (580) of presenting the results with secure access to the information.

2. A method according to claim 1, wherein the input data collection step (510) also includes the collection of data on weather conditions and crop forecasts, this data being used to dynamically adjust the availability of raw materials according to climatic variations and agricultural production cycles.

3. A method according to any one of the preceding claims, wherein the data processing step (520) includes a raw data reconciliation phase, in which the collected data is checked and adjusted to ensure consistency and reliability.

4. A method according to any one of the preceding claims, wherein the optimization algorithm implemented in step (540) is configured to minimize supply costs while maximizing the satisfaction of specific nutritional needs of animals and respecting environmental constraints.

5. A method according to any one of the preceding claims, wherein the step (550) of monitoring the consumption of raw materials by animals uses sensors and monitoring systems to collect accurate real-time data on the individual consumption of each animal, said data being subsequently integrated into the database to provide a detailed and up-to-date picture of consumption patterns, thus enabling the algorithm to adjust feed rations in a precise and personalized manner.

6. A method according to any one of the preceding claims, wherein the database update step (560) includes the use of a data management system that ensures data security and redundancy, said system including mechanisms for regular backup, data encryption, and replication across multiple cloud servers.

7. A method according to any one of the preceding claims, wherein the step (570) of calculating economic and environmental sustainability indicators includes the calculation of indicators of greenhouse gas emissions generated by livestock farming activities, water use for the production of raw materials, and land use for crops intended for animal feed.

8. A method according to any one of the preceding claims, wherein the optimization algorithm is configured to take into account technical specifications of the raw materials, including their dry matter content, nutritional value, and digestibility by different animal species, said algorithm adjusting the feed rations according to these technical specifications.

9. Product: A computer program downloadable from a communication network and / or stored on a microprocessor-readable medium and executable by a microprocessor, characterized in that it comprises program code instructions for 18 the execution of a process (500) according to one of the preceding claims.

10. Terminal-readable and non-transient storage medium storing a computer program comprising a set of instructions executable by a computer or processor to implement a method (500) according to any one of claims 1 to 8.

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