Big Data Analytics in the Agribusiness Supply Chain Management

Abstract

Importance and impact of the systems related to Agribusiness and Agri-food, are increasing around the world and demand a paramount attention. Collaboration in the inventory management is an integral part of the supply chain management, related to proactive integration among the chain actors facilitating production and supply, in especial in the agroindustrial sector of the Departamento de Antioquia, Colombia. This research establishes the main relationships between latent variables as collaboration, technology, models, optimization and inventory management, based on a literature review and applying a Structural Equation Model to a survey data of a sample of agribusiness companies. The results show that Available Technologies associated with Big Data, generates improvement of Collaboration Strategies, improving also Forecasting and Optimization; besides, Inventory Planning and Collaboration are related to Available Technologies associated with Big Data. A Poisson regression model and a Structural Equation Model estimations detect that the increasing strategies of technologies and Big Data are favorable to apply collaboration in the supply chain management, increasing possibilities to the enterprise competitiveness.

Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector

“…ACODAT was suggested in [3], is based on concepts put forward by IBM in 2001 [23,24] and has been implemented in areas such as smart classrooms [25], telecommunications [26], Industry 4.0 [7], and smart cities [4]. ACODAT is based on the paradigm of autonomic computing [8,27,28], with the purpose of providing autonomic features to systems using smart control cycles. In general, an ACODAT requires:…”

“…One of these strategies is the use of autonomous data analysis task cycles (ACODATS) defined in previous works [3] that allow the generation of knowledge models using different data sources for automation management. An ACODAT is composed of a set of data analysis tasks to achieve a given objective, such that each task has a specific role [4][5][6][7][8]: some observe the system, others analyze it, and finally, others make decisions to improve it. Thus, in ACODAT, there are interactions between the data analysis tasks in order to achieve the automation objective for which it was defined.…”

Supply Chain Management Techniques Using Big Data for Agro-Based Food Products in Bangladesh

“…Big data-driven supply chain management techniques can revolutionize the agrobased food product industry by improving efficiency, inventory management, and reducing costs. One of the most commonly used SCM techniques that leverage big data for agro-based food products is predictive analytics (Valencia et al, 2022). By integrating data from various sources, predictive analytics is crucial for informed decisions, anticipating future events, and enhancing supply chain planning (Cravero et al, 2022).…”

Agribusiness Cluster Impact Analysis on Economics Effectiveness of Soybean Producers in Benin

“…The importance and impact of agribusiness and agri-food systems increase throughout the world and require special attention. (Valencia-Cárdenas et al, 2021). This importance is manifestly in many ways.…”