The Power of Data Analytics in Transforming the Agribusiness Industry through Digital Product Passport

• Grant number: 25/08887-1
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2025
• End date: August 31, 2026
• Field of knowledge: Engineering - Production Engineering
• Principal Investigator: Mateus Cecílio Gerolamo
• Grantee: Paulo Vinícius Carneiro Perazolli
• Host Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

In order to achieve the sustainable development goals (SDGs) of the 2030 Agenda, the European Commission proposed the Circular Economy Action Plan as part of the Green Deal, popularizing the concept of Digital Product Passport (DPP). This concept involves a database that stores information about the life cycle of a product, considering aspects such as origin, materials, transport, maintenance, recycling and disposal. Although the DPP is essential to achieve a circular economy by reducing environmental impacts and closing data gaps in companies, its implementation requires great effort, investment and adaptation from organizations to collect, store and process a huge amount of data, in addition to raising critical questions about data governance and security. To overcome these challenges, it is essential that companies adopt a data-driven approach, using enabling technologies of Industry 4.0 such as Blockchain and Internet of Things (IoT). Even though the main ideas about DPP are still being developed in specific markets such as textiles, construction and batteries, this research project will aim to address the use of DPP in the agribusiness, a sector of extreme relevance in Brazil. Thus, the objective is to analyze the relationship between data analytics and digital product passports for implementation in the agribusiness industry. Thus, a search will first be carried out using the Systematic Literature Review methodology on the following topics: Digital Product Passport, Data, Data-Driven, followed by the application of the Latent Dirichlet Allocation (LDA) topic model technique, aiming to ensure that there is no bias in the results obtained during the bibliographic research. After data collection, a case study will be carried out with a company in the agricultural sector to elucidate the operation of the DPP in practice. The expected results are, in the academic focus, the theoretical basis for the implementation of the DPP through a data-driven approach and, in the business context, the study of this occurrence in a practical way, demonstrating sustainable economic and environmental implications, enabling the development of public policies. (AU)