According to the 2025 Future of Jobs report, by the World Economic Forum, Brazilian employers anticipate that the roles of Digital Transformation specialists, in AI, Machine Learning "e em" is not a complete sentence or phrase in Portuguese. It needs context. Please provide the full sentence or paragraph. Supply Chain Logistics will grow until 2030.
This growth fills a significant gap in the Logistics and Supply Chain Management sector: the lack of technical skills to implement data science, which has emerged as a crucial competency for the industry.
With the increasing reliance on accurate information for improved efficiency, it becomes essential to invest in internal talent or hire collaborators who know how to apply good data integration, processing, and analysis practices.
To provide a comprehensive overview, data science offers a detailed view of information throughout all stages of the logistics chain. Advanced analytical tools deliver numerous benefits: through in-depth data analysis, companies can predict demand, manage inventory, optimize routes, and reduce waste.
With these analyses, it's also possible to identify hidden patterns, anomalies, and trends, enabling companies to anticipate potential problems and bottlenecks. These practices not only increase operational efficiency, but also ensure swift and accurate responses to market changes and internal needs.
Operations research, in turn, employs advanced methods to solve complex problems and optimize resource allocation. Its applications range from choosing the ideal location for distribution centers to defining optimal routes and inventory levels. This approach also allows for simulating scenarios and evaluating the impact of different decisions before implementation, minimizing risks and maximizing efficiency.
In an increasingly competitive environment, mastering these operations research techniques is a strategic differentiator for professionals in the sector. Simultaneously, the ability to transform large volumes of data into actionable insights makes data science an essential skill for modern logistics and supply chain management.
Challenges along the way
While promising, these areas are still relatively new, and one of the biggest challenges is integrating legacy IT systems with new data science technologies. Many companies still use tools incompatible with modern solutions, hindering the collection and integration of relevant data.
Another challenge is cultural resistance to data-driven decisions. Many professionals still prefer to rely on experience and intuition, requiring an organizational shift that starts with leadership, promoting the value of evidence-based decisions. Furthermore, the quality and integrity of the data are crucial to avoid analytical errors that could lead to wrong decisions, demanding robust governance processes to ensure accurate, complete, and consistent information.
Despite these difficulties, obstacles can be overcome with investments in technology, training, and cultural change. Data science and operational research are essential competencies for modern logistics, not only for optimizing efficiency, but also for providing a strategic vision of the business. Companies that explore the full potential of these disciplines will be better positioned at the forefront of innovation and better prepared to compete in the market.
