According to the Future of Jobs 2025 report by the World Economic Forum, Brazilian employers predict that roles in Digital Transformation, AI, and Machine Learning and Supply Chain and Logistics will grow by 2030.
This growth fills a major gap in the Logistics and Supply Chain Management sector: the lack of technical skills to implement data science, which has emerged as an essential competency for the industry.
With the increasing reliance on data-driven decision-making to improve efficiency, it becomes imperative to invest in internal talent or hire employees who can apply best practices in data integration, processing, and analysis.
To provide an overview, data science enables a detailed view of information throughout all stages of the supply chain. Advanced analytical tools offer numerous benefits: in-depth data analysis allows companies to forecast demand, manage inventory, optimize routes, and reduce waste.
These analyses also help identify patterns, anomalies, and hidden trends, enabling companies to anticipate problems and potential bottlenecks. These practices not only increase operational efficiency but also ensure quick and precise responses to market changes and internal needs.
Operational research, in turn, uses advanced methods to solve complex problems and optimize resource allocation. Its applications range from selecting 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 operational research techniques is a strategic differentiator for professionals in the sector. At the same time, the ability to turn large volumes of data into actionable insights makes data science an essential skill for modern logistics and supply chain management.
Challenges ahead
Although 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, making it difficult to collect and integrate relevant data.
Another challenge is cultural resistance to data-driven decision-making. Many professionals still prefer to rely on experience and intuition, requiring an organizational change led by leadership to promote evidence-based decisions. Additionally, data quality and integrity are crucial to avoiding analysis errors that could lead to flawed decisions, demanding robust governance processes to ensure accurate, complete, and consistent information.
Despite these difficulties, the 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 strategic business insights. Companies that fully leverage the potential of these disciplines will be better positioned at the forefront of innovation and more prepared to compete in the market.
