According to the Future of Work 2025 report, carried out by the World Economic Forum, Brazilian employers anticipate that the roles of Digital Transformation specialist, in AI andMachine Learningand inSupply Chainand 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, that has stood out as an essential skill for the sector.
With the increasing reliance on decisions based on accurate information to improve efficiency, it becomes essential to invest in internal talents, or hire collaborators who know how to apply good integration practices, data processing and analysis.
To make a panorama, data science provides a detailed view of information throughout all stages of the logistics chain. Advanced analytical tools bring numerous benefits: from in-depth data analysis, companies can forecast demand, manage inventories and optimize routes, in addition to reducing waste.
With these analyses, it is also possible to identify patterns, anomalies and hidden trends, allowing companies to anticipate potential problems and bottlenecks. These practices not only increase operational efficiency, but also ensure quick and accurate 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 choosing the ideal location for distribution centers to defining optimal routes and inventory levels. This approach also allows for simulating scenarios and assessing the impact of different decisions before implementing them, minimizing risks and maximizing efficiency.
У все більш конкурентоспроможному середовищі, mastering these operational research techniques is a strategic differentiator for professionals in the sector. At the same time, 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
Although promising, these areas are still relatively new, and one of the biggest challenges is the integration between legacy IT systems and new data science technologies. Many companies still use tools that are incompatible with modern solutions, making it difficult to collect and integrate relevant data.
Another challenge is the cultural resistance to data-driven decisions. Many professionals still prefer to rely on experience and intuition, what requires an organizational change that starts from leadership, promoting the appreciation of evidence-based decisions. Furthermore, the quality and integrity of data are essential to avoid analysis errors that could lead to misguided decisions, demanding robust governance processes to ensure accurate information, complete and consistent.
Despite these difficulties, obstacles can be overcome with investments in technology, training and cultural change. Data science and operations research are essential skills for modern logistics, not only for optimizing efficiency, but also for providing a strategic view of the business. The companies that explore the full potential of these disciplines will be better positioned at the forefront of innovation and more prepared to compete in the market
