According to the Future of Work 2025 report, carried out by the World Economic Forum, Brazilian employers predict that the roles of specialist in Digital Transformation, 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, which has emerged as an essential competency for the industry.
With the increasing dependence on decisions based on accurate information to improve efficiency, it becomes essential to invest in internal talent or hire collaborators who know how to apply good practices in data integration, processing, and analysis.
To create a panorama, data science enables a detailed view of information throughout all stages of the supply chain. Advanced analytical tools bring numerous benefits: through in-depth data analysis, companies can forecast demand, manage inventories, optimize routes, and reduce 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.
Operations research, in turn, uses advanced methods to solve complex problems and optimize resource allocation. Your applications range from choosing the ideal location for distribution centers to defining routes and optimal inventory levels. This approach also allows simulating scenarios and assessing the impact of different decisions before implementing them, minimizing risks and maximizing efficiency.
In an increasingly competitive environment, mastering these operations research techniques is a strategic advantage for industry professionals. 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 old IT systems and 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 the cultural resistance to data-driven decisions. Many professionals still prefer to rely on experience and intuition, which requires an organizational change led by leadership, promoting the valuing of evidence-based decisions. Furthermore, the quality and integrity of the data are essential to prevent analysis errors that could lead to incorrect decisions, requiring 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 operations research are essential skills for modern logistics, not only to optimize efficiency but also to provide a strategic view of the business. 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.
