Roteiro de análise de dados e Big Data aplicado a um problema de logística

Abstract

The world is facing a transformation related to the data being generated in an increasing volume, in the most varied formats and this phenomenon happens inside organizations. Data science focuses on logistics due to its complexity and importance in the performance of companies and the challenges that all supply chain elements present. It is crucial that organizations have the capacity to extract value from the data and for that, there are no ready formats. In the development of a roadmap for data analysis and Big Data applicable to the logistics problem of missing parts at the point of use of the production line of a factory it is important to understand the general characteristics of the analysis of Big Data, such as production, storage, analysis and visualization of the data and management practices that analyze large volumes of data. In addition to the techniques, it is relevant to understand how the organizational structure and information management practices contribute to the success of Big Data in the organization. The profile of the data scientist within organizations is a professional with basic technical knowledge and deep knowledge in project management, as he usually leads multifunctional teams. There is a tendency to bring IT structures closer to other business areas, acting in a more integrated manner. Big Data brings several positive aspects to companies, supporting the interpretation of data for real time decisions, improving performance and internal efficiency and adapting products and services to customers’ needs. The challenges of implementing Big Data in organizations are related to operational issues such as access, storage, search and sharing data that is treated by Big Data in a holistic way in the organization, which can also step into the organizational culture of the company. The bibliographic review of the scientific article by WANG and collaborators (2016, p.98-110) is done to indicate possible methods applicable to the logistic problem of missing parts at the point of use of a factory production line. This problem is related to the operational planning of logistics and a possible approach would be the application of Big Data in the inventory, optimizing to define the appropriate levels of inventory. Another possible approach is the application of Big Data in logistics, using all the data generated in the chain to project flexibility in logistics operations. A suitable approach to develop Big Data in the logistics chain in this problem is a collaborative structure sharing data in the chain from the parts suppliers to the factory. The application of predictive analysis helps to understand the patterns in the lack of parts and to predict these events in advance, reducing the costs with remediative actions. The use of the optimization technique helps to understand the relationships and connections in the data, bringing insights to improve the process. Further analysis requires practical application of Big Data to the data, which is out of scope of this project.