Development of technologies and innovations encouraged companies to look for and implement innovative solutions in their practice seeking not only to increase the efficiency of activity but also towards sustainability. In this context, the aim of the research is to reveal innovative solutions for the improvement of the warehousing processes towards sustainability in the case of manufacturing companies. The methodological setup consists of two steps. First, a comprehensive literature analysis was conducted seeking to reveal and present a theoretical model based on the conceptual framework on this topic. Then, a semi-structured interview was conducted with 8 managers holding managerial positions in four Lithuanian manufacturing companies. The manufacturing companies were chosen for the research due to their durable experience in the market, which use advanced warehouse management methods in their operations. Main findings showed, that innovative solutions such as Big Data Datasets, smart networks, Drones, Robots, Internet of Things and etc., are important for the efficient warehousing processes. Furthermore, it is also necessary to emphasize the benefits of implementing of innovative solutions in warehousing processes not only in economic terms, but also for solving of social and environmental issues towards sustainability. The novelty of this study lies in its dual objective of filling a theoretical gap and of drawing the attention of companies and policy makers to the importance of innovative solutions implementation in the warehousing process towards sustainability.

This project analyzes the evolution of the manufacturing sector in Portugal from 2009 to 2021, focusing on the variations in the number of active companies across various subcategories, such as food, textiles, and metal product industries. The goal of this analysis is to understand the dynamics of growth and contraction within each sector, providing insights for companies to adjust their market and operational strategies. Key objectives include analyzing the overall evolution in the number of companies, identifying subcategories with notable changes, and providing a comprehensive analysis of observed trends and patterns. The study is based on data from PORDATA 2024, and the research employs temporal trend analysis, linear and quadratic regression, and the Pareto representation to identify patterns of growth and decline. By comparing annual data, the project uncovers periods of growth and decline, allowing for a deeper understanding of the sector’s dynamics. The findings also highlight variations in periods of economic crises and during the Covid-19 pandemic, and recommendations for action are presented to support businesses resilience and continuity. These results are valuable for companies within the manufacturing sectors analyzed and policy makers, guiding strategic decisions to navigate the complexities of the market dynamics and to ensuring long-term organizational sustainable success.