• Digital Twin
• Industry 4.0
• Simulation
• Sustainability
• Warehousing
• Wearable Sensors

Warehouses are important nodes in each supply chain and almost every industry. Their massive usage can not ignore the impact on the environmental, social and economic aspects of sustainability. To face this challenge, it is essential to seize the opportunities offered by Industry 4.0 revolution, whose technologies can support the development of greener and more efficient warehousing processes, as well as enhancing human workers performance. The growing adoption of automated technologies along with a more pervasive use of information systems and the spread of wearable sensors allow to measure and record an increasing volume and variety of data from machines, processes, and human workers. The availability and richness of such data enable to perform simulations and to conduct statistical analysis on previously inaccessible data. Moreover, they pave the way for new data-driven techniques, such as digital twin, a quantitative method that replicates a system by virtually representing all the properties and the relationships among its elements. By properly leveraging these tools, warehousing organizations can analyze, test and improve their processes to achieve higher operational efficiency. Accordingly, the main goal of this Thesis is to support the three aspects of sustainability in warehousing.
More specifically, recent methodologies and models based on Industry 4.0 paradigm were applied during the Thesis, with the aim of helping warehouse managers in the design, analysis, and improvement of sustainability aspects in material handling processes. The case studies included in this Thesis confirm the validity of the proposed approaches and suggest some relevant managerial contributions for supporting warehousing sustainability.