# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 1 Introduction  
Industry 4.0 has become a synonym for the next revolution that will enable companies to manufacture, enhance, predict, and distribute their products. The availability of new technologies, fuelled by the advances in the Internet of Things (IoT), Artificial Intelligence (AI), predictive analytics, and cloud computing, has allowed a paradigm shift, where services, hardware, and software offerings have been integrated into industry practices. Furthermore, advanced sensors, embedded software, and robotics can be seamlessly integrated to extract and identify actionable information from data to inform and facilitate an improved decision-making process. A notable consequence is increased automation, with the aim of achieving an increased level of self-sustained efficiency and optimised responsiveness to new opportunities, as well as proactively adapting and addressing new challenges.  
Developing smart industry processes provides an incredible opportunity for the manufacturing sector to enter the fourth industrial revolution. Analysing the large amounts of big data collected from sensors on the factory floor ensures real-time visibility of manufacturing assets and can provide tools for performing predictive maintenance in order to minimise equipment downtime.  
One sector that has been expanding, due to the above emerging technologies that have enhanced automated, sustainable, and cost effective packing solutions, is packing. Smart labelling and Radio Frequency Identification (RFID) tagging, as well as sensor and hardware technologies, are leading the introduction of disrupting packing approaches to integrate real-time and historical information to enhance the overall process.  
Three-Dimensional Picking and Packing are important operations in the field of logistics and supply chain management, especially in the context of Industry 4.0. Three-Dimensional Picking refers to the process of selecting and retrieving items from a storagespace, such as a bin, a shelf, or a pallet, using a robotic manipulator or a human operator. Three-Dimensional Packing refers to the process of arranging and placing items into a container, such as a box, a pallet, or a truck, using a packing algorithm that optimises some criteria, such as space utilisation, stability, or load balancing.  
Furthermore, sustainability within the packing industry is attracting increasing attention, from both the academic and the industry communities, due to the positive environmental impact, as well as optimised cost effectiveness.  
Traditional packing approaches tend to be negatively affected by various factors, such as rising material costs, environmental issues, and limited opportunities for automated and optimised activities and services.  
However, effectively packing products is based on several parameters, including (but not limited to) the volume and shape variability of the containers and pallet supports, and weight, height, width and, length constraints and requirements. The packing order plays an important role, as this would optimise the overall process, whilst enhancing its speed and potential automation.  
This survey article aims to discuss the main techniques, methods, and frameworks that are relevant to enhanced packing. The rest of the article is structured as follows: Section 2 discusses the main methodology utilised in this article. Sections 3-5 contain the discussion on the main components relevant to product packing, including different computational and theoretical algorithms and frameworks and corresponding implementation and deployment. Section 6 describes the implementation of an automated extraction and analysis of large textual data sources identified in a similar manner as per the manual assessment described in the previous sections. The aim is to identify concepts and mutual relationships that can be extracted via natural language processing (NLP) tools. Finally, Section 7 concludes this work and prompts new research directions.