Intralogistics is a crucial part of the supply chain and is continuously influenced by the drivers such as globalisation, mass customisation, and shorter product life cycles. The challenges brought about by the pandemic have accelerated the ’shifting pattern’ towards automation in transport & logistics. With the push towards higher robustness and efficiency, self-organisation is the next step to incorporate flexibility, reconfigurability, scalability, re-usability, adaptivity, and energy-efficiency in the material handling system. This aims to reduce human fatigue and inaccuracy in in-house logistics and also improve the response time. However, the feasibility of such systems in real-world applications often remains unclear owing to the complex emergent system characteristics as a result of local interactions between multiple agents and the lack of extensive operational studies. This research attempts to develop an intuitive, adaptable and scalable model to study the emergent behaviour of a system of self-organising robots for in-house material movement. By considering the industry expectations and feedback in the research and evaluation, the model also functions as a decision support tool for business cases, providing a prima facie impression on the system performance.