This paper introduces anarchic manufacturing, an extremely distributed planning and control philosophy, as the methodology for planning and controlling future smart factories. Anarchic manufacturing delegates decision-making authority and autonomy to the lowest level of entities in system elements with no centralised control or oversight. It is often postulated that traditional hierarchical structures may not be well suited to manage the state-of-the-art hyper-connected smart factories due to their reliance on communication between management layers. Distributed systems, on the other hand, are commonly perceived to be inherently more flexible, robust and adaptable than hierarchical systems due to their structure. This paper characterises distributed systems by evaluating the relative flexibility of a representative hierarchical system against an anarchic system in a job shop scenario. Multi-agent-based simulation is used to model both hierarchical and anarchic systems, which are tested for flexibility following the Taguchi method and compared against Taillard's benchmark job shop problems for overall performance. The results show that the anarchic system performs as well as the hierarchical system when subjected to unforeseen disruption, refuting the argument that hierarchical systems are too rigid and distributed systems are inherently more flexible. However, anarchic manufacturing systems, which show adaptability and self-optimising traits, provide a platform to potentially enable the emerging digital manufacturing paradigm through the free market structure especially when bandwidth for communications is limited.