Modern production processes are continuing to move towards more flexible and dynamic conditions, most clearly exemplified by mass customization, but this flexibility can also be seen in technologies like; Human-Robot Collaboration, Automated Guided Vehicle fleets for just in time delivery of parts within factories and reconfigurable manufacturing. Currently, these technologies are developing independently of one another and the supporting industrial software tools such as line balancing optimisation tools, Machine Execution Systems and fleet management tools are similarly developing independently. An alternative to developing individual technologies for each problem is the use of a shared algorithmic framework that can support all of these problem types and future research into general smart factory technology. Monte Carlo Tree Search is a relatively recent Artificial Intelligence algorithm, sometimes described as a general-purpose heuristic, that has been found to be very effective in several theoretical and game-related problems. This paper will review the current growth in research into possible industrial applications of this algorithm and how a framework utilising this algorithm can help to realise the aims of the smart factory vision.