Andrew Ma is a Postgraduate Researcher studying for his PhD in Manufacturing Systems, specifically in distributed scheduling and control of autonomous smart factories.
Andrew was awarded a MEng in Mechanical Engineering with Manufacturing and Management from the University of Bath, he has since worked for a management consultancy as an analyst in supply chain and manufacturing operations before beginning his PhD at the University of Bristol in Manufacturing Systems. Andrew has also worked in defence and aerospace in Engineering and Operations Management roles.
Andrew’s current research involves agent based modelling of distributed manufacturing systems, to characterise these systems relative to traditional hierarchical and centralised architectures.
Anarchic Manufacturing – distributed scheduling and control of manufacturing systems
- Anarchic Manufacturing on the road - Andrew has been to two conferences, CIMS (Cambridge) and CARV (Nantes) road-showing Anarchic Manufacturing systems and presenting two conferences papers discussing how Anarchic Manufacturing compares to hierarchical and centralised systems when applying scenarios on: mass customisation, scale and complexity. At CIMS, Andrew has enjoyed the networking opportunities, whether in Christ Read More
- Anarchic Manufacturing’s first journal publication - Anarchic Manufacturing has its first journal publication! Andrew explains in-depth the mechanics of Anarchic Manufacturing and challenges the assumption that distributed systems are inherently more flexible than centralised systems in the International Journal of Production Research. Here is a link to the paper: Anarchic Manufacturing Anarchic systems is going on a Read More
- The Architecture of Anarchic Manufacturing - Are traditional centralised and hierarchical structures too cumbersome for the digital age? Would a radical (and controversial) view to solving the shop-floor scheduling and control problem be better? With the rise of digital manufacturing technologies and Industry 4.0, many are considering whether current methods for scheduling and controlling (and ultimately Read More