The Magnetospheric Multiscale (MMS) Science Data Center (SDC) at the Laboratory for Atmospheric and Space Physics (LASP), at the University of Colorado, has managed MMS science and ancillary data processing and distribution since MMS launched in March 2015. The MMS SDC employs automation in nearly every part of its operations. Automation is used to start up processing “runners” that listen on queues for new processing jobs, which are triggered by configurable timing rules, including cron and operational events, or certain data/data files being available. A separate set of SDC code then automatically creates processing jobs and tracks its progress. The MMS SDC runs processing jobs for each instrument (47 different job types in total), ranging from levels l1a to l3, for “survey” and “burst” modes, plotting, and cdf creation. The SDC runs anywhere from a few hundred to over 2,000 jobs per day (on average, 1,000 jobs per day). Processing jobs are scheduled in a few different ways, from running based on a fixed schedule in cron, to being triggered by certain mission events, to being triggered by the appearance of new files. Several fail-safes have been added into the code over time to ensure failures are caught and handled, however, situations do arise where failures occur and are dealt with when something in the SDC does not work as expected. Added to these complexities is the fact that the MMS mission is incredibly time-sensitive, and requires the SDC to be available and ready to handle issues 24/7/365, which can be challenging due to the limited staffing on MMS a few years into the mission. The importance of automation in MMS SDC processing is clear. Not only does automated processing relieve some of the load from the software engineers working on the SDC, but it ensures continued smooth operation of the MMS SDC. This then allows scientists to continue their research efforts unhindered. As time has progressed, various areas for improvement, and extra automation, in this process have been implemented. This poster will focus on automation improvements to keep the system running smoothly with almost no human involvement.