A local utility company processes a variety of jobs each day including meter reading, service shut-offs, emergency response, and customer service work. For the Company, a specific workflow begins with automated meter-reading (AMR) and ends with collections/service shut-offs (CSOs) for accounts with excessively late payments (AMR-CSO workflow). There are considerable and systemic sources of variability in both the workload and resource demands of the AMR-CSO workflow including order arrival, order release schedules, order batch-sizing and maintenance scheduling. This project draws on theory from the job-shop problem to explore possible means to mitigate this variability. We hypothesized that controlling various forms of input variability would lead to reduced downstream workload variability. Using discrete event simulation we tested a variety of measures to reduce input variability in the workflow. Consistent with other literature we find that various workload control tactics have limited impact on output measures and system performance. However, we found that system is much more sensitive to resource capacity variability. One input control tactic we call Targeted Release allowed us to reduce Company capacity variability which suggested significantly improved outcomes. These initial results are promising for both the Company and for future investigation of tactics to mitigate resource capacity variability.