Leveraging PHM in Conjunction with Intelligent Scheduling to Improve Manufacturing Resilience

The scheduling of a manufacturing facility is a complex endeavor even when the equipment resources are always considered available or at least available 24/7 except for planned maintenance. However, under real-world conditions, the added complexity of unplanned downtime can significantly increase the difficulty of meeting deadlines. More reliable and efficient operations can be achieved by predicting problems and then rescheduling operations to minimize or avoid the problems' adverse effects. Stottler Henke Associates Incorporated (SHAI) has been working with the US Air Force on best practices for leveraging diagnostics, prognostics, and health management in conjunction with intelligent scheduling to improve manufacturing system resilience. The goal, then, is for diagnostic systems to identify impending faults quickly and automatically, providing the information needed to the intelligent scheduling system in order to minimize or completely mitigate the issues. Prognostic systems can estimate impending failures or rates of performance degradation; the intelligent scheduling system uses these diagnoses and predictions, along with the manufacturing deadlines and priorities, to develop mitigation strategies to minimize or avoid disruptions. The strategies include scheduling offline operations optimally to minimize the effects of the machines being offline; rescheduling operations due to machines being offline, reconfiguring systems to change their capacity and performance profiles, and/or reduce the usage of critical equipment to lengthen their remaining useful life. In the most uncomplicated cases, the schedule can be adjusted using simple strategies such as reassigning tasks from the faulted equipment to other equipment with similar capabilities. However, in many cases, more global analysis of an adjustment of the schedule is necessary to satisfy the facility's deadlines and other manufacturing goals. A system for resilience needs to be able to model and simulate the manufacturing system. That is, scenarios need to run to evaluate the manufacturing system's response to various types of problem scenarios and analyze the effectiveness of responses. This evaluation capability can be used to compare resilience strategies that specify optimal policies for employing diagnostic and prognostic capabilities and for responding to current and projected faults via rescheduling and reconfiguration. This will provide insight into the most critical pieces of equipment as related to unplanned downtime. That is, when operating under normal conditions, the most constrained pieces of equipment, may be different from the equipment that has the most significant adverse effect if it was unavailable. Intuitively this may be difficult to understand, take the example that every piece manufactured goes through one of two of the same machines that are running at full speed, but there is a 3rd machine that performs a specialized operation on a subset of very high-value parts. In this case, the two machines are the constraint on overall throughput, but it may not be evident that the 3rd machine, if down, is the constraint on profit. Being able to run scenarios will surface actual cause and effect relationships. This paper will expand on these ideas and lessons learned from real-world application of these ideas.