Problem
The Corps of Royal Electrical and Mechanical Engineers (REME) maintains all electrical equipment within the British Army. REME engineers are tasked with the maintenance, recovery, repair, and manufacturing of the battle equipment to keep it in fighting order at the battlefields and at the military bases, both at home and overseas.
Among other duties, engineers maintain the Apache Attack Helicopter, one of the world’s most advanced multi-role combat helicopters. This aircraft is very maintenance demanding: it takes about 35 hours of maintenance to cover one hour of flying. That was one of the reasons REME management considered their unit understaffed and claimed that it had to expand the mechanics department. However, their supervisors from Air Army Corps felt like the REME was being over-resourced, since on average they had enough staff to manage this workload.
Lack of agreement between these two military organizations made it clear that to solve REME’s workforce planning problem, the management needed to look deeper into data to make more evidence-based decisions. They tasked dseConsulting company with the challenge to create a robust tool for improving staffing strategies that would help them optimize scheduling and manning, and then increase the availability of helicopters. For these reasons, the consultants applied AnyLogic maintenance simulation and optimization software. The consultants aimed at:
- Representing deployment cycle processes in the digital environment.
- Analyzing cause-effect relationships between the processes.
- Determining robust manning strategies, including the targeted use of planned contractors based on sound evidence.
- Forecasting peak and drop cycles in the workload.
- Learning how human actions, one of the key components of the deployment cycle process, might impact the system.
Solution
The consultants built a simulation model for analysis and further optimization of the maintenance processes during the deployment cycle. The model included three areas of complex behavior lying within the real-life system:
- Aircrafts — in the model, they were deployed to different sites, where they were maintained by accompanied engineers, and then returned to the base where maintenance was required. Aircraft components could, in turn, randomly deteriorate during their life cycle.
- Deployment — aircraft deployment in the model was simulated for a five-year period. As it was impossible to plan the deployment for such a long term, this uncertainty was reflected in the model. In addition, consultants simulated the wearing out of aircraft components because of environmental conditions.
- People — based on the real data, consultants displayed how aircraft maintenance staff experience, individual efficiency, and stress influence deployment cycle processes.
With the AnyLogic multimethod simulation approach, consultants were able to model deployment cycle processes and handle their complexity without any simplification. Among other approaches, agent-based simulation allowed the modelers to reflect the behavior of aircraft engineers in detail, including their experience and the level of burn-out, to demonstrate more robust statistics.
In the simulation model, the following statistics were collected:
- The number of contractors at a given time of simulation and the number of aircrafts
- Utilization rate of staff over time
- Average level of stress
- Average level of experience
- Average staff efficiency
By analyzing the maintenance optimization model results, the consultants concluded that the human factor aspect played an important role in deployment cycle activities. For example, if an engineer was tired and inefficient, the job would take longer and affect all related processes. However, if new staff was recruited, the efficiency level would also drop because new recruits tend to be less experienced. As a result, the REME management had to find the balance between pushing more work to the experienced personnel, causing burn-outs, and hiring less experienced contractors.
The simulation model provided some significant insights for the management:
- When helicopters are at the deployment, the on-base staff utilization is low even though some helicopters are still at base. However, when helicopters are back, they are being maintained with a burst of employee engagement. This fact should be considered in further scheduling.
- When engineers leave for the deployment, few people stay at the base. That is why a risk emerges: if too many helicopters come back at the same time, there will not be enough personnel to service the helicopters. In addition, on-base aircraft engineers might experience heavy overload, which can negatively affect overall job results.
- As planned and emergency contractors are costly to recruit, the model allowed the management to analyze how the number of contractors could be optimized while facilitating workflows.
Result
As a result of the maintenance optimization modeling project, the consultants offered the customer a decision support tool that could be used for planning staffing policies and improving staff coordination and management.
At this stage of the project, simulation indicates that with smarter planning there is the potential for a 20% aircraft fleet availability improvement at a lower cost, in addition to the organization's prime targets.
It is estimated that the second phase of the project might save REME about $2.7 million in staff costs.