In our journey through Rostering Automation Prototyping Services contracts, we've had the unique opportunity to explore diverse and innovative rostering simulations that hold the potential to offer insightful lessons for other ANSPs. In this blog series, we're excited to share a collection of Case Studies derived from real-life simulations we've conducted for our valued clients. To maintain confidentiality, all data presented will be completely anonymized.
1. Problem description
An ATC Unit with 96 ATCOs rostered in teams on a fixed shift pattern sees significant variations in terms of overtime distribution per individual staff and increased staff leakage during the night shifts.
Compared to the previous simulation, in which 24 additional ATCOs were used to reduce overtime to under 300 hours per month, how is the workload distributed when deploying only the current headcount of 96 ATCOs?
2. Initial setup
The data used for this modelling was already uploaded to the Customer's server on the SkyRoster Cloud.
All annual leave days were considered by inputting them into the system for the simulated period (distributed randomly per employee, averaged out per year). Another notable difference with the previous simulation is that no duty leave days were registered for this one.
Type of roster: Team-based.
Roster period: 1st - 31st of May 2022.
Shift Allocation: Fixed Shift Pattern.
Pattern: MANROO (Morning-Afternoon-Nigth-Rest-Off-Off).
Morning shift: 07:00 – 14:00.
Afternoon shift: 14:00 – 21:00.
Night shift: 21:00 – 07:00.
Staff: 96 employees grouped equally in 12 teams (8 ATCOs per team).
Manpower Requirements (MPR):
The MPR was updated to accommodate all the necessary positions according to the Sector Opening Timetable and was defined using the Air Traffic Control Officer (ATC) and Supervisor (SUP) qualifications as follows:
Morning: 15 positions.
Afternoon: 15 positions.
Night: 12 positions.
Morning: 2 positions.
Afternoon: 2 positions.
Night: 2 positions.
The next step was to generate the tactical roster and solve it for the week of 2nd-8th of May 2022, using the same Sector Opening Timetable:
Working positions to be filled were Executive (EXE), Planner (PLN), Superior (SUP) and Air Flow Manager (AFM). The procedure by which the staff was assigned to the Tactical Roster was the following:
Maximum of two different worked positions, one hour each, followed by a minimum of an hour break in case of EXE and PLN.
The employees in the SUP position can perform 100% during the day shifts (M and A) and 50% during the night (N).
The AFM position is open between 07:00 and 18:00.
Both strategic and tactical rosters were generated using the SkyRoster AI-powered constraint-based scheduling engine. Afterwards, the data was imported into Power BI to observe the shifts distribution per employee and team, the overtime distribution, the time spent on position and the staff leakage.
3. Statistical results
May 2022 Strategic Roster
After solving the Strategic Roster, the entire Manpower Requirements were filled in using an additional 118 overtime shifts: 56 Morning Shifts, 57 Afternoon Shifts and 5 Night Shifts.
Compared to the previous simulation, in which 120 ATCOs would generate 488 leave days (326 Annual Leaves and 162 English Training days), in the current simulation, the 96 ATCOs have allocated only 152 leave days for the entire month of May (no English Training days are registered for this period).
May 2022 Tactical Roster
The SkyRoster Engine assigned all positions successfully in the Tactical Roster, as well, respecting the resting time specific to each position.
May 2022 Strategic Roster
It can be observed in Table 1 that the number of Morning and Afternoon shifts are almost equally distributed. The lower result for the Night shifts is due to the lower number of needed shifts in MPR for the night time. The same result can be observed for overtime shifts which are almost equally distributed between Morning and Afternoon shifts. The Night Overtime results display the lower number of overtime shifts during the night time (i.e. 5, according to Figure 3-2).
From Figure 4-1, it can be seen that shifts are almost evenly distributed between the teams, with only a slight variation due to leaves affecting employees in specific teams.
The Overtime Distribution figures show that only 5 out of 96 employees (5.2%) were not assigned to perform overtime shifts. The other employees (94.8%) have been assigned one to three overtime shifts, with only two employees from 91 employees (2.08%) performing three overtime shifts. Figure 4-2 shows that all teams have overtime shifts assigned.
May 2022 Tactical Roster
The above figure shows the Morning shift schedules and working slots distribution for all employees. It can be observed that the employees who are assigned to the SUP and AFM positions work 100%, while the employees assigned to EXE and PLN positions spend between 57.14% (4 slots) and 71.42% (5 slots) of the time in position, this percentage varying depending on how the system distributes the necessary positions.
Figure 4-5 shows the Afternoon shift schedules and working slots distribution for all employees. It can be observed that the employee who is assigned to the SUP position works 100%, the one assigned to the AFM position works 57.14%, while the employees assigned to EXE and PLN positions spend between 42.85% (3 slots) and 71.42% (5 slots) of the time in position, this percentage varies depending on how the system distributes the necessary positions.
Figure 4-6 shows the Night shift schedules and working slots distribution for all employees. It can be observed employees assigned to the SUP positions work 50% of their time, and the ones assigned to EXE/PLN positions work between 46% (4 slots), 50% (5 slots) and 54% (6 slots).
Figure 4-7 shows the entire week's distribution of Time at Work for the EXE and PLN positions. It can be observed that the maximum is 26 hours for employee C07 C07, and the minimum is 5 hours for employee A12 A12. Compared with Figure 4-8, we can observe that A12 A12 works both as EXE & PLN (5 hours) and as SUP & EXE (9 hours). Same case for A04 A04 and A08 A08.
Analysing Figure 4-9 and Figure 4-10, we can observe the teams' minimum, maximum and average time on position (in hours). In the case of EXE and PLN positions, the minimum worked hours for the teams is 11, the average is 21, and the maximum is 36 (hours).
In the case of SUP & AFM positions, the minimum worked hours for the teams is 16, the average is 28, and the maximum is 47 (hours).
Figure 4-11 shows the percentages of worked time versus breaks for EXE and PLN positions. It can be observed that no team overpasses 62.31% of time spent on position. Please check the Full Interactive Power BI Report for a more in-depth view of these stats.
Figure 4-12 shows the percentages of worked time versus breaks for SUP and AFM positions. It can be observed that most of the teams' employees performed in both positions.
From Figure 4-13, we can observe that even when including the SUP and AFM time, there is no team to work more than 65.44% of the time and less than 55,56% during the entire period. Some employees work in multiple positions, either SUP or AFM, either mixed (SUP&AFM and EXE & PLN), depending on the system assignation and qualifications.
The optimised roster for May 2022, executed with the Customer's current headcount of 96 ATCOs, minimises the variation of overtime between teams and individual employees by using only 118 overtime shifts for the entire month.
All leave days are guaranteed for the entire year, with a proportion of them (152 days) being assigned in May 2022.
Compared with the scenario from the previous simulation (i.e. Full month of April), which includes 120 employees, the current modelling uses more overtime shifts to complete all the necessary positions (i.e. 81 more overtime shifts).
The minimum, maximum and average results for regular shifts, especially for the overtime shifts, have changed, producing an increase of at least one additional overtime shift per employee apart from the one available in their contract. Only 5 of 96 employees (5.2%) were not assigned to perform overtime shifts, but only two out of 91 employees (2.08%) performed three overtime shifts.
Regarding the Tactical Roster, the planned working positions tend to behave similarly to the previous analysis.
In the Tactical Roster, the staff works in the Morning and Afternoon shifts at the planned percentage of 66%. The SUPs make the exception, who work 100% of the time in both types of shifts and AFMs, who work 100% of the time in the morning and 57% in the afternoon.
In the afternoon, there is a transition from 5 open sectors to 4 open sectors, and the AFM position is closed.
No employee or team works more than 71.43% of the time, except for the SUPs in the Morning and Afternoon Shifts.
For the night, the time spent on position is 50% for all three positions: SUP, EXE and PLN.
On average, the efficiency factor is 50% during the night shifts and 33% during the Morning and Afternoon shifts.
6. Full Power BI Report
The fully-interactive Power BI Report with anonymised data for the entire simulation can be found below.
You can download more case studies and have the opportunity to learn from the experiences of other ANSPs who have successfully optimised their rosters using SkyRoster. With this information at your disposal, you can better understand the benefits of implementing SkyRoster in your organization and achieve greater efficiency in your workforce scheduling.