What changed for three global networks

Three top-five global ground-services networks run on Cohelion.
SATS

Unlocking the true profit potential with Activity-Based Costing

Cost-to-serve visibility for below-wing operations in Singapore, down to a single flight.

Direct cost
70%
allocated to a flight
Scope
7
below-wing operations
Granularity
Per flight
customer, aircraft type, department
01

Challenge

Ground handling runs on small margins, and SATS needed both levers in view at once: the cost to serve each airline customer, and whether the handling rate per aircraft matched the work involved, particularly during peak operations or when extra resources were needed to turn an aircraft round faster. Commercial rates were largely derived from experience rather than from measured cost.

02

Approach

An Activity-Based Costing model for below-wing operations in Singapore, covering ramp, technical ramp, baggage, aircraft interior cleaning, weight and balance, flight operations, and the GSE maintenance centre. Data is pulled automatically each day from deployment systems, GSE telematics, time and attendance, and payroll, so nothing waits on manual input. A proof of concept established that around 70% of costs could be allocated directly to a flight, a figure not known beforehand.

03

Result

Profitability can be analysed not only by customer but by aircraft type, by department, and down to an individual flight. Commercial teams compare handling rates against a benchmark for the same aircraft type while operations analyse cost to serve against that same benchmark, both working from one set of numbers. Higher overtime, idle time caused by off-schedule flights, and wage rises outpacing annual rate escalations are all measurable and monitored around the clock.

"We're excited about Cohelion's Profitability solution and are eager for the future defined by data-driven operations, that will sharpen our costs and commercial negotiations."

Ranjiv Ramanathan Global Head of Special Projects · SATS
WFS

Workforce optimisation across the EMEAA cargo network

Machine learning applied to cargo volume forecasting and resource allocation across EMEAA.

Air Cargo News Awards 2025 Recognised at the Air Cargo News Awards 2025
Network
225
stations on the platform
WO rollout
40
stations in 13 countries
Forecast
94.6%
accuracy, station-customer
01

Challenge

Every manager in the cargo world has to balance incoming volume against the workforce available on site, and at WFS, part of the SATS Group, that planning was done by hand in spreadsheets drawn from several operational and HR systems. It was slow, it left room for human error, and it depended on people transcribing and adapting the data correctly. Cargo volumes are volatile and difficult to plan for.

02

Approach

Workforce Optimization is being rolled out to operational excellence, HR and IT departments across 13 EMEAA countries and 40 stations, in two phases. The first moves local spreadsheets into a single repository of weekly and monthly forecasting data. The second applies machine learning to produce an optimised forecast by airline and by day from known schedules, historical data and current trends, combining airline schedules, trucking activity, tonnages, cargo type and air waybill data with payroll hours, productive hours, absenteeism and key ratios drawn from the roster management systems.

03

Result

One version of the truth for both hours and tonnages, with variances evaluated between budget and pre-plan and between pre-plan and actuals so that the drivers of variation can be identified and acted on. WFS is explicit that the aim is service quality rather than cost reduction: the right number of people at the right time, matched to customer demand, without overstretching staff.

"The shift from traditional planning to data-driven decision-making has not only improved our operational efficiency but also reduced costs."

Jimi Daniel Hansen SVP Operational Excellence EMEAA · WFS
Swissport

A single source of truth across 312 stations

One standard for every process and KPI, maintained by a central team of four.

Coverage
312
stations worldwide
Sources
50+
systems into one platform
Central team
4
people managing group data
01

Challenge

Swissport grew quickly through acquisition, which left a diverse portfolio of services and an even more diverse IT landscape. The Global Performance & Analytics team could not view operational, HR, quality and safety data together, compare performance at regional level, or check it against previous years. A second goal was better insight into actual performance against the Service Level Agreements agreed with customers.

02

Approach

Data sources were connected at Swissport's own pace, beginning with ground handling, then cargo, then all 14 lines of business including lounge, fuelling and security, and finally Training, HR, Quality & Safety. Airports that cannot supply data automatically upload spreadsheets or complete a data-entry page, so the central warehouse always holds the full picture. Master Data Management maps every variant of customer name, station name, service, alert code and training name to the company standard without local applications having to change, which avoided a costly and risky migration.

03

Result

At month-end the platform sends named owners, country COOs and station managers, a personalised invitation to approve the data for their own area of responsibility, which made arguments about reporting discrepancies a thing of the past. Because customer SLAs sit in the same platform as the actual data, performance against those commitments is known at any moment and available in commercial negotiations. Thousands of local training courses map to twelve company categories, and a central team of four maintains data for the entire company.

"The automated data-quality checks and built-in approval and correction facilities in the platform contribute greatly to the acceptance of the data on all levels in the organisation."

Philipp Müller Head of Global Performance & Analytics · Swissport
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