How Volotea Built a Data-Driven Spare Parts Planning Operation Across Its Station Network
Moving from Static Inventory Targets to Data-Driven Spare Parts Planning
Volotea is a Spanish low-cost airline founded in 2011. Over the past decade, the airline has experienced rapid growth and established itself as one of the fastest-growing carriers in Europe, continuously expanding its network across the continent. Operating a fleet of 41 Airbus A319 and A320 aircraft across a growing network of bases across Southern and Western Europe, the airline manages a complex multi-station maintenance and supply chain operation.
As Volotea continued expanding its operational network, the airline faced increasing complexity in managing spare parts availability across multiple stations. Existing planning processes relied heavily on predefined stock targets and manual decision-making, making it difficult to maintain visibility across the network and proactively respond to operational demand.
To support a more predictive and efficient planning approach, Volotea partnered with EXSYN to implement the Supply Chain Analytics app, leveraging its Spares Forecast functionality to improve long-term demand forecasting, inventory optimisation, and operational planning.
The Challenge
Before the implementation, spare parts planning across Volotea’s growing station network was largely coordinated through Excel-based processes and predefined “ideal stock” quantities assigned to each base. As aircraft, materials, and maintenance activities moved continuously between stations, planners often had limited visibility into where inventory was available across the network and whether shortages could be resolved through transfers instead of new purchases. While the existing approach provided a basic planning structure, it became increasingly difficult to align inventory positioning with actual operational demand and the pace of the airline’s expansion:
Limited visibility of inventory availability across stations
Static stock targets that did not reflect actual consumption patterns
Difficulty identifying whether shortages could be solved through transfers instead of new purchases
Limited integration between the upcoming maintenance demand and inventory planning
Reactive decision-making around procurement and material allocation
As network complexity increased, planners needed a more dynamic way to balance stock availability, operational risk, and inventory costs.
The Solution
To address these challenges, Volotea implemented EXSYN’s Supply Chain Analytics application, leveraging its Spares Forecast functionality to support long-term forecasting and operational inventory planning.
The solution was designed to provide planners with greater visibility into spare parts demand, inventory positioning, and material availability across the airline’s station network. By integrating maintenance planning data, operational demand indicators, and inventory information into a unified planning environment, the platform enabled teams to move from reactive material planning toward a more predictive and coordinated approach.
The forecasting engine supports demand planning months and years ahead by combining:
Upcoming maintenance programmes and work orders
Historical consumption data
Inventory availability across stations
Lead times and demand variability
Unscheduled events and operational risk indicators
“Predictive spare parts planning is a natural next step in our digital maintenance journey,” said Matías Datino, Technical Senior Director at Volotea. “By combining our operational experience with EXSYN’s data expertise, we can anticipate material needs earlier and ensure our maintenance network is better prepared to support our growing operations.”
“Data-driven material planning is a key enabler for efficient and resilient maintenance operations,” said Sander de Bree, CEO of EXSYN Aviation Solutions. “Working together with Volotea, we’ve connected forecasting intelligence directly to logistics workflows, turning trusted operational data into tangible cost and reliability gains.”
From “Ideal” to “Optimum” Inventory Planning
The EXSYN solution supports both long-term planning and day-to-day operational decision-making through a set of integrated dashboards and planning views.
One of the most significant operational shifts introduced by the project was the transition from static inventory assumptions toward dynamically optimised spare parts planning.
Previously, inventory decisions were largely driven by manually defined “ideal stock” quantities assigned to each station. While effective as a baseline planning method, the approach struggled to keep pace with changing operational demand, shifting maintenance requirements, and the realities of managing inventory across a growing network.
Within EXSYN’s Supply Chain Analytics application, the Spares Forecast functionality introduced a more predictive approach by continuously evaluating historical consumption patterns, lead times, maintenance demand, inventory availability, and operational risk indicators to calculate optimum stock quantities across the network.
Rather than relying solely on fixed stock targets, planners gained the ability to make inventory decisions based on real operational conditions and forward-looking demand signals.
The platform also introduced a prioritised decision-making workflow designed to optimise existing inventory before initiating new procurement actions:
Transfer material from stations holding surplus stock
Submit pool requests where external inventory is available
Purchase material only when transfer or pooling options are not viable
This represented a fundamental shift in how spare parts planning decisions were made across the organisation — improving stock utilisation, reducing unnecessary purchases, and enabling a more coordinated network-wide planning strategy.
Integration and Deployment
The implementation integrated operational and maintenance data from AMOS together with existing inventory planning data and Excel-based workflows already used by Volotea’s materials teams. This enabled the platform to consolidate maintenance demand, stock availability, transfer activity, and procurement planning into a single operational planning environment.
The rollout followed a phased implementation approach, with the initial setup and development completed within approximately two months. The implementation allowed planners and operational users to validate recommendations against real maintenance and supply chain scenarios before expanding usage across additional workflows. Continuous feedback loops between Volotea’s planning teams and EXSYN enabled dashboards, stock calculations, and planning logic to be refined iteratively based on operational usage patterns and day-to-day planning requirements.
The project also established a scalable onboarding framework, enabling future customer implementations using AMOS to be deployed significantly faster, in some cases within only a few days.
To further improve inventory visibility and planning flexibility, the Spares Forecast functionality also takes alternate part availability into account when evaluating stock positions and material requirements across the network.
The solution is now embedded into daily materials and supply chain operations, supporting:
Procurement planning based on forecasted demand and stock sufficiency
Inventory balancing between stations
Pooling and exchange decisions
Maintenance preparation for upcoming work orders
Spare parts transfers across the station network
Identification of surplus or dormant inventory
By combining operational forecasting with actionable inventory planning workflows, the implementation helped transform spare parts planning from a largely fragmented and reactive process into a more coordinated, network-wide planning operation.
Shifting Toward Predictive and Data-Driven Planning
By introducing a more predictive and network-aware planning approach, Volotea significantly improved visibility into spare parts demand, inventory positioning, and material availability across its station network.
The implementation enabled planning teams to make faster and more informed inventory decisions by combining maintenance demand, operational data, and inventory intelligence within a unified planning environment. Rather than relying on static stock assumptions and reactive procurement actions, planners gained the ability to proactively balance inventory levels, identify transfer opportunities between stations, and prepare for upcoming maintenance activity with greater accuracy.
Key operational improvements included:
Better utilisation of existing stock across the network
Increased use of transfers before initiating new purchases
More proactive planning for upcoming work orders
Improved visibility into surplus and dormant inventory
Reduced reliance on manually defined stock assumptions
More coordinated and data-driven inventory decision-making
The project demonstrates how airlines can combine forecasting, inventory optimisation, and operational planning workflows to improve spare parts availability while maintaining tighter control over inventory growth and operational risk.
By transforming spare parts planning from a largely fragmented and reactive process into a more integrated and predictive planning operation, Volotea strengthened its ability to support a rapidly expanding airline network with greater operational efficiency and planning confidence.
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