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D-SIMSPAIR is a suite of advanced domain-specific decision-support modules for spare parts demand planning and inventory network optimisation in the aviation industry and addresses common pain points in aviation spare parts management:

  • The difficulty of assessing risks and prioritizing pre-emptive measures against technical delays and Aircraft-On-Ground (AOG) situations caused by non-availability of spare parts.
  • Field behaviour often not being in agreement with relevant planning parameters such as Mean Time Between Unscheduled Removals (MTBUR) and Repair Turnaround Time, leading to shortages which can result in flight cancellations on the one hand and investments that are not necessary on the other hand.
  • Challenges associated with designing and delivering competitive component support services that can justify higher cost and price, in an environment where MRO organisations are facing enormous cost pressure from airlines.


D-SIMSPAIR suite combines simulation techniques, mathematical modelling and optimisation algorithms with domain expertise to enable critical decisions in aviation spare parts planning and inventory network optimisation.

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Architecture & Data Management

D-SIMSPAIR has been designed for use either stand-alone or on top of an Enterprise Resource Planning (ERP) system. The various D-SIMSPAIR modules can be hosted either by D-SIMLAB or on an external Cloud or on a server within the customer’s domain.

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The D-SIMSPAIR suite is built on a number of enabling modules comprising a Dashboard from which all Use Cases can be accessed, a Field Parameter Analyser, an Inventory Network Simulator, an Inventory Optimiser and an Expendables Settings Analyser.

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Business Use Cases

The various D-SIMSPAIR use cases for spare parts planning and inventory network optimisation can be configured and stored as pre-set scenarios and can then be picked and executed for different data sets on a regular basis. Each of the use cases has predefined reports. The User is also provided with the capability to download raw output data and perform offline analysis before connecting to the underlying ERP and executing the decisions.

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