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D-SIMLAB enters Wind Energy Domain

Together with the renowned German consulting firm admoVa Consulting, D-SIMLAB is now developing new solutions to address critical challenges in Spare Parts Management in the Wind Energy domain.

D-SIMLAB Technologies and admoVa Consulting bundle their IT solution and business process optimisation competencies to address critical challenges around Spare Parts Management in the Wind Energy domain.

In recent years, sustainable reduction of operating costs has been turning into a growingly important key success factor in the Wind Energy domain, not only for operators but also for supporting service providers, with material, process and capital cost being the relevant levers. Effective spare parts management is challenging and requires solutions that take into account the domain-specific characteristics, in particular complex demand patters in equally complex inventory networks which are dependent on numerous factors.

Ambitious yet sustainable service levels resp. spare parts availability while keeping total cost low can be achieved by combining state-of-the-art decision-making methods with the underlying business processes in the best possible manner. This is where the solution provided by D-SIMLAB and admoVa comes in, taking into account wind turbine specific component classes and demand patterns, enabled by approaches that have already proven a lot of value in complex, asset-intensive domains as diverse as Aerospace and Semiconductor Manufacturing.

This allows to establish intelligent forecasting and spare parts management approaches to address typical challenges for Spare Parts Management in the Wind Energy domain as follows:


  • How many spares and what kind of order quantities are required for each part number at which locations?
  • Determination of maximum achievable service level subject to cost or budget constraints (and vice versa)
  • Differentiation of planning and control solutions to avoid excess inventory and/or low availability


  • Spare Parts Demand Forecasting, taking into account Condition Monitoring
  • Comparison of planning parameters with field behaviour, identification of troublemaker parts
  • Detection of understock situations with highest outage risk, taking into account expected profitability loss; assessment of risk mitigating countermeasure options
  • Determination of optimal repair timing, assessment of repair delay

Please approach us if you have questions, and we will be delighted to engage into a more detailed conversation.