Case Study

Using Network Optimization to Balance Local Content Requirements with Cost Minimization

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Using Network Optimization to Balance Local Content Requirements with Cost Minimization


A global manufacturer and installer of wind turbines utilizes its own assembly points throughout the world and relies upon over 100 vendors and selected international ports that serve as part of their transportation and storage supply chain. A key business challenge is to meet country- and/or local municipality-established local content requirements for wind turbine projects. Given the number and location of needed resources and a limited number of suppliers, variations in content sources could have a significant impact on the speed at which new business is acquired. Project costs and profitability, supply chain performance and project feasibility must be factored into the analysis.

The objectives of the network optimization project included:

  • Cost minimization and meeting profitability goals
  • Better and faster identification of costs relative to different sourcing scenarios including currency exchange rates
  • Improved project feasibility and pre-contract profitability analysis confidence
  • Quicker response to RFPs
  • Maximization of the utilization of a set number of assembly points and a limited number of suppliers
  • Better use of internal resources


The company utilized the LLamasoft® Supply Chain Guru® supply chain modeling platform to determine the optimal scenario given local content requirements. First, the modeling team created baseline constraint models and alternate design scenarios. Stakeholder interviews were conducted to obtain key information and buy-in. Location, cost and other critical data were imported into Supply Chain Guru and tested to determine accuracy, quick cost identification and analysis, ease of data refresh, scalability, optimal supplier and transportation utilization, user friendliness, speed and ease of application.

Supply Chain Guru analyzed every part’s cost and source relative to content constraints in order to create a detailed and robust single model of multiple cost, profit, assembly, logistics and feasibility options when compared to baseline models. The optimization engine created options under required local content scenarios by modeling all costs, currencies, different tax rates on finished/assembled parts versus individual components, sourcing points, assembly locations and end-to-end transportation options. Supply Chain Guru allowed the company to rapidly compare alternate potential what-if scenarios without recreating the entire business model.


Using Supply Chain Guru modeling technology, the company was able to:

  • Improve management and understanding of content constraints and their impact on profitability and project feasibility, including currency exchange and tax rate factors
  • Identify cost savings and cost minimization opportunities associated with different levels of content constraints and alternative flow-path balancing
  • Embed local content constraints in the modeling process
  • Enable faster response to RFPs using network optimization within local content constraints
  • Optimize transportation costs, including mode changes, port storage and total landed cost

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