Case studies

Case studies
Performance improvement

Optimizing manufacturing network through identification of cost drivers

  • Challenge

    Based in Europe, the client is an International Industrial Goods manufacturer. Having expanded in the past by both organic growth and mergers & acquisitions, the client ended up with production facilities located all around the globe with highly segmented product portfolio each, in addition to elevated logistic and personnel costs.

  • Approach

    A team combining operational experts and industry specialists collected and analyzed firstly specific data, such as production portfolio and volumes by location, cost breakdown per operational step and per product, plant performance indicators and logistics costs. Together with the client, the team aligned data bases between plants and future tendencies regarding technologies, product portfolio and market needs were identified. To better understand the actual footprint, a study on technology and operational standards used across the plant network was conducted, taking network constraints in market, technology and operational standards into account. Based on all analyses the team was able to come to the very essential part for a totally new transparency in the footprint: the cost driver matrix. With this matrix, the cost drivers behind production cost differences could be identified and various scenarios could be modeled, knowing the impact of every driving variable on each cost type. The team prepared a crucial basis for the evaluation of preferred scenarios and the assessment of network fit with future challenges. An implementation plan was developed for the main scenario.

  • Result

    The client obtained a totally new transparency regarding the cost structure in relation to origin of the costs and their influence when changing the footprint circumstances (place, portfolio per site, etc.). The cost matrix allowed the client to easily identify how different factors drive its costs and what changes could optimize its footprint through the modeling of different scenarios. The client was able to model multiple scenarios and possible target networks very close to reality due to the cost driver model, including also one-time costs, implementation risks and overall economics, as basis for assessment of network fit in future challenges.