Technology Offering
PRODUCTION FOOTPRINT OPTIMISATION
Service line
Digital Transformation
Syngroup Office
Vienna / Austria
Geography
Europe
PRODUCTION FOOTPRINT OPTIMISATION
Background
Production Footprint Optimisation combines data processing and mathematical modelling methods to: optimally allocate production sites, evaluate related risk and ensure strategic investment initiatives.

 

  • Usually, sizable investments are validated using ROI models, best and worst case scenario calculations and other well-known methods. These calculations are highly focused on the investment subject and often fail to address the impact on the footprint as a whole due to the huge amount of data and overwhelming complexity involved.
  • Production Footprint Optimisation applies state-of-the-art operations research methods to conquer the complexity and enables deci-sion makers to quickly evaluate different allocation and cost scenarios in a holistic manner.
Historic
Optimal
How it works

 

  • Re-creation of actual production footprint based on past logistic flows to customers and between production sites (Intercompany), geographic locations and production capabilities and capa-cities, as well as fixed and proportional costs of: material, labour, machines and logistics.
  • Definition of scenarios are based upon the actual footprint, including but not limited to, changes in cost-structure, opening or closing of production sites and the entering into new markets (geography, product).
  • Analytical and visual investigation of optimised scenarios and comparisons to referenced foot-prints, by looking at: capacity utilisation, redirec-tion of logistic flows and high yield footprint modifications (based upon cost reductions).
Use Case

Our client is a global manufacturer of comfort and technical foams that is undergoing a consolidation process evaluating its organically grown and fragmented footprint.

  • The primary objective is to shape the footprint in a way to yield both mid-term cost advantages as well as low long-term risks mitigation in respect to cost and market uncertainties.
  • After some initial discussions, it was agreed that an `emotion-less´ evaluation based upon numbers, facts and mathematics would provide a valuable addition to the  decision making process.
  • The necessary data was collected and fed into the optimisation model by recreating the actual footprint and deriving meaningful scenarios (e.g. new plants, market growth, shifts in cost structure). The optimised scenarios were visualised and presented with interactive features to navigate the results and review various conclusions.
  • Following this initial project the model has been further evolved to address more industry and customer-specific scenarios. The tool is now frequently used to support many projects on a global and local level that has an an immediate impact on production allocation, investments and  logistics.
Typical project tasks

 

  • Analysis of the actual footprint, production process, products and customers.
  • Gathering of data and configuration of optimisation model.
  • Recreation of actual footprint to validate the model and provide a baseline for derived scenarios.
  • Definition of scenarios to serve as a basis for decision making.
  • Consolidation of scenarios and deriving actionable findings.
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Result
10% COST REDUCTION OF FOOTPRINT ON GLABAL SCALE