Aliaxis

Aliaxis, a global leader in fluid and energy management with operations in over 40 countries, faced a persistent supply chain visibility problem rooted in data fragmentation. Production and logistics data resided in siloed systems with inconsistent naming conventions across markets, making network-level modeling nearly impossible. A 2017 network redesign initiative stalled before delivering results due to limited internal modeling capability and over-reliance on external consultants who could not maintain the model between engagements. By 2022, the company still lacked a reliable, continuously maintained view of its Pan-European supply chain — meaning network footprint decisions, make-or-buy evaluations, and route-to-market strategies were either deferred or driven by incomplete analysis, leaving significant cost and service improvement potential unrealized.

In 2022, Aliaxis implemented AIMMS SC Navigator with implementation partner Haskoning to build a Pan-European digital twin of its supply chain network. The deployment followed a deliberate capability-building model: Haskoning led the initial proof of concept and data integration work, while simultaneously transferring modeling knowledge to a newly appointed dedicated internal resource — enabling Aliaxis to independently run scenarios over time. The digital twin uses standardized Excel templates to manage data inputs across all supply chain cost layers, enabling rapid scenario runs without external dependency. Rather than operating as a standalone analysis tool, the Digital Twin & Simulation model was embedded into Aliaxis's Integrated Business Planning (IBP) process through a cross-functional Sourcing Board, ensuring outputs directly inform decisions on network consolidation, Incoterms comparisons, and new product route-to-market strategies.

The digital twin delivered measurable impact across cost, speed, and sustainability. Scenario analysis across network footprints and transportation configurations identified potential logistics cost reductions of approximately 8–9% of total logistics costs. Simulation of distribution site consolidation scenarios revealed pathways to substantial inventory reductions. Decision speed improved dramatically — analyses that previously required months of external consultant engagement can now be completed internally within days. CO₂ emissions were integrated as a tracked KPI across network scenarios, establishing the data foundation for sustainability-driven optimization. As Stefan Ostertag observed: "Without the SC Navigator model, we would not have been able to identify this potential. It gave us a clear view of how network changes impact costs, stocks, and service."

• Dedicate an internal owner to the model from day one; without a named resource responsible for keeping the digital twin current, the model risks becoming a stale artifact rather than a living decision tool.
• Use implementation partners for initial build and knowledge transfer, not indefinite operation — the goal is to internalize capability, not outsource it permanently.
• Embed the digital twin into a formal governance process (such as an IBP Sourcing Board) to ensure outputs drive actual decisions rather than informing offline analysis.
• Begin tracking CO₂ as a metric early, before it becomes an optimization variable — building the data foundation ahead of regulatory or strategic requirements reduces costly rework later.