Choosing locations, assigning customers, placing products — in an industry where cold chain, regulatory approval and lead time are non-negotiable. W2MO Supply Chain Engineering puts these decisions on a data-driven foundation.
Total network cost through optimised site choice and assignment.
more customers reachable within the committed 24h/48h window.
Data-driven decisions with comparable, side-by-side scenarios.
Five typical situations from day-to-day management — you will recognise at least one of them.
M&A consolidation without a systematic network analysis: sites were inherited, not strategically chosen. Which one stays, which one goes, who gets which products — the question is there, the answer is not.
One product 2–8 °C, another below −20 °C, a third at room temperature — and on top of that, marketing authorisations that only allow certain goods from certain locations. Every reshuffle becomes a regulatory project.
Growth in a market accelerates, existing sites are bursting at the seams, transport costs are climbing. A new location is overdue — but the decision rests on two slides and an Excel sheet.
Pharmacy wholesalers are supplied from the warehouse they have „always" been supplied from — not from the one that is closest today, or the cheapest.
Pandemics, geopolitical tensions, tariff changes: every supply chain that looks stable today can become a bottleneck tomorrow. Without scenario modelling, resilience stays a claim.
Once the sites are set, the actual work happens in the assignments — and in pharma those assignments carry regulatory, medical and commercial consequences.
Which customer is served from which warehouse? That decision drives transport cost, lead time and service level — and depends on more than just distance.
Which product is stocked at which site? In pharma this carries more weight than elsewhere — authorisations, temperature zones and serialisation obligations all weigh in.
Six topics that come up in almost every first conversation — usually several in parallel.
Four steps that have proven themselves in comparable projects. Typical project duration: 6 to 12 weeks — depending on data availability and complexity.
Digital twin of your existing network: sites, customers, product flows, costs. Data usually from Excel or CSV exports — or via W2MO connectors directly from SAP and other systems if preferred.
Mathematical optimisation with no constraints: what would the best solution be if nothing got in the way? Centre-of-gravity analysis and a location-candidate grid produce the theoretical cost optimum as a benchmark.
Adding constraints: capacities, regulatory approvals, cold-chain requirements. Up to four models in parallel in the scenario manager — consolidation, expansion, near-shoring, resilience.
Consolidation, recommendation, implementation roadmap. A solid basis for the board decision — and, if wanted, a handover into a site-design model for the selected warehouses.
The result is not a one-off network decision, but a living model of your supply-chain network — updatable any time markets, portfolio or regulatory boundaries change.
Network decisions involve many stakeholders — procurement, sales, regulatory, finance. With AI assistance, the model becomes a conversation partner instead of an expert tool.
„What happens to transport cost if we close the Hannover site?" or „Which products would have to be stocked redundantly in two locations?" — answers directly from the digital twin.
The MCP architecture lets AI models access the network digital twin, scenario data and optimisation algorithms directly — no manual interface work.
Pharma data is often spread across several systems — ERP, regulatory databases, cold-chain monitoring. AI-generated mappings accelerate consolidation and surface data gaps early.
Connect any LLM — Claude, ChatGPT, Gemini or your own — through the W2MO MCP server. You choose the model, you control the data. No vendor lock-in.
Values from comparable pharma projects over recent years. The exact range depends on the size and structure of the network.
logistics cost across the network through optimised sites and assignments.
more customers reachable within the committed service-level window.
transport kilometres and the CO₂ emissions tied to them.
compared in parallel in the scenario manager — cost, service, resilience, CO₂.
For an annual transport budget in the mid double-digit million range, even 10 % network optimisation potential translates into a single-digit million contribution per year — durably, not as a one-off. On top of that come effects that are harder to quantify but often more important: higher service reliability, lower compliance risk and stronger resilience against disruption.
The ranges above depend heavily on the size and structure of the existing network. A first assessment based on rough indicators is possible within a few days.
From the network model to site design in a single platform — end to end, on the same data foundation.
Digital twin of the network with as-is analysis and scenario optimisation.
Tactical planning of volume flows across the optimised network.
Up to four network models kept in sync and directly comparable.
Bridge from network to individual sites with volume and flow data.
Five topics that come up in almost every preliminary conversation.
Yes. Experience shows that data gaps are the rule, not the exception. The W2MO smart-data logic detects gaps and inconsistencies systematically and proposes plausible corrections. Data is usually supplied via CSV exports — a direct connection to SAP or other systems is possible, but not a prerequisite. The project start does not depend on perfect data — only on knowing what we are computing with.
Regulatory requirements are configured as constraints — which SKUs may be stored at which sites, which temperature class is required, which markets may be supplied from which site. The system itself has no opinion on the underlying regulations; maintaining those rules sits with you and your regulatory team. Once captured, the model only computes variants that respect them — non-compliant assignments are excluded automatically.
Yes. W2MO's non-relational data structure and parallelised algorithms are built for exactly this kind of volume. Networks with hundreds of millions of order lines and large customer bases are a standard case, not an edge case.
Yes — that is in fact the central idea. The scenario manager keeps up to four models in sync in parallel. Customers typically compare, for example, the status quo, a consolidation variant, an expansion variant and a resilience variant. Filters by product or customer groups allow additional regional or category-specific analyses.
In the network model we define sites, volumes and requirements. For selected sites that data can be carried over into a site-design model — volumes, inbound and outbound flows and service requirements are available as a starting point. This is not automatic; it is a deliberate next project step that you initiate. The benefit: you work in one platform throughout — from the strategic network model to the operational warehouse layout.
In 60 minutes we'll walk you through W2MO on a pharma-logistics example — with your typical flows and KPIs. No slides. The real twin.
30 minutes, focused on your network situation — free of charge and without obligation.
Book a slot →The real twin on a pharma example — scenarios compared live in the model, not on slides.
Request a demo →„Network design in pharma" — approach, data requirements, worked examples.
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