Two systems into one source of spend truth.
When two companies merge, procurement inherits two of everything. Mithra normalizes both supplier bases and surfaces consolidation savings in weeks, not quarters.
After a merger, procurement inherits two of everything.
Two ERPs, two supplier bases, two taxonomies. Names don't match, and the savings clock is ticking.
It's a people problem too: adoption stalls when staff fear the merger. One trustworthy view of spend moves the conversation from fear to value.
From two unmerged systems to one consolidation plan.
Supplier normalization
Match the same supplier across both companies even when names differ — well beyond fuzzy matching, and it holds up on smaller suppliers.
Material & service harmonization
Unify what's bought across both entities, materials and services handled separately.
Category re-mapping
Rebuild one common taxonomy from two divergent structures, catching mis-booked suppliers.
Consolidation agents
Surface where the merged entity buys the same thing twice — strategic vs. tail-spend aware.
Atlas reporting
Leadership's high-level view on demand: top categories, key suppliers, total spend.
Records management · merged entity
Where is the merged company buying the same thing twice?
Once both supplier bases are normalized, the agents surface every overlap, ranked by value, with the evidence behind it.
- Context-aware recommendationsThe engine won't suggest dropping a strategic supplier for a tail-spend one.
- Every finding carries evidenceThe duplicate contracts, the rate gap, and the estimated annual saving.
- Board-ready prioritizationHighest-value, highest-confidence overlaps first, so the savings clock starts on day one.
Two divergent taxonomies, rebuilt as one.
Mithra re-maps both pre-merger structures into one common tree, catching mis-booked suppliers along the way.
- One common taxonomyBuilt from both source structures, reviewed by exception.
- Mismatches caughtSuppliers booked under conflicting categories are flagged and re-mapped.
- Materials and services handled separatelyBecause their data behaves very differently.
| Supplier | Source category | Mithra category | Spend |
|---|---|---|---|
| Iron Mountain | Facilities (B) | Records & data mgmt | €1.2M |
| Aon | Professional svcs (B) | Insurance brokerage | €3.4M |
| Microsoft | IT / Software (A) | IT & software | €2.1M |
| Mainframe Services | Hosting (A) | Infrastructure | €0.9M |
| Securitas | Office (B) | Guarding & security | €0.6M |
What makes the consolidation reliable.
Clean data first
A cleaning algorithm customized per client — the foundation that makes the agents reliable, and the moat versus DIY.
Built for scale
Homegrown tools break at millions of rows. Mithra serves them performantly against one consistent model.
Context-aware
Suggestions that know strategic from tail-spend suppliers — not naive matches the team can't trust.
Proven at scale
Already serving large multi-entity clients across manufacturing, retail and FMCG.
Clean data and consolidation savings, at enterprise scale.
Energy100% of spend covered and correctly categorized across the group in three weeks.
- Consolidation opportunities surfaced across the supplier long tail
- Overlapping spend ranked by value, ready to action
ManufacturingMaterial and supplier data from multiple entities harmonized into one governed view.
- Duplicate suppliers resolved across entities
- A governed foundation the team maintains, not a one-off cleanup
Post-merger consolidation questions, answered.
Just went through a merger?
See how Mithra unifies two supplier bases, harmonizes categories, and surfaces consolidation savings in weeks, not quarters.