Standardising SAP Plant Maintenance Data Across 15 Countries
How a global brewing group replaced spreadsheets, e-mail approvals and manual mass updates for five SAP PM object groups with vise Data Run: central governance, local data ownership, 16 expert users in four regional clusters, and an auditable path from Excel template to SAP.
· Visehub · updated 3 November 2026
Case study · SAP Plant Maintenance · vise Data Run · 15 countries, in Europe and beyond · 4 regional clusters · 16 licensed expert users · 5 PM object groups
Most SAP PM problems don’t come from a missing transaction
They come from the process around the master data. A global brewing group — the same group that has run vise DMW since 2017 — running SAP across 15 countries, from Europe to the Caribbean, had the transactions it needed. What it had, market by market and cycle after cycle, was spreadsheets, e-mail approvals and manual mass updates — and that approach had reached its limit. The shift described here is from a one-off clean-up to an ongoing data-quality capability, run by the business.
The challenge: even on one SAP platform, PM data drifts apart
Markets sharing one SAP platform still diverge. Equipment descriptions follow local habits. Functional-location structures differ by country. Maintenance plans go obsolete without anyone noticing. Mandatory attributes are missing in some markets and over-filled in others. All of it was coordinated through spreadsheets, e-mail approvals and manual SAP transactions — and all of it shows up downstream in maintenance scheduling, work-order quality, spare-parts planning and, eventually, in any S/4HANA migration.
No central visibility
No single record of who changed what. Validation applied differently in each market. A weak audit trail that made changes hard to verify or reconstruct.
Repeated manual effort
The same spreadsheet-and-e-mail routine in every market, every cycle. Nothing reusable between cycles or between markets — duplicated effort with no compounding benefit.
Specialists as a bottleneck
A small group of SAP specialists sat between every regional update and the live system: queues, and dependency on scarce expertise.
What we standardise: five PM object groups
The objects that carry the maintenance process, governed centrally with local data ownership:
| # | Object group | Scope |
|---|---|---|
| 01 | Equipment | Master records, attributes, planner groups, work centres, naming |
| 02 | Equipment BOM | Bills of material for technical objects: components, item data, links to parent equipment |
| 03 | Functional locations | Hierarchy, structure indicators, organisational data, naming |
| 04 | Maintenance plans | Scheduling parameters, cycles, items, alignment to central strategies |
| 05 | Task lists | Operations, work centres, control keys, durations, harmonised across markets |
The process: one standardised path for every market
Validated before SAP, controlled during execution, auditable end to end — and business-led. End users prepare and load their own data through standardised Excel templates. No IT ticket per change, no ABAP, no custom development.
| Step | What happens | Who / where |
|---|---|---|
| 01 Prepare | A business user fills a standardised Excel template | Business user |
| 02 Validate | Data is checked against SAP reference data and business rules before it reaches SAP | vise Data Run |
| 03 Approve | Role-based workflow with four-eyes approval | Defined approvers |
| 04 Execute | Dry-run simulation, then load through standard, SAP-supported interfaces | vise Data Run → SAP |
| 05 Verify | Results and the full audit trail are recorded | SAP as system of record |
Why it holds: data is never written directly to database tables, so SAP’s own consistency checks stay active. Every run keeps the source file, the submitter, the validation results, the approvals, the execution status and SAP’s responses.
The operating model: central expertise, local ownership
Sixteen trained, licensed expert users support 15 countries across four regional clusters. Local teams stay responsible for their own data; the expert users coordinate validation and execution through one shared platform.
Self-service
The client’s own expert users run the full process end to end, on a platform already configured for their PM objects and rules.
Or managed
Visehub provides embedded support and runs validation and execution on the client’s behalf — for organisations that want the governance without the operational workload.
The outcomes
- Business-led, no IT dependency. End users create and load their own data through Excel templates; no ABAP or custom development per change.
- Consistent data. The same templates and rules for comparable PM objects in every market — reliable scheduling and reporting across countries.
- Errors caught early. Invalid or incomplete records are stopped before SAP execution.
- Less manual SAP effort. Larger volumes processed without editing each object by hand.
- Full visibility and audit. Every run, validation, approval and execution tracked and reconstructible.
- Experts freed up. Specialists focus on exceptions and governance, not spreadsheet formatting.
- Fast market onboarding. New countries adopt processes already configured and tested elsewhere.
Part of a broader data operating layer
vise Data Run is one piece. vise DMW handles migration and data quality before go-live; vise LoV-MAP handles value governance and mapping. Together they cover migration, mapping, data readiness and recurring BAU maintenance — for SAP modules beyond PM, and for asset platforms such as IBM Maximo.
Build a scalable SAP Plant Maintenance data process
Want to see how each of the five object groups would run through a controlled vise Data Run process in your landscape? Start with a 30-day pilot on one object and one country.