How to connect Power BI to MRPeasy
Short answer
Because MRPeasy is hosted with no direct database access, connect Power BI through the REST API using Power Query, or better, load API data into a warehouse on a schedule and point Power BI there. Build dashboards for on-time delivery, shortages and inventory, then add natural-language Q&A on top.
MRPeasy has solid built-in reports, but owners and finance leads often want blended views: sales against capacity, inventory against open demand, or on-time delivery by customer across years. That is where Power BI earns its place.
The constraint is the lack of a database connection. Unlike on-premise ERPs, you cannot point Power BI at a SQL server. You have three real routes: scheduled report exports, direct API calls from Power Query, or a pipeline into your own database.
The best route depends on volume. A small shop can live with API-based refreshes. A shop with years of history and many lines will want a warehouse so refreshes are fast and the vendor API is not hit repeatedly.
What teams use it for
On-time delivery by customer
Compare promised and actual ship dates per customer and product family, and trend them by month.
Material shortage board
Show components short for the next four weeks of manufacturing orders, with vendor and expected receipt date.
Inventory health
Months of cover, slow movers and stock value by category against open demand.
Margin by product
Join sold price with BOM and routing cost to see where margin is thinning.
Work center load
Scheduled hours against available hours by week, to spot bottlenecks before they bite.
Purchasing performance
Vendor on-time rate, lead time drift and price changes over time.
MRPeasy integration surface
- REST API with key and secret authentication, callable from Power Query using the Web connector with headers set in a query function. Confirm endpoints, pagination and field names in your account documentation.
- Built-in report and list exports to CSV or Excel, which can be dropped on SharePoint or OneDrive and read by Power BI as a simple, lower-fidelity route.
- No direct SQL access to the hosted database. Any claim of a live connector to the vendor's tables should be treated with caution.
- API rate limits and pagination, which make full history refreshes slow. Incremental refresh by modified date is worth the effort.
- A warehouse such as SQL Server, PostgreSQL or Azure Synapse filled by a scheduled job is the stable middle layer.
How it works
- 1
Choose the data path
Exports for a quick start, Power Query on the API for a mid-size shop, a warehouse pipeline for anything with lots of history or many reports.
- 2
Design the model
Build fact tables for order lines, manufacturing order components, purchase order lines and stock movements, with dimensions for item, customer, vendor and date.
- 3
Extract on a schedule
A small job calls the API with modified-since filters and upserts into the warehouse, keeping a log of each load.
- 4
Connect Power BI
Use Import mode against the warehouse. If the warehouse is on-premises, install the on-premises data gateway on a server that can reach it.
- 5
Build core dashboards
Start with delivery performance and shortages, check numbers against MRPeasy reports, and only then expand.
- 6
Add Q&A
Tidy field names and synonyms in the model so Power BI Q&A or Copilot, where licensed, can answer plain-language questions, or add a separate chat layer over the warehouse.
Example flows
Nightly API-to-warehouse load
Trigger: A scheduled job at 02:00.
- Call the API for orders, purchase orders and stock changed since the last run.
- Upsert rows into staging then fact tables.
- Record row counts and any failures.
- Trigger a Power BI dataset refresh.
Result: Dashboards open each morning with yesterday's data without anyone exporting a file.
Ask a question of the dashboard
Trigger: A manager types a question in the report's Q&A box.
- Q&A maps the words to model fields and measures.
- A visual is returned, such as late orders by customer this quarter.
- The manager pins it to a dashboard.
- The data team reviews synonyms for any question that missed.
Result: Common questions no longer wait for an analyst to build a report.
Keeping data on-prem
MRPeasy data originates in the cloud, but the reporting stack can still be private. Pull data into a warehouse inside your own network or tenant, then use Power BI Desktop and an on-premises data gateway, or Power BI Report Server if licensing allows, to keep reporting close to your controls. For natural-language questions where data cannot go to a cloud AI service, put a locally hosted model over the same warehouse with read-only database credentials, so questions are translated to queries that run on your infrastructure. The vendor API call that fills the warehouse is the only outbound dependency. Check Power BI licensing, since Report Server and Q&A or Copilot features depend on the licence tier.
Security checklist
- Use a read-scoped API key for the extract job and store it in a secrets vault.
- Apply row-level security in Power BI for users who should only see their region or customer set.
- Restrict warehouse access to the load job and a read-only reporting account.
- Review who can export data from published reports and datasets.
- Document the refresh schedule and keep load logs for audit.
Ways to get it done
Scheduled exports into SharePoint read by Power BI
Effort: Days
Small shops needing a few simple dashboards fast.
Power Query directly on the API
Effort: 1-2 weeks
Mid-size data and one analyst comfortable with M code.
Warehouse pipeline, with or without Netray
Effort: 2-6 weeks depending on scope
Larger history, many reports or a need for private natural-language Q&A.
Pitfalls
- !Hitting the API from every report refresh. Centralise extraction and respect rate limits.
- !Reporting off exports with different filters each time, producing numbers that cannot be reconciled.
- !Forgetting deleted or cancelled records. Plan for status changes and soft deletes in your upsert logic.
- !Mixing time zones for promised and actual dates, which distorts on-time metrics.
- !Publishing dashboards before reconciling totals with MRPeasy's own reports.
Frequently asked questions
Is there an official MRPeasy Power BI connector?
We cannot confirm a native certified connector, and offerings change. Check the vendor's integrations list and the Power BI marketplace. Otherwise use Power Query over the REST API, scheduled exports, or a warehouse fed by the API.
Do I need an on-premises data gateway?
Not for pulling directly from a cloud API in Power Query, in most setups. You need the gateway when the data sits in a warehouse or file share inside your network and the Power BI service must refresh from it.
Can I get real-time dashboards?
Near-real-time at best. API limits and refresh frequency mean intervals of minutes to hours are realistic. For a live shortage view, a webhook-driven or frequent incremental load into the warehouse works better than full refreshes.
Which dashboards should I build first?
On-time delivery and material shortages, because they connect directly to customer promises and daily planning. Then inventory cover, and margin by product once costing data has been reconciled with finance.
Can staff ask questions in plain English?
Yes, via Power BI Q&A or Copilot where licensed, which depend on a clean semantic model. For private or more flexible questions, a locally hosted model over the warehouse can generate and run read-only queries.
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