SyteLine & CloudSuite Industrial data, extracted properly
The hard part is never the chart — it is getting job, material and shipment records out of SyteLine on a schedule. A senior US-based engineer settles the extraction route, models it, and puts job cost, on-time delivery and margin by part on one page that refreshes itself.
Representative dashboard — sample data
Extraction first, visuals second
An Infor SyteLine Power BI dashboard succeeds or fails on one decision made in the first hour: how the data leaves the ERP. SyteLine — sold today as CloudSuite Industrial, or CSI — keeps every job, material issue, receipt and shipment in a Microsoft SQL Server database, but what you can reach depends entirely on whether you run on-premises, single-tenant, or multi-tenant in Infor's cloud. That answer, not the chart type, decides the whole build.
So we start there. We agree the access route with your Infor administrator, land the records incrementally, shape them into a star schema keyed on job, part, site and date, and schedule the refresh. What comes out is job and work-order costing, on-time delivery, margin by part and customer, and inventory that agrees with the ERP — from one model instead of five exports.
What we build from SyteLine
Buy a block of prepaid Flex hours and, if you're unhappy within the first 4 hours, we refund you in full — no questions asked. We scope each task and quote the hours before work starts, hours deduct only for work you approve, and your banked hours never expire.
Why SyteLine builds stall
Every problem below is an extraction or modeling problem. None of them is solved by picking a better chart, and all of them are what a senior engineer is actually being paid for.
On-premises sites can usually hand over a read-only SQL replica. Multi-tenant CloudSuite generally cannot, and points reporting at the Infor Data Lake or the IDO web interface instead. We establish which of these your subscription allows before a single hour is spent modeling.
SyteLine is multi-site by design, so the same part or customer number can live in several sites with different costs behind it. Site becomes an explicit dimension in our model and shared master records are agreed up front, so a group roll-up is not a double count.
A job is not one row. Cost accumulates by job and suffix, by operation and by material line, and estimates live apart from the actuals. Getting that grain right is what lets a margin number on the summary page drill to the operation and the transaction that caused the variance.
Years of material and labor transactions cannot be pulled from scratch every morning. We key the extract on the row and record-date columns your version populates, so each refresh moves only what changed and the ERP is never asked for its whole history again.
The BI content bundled with CloudSuite is modeled on Infor's standard structures. The moment you need a user-defined field, a costing rule it does not carry, or SyteLine joined to CRM or shipping data, you are past its edge — which is usually the week someone calls us.
Through whichever route your deployment supports: a read-only replica of the SQL Server database, the Infor Data Lake for multi-tenant CSI, the SyteLine IDO web interface for scoped application-layer reads, or a scheduled extract into a landing database. We land the records, model them, and refresh on a schedule — the report never queries production live.
Which one you get is not a preference, it is a fact about your subscription. Older on-premises SyteLine and single-tenant cloud instances are typically willing to expose a read-only replica, and that is the best case: full SQL access, long history, and cheap incremental loads. Multi-tenant CloudSuite Industrial generally does not hand out direct database access at all — Infor's own reporting path there runs through the Data Lake, reached with its published query interface, which usually means landing the results somewhere Power BI can refresh from on a schedule. The IDO web interface sits between the two: it reads through the application layer, so business logic and security are respected, but it is better suited to scoped, moderate-volume pulls than to a decade of transactions.
Whichever route we take, the pattern downstream is the same. We land a copy, run it incrementally so each refresh moves only changed rows, and let Power BI query the modeled copy rather than the ERP. That keeps refresh inside the documented limits — Microsoft's incremental refresh reference (opens in new tab) explains how the partitioning works — and where the source sits on your own network, an on-premises data gateway (opens in new tab) carries the connection. If your workspace runs on capacity, we can also expose the finished semantic model through its XMLA endpoint (opens in new tab), so your own team can query or extend the model with standard tooling instead of asking us for every change.
This is the honest comparison behind the scope estimate you approve before any hours are spent. We confirm the route with your Infor administrator first, because the wrong assumption here is the one that turns a five-day build into a five-week one.
| Route out of SyteLine | Best for | Tradeoff |
|---|---|---|
| Read-only SQL Server replica | On-premises and single-tenant sites; long history and cheap incremental loads | Needs DBA cooperation and, on your own network, a gateway |
| Infor Data Lake (multi-tenant CSI) | Cloud CloudSuite Industrial, where direct database access is not offered | Reached through Infor's query interface, so results usually land in a staging database first |
| SyteLine IDO web interface | Scoped reads that must respect application logic and user security | Application-layer volume limits make it a poor fit for full transaction history |
| Scheduled extract to a landing database | Any instance where the route above is throttled or restricted | Adds a moving part that has to be monitored and documented |
| Landed copy + star schema | Every production build, on top of whichever route above applies | Adds a storage layer — which is the point, not a cost |
Every view below maps to records your instance already holds. Table names follow the standard SyteLine schema; your version, your modules, and any user-defined fields your team has added change the detail, so we confirm the mapping against your own database before quoting the work.
| Dashboard area | SyteLine / CSI records it reads | Question it answers |
|---|---|---|
| Job & work-order cost | Job headers and their material and routing detail (job, jobmatl, jobroute), plus posted labor and material transactions |
What has this job actually consumed, by operation and material line? |
| Estimate vs actual variance | Estimated cost on the job and routing records against posted actuals, at the job-and-suffix grain | Which open jobs are running over estimate, and where did it start? |
| On-time delivery | Customer order headers and lines (co, coitem) with promise, request and actual ship dates |
Are we hitting the date we promised — and the one the customer asked for? |
| Margin by part & customer | Order lines and invoiced revenue against item cost records (item) and the customer master (cust) |
Which parts and accounts are actually earning, once cost is attached? |
| Inventory & material | Item master with per-site quantities and locations (item, itemwhse) and material transaction history |
What do we hold by site, what is aging, and what is short against demand? |
| Purchasing & vendor performance | Purchase order headers and lines (po, poitem) with receipts and the vendor master (vendor) |
Which suppliers deliver complete, on time, and at the agreed price? |
The same handful, on nearly every engagement. None of them is exotic; all of them quietly produce a number that looks reasonable and is wrong, which is worse than a report that obviously fails.
This page is about the ERP record: what a job cost, when it shipped, and what the part earned. Machine-level analysis is a different data source and a different build — if you want OEE, downtime reasons and scrap from an MES or historian, start at Power BI for manufacturing and logistics instead.
The two are complementary and often sequential. Most SyteLine clients start with the financial and delivery picture, because it comes from a system that is already the book of record and needs no new instrumentation, then add shop-floor telemetry later once the cost model is trusted. Running it the other way around tends to produce a beautiful line-side screen that nobody in finance believes. If you also run a second ERP in the group, our Epicor Power BI dashboard page covers the equivalent build on Epicor Kinetic and ERP 10 — a different system with the same job-costing questions — and because SyteLine sits on SQL Server, our SQL Server Power BI dashboard page explains how we build directly on a database replica.
Discrete manufacturers, job shops, make-to-order and mixed-mode plants running SyteLine or CloudSuite Industrial — usually with a controller or operations leader tired of rebuilding the same workbook. You buy prepaid Flex hours that never expire: Starter $2,500, Growth $4,600 or Power $8,000, with rates from $100/hour on the largest block.
Most tasks ship in 3–5 business days once you approve the scope estimate; a first ERP build is larger and gets a clear timeline up front. The usual pattern is a Growth or Power block for the extraction route, the model and the first pages — that work is front-loaded — then the remaining hours spent on extra views, DAX changes and refresh care. There is no project minimum and no retainer: you send a request, we reply with a scope and the hours it needs, and hours deduct only for work you approve. The mechanics are on how prepaid Flex hours work, and the modeling discipline behind every build is described on our Power BI data modeling and DAX page.
Everything. The Power BI file, the semantic model and DAX, the extraction logic, the refresh schedule and the service account all live in your tenant and your Infor instance. There is no black box, no licence to us and no lock-in — if you never send another request, the dashboards keep running.
We publish to your workspace, document the access route and every record we pull, and walk your team through the model so someone internal can maintain it. Most clients then keep a few banked hours for what follows: a page for a newly opened site, a measure that has to track a changed costing method, or a refresh that broke when credentials rotated after an Infor upgrade. That is post-launch support without a retainer — the same hours, spent when you need them.
business days, typical
hours, never expire
own everything
Extraction, modeling, DAX and refresh care all draw from the same banked hours. See Flex pricing →
How it works
Tell us your version, whether you run on-premises, single-tenant or multi-tenant CloudSuite, how many sites you report on, and the views you need first. We check what your administrator can actually open up, then reply with a scope and an hours estimate before any work begins.
A senior US-based engineer sets up the access route, lands the records incrementally, builds the star schema on job, part, site and date, and writes the DAX — then ties job cost and shipment totals back to the ERP reports your team already trusts before anyone sees a chart.
We publish to your workspace, configure the gateway and scheduled refresh, document the extraction route and the model, and hand everything over. You own it outright; we keep the refresh healthy from your banked hours whenever you want us to.
Representative perspective
Three sites, three versions of the same part number, and a group margin report nobody trusted. Fixing the site key mattered more than anything that ended up on the screen.Controller, multi-site discrete manufacturer
Representative perspective
We knew job variance a week after the job closed, which is the same as not knowing it. Seeing it while the job is still open changed what the estimators do.Operations manager, make-to-order job shop
Tell us your SyteLine version, your deployment and the views you need. We'll confirm what your instance can give us, scope the task and quote the hours before any work begins — drawn from prepaid Flex hours that never expire, with no project minimums.
FAQ
There is no first-party Infor connector in Power BI, so the route depends on your deployment. On-premises and single-tenant sites usually give us a read-only SQL Server replica. Multi-tenant CloudSuite Industrial normally routes reporting through the Infor Data Lake or the IDO web interface instead. We confirm what your subscription and administrator allow before quoting hours.
Yes, and it is usually the first thing we fix. SyteLine is multi-site by design, so the same item, customer, or vendor number can exist in more than one site with different costs attached. We add site as an explicit dimension, agree which master records are shared, and label every measure so a group total is never an accidental double count.
The analytics content bundled with CloudSuite is modeled on Infor's standard structures, which is exactly where it stops. Most teams call us once they need user-defined fields, a costing rule the standard content does not carry, or SyteLine joined to a CRM, shipping, or payroll system. Power BI takes the same records further without replacing what already works.
Twice, deliberately. One measure compares the actual ship date against the date you promised on the order line; a second compares it against the date the customer originally requested. They answer different questions and usually produce different percentages, so we build both, label them plainly, and let you decide which one goes on the leadership page.
No. We are an independent, US-based Power BI practice and claim no Infor or Microsoft partnership. Our engineers are Microsoft-certified, and we build only with documented, supported tooling: the access route your Infor administrator approves, plus Power BI's published gateway, incremental refresh, and semantic model features.
Book a free demo
Tell us the task. We scope it, you approve, and a senior consultant delivers — typically in 3–5 business days. Prepaid hours that never expire.
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