Infor SyteLine

Power BI Integration with SyteLine

Power BI is the most widely adopted third-party BI platform for SyteLine environments, especially for organizations that standardize on the Microsoft analytics stack. Power BI connects to SyteLine's SQL Server database via DirectQuery for real-time dashboards or Import mode for complex analytical models. The key to successful Power BI integration is building a well-designed data model that navigates SyteLine's normalized schema and exposes manufacturing metrics through intuitive visualizations.

Connection Modes: DirectQuery vs. Import

DirectQuery passes DAX queries directly to SyteLine's SQL Server, providing near-real-time data at the cost of query performance. Import mode loads SyteLine data into Power BI's in-memory engine on a scheduled refresh, enabling fast interactive analysis but with data latency. Hybrid models combine both: use Import for large historical tables (SLGlTrans, SLInvTrans) and DirectQuery for volatile operational tables (SLCos, SLJobs) that need current data.

  • Configure DirectQuery connection to SyteLine SQL Server for operational dashboards requiring sub-minute data freshness
  • Use Import mode with scheduled refresh for analytical reports over historical data (financial periods, trend analysis)
  • Implement hybrid model: DirectQuery for SLCos, SLJobs, SLWhseitems; Import for SLGlTrans, SLItems, SLCustomers
  • Set up Power BI gateway for on-premise SyteLine databases to enable scheduled refresh from Power BI Service
  • Configure row-level security (RLS) in Power BI matching SyteLine site/warehouse permissions per user role

Data Model Design for SyteLine

SyteLine's highly normalized schema requires careful denormalization in Power BI's data model. Create a star schema with fact tables derived from transaction tables (SLCos, SLJobs, SLPoitems, SLInvTrans) and dimension tables from master data (SLItems, SLCustomers, SLVendors, SLWhse). Relationships between facts and dimensions use the natural keys (item, cust_num, vend_num) that SyteLine uses as primary identifiers.

  • Build sales order fact from SLCos JOIN SLCoitems with measures: order_qty, unit_price, extended_amount, margin
  • Create production fact from SLJobs JOIN SLJobRoutes with efficiency, scrap, and schedule adherence measures
  • Design inventory snapshot fact from SLWhseitems with qty_on_hand, qty_alloc, reorder_point, and safety_stock
  • Define date dimension table with fiscal periods matching SLGlPeriods for proper financial reporting alignment
  • Set relationship cardinality correctly: one-to-many from SLItems to SLCoitems, SLItems to SLWhseitems

DAX Measures and Manufacturing KPIs

DAX measures transform raw SyteLine data into actionable manufacturing KPIs. Common measures include on-time delivery percentage, production efficiency, inventory turns, order backlog value, and gross margin by product line. Time intelligence functions (SAMEPERIODLASTYEAR, DATEADD) enable period-over-period comparisons critical for manufacturing performance management.

  • On-Time Delivery: DIVIDE(CALCULATE(COUNTROWS(Sales), Sales[ship_date] <= Sales[due_date]), COUNTROWS(Sales))
  • Production Efficiency: DIVIDE(SUM(JobRoutes[run_hrs_t_conv]), SUM(JobRoutes[run_hrs_t])) for actual vs. standard
  • Inventory Turns: DIVIDE([Rolling12M_COGS], AVERAGEX(VALUES(Calendar[Month]), [MonthEnd_OnHand_Value]))
  • Order Backlog: CALCULATE(SUM(CoItems[price] * CoItems[qty_ordered]), CoItems[stat] = "O") for open order value
  • YoY Revenue Growth: DIVIDE([Revenue] - CALCULATE([Revenue], SAMEPERIODLASTYEAR(Calendar[Date])), CALCULATE([Revenue], SAMEPERIODLASTYEAR(Calendar[Date])))

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