SyteLine BI and Analytics Dashboard Guide

Analytics Architecture for SyteLine

SyteLine analytics can be delivered through three approaches: Infor Birst for embedded, pre-built manufacturing analytics; direct database connections from tools like Power BI or Tableau; or a data warehouse that consolidates SyteLine data with other sources. The right approach depends on your analytics maturity. Start with Birst for standard manufacturing KPIs, add Power BI for custom departmental dashboards, and invest in a data warehouse only when cross-system analytics become a priority.

• Start with Infor Birst pre-built dashboards for standard manufacturing KPIs with minimal setup effort
• Use Power BI with DirectQuery for departmental dashboards requiring custom calculations and visuals
• Build a data warehouse when analytics need to combine SyteLine data with CRM, MES, or IoT sources
• Implement row-level security in analytics tools matching SyteLine's site and company security model

Manufacturing KPI Design

Effective manufacturing dashboards focus on actionable KPIs, not vanity metrics. The top KPIs for SyteLine environments are: on-time delivery (OTD), schedule adherence, first pass yield, inventory turns, and capacity utilization. Each KPI should have a clear definition, data source, calculation method, target, and alert threshold.

Avoid dashboard overload, 5-7 KPIs per dashboard with drill-down capability is more effective than 50 metrics on a single screen.

• Define each KPI formally: name, calculation formula, data source, target, and alert threshold
• Limit dashboards to 5-7 KPIs with drill-down paths for detailed investigation when needed
• Align KPIs to organizational levels: executive summary, plant manager detail, supervisor operational
• Include trend analysis (rolling 12 months) alongside current values to show improvement trajectory

Self-Service Analytics Enablement

The highest-value analytics outcome is a self-service culture where planners, supervisors, and managers can explore SyteLine data independently. This requires curated data models that present SyteLine's complex schema in business-friendly terms, training on analytics tools, and governance to ensure data consistency. Published datasets with documented metrics prevent the dashboard sprawl and conflicting numbers that undermine analytics credibility.

• Create a semantic data model that translates SyteLine's technical schema into business-friendly dimensions
• Publish certified datasets with defined metrics to prevent conflicting calculations across dashboards
• Train power users in each department to build and maintain their own dashboards and analyses
• Establish dashboard governance: naming standards, refresh schedules, ownership, and retirement criteria