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Adopted daily by maintenance teams running mission-critical SAP operations at scale
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Case Studies
BRF Shares Their Maintenance Transformation
An Interview with Saint Gobain
Turning Around a Reactive Maintenance Environment at Ingredion
Industry Insights to Fuel Your Results
Asset-intensive businesses to achieve their EAM digital transformation
01. What are maintenance analytics?
Sigga’s maintenance analytics go beyond static SAP reports by surfacing execution-level insights such asreal time spent versus planned hours, user adoption trends, unrecorded activity and performance gaps. These advanced dashboards reveal what actually happened on the floor and where the gap between plan and reality is costing productive time.
02. How does Sigga Analytics help maintenance managers find hidden workforce capacity?
Sigga compares actual labor confirmations against planned hours at the work order level, surfacing tasks where estimates are consistently over or underestimated. This identifies where capacity is being absorbed by planning assumptions rather than real work, giving managers the data to recalibrate labor standards, redistribute workload and recover productive hours that were previously invisible in aggregate SAP reports.
03. How does Sigga Analytics support user adoption and continuous improvement initiatives?
Sigga tracks system usage week over week across teams, showing which users and groups are actively engaging with workflows and where adoption is lagging. This gives implementation leaders an evidence base for targeted follow-up, training adjustments and process corrections before low adoption becomes a data quality problem that undermines the broader maintenance improvement program.
04. How does maintenance analytics accelerate the path toward predictive maintenance?
Every work order, form submission and mobile interaction captured through Sigga feeds a growing data layer in SAP that analytics dashboards translate into actionable patterns like deviation trends, recurring failure signatures and productivity benchmarks that condition-based and predictive strategies depend on. Organizations that start with execution analytics build the structured data foundation that makes the transition from corrective to predictive maintenance achievable rather than aspirational.