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For laboratories to make strategic decisions, data needs not only to be collected but also to be interpreted. The LIMS/LBYS Reporting and Data Analysis Module integrates all data—from sample acceptance to result approval, equipment suitability to quality records—into a single data model, generating real-time dashboards, customizable reports, and statistical analyses. This module makes cycle times, OOS/OOT rates, capacity utilization, and costs visible; areas for improvement are identified based on evidence. For details: Reporting and Data Analysis.
Real-Time Dashboard KPI & SLA Tracking SPC & QC Charts Data Warehousing & ETL Audit Trail & E-Signature
Who Sees What? Role-Based Boards
Operation Panel
Pending samples/jobs, equipment availability, queues, and daily targets. Practical, real-time visibility for the operator.
Quality Dashboard
OOS/OOT trends, deviation-CAPA status, QC statistics, training and documentation compliance.
Control Panel
Cycle time (TAT), capacity utilization, cost/KPIs, supplier performance, and SLA compliance.
Data Modeling and Integration
The module associates fields from the Sample Management, Analysis and Methods, Quality, and Equipment modules with common keys. Measurements from devices are linked with method versions and acceptance criteria, allowing results to be interpreted within their context. Data can be retrieved from external systems (ERP, QMS, CMMS) via ETL processes; trend analyses are performed while maintaining a historical data track (SCD) in the data warehouse.
Report Types
Report Its purpose Highlights
Operational Reports Daily workload, pending tests, queue management. Live filters, drag-and-drop columns, import/export.
Quality Reports OOS/OOT, deviation–CAPA, training–document compatibility. Root cause breakdowns, closing times, risk scores.
Compliance & Auditing Audit trail summaries, e-signature rates, version control. Audit packages, timestamped event lists.
Finance & Cost TCO per asset, consumable/reactive consumption, labor times. Unit costs, order-invoice matching, budget tracking.
QC and Statistical Analysis
The module monitors method performance using Levey–Jennings control charts, Westgard rules, Shewhart X̄–R/P–NP/C–U charts, and trend/shift analysis. It integrates with regression and linearity checks, R² thresholds, and calibration curves. Automatic alerts and rerun suggestions can be generated for OOS/OOT detections; results are filtered based on method/device/consumable lot context.
Alerts, Subscriptions, and Scheduled Deliveries
Instant alerts are triggered upon exceeding critical thresholds (e.g., TAT target, QC limit violation, device non-compliance). Users can subscribe to reports; scheduled deliveries are received in PDF/CSV/Excel formats. Scheduled reports are automatically generated with desired breakdowns and filters and shared via email or secure connection.
Self-Service BI: Generate Your Own Report
With drag-and-drop field selection, pivot tables, multiple filters, and save-and-share features, teams can create their own views from a single source of truth. The authorization model determines who can see which field/sample, enabling agile reporting without compromising data security.
Export and Sharing
- Output in PDF/Excel/CSV/JSON formats and scheduled delivery.
- Providing data to BI tools (e.g., enterprise data warehouse) via API.
- Proof of immutability through signed reports and version control.
Data Governance and Security
All views are recorded with the audit trail. Role-based authorization, data masking, and field-level authorization are implemented. Data integrity, compliant with ALCOA+ principles, is supported by e-signatures and timestamps. All reports and evidence sets requested during audits can be provided from a single source.
Measure, Manage, Improve: Recommended KPIs
Cycle Time (TCT)
Median/mean TAT, within-SLA results, bottleneck stations.
Quality
OOS/OOT ratio, CAPA effectiveness, number of QC alerts, and violation distributions.
Capacity & Efficiency
Hourly throughput per device/analyst, queue lengths, rework rate.
Cost
Cost per test, consumable consumption, reagent losses, rework cost.
Value through Scenarios
Case 1: When the QC chart shows a Westgard 13s violation, the system flags the relevant method-device-lot combination, alerts the analyst, and suggests secondary testing for verification if necessary. Case 2: When a drop in the TAT %SLA is observed, queue analysis identifies a bottleneck; managers optimize shift/device scheduling based on data. Case 3: An OOT trend is detected in supplier lot changes; quality teams support root cause and CAPA flow with reports.
Suggested Steps for a Quick Start
- Define priority KPIs (TAT, OOS/OOT, rework, capacity).
- Clarify the data dictionary and field mappings; create an ETL/integration plan.
- Design role-based dashboards; implement access and masking rules.
- Define alert thresholds and scheduled report subscriptions.
- Verify in a pilot program; then initiate the corporate rollout and optimization cycle.
Tip: Track KPIs not only with the average, but also with the median and percentile (P90/P95). A few extreme values in the tails can spoil the whole picture; percentiles better capture operational reality.
For more information and module features, visit the official page: Reporting and Data Analysis.
