Use Trusted Data to Improve Quality, Asset Performance, and Energy Efficiency
Artha helps manufacturers connect quality, asset, maintenance, energy, IoT, and operational data to support predictive insights, better reporting, and smarter operational decisions.
Modern Solution Brief
Quality control, equipment maintenance, and energy usage are critical manufacturing costs. However, the data needed to optimize them is fragmented—stored in isolated plant lab records, paper logs, equipment files, and utility invoices. Artha unifies these datasets, enabling leaders to trace defects, schedule maintenance proactively, and identify energy optimization opportunities.
Domain Specific Platforms
Unifying sensor feeds, repair archives, and energy logs into structured registries.
Quality Data
- Quality Data Integration Connect lab test databases and sensor logs with production runs to correlate defect trends.
- Scrap & Rework Visibility Track scrap volumes per shift, machine, and material batch to locate excess waste.
- Defect Root-Cause Mappings Unify inspection notes and process histories to isolate variables that trigger faults.
Asset Uptime
- Equipment Telemetry Integration Ingest machine logs and vibrational data to build predictive maintenance models.
- Maintenance History Mappings Track repairs, parts replacement cycles, and machinery lifetimes to optimize scheduling.
- Spare Parts Data MDM Clean and map spare parts records to ensure plants order correct components.
Energy & ESG
- Plant-Level Energy Analytics Track power, water, and fuel usage across shifts and production lines.
- Sustainability Reporting Support Consolidate energy metrics into trusted reports that satisfy regulatory compliance checks.
- Energy Consumption Insights Identify anomalies in machine line consumption to locate inefficient assets.
Frequently Asked Questions
Get answers to questions regarding this solution area.
We build the underlying data pipelines that ingest and format vibrational, temperature, and maintenance telemetry. This structures the data needed to train and run predictive models.
Correlating power usage directly with machine states and production cycles helps supervisors locate lines that waste energy while idling, supporting operational changes.
Ready to Begin?
Connect with our data integration and enterprise platform specialists to scope your project.
