Dataraft in Action: 7 Real-World Industrial Analytics Use Cases

1. Why Dataraft for Industrial Analytics

Industrial teams are drowning in sensor feeds, simulation outputs, test rigs, and Excel trackers, but still rely on intuition and spreadsheets for critical decisions. Dataraft gives engineers and operations teams a no-code way to explore data, build predictive models, and optimize parameters without waiting for a central data science team.

Dataraft combines automated regression modeling, optimization engines, and GenAI-assisted “ask the data” analytics so domain experts can move from “what happened?” to “what should we do next?” in hours, not weeks.

2. Optimizing Steel Pellet Plants with AI/ML

Pellet plants sit at the heart of integrated steel manufacturing, where a small improvement in pellet strength or fuel efficiency drops straight to the bottom line. Dataraft’s AIML-driven optimization framework was deployed at a steel pellet plant to maximize pellet strength (targeting 250–300 units) while minimizing gas consumption.

The plant provided 210 samples across 68 parameters (ore characteristics, furnace settings, operational data), collected twice per shift over 35 days. Dataraft automatically profiled the data, surfaced rule/statistical alerts, and proposed the top three analyses for optimizing pellet strength and gas usage.

Key steps:

• Conversational data exploration to understand strength vs. gas trade-offs and raw material impact.

• Sensitivity analysis with multiple ML techniques to rank the most influential parameters on pellet strength.

• Correlation views showing feed rate directly proportional to pellet strength and burn-through inversely proportional to gas consumption.

Optimization results:

• Model accuracy within a 10% error band despite short data history.

• Pareto front showing trade-offs between strength and fuel; “best-of-both” setting delivered pellet strength of 261.1 with fuel reduced from 29,047 to 22,122 units.

3. Cell Analytics for Battery Health and Remaining Useful Life

Lithium-ion cell performance drives warranty risk, safety, and TCO in EVs and energy storage. Dataraft’s Cell Analytics use case uses NASA’s battery dataset with four cells (B0005, B0006, B0007, B0018) under standard CC–CV charging at 1.5 A and discharging at 2 A down to cell-specific cut-off voltages.

Typical questions answered:

• Are all cells charging correctly to rated voltage and discharging correctly over cycles?

• Which cell shows faster capacity fade and is likely to fail first?

• How do max temperature, average voltage, and charge current influence capacity fade?

Dataraft flow:

• Configure a study with capacity fade as the target and cycle number, average voltage, average charge current, and max temperature as predictors.

• Apply domain rules (e.g., EOL at 30% fade from 2 Ah to 1.4 Ah) and automatically flag rule violations.

• Use heatmaps to reveal that max temperature impacts fade, but average discharge voltage has the strongest correlation.

Outcome:

• For B0005, Dataraft shows stable 4 V discharge voltage while capacity gradually drops, accelerating after ~35 cycles.

• For four-cell comparison, all cells charge to full voltage, but B0006 is identified as discharging faster than others. ​

• The model quantifies cycles to reach 50% capacity for each cell, enabling proactive replacement strategies.

4. Engineering Analytics: From Simulation to Road-Test Feedback

Dataraft is already used in engineering organizations to squeeze more value out of simulation data, BOMs, and test results. Several short-form use cases can be stitched into one narrative around Design Analytics.

4.1 Spot Weld and MDO Optimization

Using simulation data, Dataraft’s MDO (multi-disciplinary optimization) capabilities identified sensitive parameters in body structures and achieved mean error within 2.5%.

For spot weld optimization, engineers achieved:

• 4 fewer spot welds in 50% of the time using one-third of the samples compared to the earlier method.

• Compounded savings at vehicle volumes through reduced welds and reduced simulation runs.

4.2 Curb-Weight Prediction from BOM Data

By combining BOM attributes with competitor benchmark data, Dataraft predicted target curb weight with ~5% error. This allowed engineering teams to estimate weight for planned dimensions and steer design targets without complete CAD or test data.

4.3 Road-Test and NVH Analytics

Using road test data, including OBD channels and derived features, Dataraft built models to predict clutch failure and identify the most reliable failure-prediction strategy. NVH/customer feedback logs (buzz, squeak, rattle) were converted from free-text narrations into structured signals using text analytics, helping teams detect quality degradation patterns during warranty life and improve JD Power-type scores.

5. Industrial Automation & Preventive Maintenance

Dataraft also underpins workflow automation and predictive maintenance across industrial sectors, combining IoT, SCADA, and business data.

5.1 Automated Regression and Optimization Engine

At platform level, Dataraft provides:

• Automated regression modeling: users select dataset and target (e.g., sales, inventory, maintenance hours) and receive tuned models with metrics, feature importance, and what-if analysis.

• Optimization engine: users define objectives and constraints; Dataraft searches for optimal parameters and can push corrections back to processes via APIs.

5.2 Lens Manufacturing Workflow Automation

For a large US lens manufacturer, Dataraft supported a web-based automation portal that replaced Excel-based job tracking and reduced per-job turnaround from 1.5–2 hours to around 5 minutes. The solution removed expensive PLM licenses for some users, improved productivity tracking, and provided a single point of access for job status.

5.3 Engineering Drawing Review Workflow

A leading engineering manufacturing solutions provider used Dataraft to digitalize drawing and document review workflows. The goals were traceability of feedback, standardized commenting, and digital sign‑off for manuals, drawings, and illustrations. The responsive web application reduced review cycle times and centralized historical feedback for future reference.

5.4 Oil & Gas: Well Monitoring and Preventive Maintenance

In midstream oil and gas, Dataraft powers a preventive maintenance and well-status monitoring solution. The system:

• Ingests minute-level data for ~45 wells from OSI PI Server, cleans it using statistical outlier handling, and pushes it into MSSQL.

• Uses PowerApps/PowerBI front-ends with role-based views and single-select filters across screens.

• Raises automated alerts based on dynamic thresholds for key indicators, and correlates well-health with chemical analysis of water samples.

Benefits:

• Fully automated architecture for continuous visualization of pressure, quantity, and filter performance.Automation-use-cases.pptx​

• Reduced operating cost via early well-failure alerts and better maintenance scheduling.

5.5 Advance Warning for Blood Separation Devices

For a medical devices setup, Dataraft consumes device logs and alarms into a central database and runs:

• Correlation and segment analysis.

• Time-series decomposition and Isolation Forest-based anomaly detection.

• Similar-issue matching across devices and trend‑based advance warnings.

This enables proactive service interventions before device downtime impacts hospital operations.

5.6 IoT-Based Process Optimization Loop

A Dataraft-powered Azure IoT architecture demonstrates closed-loop optimization:

• Data from SCADA/PLCs, sensors, machines, and SAP/ERP/CRM is ingested via MQTT into Azure and Dataraft.

• Dataraft Optimizer proposes parameter value combinations; Rapid Modeller compares current vs. optimized values.

• Feedback controllers apply nearest optimized parameter sets, while new data feeds the next optimization cycle.

This pattern is generic enough for continuous-process industries—steel, cement, chemicals, food processing.

6. Oil & Gas Analytics: From Porosity to Integrated Asset Optimization

The dedicated oil & gas deck gives you another rich blog (or a deep section in this one) showcasing Dataraft’s domain-adapted analytics.

6.1 Predictive Maintenance of Equipment and Pipelines

Dataraft (or combined with platforms like GE Predix) uses compressor loading, pump RPM, turbine and heater-treater thermodynamics to predict failures and schedule maintenance. Telemetry from pipeline sensors (pressure, flow) is pattern-matched against healthy sections to target pigging and inspections, replacing purely reactive maintenance.

6.2 Insights on Wells and Artificial Lift Performance

For wells that undergo multiple design changes and artificial lift deployments, Dataraft uses reservoir/well data to:

• Perform decline-curve analysis under current conditions.

• Identify key influencers like DaysOld, average wellhead temperature, choke size, and wellhead pressure.

• Run what-if analysis to test production strategies without physical interventions.

Rule-based insights (e.g., specific combinations of choke size, pressure, and DaysOld leading to desired vs. poor production) guide field teams in selecting ALS configurations.

6.3 Porosity Estimation from Neutron/Thermal Logs

Neutron and thermal neutron logs are fed into Dataraft as predictors for NPOR (measured porosity). ML classification achieved ~85% accuracy on unseen data, binning porosity into poor/good/desired intervals and mapping them across depth.

Insights include:

• 50–75% of the logged section with porosity 9–14% and streaks up to ~27%.

• Scatter plots showing NPOR inversely related to far-neutron counts and positively related to thermal neutron porosity, consistent with physics.

6.4 Integrated Asset Management and Optimization

The deck also sketches how big data platforms (Hortonworks, Azure, GE iFIX) plus ML/NN can replace time-consuming mechanistic simulators. Dataraft can sit inside this architecture to:

• Integrate seismic, logging, and PTA data to pick optimum drilling locations.

• Combine reservoir, drilling, and production data to optimize field development plans.

• Optimize process terminals and refineries based on cost functions and throughput objectives.

7. Dataraft platform capabilities under the hood

Across all these use cases, several common Dataraft features recur:

• No-code exploration: automatic data profiling, rule/statistical alerts, and “tell me what I can do with this dataset” suggestions.

• Multi-model sensitivity analysis: robust ranking of influential variables (e.g., pellet strength drivers, well productivity factors, battery degradation factors).

• Advanced optimization: multi-objective algorithms like NSGA-II for strength vs. fuel or conflicting KPIs in other processes.

• Industrial integration: connectors to SCADA/PLCs, OSI PI, SAP/ERP, and Azure IoT; APIs for closed-loop corrections.

• GenAI assist: text-to-analysis prompts, SQL/analysis suggestions, and dashboard creation without coding.

The industrial world is moving fast—from reactive spreadsheets to proactive, AI-augmented decision-making. The seven use cases above show what’s already possible today when domain experts get direct access to powerful no-code analytics, automated modeling, multi-objective optimization, and GenAI assistance.

Dataraft isn’t just another dashboard or BI tool. It’s the bridge that lets engineers, operations leaders, and plant managers turn months of waiting into hours of insight—and turn insight into measurable bottom-line impact.

Ready to stop drowning in data and start making faster, better decisions?

→ Book a 30-minute demo and see Dataraft analyze one of your own datasets live.