Battery cells & packs
Catch electrode variation before formation confirms it
In cell manufacturing the cost of a defect compounds: a coating deviation becomes a formation loss weeks later, after significant value has been added. The platform correlates upstream process data with downstream electrical results so drift is caught in the same run rather than in the yield report.
- formation scrap
- -28%
- largest deployed footprint
- 18 GWh
- coating drift alert
- < 2 s
- gigafactories connected
- 4
Standards supported
- IATF 16949
- ISO 9001
- Customer-specific requirements
Typical monitored characteristics
- Coating weight
- Thickness
- Moisture
- Alignment
- Weld resistance
- Formation capacity
Where the cost sits
The problems we start from
These are the recurring findings from discovery workshops in this sector. If none of them describe your operation, a pilot is probably premature — and we will say so.
Operational problems
Yield loss is discovered after formation
Electrical results arrive days or weeks after the process deviation that caused them, so the corrective action lands long after the affected material has moved on.
Upstream and downstream data never meet
Coating, calendering, assembly and formation each keep their own records, making cross-stage correlation a manual data-science project.
Ramp-up hides systematic variation
During ramp everything is moving at once, so it is genuinely difficult to separate a process problem from a recipe change or a material change.
Scrap value is high and rising
Scrapping a formed cell destroys far more value than scrapping an electrode, yet detection usually happens at the expensive end.
Engineering constraints
Continuous and discrete processes in one chain
Coating is continuous by length while assembly is discrete by cell, so genealogy has to bridge web position and cell identity.
Very high data rates
Inline gauges produce thousands of readings per minute per lane, which has to be aggregated without losing the signal.
Recipe churn during ramp
Limits and setpoints change frequently, so control limits must be versioned and analysis must respect the revision in force.
Multi-vendor equipment
Lines combine Asian, European and in-house equipment with inconsistent interfaces and tag naming.
Platform capabilities
How the platform is configured for battery manufacturing
Same architecture, same modules — configured against the characteristics, sampling logic and evidence expectations of this sector.
Web-to-cell genealogy
Maps continuous coating position to discrete cell identity so a downstream failure can be traced to a specific lane and metre of electrode.
High-rate SPC
Streaming capability analysis per lane and per gauge with configurable subgroup logic for continuous processes.
Cross-stage correlation
Correlates coating, calendering and assembly parameters with formation and end-of-line electrical results to expose the real driver of yield loss.
Vision on electrode defects
Classifies pinholes, streaks, agglomerates and edge defects, tying each detection to web position and downstream cell.
Recipe and limit versioning
Every analysis respects the recipe revision in force at production time, which keeps ramp-phase data interpretable.
Business outcomes
What changed for operations like yours
Customer-reported figures measured against documented pre-deployment baselines. Ranges, not single numbers, because process maturity dominates the result.
- Formation scrap
- -28%
- Time to detect drift
- -19 days
- Cell yield
- +3.4 pt
- Cell-level traceability
- 100%
Formation scrap
First quarter after coating-line rollout
Time to detect drift
From formation report to same-run alert
Cell yield
On lines with full upstream coverage
Cell-level traceability
Electrode position to finished cell
| KPI | Typical movement | Measurement note |
|---|---|---|
| Formation yield | +2 to +4 pt | Depends on maturity of upstream instrumentation |
| Scrap value avoided | -20% to -30% | Weighted by value added at detection point |
| Drift detection lag | Days → seconds | For instrumented characteristics |
| Ramp learning cycle | -40% cycle time | Faster confirmation of recipe changes |
| Traceability coverage | 100% of cells | Within instrumented process steps |
Expected ROI
A value case your controller can interrogate
Every line below is an assumption, not a promise. During a pilot each one is replaced with a measured figure from your own baseline, which is what makes the business case defensible in a capital review.
| Value driver | Assumption | Annual |
|---|---|---|
| Formation scrap avoided | 28% reduction on formation losses | €1.34 M |
| Electrode rework avoided | Earlier detection shifts loss upstream | €260k |
| Ramp acceleration | Two weeks earlier to target yield | €480k |
| Engineering effort | Manual correlation work removed | €120k |
| Indicative total | Before platform and integration cost | €2.20 M |
Payback
4–7 months
From first connector to cumulative break-even
How we validate it
- Baseline recorded before any change
- Success criteria written into the pilot scope
- Measured comparison in the pilot report
- Exit conditions agreed up front
Illustrative model. Value is dominated by where in the process detection moves to, which is quantified during the pilot.
Coating thickness variation that previously surfaced as formation loss weeks later is now detected within the coating run; formation scrap fell 28% in the first quarter.
Related sectors: Automotive, Semiconductor, Electronics & EMS
Start the evaluation
Find out where quality drift is hiding in your plant.
Bring one line, one defect family or one audit workflow. We will map the available data sources, quantify the cost of the current detection delay, and show the fastest route to measurable control.
45-minute technical walkthrough
With a solution architect who knows manufacturing data, not a scripted demo.
NDA before any data review
We can assess feasibility from sample exports without production access.
Written pilot scope
Baseline metrics, success criteria and exit conditions agreed up front.