Skip to content
Vienna and Carinthia, November 15 to 20
Book a Call

Data analytics and applied AI for quality-critical manufacturers

Turn Production Data Into Fewer Rejects.

See where quality is slipping while production runs, find the cause, and act before more parts are affected.

Book a Call

Three Ways We Help. Pick One.

Start with the question that costs you the most. The method follows the problem, never the other way around.

  • Advanced Analytics

    Track Quality and Investigate Losses

    Watch rejects, yield and output as production runs. When something moves, trace it across parts, machines, materials and process conditions.

    See the analytics detail

  • Predictive AI

    Recognize Quality Risk Earlier

    We test whether your history gives useful early warnings, checking reliability, false alarms and response time before anything goes live.

    See the AI detail

  • Intelligent Automation

    Move From Findings to Action

    When a finding is proven, act on it: notify the right team, recommend the next step, or trigger an approved action.

    See the automation detail

Your engineers validate findings and approve rules or actions before anything runs. They keep control of critical production decisions.

A rejected part is only part of the cost.

Scrap is the loss you can see. Behind it sit rework hours, repeat inspections, engineering time, and the risk of a defect reaching your customer.

  • Inside the plant

    • Scrapped materials and rejected production
    • Rework and reinspection
    • Production time making or correcting rejected parts
    • Engineering time assembling and reviewing evidence

    Beyond the plant

    • Customer rejection and containment
    • Complaints and repeated corrective-action requests
    • Warranty claims or cost-recovery exposure
    • A weaker supplier scorecard, and the next order that quietly goes elsewhere

  • We start with the costs your team can verify. Not every reject is a lost sale, and we will not pretend it is.

What a One-Point Yield Improvement Could Mean

Small yield gains add up fast at production scale. What they are worth depends on your volume, your costs and what changes in the process.

Illustrative calculation

Take 200,000 parts a year. Moving from 93% to 94% first-pass acceptance means 2,000 fewer rejects. At an assumed avoidable loss of $20 per reject, that is $40,000 a year. Illustrative arithmetic; your numbers will differ.

  • 1 percentage pointHypothetical acceptance-yield improvementIllustrative example
  • 2,000Fewer rejects at the same assumed production volumeIllustrative example
  • $40,000Potential avoided loss at an assumed $20 per rejectIllustrative example

Your calculation may differ. Avoidable loss is not automatically selling price; scrap recovery, rework options and implementation costs also count.

Useful context. Not your plant's forecast.

Industry research can help frame the conversation. Your business case still needs your own baseline.

5.0%Cross-industry median scrap and rework costs as a share of cost of goods soldAPQC manufacturing benchmark, 2025 median

The APQC denominator is cost of goods sold, not revenue. A cross-industry benchmark, not a forecast for any one manufacturer.

View the APQC benchmark
41%Selected supplier-related quality issues as the category where a 25% reduction would have the greatest operational impactLumafield/NewtonX industry survey, 2026
58%Believed at least a quarter of their full quality costs were not being measuredLumafield/NewtonX industry survey, 2026

A survey conducted independently by NewtonX for Lumafield with 210 manufacturing quality decision-makers in the United States and Canada. The second figure covers total quality costs, not scrap alone.

Read the research summary

One Quality Problem. Four Points of View.

A reject looks different from the lab, the floor, the boardroom and IT. We shape the analysis around the decision each team needs to make.

  • Quality and process teams

    See likely defect drivers and what still needs testing, to focus engineering investigation, not replace it.

  • Plant and operations leaders

    See what rejects, rework and slow responses cost the plant each week.

  • Business and finance leaders

    Judge proposals on expected value and your own baseline, not on technology promises.

  • IT and data teams

    Work through sources, identifiers, access and integration in your systems; agree on security and deployment first.

See the data and architecture approach

Advanced Analytics

Give Your Engineers a Head Start.

See how rejects, yield and output move as production runs, then investigate which parts, materials, machines or conditions are driving it.

  • Compare like with like

    Track accepted and rejected production as data arrives, then compare relevant parts, processes, equipment and materials to see where losses concentrate.

  • Follow the pattern across records

    Inspection, production, material and maintenance records in one view. No more chasing spreadsheets.

  • Keep conclusions inspectable

    Show the records and assumptions behind a finding; engineers decide whether it is a lead, a confounder or a validated cause.

What you get: timely visibility, a sharper investigation and a clear next step. Not software that claims causality from any dataset.

Compare accepted and rejected production in contextAccepted production and rejected production both feed one shared context panel that lists part, material, equipment and conditions. The comparison goes to engineering review.AcceptedproductionRejectedproductionSHARED CONTEXTPartMaterialEquipmentConditionsEngineeringreviewENGINEERS DECIDE

Predictive AI

An earlier warning must earn its place.

A model is useful only when its signal is reliable and early enough to act on. We test that against your problem, history and response.

  • Check the history

    Confirm that process readings and quality outcomes link, and that the data covers the operating conditions the model would meet.

  • Test on information available at the time

    We test the model only on what was knowable at the time, never on the final inspection result it is supposed to predict.

  • Evaluate the warning, not just the score

    Review missed risks, false alerts and response time with the engineers involved; agree on monitoring and ownership before deployment.

If the evidence does not support a useful model, we say so, and show you the gap.

Recorded conditions, a potential warning, then the quality outcomeA line of recorded process conditions runs over time. A dashed marker shows where a potential warning could appear, before the quality outcome at the end of the line. Illustrative sequence, not a client result.Recorded conditionsPotential warningQuality outcomeTIME
Illustrative sequence, not a client result

Intelligent Automation

Close the Loop: From Finding to Fix.

A finding nobody acts on changes nothing. We automate the agreed next step, from alerting the right team to triggering approved corrective actions.

  • Trigger the response

    Send alerts, recommend corrective steps or start actions when defined quality conditions are met.

  • Apply the right approval

    Some actions run automatically under agreed conditions; others require an authorized approval. You set the rules.

  • Keep the record of action

    Record the trigger, approval when needed, action taken and outcome so engineers can trace decisions.

Equipment-level actions depend on agreed plant, safety and IT/OT controls. Engineers retain authority over critical production and quality decisions.

Finding, named owner, review, response recordedFour steps in a row: a finding is routed to a named owner, reviewed, and the response is recorded. Review and response sit under the label engineer-approved action; actions follow the plant's agreed approvals and controls.FindingNamedownerReviewResponserecordedENGINEER-APPROVED ACTIONAlerts, recommendations and business-approved actions,under agreed approvals, safeguards and tracking.

Questions before we begin

Is this only for manufacturers without good reporting?

No, reporting may already work well. Your next need could be a deeper explanation, a tested early warning or a better response workflow.

Does Datansh identify the root cause?

We help your team investigate likely contributors from the production and quality evidence; your engineers validate causes and corrective actions. A correlation is never treated as a confirmed cause.

Does every engagement include AI?

No, we use the approach that fits the problem. AI belongs where it adds a useful capability and the data supports a meaningful test.

Do we need new software before starting?

Not necessarily; we start with the systems and records you already have. Any new integration or platform decision follows from the agreed scope, not from a need to replace your existing tools.

Can we begin with automation if the problem is already understood?

Yes, a known finding with an unreliable handoff is a valid starting point. We agree on the workflow, owner, evidence and response measure before building it.

Pick the Quality Question That Costs You the Most.

A source of loss, an emerging risk or a slow response. Bring it, and we will define a practical way to test the next step.

Book a Call

Or write to info@datansh.com