How we work
Start Small. Prove It. Then Scale.
Agree the quality priority, the number worth improving and the decision the work should support. Then choose your starting point.
First, make the question clear.
The problem
What is rejected, reworked or delayed, or which quality risk or response needs to improve?
The measure
Choose the relevant measure, such as rejection rate, scrap cost, first-pass yield, rework hours or response time.
The decision
Define who will use the result and what they must decide; a report, a warning and an action workflow serve different purposes.
A first conversation is enough to check the fit. No presentation, no data handover.
We Build on What You Already Have.
Use the checks you already make
Start with inspection, production, process, material or test records, and agree on what they can and cannot answer.
Add the capability that is missing
An investigation view, a predictive test or an approved response workflow, without rebuilding what already works.
Validate with the people who know the process
Review assumptions, findings and proposed actions with the quality, process and operations owners.
Make the next decision explicit
Decide whether to implement, extend, refine or stop; a larger engagement is not an automatic outcome.
Start Where It Hurts, Not at Step One.
Connecting records, understanding losses, investigating drivers, testing warnings or organizing a response: the useful starting point depends on what your team already has.
Start where needed
Connect
Link the records needed to examine the question.Understand
See where quality loss occurs and which patterns need attention.Investigate
Compare likely contributors and validate the findings with your engineers.Anticipate
Test whether past conditions give a useful early warning.Respond
Put findings into an agreed review and action process, then track what happens.
Capabilities, not compulsory stages: a manufacturer with good reporting can start with a predictive test or a response workflow.
Two starting points. One clear purpose.
We agree the problem and the KPI on the first call. Then you pick the entry project that fits what you already know.
Option 01: Investigate
Understand the loss
Take one reject or yield problem that keeps costing you, and use production, inspection and scrap records to find where the loss concentrates.
- Define the quality question and the production scope.
- Review the records and the gaps.
- Establish the baseline the data supports, then the next test or decision.
2 to 4 weeks. Fixed scope and fee. Best when the loss needs a closer look.
Option 02: Validate
Test an approach
When the problem is clear, test one improvement idea on plant data against an agreed KPI and decide on rollout.
- Define the approach and the evidence needed to test it.
- Build a bounded analysis, model or workflow test.
- Review the result with the team; state the limitations and the case for, or against, rollout.
6 to 10 weeks. Fixed scope and fee. Best when the problem is already defined.
Choose either starting point. There is no mandatory sequence. Final scope, fee, timeline and success criteria are agreed in the proposal.
Put it into daily use
Where the result supports it, integrate the agreed capability into the team's reporting, investigation or response process.
Keep it working and improving
Ongoing support can cover data quality, model or workflow monitoring, and agreed extensions to other lines or problems.
What you can expect from the work
A clear question
We agree on what you need to understand or improve, and how the result will be judged.
Evidence your team can examine
Findings come with the records, assumptions and limitations behind them; your engineers validate causes and actions.
A practical next step
A better investigation, a tested warning signal or an agreed response workflow; further implementation depends on what the work proves.
Clear boundaries from the start.
Engineers validate the decision
We provide evidence and decision support; your engineers validate causes, process changes and corrective actions.
No machine write-back
Nothing writes back to a machine; an agreed automation can create an alert or workflow task without changing production settings.
No unsupported prediction promise
We test whether the data supports useful early-risk signals; a reporting project is not proof of a predictive model.
No assumed savings
The business case uses the plant's baseline and stated assumptions; potential value and achieved improvement are reported separately.
Your Systems Stay. We Connect.
The evidence may sit in ERP, MES, QMS, a historian, inspection systems or spreadsheets; we connect what the use case needs rather than replace the stack.
Access and permissions are defined with your IT team; read access to production sources and approved workflow actions stay separate.
Production and quality data can remain inside the customer's approved on-premise, German/EU cloud or existing Azure environment. Datansh does not require production datasets to be relocated to India.
Practical questions
Which starting option is right for us?
Must we complete Investigate before Validate?
What should we bring to the first call?
Can historical exports be enough?
What happens when the records do not support the proposed approach?
Will you replace our current systems?
What happens after an entry engagement?
A Good First Step Is One You Can Say Yes To.
One quality priority. One number that matters. A clear way to judge the result. That is what we work through on the first call.
Book a CallOr write to info@datansh.com