Beneath AnalyticsIndustry
Manufacturing
Most plants log plenty of machine data and look at it a month later. We connect machine, line and quality data so you can see where output is lost this shift, catch the conditions that lead to defects before a batch finishes, and plan from live orders instead of old spreadsheets. Process owners and process engineers also lose a large share of their week to writing and updating documentation, so we build workflows that draft it for them to review.
Where this sector loses ground.
For manufacturers who want less scrap and more output from the equipment they have. We connect machine, line and QA data, report OEE where the floor can act on it, build predictive quality checks, and take the documentation load off process owners and process engineers.
Defects found at the end of the line
Quality problems are only spotted once a batch is finished, so scrap and rework pile up before anyone can step in.
OEE explained in hindsight
Availability, performance and quality losses are reported monthly at best, too late to change the shift that caused them.
Machine data stuck on the floor
PLC and sensor output stays local, without the pipelines and models that would turn it into alerts and forecasts.
Documentation eating the process team's week
Process owners and process engineers spend a large share of their time writing and updating procedures, work instructions and change records by hand, time that is not spent improving the process.
Planning on stale spreadsheets
Demand and inventory planning runs on manual extracts, so production schedules lag behind what orders and suppliers are doing.
How we work in this sector.
Sequence matters: govern the data first, make every reported number traceable, then build the models and automation that act on it.
Bring machine, line, QA and order data into one warehouse, with monitoring on every pipeline.
Calculate OEE by line and shift and put it in front of the floor while there is still time to act.
Build predictive quality models that flag the conditions linked to defects before the batch is complete.
Automate planning and compliance reports, so analysts spend their time on improvement and not on extraction.
Automate the writing: workflows that draft and update procedures, work instructions and change records from your process data, with the process owner reviewing and approving each one.
Where this usually starts.
The solutions we most often deploy in this sector. Each page sets out the capabilities, platforms, and outcomes you can expect.
- Tracking & Analytics
Analytics & Business Intelligence
Analytics and business intelligence means Beneath Analytics turns your tracked data into decisions: attribution modelling, predictive models, and dashboards built around the questions your team actually asks and adopted by the people who use them.
View solution - AI Consulting & Development
Data Science
Data science answers the questions your dashboards cannot: who will churn, what demand will be, which segments matter. Beneath Analytics builds churn models, forecasts, and segmentation on your own data, integrates them into the tools your team already uses, and validates them with properly designed experiments.
View solution - AI Consulting & Development
AI Workflow Automation
AI workflow automation removes the manual work between your systems: compliance reports, risk summaries, inventory updates, and weekly reporting compiled by hand. Beneath Analytics maps the process, builds pipelines with n8n, Python, or dbt, then monitors them, so reports arrive on time without someone spending days assembling them.
View solution
Common questions.
Not usually. Most plants already log data somewhere: historians, MES or PLC exports. We connect to those sources and do not touch production equipment.
Enough examples of defects and normal output, typically several months of production. If outcomes are not recorded consistently, we start there, because a model is only as good as its labels.
Recurring documents that follow a pattern: standard operating procedures, work instructions, change and deviation records, shift and quality summaries. Workflows draft them from your process and production data, and the process owner or engineer reviews and signs off. Nothing is published without that approval.
Yes. The output feeds the same improvement cycle: measured loss, prioritised causes, and proof that a change really improved the number.
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