Industrial AI & automation
Each of these targets a specific business problem. For every one we explain what it does, the problem it solves, what information it needs, what implementation involves, and how success is measured. Not every legacy machine or PLC can be integrated without an engineering assessment — we say so honestly.
Problem: Manual, spreadsheet-driven planning that can't react to changes.
What it does: AI builds and re-sequences production schedules from orders, capacity and material availability, so the line runs the right jobs in the right order.
Needs: Order book, machine/line capacity, changeover times, material lead times.
Implementation: Connect order and ERP data, model constraints, pilot alongside the planner before automating.
Measured by: On-time delivery %, schedule adherence, changeover hours, planner time.
Problem: Nobody knows true output or where hours are lost until it's too late.
What it does: Turns machine and manual data into live OEE, output and loss reporting instead of end-of-week spreadsheets.
Needs: Output counts, downtime reasons, shift logs; sensors or manual entry.
Implementation: Define the metrics that matter, wire up data capture, build dashboards and alerts.
Measured by: OEE, downtime by cause, output per shift, reporting lag.
Problem: Defects caught late, inconsistent manual inspection, rework and returns.
What it does: Machine-vision models flag defects, dimensional errors or missing components on the line.
Needs: Sample good/defect images, defect definitions, line speed and lighting.
Implementation: Camera and lighting rig, model trained on your defects, human-in-the-loop sign-off first.
Measured by: Escape rate, scrap/rework cost, inspection throughput, consistency.
Problem: Unplanned breakdowns, firefighting, over- or under-maintenance.
What it does: Monitors equipment signals to predict failures before they stop the line.
Needs: Critical-asset list, failure history, available sensor data (vibration, current, temperature).
Implementation: Instrument the critical assets, baseline normal behaviour, alert on drift.
Measured by: Unplanned downtime, mean time between failures, maintenance cost.
Problem: Slow or error-prone manual checking, counting and sorting.
What it does: Visual automation for counting, sorting, reading codes, guiding robots and verifying assembly.
Needs: What must be seen/decided, part variation, speed and environment.
Implementation: Select cameras/optics, train on representative parts, integrate the decision into the line.
Measured by: Accuracy, throughput, labour redeployed.
Problem: Stockouts alongside overstock, cash tied up, expedited freight.
What it does: Forecasts demand and recommends stock and reorder levels to cut both shortages and excess.
Needs: Sales/consumption history, lead times, service-level targets.
Implementation: Clean history, build forecast, pilot recommendations against current policy.
Measured by: Stockouts, inventory value, write-offs, service level.
Problem: Fragmented spend, price creep, no view of supplier reliability.
What it does: Analyses spend, prices and supplier performance to support better buying decisions.
Needs: Purchase history, supplier delivery/quality records.
Implementation: Consolidate spend data, surface patterns, track supplier scorecards.
Measured by: Price variance, supplier on-time/quality, spend under management.
Problem: Skilled staff lose hours to copy-paste and chasing paperwork.
What it does: Automates repetitive admin: data entry, order processing, document handling, approvals.
Needs: The repetitive processes, systems involved, volumes.
Implementation: Map the process, automate the stable steps, keep people on judgement work.
Measured by: Hours recovered, processing time, error rate.
Problem: Documentation is late, inconsistent or missing for audits.
What it does: Generates work instructions, shift handovers, compliance records and management reports automatically.
Needs: Current documents, data sources, audit requirements.
Implementation: Template the documents, connect the data, review workflow.
Measured by: Time to produce reports, audit findings, completeness.
Problem: Data trapped in machines and spreadsheets that don't talk to each other.
What it does: Connects machines, ERP, quality and maintenance data into one trustworthy source.
Needs: Systems inventory, PLC/machine access, what decisions the data must serve.
Implementation: Assess integration feasibility per machine, build the data layer, govern it.
Measured by: Data availability, manual re-keying removed, decision speed.
Problem: Hard-to-fill, high-turnover or unsafe manual roles; cycle-time limits.
What it does: Robots and cobots for repetitive, heavy or hazardous tasks — welding, handling, packing, tending.
Needs: Task detail, volumes, cycle time, safety constraints, payload.
Implementation: Assess feasibility, select robot vs cobot vs fixed automation, integrate and guard.
Measured by: Cycle time, output, labour redeployed, safety incidents.
Problem: Knowledge walks out the door; new staff take months to get productive.
What it does: Assistants that answer from your manuals, SOPs and maintenance history so expertise isn't locked in people's heads.
Needs: Documents, SOPs, maintenance logs; access rules.
Implementation: Index the knowledge, restrict to verified sources, pilot with a team.
Measured by: Time to find answers, onboarding time, repeat questions.