From Shop Floor
to Smart Floor
Support across the entire operational lifecycle — planning, sourcing, production, quality, maintenance and fulfilment — combining domain expertise with AI, automation and data-driven intelligence.
less unplanned downtime with predictive maintenance
McKinsey benchmark
the OEE most factories actually run at — half their true capacity
Industry average
OEE lift documented from advanced analytics in food & beverage
BCG case study
We support manufacturers across the entire operational lifecycle — from planning and sourcing through production, quality and maintenance to fulfilment. By combining domain expertise with AI, automation and data-driven intelligence, we streamline operations, lift asset utilisation, strengthen supply-chain resilience, and accelerate the journey towards smarter, connected manufacturing.
Across the Whole
Operational Lifecycle
Planning & Scheduling
AI demand forecasting and production scheduling that replace the spreadsheet — so the plan survives contact with the shop floor.
Sourcing & Supply Chain
Supplier risk visibility, inventory optimisation and scenario planning that turn supply volatility from a crisis into a variable you manage.
Production & OEE
Machine monitoring, MES integration and live dashboards that show where availability, performance and quality losses actually hide.
Quality & Traceability
Computer-vision inspection and batch-level traceability — 95%+ defect detection, recall-ready records, and a shrinking cost of poor quality.
Maintenance & Asset Care
Condition monitoring and failure prediction that catch breakdowns before they happen — downtime scheduled on your terms, not the machine's.
Fulfilment & Logistics
Finished-goods visibility, dispatch automation and DIFOT analytics — because on-time-in-full is where customers grade the whole factory.
The Numbers Behind the Promise
Less unplanned downtime — predictive maintenance
Defect detection accuracy — vision-based QC
Lower forecast error — AI demand planning
Energy use cut by AI-based production scheduling
Benchmarks: McKinsey, Deloitte, BCG, MDPI Sustainability (2023–25). We baseline your plant before promising a number.
A production week, in practice
Today
- Planning lives in a spreadsheet that drifts by Tuesday
- Breakdowns discovered when the line stops
- Quality checked by sampling, after the fact
- Stock counted by walking the floor
- OEE estimated, and usually overestimated
Working with us
- Schedules built from live demand, capacity and stock
- Sensors flag a failing asset weeks before it fails
- Every unit inspected by vision, at line speed
- Inventory tracked continuously against the plan
- OEE measured per machine, per shift, per cause
Predictive maintenance is associated with 30–50% less unplanned downtime (McKinsey), and vision inspection exceeds 95% defect detection. We baseline your plant before promising any number.
How AI Shows Up on the Floor
Use Case 01
Maintenance Before the Breakdown
IIoT condition monitoring and failure prediction
For an SME plant, downtime runs $1,000–$5,000 a day, and an unscheduled stop costs about a third more per minute than a planned one. Predictive maintenance flips the economics: sensors watch vibration, temperature and current draw; models flag the bearing weeks before it fails; the repair happens on Sunday, not mid-run.
Adopters report 30–50% less unplanned downtime, with most reaching payback inside 18 months — one of the best-evidenced returns in industrial AI.
- IIoT sensor & condition-monitoring rollout
- Failure prediction & smart alerting
- Maintenance planning & CMMS integration
- Critical-spares optimisation
Use Case 02
Planning Beyond the Spreadsheet
Forecasting, scheduling and one source of shop-floor truth
ERP handles the transactions, spreadsheets fill the gaps, and shop-floor reality drifts from the plan by morning tea. Unifying ERP, MES and machine data gives planners a live picture — and AI forecasting cuts demand error 20–50%, which flows straight into lower stock, fewer stockouts and honest lead-time quotes.
For contract manufacturers, live capacity visibility is the difference between winning the quote and regretting it.
- AI demand forecasting & S&OP support
- Production scheduling optimisation
- ERP–MES–shop-floor data unification
- Live capacity & quoting visibility
Use Case 03
Quality That Never Blinks
Vision inspection, traceability and root-cause analytics
Cost of poor quality typically runs 5–25% of sales — and four in ten manufacturers can't put a number on theirs. Camera-based inspection catches defects human eyes miss, at line speed, on every unit, around the clock. Batch-level traceability turns a potential recall from an existential threat into a contained event.
Digital work instructions and root-cause analytics close the loop: fewer escapes, faster answers, less tribal knowledge walking out the door.
- Computer-vision quality inspection
- Batch & recall traceability (HACCP/ISO-ready)
- Digital work instructions for operators
- Root-cause & first-pass-yield analytics
Energy prices, skills shortages and supply shocks have made "she'll be right" a losing strategy — yet only around 30% of Australian SME manufacturers have adopted AI. That's the opening: modest, well-chosen automation compounds fast, and federal and state programs will often co-fund the journey.
What's Your Real OEE?
Most plants that measure honestly find 15–20 points of hidden capacity. A short readiness review will show you where yours is — and what it would take to claim it.