Same factory. Different questions.

AI pays back on accurate, contextualised operational data. Build that foundation with the AI strategy, and each delivers more because of the other.

Standard OEE across sites shows where hidden capacity sits, which is usually the cheapest growth available.

Fast, precise traceability turns a potential company-wide recall into a contained, documented event.

Investors and customers increasingly ask for product-level carbon and energy data, which only the shopfloor can provide.

Accurate, real-time inventory and WIP free up working capital and remove surprises at the stock count.

Actual material and labour consumption per order replaces standards, so product costs reflect reality.

Real cycle times feeding the plan mean fewer expediting costs and less overtime.

A clean operational data foundation lets finance model the impact of changes before committing the money.

One definition of OEE and downtime across every plant turns site comparisons into a source of improvement rather than argument.

Digital instructions and skills management let new starters perform like experienced ones much sooner.

Faster, data-driven rescheduling helps the network absorb disruption.

Comparable data across sites is what makes network-level analytics and AI possible.

Contextualised data captured at the point of work is the foundation for every analytics and AI ambition the business has.

A shopfloor platform at ISA-95 level 3 gives a clean boundary between ERP and the equipment.

Genealogy is a data model problem as much as a process one, and it needs designing once, properly.

Clear ownership of stock across systems stops two of them arguing about the same bins.

AI and a consistent operational data foundation scale together. Each new site adds history the models can learn from.

Frontline tools are where adoption is won or lost, and where trust in digital starts.

Production execution is usually the backbone that other use cases build on.

Quality use cases often show measurable value quickly, which builds momentum across the programme.

Consistent OEE and loss data across sites shows exactly where to focus support and capital.

Network-wide visibility of quality issues stops one site's problem becoming every site's problem.

Live order status lets you rebalance work between plants when demand or supply shifts.

Comparable data makes it possible to spot patterns across the network rather than plant by plant.

Real-time output and downtime, with reasons, means the morning meeting starts from facts rather than debate.

Digital instructions and skills enforcement make the plant less dependent on a few experienced people.

Complaints and audits answered in minutes, not days.

Production and maintenance working from the same downtime data ends the blame game.

Automatic loss capture, including micro-stops, gives a continuous loss tree instead of a one-off study.

Digital standard work makes the standard easy to follow and easy to check.

First pass yield by step shows where hidden rework sits.

Pattern finding across shifts and products points improvement teams at the right problems.

Live line status and downtime reasons let you act within the shift, not the day after.

A prioritised list of released orders that reflects reality shows what to run next and why it changed.

Skills checks and digital handover keep every shift starting from the same picture.

Line-side material visibility stops lines waiting for parts the system thought were there.

Checks triggered by what's actually running, with SPC at the line, catch drift before it becomes scrap.

Complete genealogy turns recalls and complaints into precise, documented answers.

Digital instructions and sign-offs build the quality record as the work is done.

Contextualised data makes root cause analysis faster, because the process conditions are already linked to the result.

Stops captured from the equipment, with reasons, give reliable MTBF and MTTR and a fair split of the causes.

Condition data combined with downtime history is the starting point for predictive maintenance.

Maintenance windows planned around the production schedule, rather than fighting it.

Operators raising faults from their own screen, and carrying out autonomous maintenance, catch problems early.

Real cycle times and setup times feeding back make every new plan more realistic.

Accurate stock and WIP, updated as material is consumed, mean the plan starts from reality.

Live order progress shows early when a job will finish late.

Patterns in actual performance help forecast capacity and lead times more accurately.

Material tracked to the line, with consumption recorded as it happens, keeps the system and the shelf in step.

Replenishment triggered by the actual production schedule stops lines waiting.

Lot tracking from receipt to finished goods makes FIFO and shelf life enforceable.

Quality holds visible at every point where material can be moved or consumed.

Process data linked to the order and the outcome is what analytics and AI need to find root causes.

SPC and capability data from the line show where the process drifts.

Digital work instructions mean an engineering change reaches every workstation at once.

As-built records show exactly what went into each unit, for investigations and ramp-up.

Energy and waste measured by machine and product turn ESG reporting from estimates into facts.

Digital safety checks and skills enforcement make sure the right people follow the right procedure.

Well-maintained equipment is safer and uses less energy.

Product-level data makes it possible to answer customer carbon questions.

Publishing contextualised data, rather than raw tags, makes every downstream consumer simpler.

A shopfloor platform at level 3 gives the control layer and ERP a single, well-defined partner.

Machine connectivity serves maintenance and production from the same connection.

Genealogy depends on reliable, time-synchronised data from the equipment.

Digital work instructions and skills tracking capture expert knowledge and guide new starters step by step.

AI assistants can answer an operator's question from the SOPs at the moment they need it.

Guided work with built-in checks lets new people get it right first time.

Modern, intuitive shopfloor screens make the job easier to learn and more satisfying.

Digital handover and skills visibility mean you start the shift knowing what's waiting and who can do what.

Live line status with downtime reasons shows where to go first.

Faults raised from the line go straight to maintenance, with the context they need.

A clear, prioritised list of released orders removes the guesswork about what to run next.

Clear, current instructions and simple checklists on one screen, in your language.

Counts and downtime captured from the machine, so you're not writing them down.

Checks prompted at the right moment, so nothing is missed and nothing is done twice.

Raising a fault in a couple of taps, and seeing when help is coming.

Fault reports with the machine state and recent history attached shorten diagnosis.

Digital procedures and checklists on a tablet at the machine, with sign-off built in.

An assistant that can find what was done the last few times this fault happened.

Spare parts availability visible before the job starts.

Checks triggered by events such as a material or tool change, with results captured straight from the gauge.

Every result linked to the lot and the operator automatically.

Digital checklists and sign-offs complete the record as you go.

Holds that block material in every system, as soon as they're placed.