Run Operations
From One Live View

Machines and MES generate data all day, but it lands in reports the morning after. By the time a problem is visible, the shift that caused it is over.

• Stream machine, MES and quality data into one near-real-time view
• Alert on downtime, scrap and cycle-time drift as it happens
• Put the signal in front of the people who can act on the line

Production issues caught and worked during the shift, not explained the day after.

Defects surface at final inspection or, worse, at the customer, long after the process drifted. Root cause is a detective exercise across disconnected data.

• Correlate defects with process parameters, material lots and stations
• Detect the drift that precedes a defect, upstream
• Trace any escape back to its root cause fast

Defects caught closer to their source, cutting scrap, rework and escapes.

Run-to-failure means surprise breakdowns; fixed schedules mean servicing healthy machines. Both waste money, one in downtime, the other in labour and parts.

• Model failure signatures from sensor and maintenance history
• Flag machines trending toward failure before they stop
• Shift servicing from the calendar to actual condition

Fewer surprise breakdowns and less needless servicing, with maintenance aimed where it's due.

Planning, supply and delivery data sit in disconnected systems across plants and regions, so a disruption is only visible once it has already become a shortage.

• Engineer a unified data layer joining ERP, supplier EDI, transport and inventory
• Build a real-time control tower giving one predictive view of supply risk
• Push exception alerts into the planning workflow before issues become shortages

Risk seen and worked before it turns into a stockout - on comparable builds, roughly 40% fewer (illustrative).

Safety stock is set by habit, so cash is tied up in slow movers while the parts that matter still stock out. High inventory and poor service coexist.

• Segment SKUs by demand pattern, variability and criticality
• Right-size safety stock and reorder points to service targets
• Rebalance stock toward where it protects service

Working capital released from the wrong stock while service on the right stock improves.

Production plans are assembled by hand across spreadsheets. They take days to build, ignore real constraints, and are stale the moment demand or supply moves.

• Model real capacity, material and changeover constraints
• Generate feasible plans that balance service, cost and utilisation
• Re-plan quickly when demand or supply shifts

Plans that respect the constraints and can be redone in hours, so the schedule tracks reality.

Everyone knows a process is slow or costly, but with no measurement the waste can't be sized, so it never rises up the priority list.

• Measure cycle time, cost and rework across the process
• Quantify where effort and money leak
• Rank the fixes by the value they release

Operational waste measured and ranked, turning a vague sense of inefficiency into a costed action list.

More people are thrown at a slow process and it stays slow, because the real constraint is somewhere no one has identified.

• Map the end-to-end flow and where work piles up
• Locate the true constraint, not the assumed one
• Focus improvement on relieving it

The real bottleneck found and relieved, so throughput improves without just adding cost.

Sites doing identical work get very different results, and the good practices that explain the gap never get identified or shared.

• Benchmark performance across sites on common metrics
• Isolate what the top performers do differently
• Codify and roll out the practices that work

The best-performing way of working identified and spread, lifting the laggards toward the leaders.