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Practice 03

Downtime you can plan for.

A machine that fails unannounced costs more than the part. We turn the telemetry you already collect — or can collect cheaply — into a maintenance schedule, a spares forecast and a route plan that reflects the roads you actually drive.

What this covers

Assets, stock and movement.

Built for the sectors that carry this economy: gold and bauxite operations, agriculture, utilities, transport and the import chains that feed them.

Condition monitoring

Equipment health

Vibration, temperature, pressure and runtime signals turned into a degradation curve — with an alarm threshold your maintenance team helped set.

Maintenance

Service planning

Work orders generated from predicted condition rather than the calendar, sequenced against production so the fix lands in a planned stop.

Agriculture

Machinery and yield

Harvester and irrigation equipment monitoring, plus yield signals that inform when to move rather than when to buy.

Fleet

Vehicles and routes

Servicing predicted from usage, and routing that accounts for the Surinamese road network instead of a European default.

Supply chain

Inventory and suppliers

Demand-driven stock levels, reorder points that respect real lead times, and supplier risk where a single importer is the only source.

Trade

Customs and import

Documentation prepared in advance, duty and clearance timing modelled, and delays flagged before they cost a demurrage charge.

What changes

Targets we set before we start.

Industrial projects live or die on the first quarter of data. We state what we expect, and we report what happened — including when a sensor is simply too noisy to be useful.

  • 60% less unplanned downtime on the asset class in scope — a design target, not a guarantee.
  • 25% lower logistics cost through routing, load planning and stock positioning.
  • Fewer emergency purchases, because spares are forecast from predicted failures rather than panic.

How we deliver

Instrument, model, then trust.

A model is only as good as the data path under it. We spend the early weeks on measurement, not on dashboards.

01

Audit the data path

What is measured, how often, where it lands, and what breaks. Missing instrumentation is quoted before any modelling begins.

02

Model against failures

Trained on your maintenance history where it exists; where it does not, on condition thresholds agreed with your engineers.

03

Wire into the schedule

Predictions become work orders in the system your planners already use. A prediction nobody schedules changes nothing.

What fails without warning today?

Name the asset class and what you already measure. If the answer is “nothing yet”, we will quote the cheapest instrumentation that would change that.

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