What Asara does in an industry

Pumping regimes, environmental limits and measurements lived in different programs and spreadsheets, so a limit breach and its production cause only met by hand, while a report was being prepared.

• The pipeline's operating regime sheets are kept in the system next to the company's finance and documents.
• Environmental limits and measurement results sit on the same data model as production figures.
• AI compares sections against each other and against history and flags deviations before they reach a report.

Cost, downtime and breach are visible in one picture; the management report is assembled from live data.

Several shippers deliver oil of different quality into one pipe. The dispatcher needs to know where each batch is now, when it reaches the delivery point and how it mixes with its neighbours.

• A batch journal: movement of volumes along pipeline segments, with injections and withdrawals.
• Hydraulics and mixing are computed by a model.
• What-if scenarios: a change in the pumping programme immediately shows the shift in batch arrival times.

The dispatch room sees batch positions and arrival forecasts on one screen and tests decisions on the model before changing the regime.

Network, subscribers, volumes and environmental limits are tracked in industry systems apart from finance, and the company-wide picture is stitched from exports.

• The gas distribution industry module switches on inside the same system that holds finance, people and documents.
• Network assets sit on a map; their figures are read together with the economics of the section.
• Management questions about the network and subscribers are asked in plain words and answered from the data.

Industry and management data read together: a network deviation shows up in the company's economics at once.

Incidents, work permits and the state of network assets are tracked separately, and the link between safety, repairs and money is not visible.

• Network assets, incidents and work permits are kept in one system with finance and documents.
• AI watches deviations by section and highlights risks.

Management sees the safety and the economics of the network in one picture.

Incidents, work permits, emergency response plans and environmental indicators are kept in different domains, and a manager struggles to see a risk before it becomes an accident.

• Incidents, work permits and safety regulations live next to production and finance.
• AI detects deviations and risks by unit and over time.
• Follow-up assignments from an investigation are issued and tracked in the system.

The safety service and management work from one picture of risks and see measures carried out.

Production orders, load and safety are counted apart from finance, and the cost of downtime shows up late.

• Production processes and industrial safety are part of the same system as company management.
• Forecasts and tasks are built on the same data; execution is tracked in the system.

Downtime, a breach and their cost are visible together, at the moment they happen.

The fund portfolio, commitments and their documents are kept in spreadsheets and correspondence, and the consolidated picture for the board is assembled by hand.

• Funds, commitments, documents and finance in one environment with roles and access domains.
• The board sees the consolidated picture; each unit sees its own domain.

The board report is assembled from the system for any meeting.

Accident data exists, but it only answers “what happened”. An oversight body needs to know where the next accident will occur, why, and who is responsible for the section.

• Data on accidents, the road network and weather is gathered into one store.
• Machine learning models score risk by road section and rank regions.
• Causal analysis links accident rates to the section's condition; AI writes up the analysis and prepares oversight materials.

From a map of accidents to a risk forecast and targeted measures for specific sections and responsible parties.

The regulator needs a single register of unmanned aircraft and their operators, control over permits and restrictions and an overall picture of flights.

• Registration of aircraft, owners and operators; tracking of specifications and status.
• A single map of aircraft, monitoring of active flights, restricted zones.
• A panel for regulators: registered versus unregistered aircraft, analytics and reporting.

The state sees the fleet of unmanned systems and its movements in one system and manages permits from it.

Information on telecommunications infrastructure and coverage is spread across operators and agency forms; the national picture is assembled by request.

• Infrastructure assets sit on a map; figures are read by region and settlement.
• Events and deviations are ranked by importance; each type has its own response procedure.

An operational domain: the state of the infrastructure is visible in real time, and the response is traced through to completion.

Freight indicators, the state of nodes and inspection results live in different systems, and the holding's management needs one picture of the network.

• A network map with indicators by node and direction.
• Key indicators, inspections and an event timeline in one situation center.

Management sees the whole network and examines deviations by node without assembling digests by hand.

Maps of natural, industrial and other risks are held in a proprietary GIS that the agency cannot develop itself.

• Risk layers and point assets on a map built on an open stack that is handed over to the client.
• Ranking of territories by risk and a search for hazards around an asset.
• Regional units develop their own layers themselves.

The atlas becomes the agency's own tool: data, maps and code stay with the client.

Transit shipments are controlled by documents and checkpoints; between them the cargo is unobserved.

• Shipments with navigation seals are visible on a map along the whole route.
• Route events, such as deviation, delay or opening, are ranked by importance, each type with its own response procedure.

The situation center sees transit as a whole and responds to an event en route.

Indicators for regions, commodity markets and food prices arrive from different sources at different frequencies, and the link between them, the shelf price as the result of market balance and regional income, is not visible in one system.

• Data on regions, markets and prices is gathered into one data model with a shared methodology.
• The forecast shows what happens without intervention and what a specific measure delivers.
• An analytical domain: state of affairs, event, analysis, forecast, decision, result.

A measure is tested by calculation before it is adopted and assessed on the same indicators afterwards.