Marker.io integration Marker.io integration END

Operational efficiency

AI analytics provide an unmatched analysis across supply chain, logistics and field operations, while determining the most impactful variables on your KPIs.

Let your data tell the story

By analyzing vast datasets, AI can instantly identify and mitigate the root causes behind operational fluctuations (unexpected slowdowns and breakdowns), taking cost out of the business, without hurting the customer experience or your ability to innovate and grow.

SparkBeyond’s Discovery builds a complete picture of operational activities that tells the story of where processes can be tightened up, errors eliminated and external spending reduced.  To generate accurate, actionable insights, the platform combines internal data, including OS, historic/real time land and sea telemetry, with SparkBeyond’s library of external data, including geospatial, weather and IoT and test millions of hypotheses every minute.

Benefits include journey optimization (route, speed), lower fuel consumption, maximized oil well production, better shutdown planning, automated order and logistics processes, cost-effective manufacturing and delivery partners, better labor utilization and higher stockroom productivity.

Applicable across discrete industries like CPG, automotive, electronics, textiles and aerospace and process industries like food and beverage, chemicals, oil & gas and pharma, Discovery’s multi-layered insights help operational teams dramatically cut waste in raw material, energy, labor and machine time and reduce environmental impact.

Discovery’s production-ready models adapt automatically to changes in the environment, giving you continuous, accurate insights and recommendations which are fully explainable and clearly visualized to enhance process understanding across the full workflow.  

Volatile commodity prices and industry market valuations are forcing mine operators to cut costs and boost productivity.  A global mining company wanted to reduce operating costs, and identified fleet fuel consumption – accounting for 30% of an average mine’s variable cost – as the primary driver.  SparkBeyond analyzed fuel consumption (per unit of distance travelled/per unit of material transported), scanning datasets such as tyre pressure, refueling data, fuel and oil quality and road gradient and quality.  Outcomes:

  • Fuel consumption cut by 10%.
  • Actionable insights on optimal speed and payload, maintenance scheduling and overall fleet management.

Using AI-powered analytics to leverage data can help organizations detect and mitigate the root causes of process inefficiency across the entire value chain.

Features

No items found.
No items found.

It was easier in this project since we used this outpout

Business Insights

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Predictive Models

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Micro-Segments

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Features For
External Models

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Business Automation
Rules

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Root-Cause
Analysis

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Join our event about this topic today.

Learn all about the SparkBeyond mission and product vision.

RVSP
Arrow