Accurately forecasting energy demand is crucial for operating electricity networks. This dashboard takes live data from the Belgian electricity network to forecast model and future load. Over time the patterns in the data can change, these can be evolving trends such as the adoption of electric vehicles, or step changes such as those seen through the recent pandemic.

By Building models at the point of need, we ensure forecasts are always reflecting the latest conditions and InstantML provides the automation required to implement at scale without the need for manual tuning.

When products are perishable, the value of accurate forecasts increases. Planning the correct stock levels for each product line or category ensures demand can be met with minimal waste. Beyond this however is the ability to understand what is driving sales and how demand can be controlled through promotions to optimise the outcome for the retailer.

Timing the servicing of aircraft engines is a critical calculation. Service regimes that rely on service hours and cycles alone can be unreliable and inefficient, with units degrading at varying rates when operating under different conditions. Servicing a unit too early adds unnecessary costs, waiting for a problem reveal to itself could result in unit failure, secondary damage, and significantly increased costs.

There are various ways to look at this data; forward looking to predict the remaining useful life of the asset, and in real time to identify if the unit is within a window of service, or backward looking to analyse why the unit failed. Both of the below solutions analyse engine sensors and settings to better inform the planning of services accross their fleet.

 

 

 

 

 

 

 

 

The characteristics of products are the sum of the input materials and conditions. Once these input conditions are modelled, the characteristic in question can be predicted across various scenarios.
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In this case the strength of concrete is modelled for a variety of different concrete mixes as the concrete dries over time. This enables a batch to be made up that both optimises the input costs and provides an understanding of how long the final product will have to be stored for prior to being shipped and/or installed.

This case demonstrates a reduction in storage costs from better understanding the evolution of the individual concrete mixes, but can similarly be applied to reduce input costs or substitute ingredients which are unavailable.  

 

 

 

 

 

 

 

 

The characteristics of products are the sum of the input materials and conditions. Once these input conditions are modelled, the characteristic in question can be predicted across various scenarios.

In this case the strength of concrete is modelled for a variety of different concrete mixes as the concrete dries over time. This enables a batch to be made up that both optimises the input costs and provides an understanding of how long the final product will have to be stored for prior to being shipped and/or installed.

This case demonstrates a reduction in storage costs from better understanding the evolution of the individual concrete mixes, but can similarly be applied to reduce input costs or substitute ingredients which are unavailable.