Case Studies

 
 
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Field Service

Operating as a company with field work operations could be a challenge. Hiflylabs helps its clients with predictive analytics to improve time prediction for field tasks which leads to reduced work costs; to create technician utilization dashboards, and to optimize work schedule which results in the reduction of wasted work time by up to 20%.

 

 
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Campaign Targeting

Results from past campaigns carry much valuable information, and Hiflylabs succeeded in helping its clients to find and utilize this information. Using machine learning models trained on this data, prediction of customer conversion and optimization of communication channels are possible, which could boost campaign success rate by up to 200%.

 
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Manufacturing / industrial IOT

There are many areas where manufacturing processes can be optimized: item failure / increased item failure rate prediction by monitoring and analyzing shop-floor sensor data; reduction of cycle time by eliminating unnecessary buffers after understanding and the analysis of the actual processes; avoiding unexpected downtime by root-cause analysis. Hiflylabs can solve these challenges in the optimization process and can reduce failure-caused costs by more than 15%.

 

Predictive maintenance

One of the biggest challenge in maintenance is predicting unexpected events, especially if they happen at the same time. Since more than 50% of failures are predictable, the number of unforeseen incidents can be significantly reduced. Hiflylabs has proven the ability to discover patterns resulting maintenance problems, predict failure of certain system elements and optimize maintenance cycles.

 
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Wholesale demand forecasting

Increasing the satisfaction of customers and decreasing the costs are both benefits of demand forecasting. Hiflylabs has the expertise to predict demands of wholesale hubs/warehouses, optimizing between out-of-stock and storage costs. Since the demands are highly affected by external factors, such as promotions, weather, seasonality and holiday, they could be included in the prediction process. As a result, forecast prediction error can be reduced by up to 20%.

 

Electricity fraud

Electricity fraud in residential and SME sectors are up to 3,5% of total usage. Traditional selection processes are inefficient to focus control resources on highest risk usage points. As the first step, Hiflylabs unified separate data sources and developed predictive models that score usage point according to associated risks. On the top of these, our experts developed a risk assessment system that provides a list of risky electricity meters for the control department each month. Based on the projects' results 2x increase in fraud detection accuracy has been reached, which means 6x project ROI.