Our customer, a global leader in plastics, chemicals, and refining, needed a bespoke solution to enhance operational efficiency and streamline production. They aimed to improve data collection, processing, and analysis while implementing predictive maintenance, demand planning, and forecasting.
The customer required a custom approach to better collect, store, process, and analyze massive amounts of data.
They initially had a solution that implemented Machine Learning models and transformed the data onto dashboards for further analysis which required a custom frontend. Additionally, the customer wanted to implement predictive maintenance strategies with a focus on maintenance, demand planning, and forecasting.
Currently, we’re collaborating on developing a solution to help them utilize big data across a wide range of operations, optimizing production efficiency, forecasting machine reliability, planning maintenance, and effectively managing disruptions in the supply chain.
Contact us to see how our data engineering solutions can boost efficiency and optimize processes.
Our expert team solved the customer’s problem by delivering services including frontend and backend engineering as well as the implementation of predictive analytics for a solution that:
Visualizes parameters:
including those of very specific assets for easy access to historical changes, relationships, and expected future values.
Consolidates data:
centralizing real-time data from IoT Hub through EventHub into the system, aiding early anomaly detection for engineers.
Allows customization:
for improved clarity and better decision-making processes across many areas.
Provides real-time alerts:
enabling proactive maintenance and presenting potential breakdowns that could cause production disruptions.
Schedules bookings for large scale industrial equipment:
managing their usage, and scheduling maintenance and cleaning sessions.
Handles demand planning and forecasting:
including mass energy and carbon emission management.
Reduces costs:
through gradual digitization and implementation of process automation spread out into a multi-year plan.
Our expert team addressed the customer's challenges by delivering a suite of services including frontend and backend engineering as well as the implementation of predictive analytics for a solution that:
Visualizes parameters:
offering easy access to the historical changes, relationships, and expected future values of very specific assets.
Consolidates data:
centralizing real-time data from IoT Hub through EventHub into the system, enabling early anomaly detection for engineers.
Allows customization:
improving clarity and decision-making processes across many areas.
Provides real-time alerts:
enabling proactive maintenance and early detection of potential breakdowns that could disrupt production.
Schedules bookings for large scale industrial equipment:
managing the usage, maintenance, and cleaning sessions of the equipment.
Handles demand planning and forecasting:
managing mass energy usage and carbon emissions.
Reduces costs:
through gradual digitization and implementation of process automation over a multi-year plan
By employing effective data engineering practices and implementing features like real-time alerts, data consolidation, and visualization, we enabled data-driven decisions and a proactive approach to maintenance and demand planning. This proactive approach has been pivotal in achieving the 20% reduction in downtime.
Our collaboration in predictive maintenance incorporates essential domain knowledge and leverages state-of-the-art Machine Learning solutions. This approach helps customers scale their processes, transition to production, and enable live inference at operating sites. Implementing AI solutions significantly reduces real production costs by decreasing the frequency of maintenance periods.
Contact us today to learn how our comprehensive data engineering solutions can optimize your processes, minimize downtime, and drive efficiency in your organization.
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