Inaccurate tank levels lead to inefficiencies in device maintenance and operational planning
Need for accurate forecasts to minimize leaks, spillage, and downtimes
Identify equipment failure as well as maintenance issues before shutdowns
Built predictive model to forecast tank levels using onsite sensor reading and historical data. Tank forecasts leveraged to schedule tank pickups and minimize downtime at site
Leveraged Azure Machine Learning for rapid parallel model testing and development. Tested different advanced machine learning algorithms (Neural Network Regression, Poisson Regression, Decision Forest Regression) enable accurate prediction
Build Power BI dashboards available on PCs and mobile devices to monitor oil wells and other assets in the field. Based on data it can generate real-time alerts.
Power BI dashboard to analyze field assets, plan activities and monitor for failures
The minimized time needed for resolution across device maintenance issues
Schedule jobs/trucks/maintenance optimally, reducing cost and increasing revenue
About Neal Analytics
Neal Analytics is a cloud, data, and AI Microsoft Gold consulting partner supporting data-driven transformation initiatives from data strategy to solution design, architecture, development, operationalization, and support.
Our expertise spans across migration and modernization, data science, AI/ML, IoT, edge computing, BI, application development, and RPA.
With a focus on right-sized and pragmatic approaches towards digital transformation, Neal leverages Agile methodologies and flexible engagement models to deliver measurable customer value.