A Cybernetic and Computer System engineer who specializes in predictive analytics, ML and AI, Oscar Cruz brings a wealth of industry experience and a passion for making a difference in the world through technology. After graduating from La Salle University with an engineering degree focused on cybernetic and computer systems ?" Cruz worked in software development, business intelligence and analytics for a number of organizations spanning industries from construction, mining and transportation to government and education.

Through his work, he built global teams to deliver analytics to companies and public bodies across Canada, USA, Mexico; including UofC, SAIT, Bow Valley College, BURNCO, Canadian Western Bank, Alberta Energy Regulator, VIHA and WestJet. Cruz has developed statistical material for Data Analytics Program, adding hands-on skills, including dimensional modeling, ML, AI and predictive analytics, while also helping build the Analytics bootcamp program for SAIT, which teaches students high-demand skills in machine learning and artificial intelligence. He keeps a hand in industry as a managing partner and business analytics practice leader for Bow River Solutions, a consulting firm specializing in data management, working with vendors, such as IBM, Tableau, SAP, Informatica and Microsoft.

Tech Talk

Title: Using Predictive Analytics, Machine Learning and AI, to Prevent Downtime of your Critical Equipment

Description:

Asset-intensive industries like oil & gas, mining, energy and utilities use complex equipment such as rotary, compressors, haul trucks, and turbines in their day-to-day operation. Any unplanned downtime or major unforeseen equipment failure negatively impacts production, which affects the organization’s financial performance. However, increasing instrumentation (“smart technology”) of equipment and infrastructure and wireless communications are enabling organizations to acquire volumes of asset performance data and become proactive in monitoring the condition of these assets. Furthermore, analytics are enabling organizations to develop sophisticated models of asset performance, predict component and equipment failure and assess the health of in-service equipment. Driven by predictive analytics you can now detect even minor anomalies and failure patterns to determine the assets and operational processes that are at the greatest risk of problems or failure. Advances in analytic algorithms enable organizations to identify signs of possible failure well in advance of previous methods. What-if analysis allows an organization to investigate potential scenarios to determine the most appropriate (economic, efficient, safe) means of responding to pending equipment failure. Automated decision management can then recommend the best action to take in anticipation of equipment problems. This session will explain how the capabilities of Predictive Maintenance and Quality solution are being used by oil & gas, mining, energy and utility organizations worldwide to integrate relevant equipment data, including real-time, build models that predict maintenance needs, monitor asset performance, provide timely alerts, and recommended appropriate actions. These integrated capabilities allow these organizations to deploy limited resources more cost effectively, maximize equipment uptime and enhance quality and supply chain processes. 

 Use-case examples:

• Predict the failure of a monitored asset in order to fix it and avoid costly downtime
• Identify the root causes of asset failure to take corrective actions
• Minimize product quality and reliability issues to meet customer delivery schedules
• Others...