Digital technology has improved industrial operations in countless ways. Older, outmoded forms of data-driven technologies, however, have resulted in data silos and data lakes, leaving useful data largely inaccessible and the insights that data might support unattained.
This white paper will discuss the evolution of maintenance strategies, including the use
of advanced pattern recognition and machine learning technology to enable the shift to
more proactive and optimized strategies. Moving to a Predictive Maintenance strategy
helps asset intensive organizations increase asset performance, reliability and availability
to ultimately maximize economic return.
By refocusing data management strategies and taking a holistic approach to build the
right data foundation, upstream oil and gas companies can use new tools to optimize
production, increase safety, and improve bottom-line results.
The manufacturing plant is a heavy energy consumer – energy overheads are always the
largest or second largest factory operating expenses. Energy losses and optimization can
be strategically managed by using modern technologies such as big data management,
manufacturing intelligence and advanced analytics to measure, visualize and analyze
energy data in a business context. This forms the foundation for the continuous
improvement process. Understanding and correlating energy data with operations data
can bring in required changes, enabling factories to advance operational performance,
control costs and stay competitive.