Advanced Industrial Analytics

By Greg Gorbach

Category:
ARC Report Abstract

At every turn, the industrial space is becoming much more dynamic. More than ever, manufacturers and other industrial organizations must quickly adapt and respond as markets, technologies, ecosystems, and competitors change. They need to recognize or, ideally, anticipate rapidly changing situations and to take appropriate measures in time to make a difference. Feeling the pressure, almost every division, every department, every cost center, and every aspect of manufacturing now seeks more and better information. And while localized analytics can certainly help, the enterprise benefits even more when information analytics initiatives span multiple divisions and departments, because new insights may arise when data from a variety of sources are considered.

Industrial companies are moving to a culture and business model in which all decisions are made based on analysis of operations and business process data. Throughout the organization, these companies employ software to collect, contextualize, visualize, and analyze data to gain new insights. The common question is, "What does the data tell us?" Armed with new insights, organizations can anticipate changes and drive better business results. The culture of an information-driven company encourages decisions based on quantifiable information and analysis.

The use of analytics in industrial companies is growing rapidly. For more than a decade, the information workhorse has been the business intelligence (BI) platform, supplemented by enterprise manufacturing intelligence (EMI) in the plant. These systems excelled at helping users discover and understand the underlying reasons and details about what happened and why. Now, with the industrial space becoming much more dynamic, manufacturers are turning to advanced analytics and machine learning to support predictive and prescriptive solutions.

Today, the analytics market is extremely fluid. More companies are pursuing analytics solutions and more employees throughout the enterprise want more and better decision tools. And the increasing focus on Industrie 4.0 (I4.0) and Industrial Internet of Things (IIoT) is driving demand for predictive maintenance solutions, which rely on advanced analytics.

Table of Contents

  • Executive Overview
  • The Evolution of Industrial Analytics
  • Cloud Application Platforms
  • Introduction to Advanced Industrial Analytics
  • Advanced Analytics Use Modes
  • Recommendations

 

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Keywords: Industrial Analytics, Industrial Companies, Machine Learning, Industrial Internet of Things (IIoT), ARC Advisory Group.

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