Analytics & Machine Learning

The use of analytics and machine learning in industrial companies and cities 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 and infrastructure space becoming much more dynamic, companies and cities 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 making 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.

All companies and cities can benefit from enhanced insights into market demand, operations, inventory, and corporate performance. Analytics provide the information to effectively manage global markets, supply chains, and operations. 

Use of Machine Learning, Artificial Intelligence, Cognitive Computing Is Exploding

Artificial intelligence, or machine learning, underlies many now-common consumer products.  Netflix recommends what to view; Siri, Cortana, and Alexa respond to voice; and Amazon uses this technology to optimize product recommendations.

In the industrial space, we’re seeing considerable interest in using these technologies to optimize asset maintenance, production operations, supply chain, product design, field service, and other areas.  Being interested is one thing, but understanding how to obtain and use the technology for a specific purpose is quite another.  ARC analysts can work with your team to help select the right technologies for your specific applications.


Build Business Case Consensus

Applying analytics in an industrial setting is a complex endeavor.  To help you build internal consensus while avoiding unnecessary costs and false starts, the guide answers key questions, such as: 

  • What are the analytics modes and how do they differ?
  • What roles and responsibilities are required?
  • How important is unstructured data to predictive analytics?
  • How is edge data being used?
  • Where do industrial platform with IoT services fit in analytics?

ARC Technology Selection Process

ARC Coverage Areas


    ARC's team of analysts are dedicated to researching, analyzing, and consulting in these areas:

    • Artificial Intelligence
    • Big Data
    • Business Intelligence Platforms
    • Cloud Application Platforms
    • Cognitive Computing
    • Enterprise Manufacturing Intelligence (EMI)
    • Machine Learning
    • Predictive Analytics
    • Prescriptive Analytics


    Related Technology Selection Guide

    ARC Client Quote

    "ARC's STAR Supplier Selection process was extremely effective.  By working through the process, we received valuable information that helped the teams understand what technologies and options are currently available.  ARC helped us develop a coordinated solution from the smorgasbord of technologies available that we believe will successfully address our client's unique challenges.  One of the key benefits was to hammer out the differences and get the project teams on the same page, allowing us to get buy-in and alignment from our management."

    Solaris Management Consultants