We now live in a world where data is the new currency and is a primary driver to technological advancements. This leads entire industries to constantly seek innovative ways to enhance their productivity and efficiency through Artificial Intelligence (AI) and Business Intelligence (BI) initiatives. The manufacturing sector is no exception, as it embraces the power of AI and BI through digital transformation projects to usher in a new era of automation and optimization.

Ideally, these transformative technologies should reshape traditional manufacturing processes, resulting in improved product quality, reduced costs, and streamlined operations. But, are they truly as effective as originally planned?

Today, we delve into the critical success factors to ensure AI and BI initiative success in manufacturing and explore how to ensure the desired impact.

Data: The Foundation For AI and BI Success

With the manufacturing sector under intense pressure today amidst factors like rising labor costs and supply chain issues, the impact of AI and BI initiatives will only expand in the years to come. Right now, these technologies have the potential to completely transform how goods are manufactured by making factories more intelligent and efficient while revolutionizing operations in a myriad of ways, providing manufacturers with the ability to:

  • Cut operational costs
  • Reduce waste
  • Improve sustainability
  • Enhance product quality
  • Enhance inventory management
  • Ensure regulatory compliance
  • Increase yield
  • Avoid downtime
  • Improve the manufacturing process

At the heart of every AI and BI initiative is data. According to Joe McKendrick in his Forbes article titled The Data Paradox: Artificial Intelligence Needs Data; Data Needs AI, “Artificial intelligence is a data hog; effectively building and deploying AI and machine learning systems require large data sets.” So, ensuring the success of your AI or BI initiative will be directly proportional to your ability to generate data.

Data: Quality Versus Quantity (Or Both)

When it comes to data for AI and BI initiatives, the primary issues will revolve around questions of sourcing, and specifically for:

  • quantity versus quality
  • machine-generated or human-generated
  • raw data or contextualized for value

One area where AI and BI is having a profound impact is machine health and predictive maintenance. Traditionally, manufacturers have relied on scheduled maintenance practices, which have often led to unnecessary downtime and increased costs. However, with the power of AI and BI, manufacturers can now employ predictive maintenance strategies that analyze real-time data from sensors and machines to detect anomalies and predict potential failures.

For example, within a manufacturing plant, IIoT sensors and beacons combined with AI algorithms can monitor data from the assembly line, such as temperature, vibration, and power consumption, to identify patterns that indicate impending equipment failure. By detecting these warning signs early on, maintenance teams can schedule repairs or replacements proactively, avoiding costly breakdowns and optimizing downtime. This proactive approach helps prevent unexpected disruptions, reduce unplanned maintenance expenses, and maximize overall equipment effectiveness.

Ensuring the success of your AI and BI initiative will require you to generate both the necessary quantity and quality of contextualized data from all target systems and processes within your plant or facility.

Filling Manufacturing Blind Spots

Unfortunately, getting the necessary quantity of contextualized (read “quality”) data is easier to plan and harder to accomplish. Ask a Gartner analyst how much data is automatically generated by systems and processes, and they will tell you that for upwards of 90% of manufacturers there are only 10-20% of their systems generating digital data. 

So, the reality is that with an average of 75% manufacturing blind spots, AI and BI initiatives are most likely skewing reporting and analysis based on the limited data available. This, in turn, leads management to make decisions with an incomplete picture of the reality of what is happening.

The good news is that filling these blind spots with digital data in real-time is now possible with the latest in IIoT asset tracking and digital twin solutions. If you have 30 mins, then Thinaer can take you through a process customized to identify your current blind spots so you will better understand where your data gaps prevent a complete picture for your AI and BI initiatives.

Let’s discuss your data issues and understand where your manufacturing blind spots put your AI and BI initiatives at most risk. Our experts are ready to answer all of your questions and help you get a handle on your digital transformation processes and AI/BI initiatives.

By Product

Thinaer Sonar

Classified Area

API

Digital Twin

Asset Tracking

By Industry

Manufacturing

Defense & DoD

Aerospace

Aviation

Healthcare

Consumer Packaged Goods

By Product

Thinaer Sonar

Classified Area

API

Digital Twin

Asset Tracking

By Industry

Manufacturing

Defense & DoD

Aerospace

Aviation

Healthcare

Consumer Packaged Goods

Blog Articles

i

Case Studies

eBooks

Data Sheets

Videos

About

Careers

News & Press

Partners

Contact