Industrial applications of Artificial Intelligence and Machine Learning – Part 2/7

The use cases for artificial intelligence (AI) and machine learning (ML) in industrial markets are quite broad and diverse, but behaviorally they are somewhat similar. Like a valued butler, a good AI or ML application is a helpmate, anticipating needs, managing tasks, and providing trusted advice (recommendations). Below are some examples of the kind of valuable assistance AI and ML can provide throughout a product’s or an asset’s lifecycle.

Predictive Maintenance and Field Operations

The most commonly cited industrial application of AI is predictive maintenance, e.g. the ability to predict when an equipment failure will happen to avoid expensive downtime costs. Few people realize how much AI can be used upstream, downstream, and beyond these specific AI-powered predictive models in order to design and scale the benefits of AI to all field operations.

ML-enabled field support can assist with:

Maintenance, Repair and Operations planning,
Generation of preventive and predictive maintenance recommendations,
Analysis of quality issues,
Automation of routine operations and maintenance tasks using automation software, robots, autonomous vehicles and drones,
Interpreting and funneling operational data back to teams working on service design, and
Interpreting and sharing performance data, along with other quality-related data like customer feedback and warranty information, to manufacturing teams.

Design

In the conceptual phase of product development, ML is applied in combination with virtual engineering models and simulation for iterative design.

With these technologies, millions of design options can be cycled through in an instant, with recommendations automatically generated for optimal solutions based on multiple criteria (cost, sustainability, time, regulatory requirements, etc.).

AI and ML are also valuable in the earliest ideation stages. They are being used in cognitive search systems to help designers explore existing design concepts via both text and image searches. And they can help designers understand customer demand through analysis of sources like social media or internal customer feedback systems.

Testing

ML can also be used to develop highly accurate digital models of both physical objects and systems. This enables the development of realistic behavioral models that can be used to run performance simulations. This use case is so well-established that physical prototyping has been all but eliminated in some industries (architecture, automotive, aerospace).

Manufacturing

ML-powered digital modeling and simulation (including virtual reality systems) are also being used to 1) plan production lines and systems, 2) develop and integrate smart equipment, smart robots and production-line drones, 3) recommend and execute proactive maintenance (preventive/predictive maintenance), and 4) funnel important production data back to teams working on product design and specifications.

Sales & Marketing

In commercialization phases, AI and ML applications are being used to:

Predict demand trends,
Deliver highly personalized/micro-targeted marketing,
Create intelligent, multi-lingual bot assistants for self-service ordering and support,
Power sales- and marketing-related virtual and augmented reality applications, and
Customize products and services.