Ravi Chalaka

Artificial Intelligence is a Game Changer for Industrial IoT

Blog Post created by Ravi Chalaka on Nov 13, 2017

AI and IoT Blog Series
By Ravi Chalaka

 

In a recent speech, Russian President Vladimir Putin said that whoever reaches a breakthrough in developing artificial intelligence (AI) will dominate the world.[1] Chinese government has made AI development a priority by investing billions to get ahead in its development and application. Our future will be transformed in ways we don’t yet fully comprehend with application of AI. AI is a broad science and recent developments have been focused on machine learning and deep learning. It is clear that leveraging AI and the Internet of Things (IoT) can solve problems and unlock new opportunities, saving time and money in consumer, commercial and industrial environments. While we all see everyday examples in the consumer world like Amazon Alexa, Apple Siri and Tesla’s semi-autonomous cars, AI is being applied to transform industrial processes involved in the production of goods with greater impact to top and bottom lines. By combining industrial assets with intelligent software, AI paves the way for smart manufacturing with end-to-end operational visibility through predictive quality and dynamic scheduling, resulting in optimal production efficiency.

 

We’re not sure about world domination but AI is pervasive throughout our society, and the machine learning variant of AI that learns with or without human supervision is already a game changer for manufacturers using Industrial IoT (IIoT). The auto industry is an early adopter of AI, where it not only helped to make autonomous driving a reality but continues to develop ways to make cars more efficiently as well as making them safer. Across industries, manufacturers are integrating AI into their operations to improve quality, speed and functionality, and accelerate innovation.

 

Network, Iot, Internet Of Things, Connection, Cloud

Examples of AI in Industrial IoT Use Cases

Predictive Quality: Product recalls result in 80 percent of all financial losses.[2] AI can help minimize the number of defects and related recalls by comparing live data to standard deviation models. In the production process, when an anomaly occurs, an AI enabled solution can trigger real-time alerts, and the traceability makes it easy to pinpoint the root cause of production problems for fast resolution. Intelligent software gathers and analyzes meaningful human, machine and material data in real time, detecting production issues as they happen. With cameras on the shop floor, manufacturers can apply advanced image analytics to detect quality issues that are not visible to the human eye. With real-time notification and visualization tools, operators can respond to prevent issues before they impact production, reducing unplanned downtime, minimizing product defects and operating costs, and increasing production throughput. Using advance algorithms, AI even identifies patterns that can improve product quality over time.

 

Dynamic Production Scheduling: Modernized production processes provides increased operational availability, better on-time delivery and higher asset utilization. Using sensor data from man, machine, material, methods and applying advanced statistical modelling techniques, manufacturers can manage production flow while monitoring the health of assets deployed in the plant, and determine the best route and production velocity to achieve target quantity and quality of goods. Using an IoT platform enabled with AI, manufacturers can seamlessly interface with manufacturing controls to analyze shop floor data, proactively optimize production schedules, simulate workflows and reduce bottlenecks. For example, using value-stream visualization, manufacturers can observe key production planning factors in real time, allowing them to pinpoint when production process bottlenecks will occur based on predetermined conditions.

 

Disruption and Opportunity

Every industry is being affected by a wave of disruptive technologies and newcomers. To survive even the most established industrial companies must be able to transform vast amounts of data into insights that can be used to inform better decisions and improved outcomes. Today’s smart factories link data from supply chains, design teams, production lines and quality control to provide an end-to-end view of operations, and AI enables the predictive and analytic capabilities that drive greater efficiencies.

 

With over a century of global operational technology (OT) and decades of information technology (IT) experience, Hitachi is at the forefront of developing AI. Its Lumada IoT platform harnesses the power of Ai and IoT to enable manufacturers to reduce production costs, achieve near-zero defects, and improve product quality and worker productivity across multiple manufacturing sites. It gives manufacturers the intelligence they need to drive continual improvements and be industry leaders.

 

References:

[1] Meyer, David. Vladimir Putin Says Whoever Leads in Artificial Intelligence Will Rule the World. Fortune. September 4, 2017.

[1] Harrison, Ian, and Adrian Parker. The True Cost of Product Recall. Lockton Companies LLP. April 2015.


 


[1] Meyer, David. Vladimir Putin Says Whoever Leads in Artificial Intelligence Will Rule the World. Fortune. September 4, 2017.

 

[2] Harrison, Ian, and Adrian Parker. The True Cost of Product Recall. Lockton Companies LLP. April 2015.

Outcomes