AI Roots Out Waste and Inefficiency in Manufacturing

Manufacturers are using artificial intelligence to cut losses and waste. Computer vision can identify and address issues in production that directly impact the success of a business, both financially and sustainably.


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Image credit: Flutura

Companies use artificial intelligence to collect and analyze data that can provide insights on business operations, customer engagement, and everything in between. As companies make sustainability a priority, they are turning to AI to help them become better corporate citizens.

Manufacturers lose 2-4 percent of their product to off-spec production, which can cost a company millions of dollars per year. Nestlé is tackling this issue head on. The food and beverage company announced its goal of reaching net zero carbon emissions by 2050.

Eye on the Line

To reach this goal, Nestlé uses automation, AI, predictive maintenance, and robotics to optimize its assembly line processes. Nestlé has deployed Seebo’s AI technology, which is designed to cut waste from yield and improve quality on complex production lines.

Seebo integrates with a company’s existing sensors and quality control systems to assess inefficiencies. The Seebo Process-Based Artificial Intelligence software models the production process to identify where and how waste occurs. Once it determines the root cause, the Seebo software suggests optimization techniques to reduce or prevent waste. Machine learning capabilities allow Seebo to predict when waste will occur and to send notifications to prevent inefficiency on the line.

Nestlé is also using real-time data to assess its supply chain and procurement operations. The company has deployed machine learning tools to help automate business operations and improve employee work processes. The technologies deployed are part of Nestlé’s goal of creating a “cognitive enterprise” that uses artificial intelligence to streamline business processes and boost profits.

Metal tanks on manufacturing floor

Image credit: Seebo

Predict and Prevent

Another IIoT solution for manufacturing comes from Houston, Texas-based Flutura, which aims to eliminate production line loss through the use of IoT. The company’s Cerebra machine intelligence platform uses AI, computer vision, and machine learning to collect and analyze data from IoT sensors, machine signals, and operational processes to determine business processes and predict faults. Operators can access the data on a dashboard.

Cerebra can determine the root cause of a problem, predict faults, and prevent failures, saving the company from lost product and downtime. Cerebra’s Automated Root Cause Analysis (RCA) can identify flawed components, erroneous input parameters, and system or process irregularities quickly, so manufacturers can respond quickly and avoid a shutdown. In some cases, Cerebra can warn of potential problems 60 minutes in advance, so operators have ample time to intervene if needed.

In addition to reducing production failures and downtime, machine intelligence can increase the life of the equipment and improve maintenance schedules. It also can reduce fuel and energy consumption, helping manufacturers reach sustainability goals.

Flutura teamed with Intel to expand Cerebra’s capabilities to improve health and safety across entire industrial facilities. Cerebra integrates computer vision with the facility’s asset monitoring and diagnostic data to ensure a safe working environment. Video analytics and AI models can determine proper use of safety gear and ensure secure access inside and outside the facility.

Part of the Intel AI Builders program, Flutura used the Intel® DevCloud to optimize Cerebra, which is powered by Intel® Xeon™ Scalable processors, for use in cloud-based, on-premises, and edge-based deployments. Flutura built its algorithms with TensorFlow, using the Intel® Distribution of OpenVINO™ toolkit.


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