A Practical Approach to AI/ML at the Industrial Edge Real time intelligence is key to smarter manufacturing

Elizabeth Samara Rubio, Chief Business Officer, SiMa.ai

In today’s rapidly evolving business landscape, there is quantifiable impact to be gained by AI-enabling the development of products, the production lines, and the processes that operate at the edge and serve as the lifeblood of the manufacturing industry. Industrial organizations must embrace AI/ML to increase revenue, reduce operational expenditures (OpEx), and gain competitive advantage. The growing demand for AI/ML that can keep up with manufacturers’ needs for real time data using intelligent platforms to synthesize data strategy, governance, and Machine Learning Operations (MLOps) presents a real opportunity for change.

This blog post recognizes the challenges faced by the manufacturing industry in fully realizing the benefits of utilizing AI/ML at the edge. Importantly, it explains how SiMa.ai is uniquely architected to get organizations on a path to quickly deploying and realizing value from industrial edge AI initiatives today.

The Cost of Quality in Manufacturing 

The financial implications of poor factory performance can plague a company’s bottom line. In the automotive industry, bad welds result in a staggering $9.9 billion loss per year, according to McKinsey; warranty claims cost manufacturers over $4 billion in a single quarter (Warranty Week). With the implementation of AI/ML technologies, manufacturers can expect to reduce inaccuracies over time with automated and efficient inspection processes. Improving such processes in real-time impacts shop floor data, optimizes production scheduling, better predicts defects and spots bottlenecks, and reduces waste – all of which contribute to less costs taken on by the manufacturer.

Real Time, Secure, Energy Efficient AI/ML at the Industrial Edge

Manufacturing companies across various sectors heavily rely on control systems to detect product defects and eliminate them prior to shipment. However, traditional inspection methods fall short when it comes to detecting defects in high-speed production lines that operate at speeds exceeding 70 miles an hour. Manual inspection becomes incompatible and unreliable under such circumstances.

Data transfer between the factory floor and the cloud is also highly latent and cost intensive, presenting a significant barrier to an organization’s ability to harness real time intelligence and act quickly to remedy a production line, defect or process. Alternatively, Edge AI/ML drives multiple inferences per millisecond, facilitating application performance that inspects and detects defects at incredibly high speeds to proactively reduce waste and costs.

SiMa.ai for the Industrial Edge

Current efforts to bring greater intelligence to the factory floor have made improvements, but continue to be hampered by limitations on precision, coverage, and cost — with organizations often forced to make trade offs between the three. In-line, process, and on-machine quality inspection systems provide limited coverage, come with high inspection costs, and require significant capital expenditure. Additionally, rules-based inspection systems struggle to adapt to variations in lighting conditions, part positioning, and product SKUs.

The introduction of AI-enabled automation that complements human and manual methodologies is going to bring a new level of collaborative intelligence to industrial manufacturing. High performance AI/ML applications with ultra low latency, greater power efficiency, data privacy and security, and significant cost savings – all fully enabled by the SiMa.ai MLSoC – will become the norm as AI/ML implementations at the industrial edge grow in scale.

SiMa.ai delivers on both performance and power while addressing the needs of manufacturers across use cases. With SiMa, manufacturers can automate inspection and quality control processes, detect defects with precision, improve quality, and streamline their processes to drive efficiency.

SiMa.ai unlocks new levels of predictive maintenance with real time data processing. By automating equipment failure detection with AI/ML, manufacturers can improve their maintenance processes by an average performance increase of five to 10 percent and maintenance planning time by 20 to 50 percent. Achievable with the help of AI models that run continuously, analyzing vast amounts of data using sensors and IoT devices identifies patterns and anomalies that precede equipment malfunctions.

Manufacturing customers adapt and fix faster with SiMa.ai’s closed-loop automation, which extends beyond defect detection and maintenance. While the cloud is good for processing data that is not time-driven, the edge uniquely meets the needs of industrial automation applications whether the goal is real time supply chain production monitoring, workload optimization, computer vision-guided assembly lines, or quality management.

Help your business get ahead or unlock new innovations today by adopting the SiMa.ai MLSoC and an integrated approach that synthesizes data strategy, governance, and ML Ops in a unified view in real time. Learn more about how SiMa.ai uniquely addresses industrial customer challenges in our on demand webinar, how to make the case for AI/ML deployment inside your organization today using our Industrial Deployment Guide, or jump right in and order your DevKit today to get started.