For AI in manufacturing, start with data

He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School. Consider the example of a factory maintenance worker who is intimately familiar with the mechanics of the shop floor but isn’t particularly digitally savvy. The worker might struggle to consume information from a computer dashboard, let alone analyze the findings to take a particular action.

• In manufacturing, ongoing maintenance of machinery and equipment represents a significant expense and a negative impact on the bottom line.
• Now, terabytes of data flow from almost every tool on the factory floor, giving organizations more information than they know what to do with.
• Accurate demand forecasting helps manufacturers reduce risk and increase overall supply chain efficiency.
• Unlock the potential of AI and ML with Simplilearn’s comprehensive programs.
• In manufacturing, for instance, satisfying customers necessitates meeting their needs in various ways, including prompt and precise delivery.

Fero Labs is a frontrunner in predictive communication using machine learning. The benefits they’ve found from automation include a reduction in operational costs by up to 40%; an increase in the manufacturer’s control over processes; improved employee performance; and significantly lower downtime. Machine learning solutions can promote inventory planning activities as they are good at dealing with demand forecasting and supply planning. AI-powered demand forecasting tools provide more accurate results than traditional demand forecasting methods (ARIMA, exponential smoothing, etc) engineers use in manufacturing facilities.

The Factories of the Future Can…

Manufacturers can use AI to forecast demand, dynamically shift stock levels between multiple locations, and manage inventory movement through a bafflingly complex global supply chain. Simulation–advanced computer modeling–is revolutionizing every method and procedure in the manufacturing industry. It’s enabling manufacturers to carry out tests and run experiments in virtual worlds instead of the real one, where they’re expensive, time-consuming, and potentially unsafe. Sridevi Edupuganti is an innovative leader known for strategically enhancing business opportunities through technology planning, orchestrating roadmaps, and guiding technology architecture choices. With a rich career spanning over two decades as a Senior Business and Technology Executive, she has driven teams to empower customers for digital transformation.

Manufacturers can increase production throughput by 20% and improve quality by as much as 35% with AI. The fusion of AI intelligence and manufacturing has brought about a transformative shift in industrial processes, leading to increased innovation across the manufacturing sector. Finnish elevator and escalator manufacturer KONE is using its ‘24/7 Connected Services’ to monitor how its products are used and to provide this information to its clients. This allows them not only to predict defects, but to show clients how their products are being used in practice.

What is artificial intelligence?

As a true visionary, Gopi is quick to spot the next-generation technology trends and navigate the organization to build centers of excellence. K is a distinguished sales leader with a remarkable journey that spans over 15 years across diverse industries. Her expertise is a fusion of capital expenditure (CAPEX) machinery sales and the intricacies of cybersecurity. Sugandha is a seasoned technocrat and a full stack developer, manager, and lead.

Software powered by artificial intelligence can help businesses optimise procedures to maintain high production rates indefinitely. To locate and eliminate inefficiencies, manufacturers may use AI-powered process mining technologies. After detecting an issue and classifying it, they use automated protocols to prevent the problem from escalating and trigger alerts.

Using AI for quality control

This is because OCR is able to identify data directly from scanned/printed images, thereby reducing data entry time. Then, the object detection model can be trained and applied to the company’s computer vision system so that PPE is detected in real time. Computer vision helps manufacturers with detection inspection via automated optical inspection (AOI). Using multi-cameras, it more easily identifies missing pieces, dents, cracks, scratches and overall damage, with the images spanning millions of data points, depending on the capability of the camera. AI can help enhance supply chain activities, such as optimizing inventory levels, and identifying potential supplier issues.

Until recently, simulation was highly complicated and required immense computing power. Thanks to AI technology, simulation is now 100 times faster and more usable–and affordable–than ever. As a digital leader responsible for driving company growth and ROI, he believes in a business strategy built upon continuous innovation, investment in core capabilities, and a unique partner ecosystem. Gopi has served as founding member and 2018 President of ITServe, a non-profit organization of all mid-sized IT Services organization in US. Gopi is the President and CEO of Saxon Inc since its inception and is responsible for the overall leadership, strategy, and management of the Company.

Preparing Enterprise Data for Generative AI

Factory worker safety is improved, and workplace dangers are avoided when abnormalities like poisonous gas emissions may be detected in real-time. Besides these, IT service management, event correlation and analysis, performance analysis, anomaly identification, and causation determination are all potential applications. Machine vision is included in several industrial robots, allowing them to move precisely in chaotic settings. Edge analytics uses data sets gathered from machine sensors to deliver quick, decentralized insights. AI for manufacturing is expected to grow from $1.1 billion in 2020 to $16.7 billion by 2026 – an astonishing CAGR of 57 percent.

The concept proposes to modernize how horizontal transporters can communicate with AI-based, high-level machinery in real time. The main idea is to optimize all truck movements in the warehouse and make them interconnected. For manufacturers, warehouse automation becomes a relevant solution to minimize manual labor and reduce operational costs. Automated warehousing also helps companies process orders quicker and ensures more accurate scheduling.

The Four Types of AI in Manufacturing

To help with this, FANUC developed ZDT (Zero Down Time), a piece of software that gathers images from cameras, before sending them (and their to the cloud. After they’ve been processed, they can spot any potential issues that may appear. If there are poor lighting conditions or blurring to the text/image, OCR’s capabilities could be lessened. However, there are already solutions in place that ensure OCR can overcome its challenges, while its deep learning processes ensure the system is able to achieve familiarity with printed texts super fast.

Deep learning is essential because without it, training object detection algorithms to process huge swathes of data is impossible. And without these huge swathes of data, the computer vision system isn’t able to correctly differentiate objects, as well as contextualise them. Computer vision also assists operators with Standard Operating Procedures when the operators have to switch products numerous times in one day.

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