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Explore top manufacturing ERP software on the market, as well as manufacturing ERP functionality, integrations, adoption roadmap, and business value.

Typically powered by machine learning (ML) algorithms, it can perform different tasks from product counting to execution quality control with defect (anomaly) detection.

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To reap the full benefits of this technology, however, companies will need an upgraded tech ecosystem, a solid data management and integration strategy, and the expert guidance of a trusted partner like Itransition.

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Learn how machine learning can help manufacturers to improve operational efficiency, discover real-life examples, and learn when and how to implement it.

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Scheme title: Main components of a computer vision system for manufacturing Data source: IEEE — Computer vision techniques in manufacturing, 2022

Increased productivity and minimized machine downtime via automation and computer vision-based maintenance (up to 50%, based on McKinsey's estimates) translate into a general reduction in operating costs.

Implementing an ML-based computer vision solution in a manufacturing scenario will involve business-oriented and technical steps. Here’s a brief rundown of the main adoption stages.

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Computer vision systems cover various use cases and operational scenarios. These include, for example, product design, automated product assembly, quality inspection, predictive maintenance, inventory management, supply chain traceability, and safety monitoring.

Thanks to modern deep learning algorithms and neural networks, computer vision systems for object or anomaly detection and other manufacturing-related tasks have achieved close to 99% accuracy, as reported by SAS.

Computer vision (CV) in manufacturing involves AI-powered systems that can capture, analyze, and understand visual inputs from the physical world (such as factories and other industrial sites) to help humans take proper actions and assist them in various production processes.

Combined with ML-powered anomaly detection, computer vision enables automated visual inspection to identify defective products with any type of anomaly (e.g. scratches or dents).

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Scheme title: Top 10 technologies helping manufacturers meet their business goals Data source: ibm.com — 2021 Digital transformation assessment Covid-19: a catalyst for change

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When implementing computer vision in manufacturing, you can face some technical challenges. Keep in mind the following recommendations to streamline its adoption.

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Computer vision-driven robots operate with surgical precision, ensuring better product quality, minimized human error, and an overall reduction of 10-20% in QA operations cost, as reported by McKinsey.

Read the story behind Itransition’s 5-year collaboration with a leading UK furniture manufacturer on web, mobile and VR solutions.

Deloitte highlighted that computer vision systems in manufacturing enable organizations to identify dangerous malfunctions, monitor workers' conditions, and detect any sign of fatigue or discomfort.

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A study by the Institute of Electrical and Electronics Engineers suggested a potential system framework to integrate computer vision into your existing manufacturing environment. These are its key components and related processes:

Comprises cameras that collect visual data from the manufacturing system and send it to the CV system via the Internet of Things (IoT).

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Processes the previous results with rule-based or more complex artificial intelligence algorithms to trigger a suitable course of action.

Explore how manufacturing companies implement digital twins to increase efficiency, encourage innovation, and drive more value.

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Provides the sensing module with uniform illumination and enables it to capture clear images of the manufacturing system.

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A set of robots that interact with the manufacturing system according to the strategies defined by the decision-making module.

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Image title: Volvo Cars’ visual inspection system Atlas Data source: assemblymag.com—AI-based vision technology aids vehicle inspection

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Along with other technologies driving digital transformation in manufacturing and the shift towards the Industry 4.0 model, computer vision proved to be a valuable ally for this sector. Indeed, its implementation has often resulted in significant cost cuts, superior quality control, higher production output, and increased worker safety.

According to Deloitte, adopting computer vision, automation, and other smart factory initiatives accelerates manufacturing cycles, resulting in a 12% growth in labor productivity and 10% in total production output.

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Rely on Itransition's computer vision consulting and implementation services to fully seize the benefits of this technology in your business scenario.

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