4.3 out of 5 stars
(416 Ratings)
Jama Connect®, a product development platform, uniquely creates Living Requirements™. This digital thread is created through siloed, test, and risk activities to provide end to end compliance, risk mitigation, process improvement, and compliance. Companies creating complex products, systems, and software can now define, align, and execute on what they need. This reduces the time and effort required to prove compliance and saves on rework. You can be sure of success by choosing a solution that is easy-to-use, flexible, and offers support and services that are adoption-oriented.
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4.9 out of 5 stars
(4 Ratings)
Epicor Connected Process Control (CPC) is a flexible no-code/low-code MES and manufacturing operations platform that helps manufacturers standardize production and assembly processes, improve quality, and increase visibility across the shop floor.
Using digital work instructions with multimedia content, manufacturers can guide operators through critical tasks, enforce process control, and improve consistency across products, workstations, and production lines.
CPC connects equipment and devices to collect production and quality data in real time, helping manufacturers identify issues faster, reduce waste, and support continuous improvement.
CPC provides product traceability with a complete historical record of each product's build and inspection history. Manufacturers use CPC to support assembly verification, error proofing, quality inspections, operator guidance, and other connected manufacturing initiatives.
For manufacturers with complex product variations, CPC can dynamically present the correct work instructions based on the product or configuration being built, helping ensure products are assembled accurately and consistently.
Available on-premises or in the cloud, CPC scales from a single work cell to enterprise-wide deployments.
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Amazon Lookout for Vision
Effortlessly develop a machine learning (ML) model capable of detecting anomalies in your production line with just 30 images. This technology allows for the identification of visual defects in real time, thereby minimizing and averting product flaws while enhancing overall quality. By leveraging visual inspection data, you can prevent unexpected downtime and lower operational expenses by proactively addressing potential problems. During the fabrication and assembly stages, you can identify issues related to the surface quality, color, and shape of products. Additionally, you can recognize missing components, such as a capacitor that is absent from a printed circuit board, based on their presence, absence, or arrangement. The system can also identify recurring defects, like consistent scratches appearing on the same area of a silicon wafer. Amazon Lookout for Vision serves as a machine learning service that employs computer vision technology to detect manufacturing defects efficiently and at scale. By automating quality inspections through computer vision, you can ensure higher standards in product quality and consistency. This innovative approach not only streamlines the inspection process but also empowers businesses to maintain competitive advantages in their respective markets.
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Scanflow
Scanflow Inspect puts an AI inspector on your production line. One compact unit bundles the camera, lighting, edge computing and software, so it can be mounted on a conveyor, at a workstation or at the end of the line. Every part is checked at cycle time, not just a sample, so faults are flagged before they travel downstream. It spots issues like thread and diameter drift, missing components and wrong kits, and it measures dimensions as it inspects. Coverage runs across castings, forgings, machined components, sub-assemblies, complete systems and vehicles. Teams can ask the built-in QC AI Agent plain questions about defects, batch pass rates and reject trends. The Auto-Annotation Tool lets your own engineers teach the system a new part within hours, with no data-science backlog. Start with a Day 1 demo on your parts, run a one-line pilot over two weeks, then go live. Pilot benchmarks: 95%+ detection accuracy and 2x faster than manual checks.
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