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Solving Manufacturing Challenges with Micro-Solutions

July 14, 2021


Micro-solutions in manufacturing: Solving one challenge at a time to achieve productivity excellence

Smart manufacturing presents new challenges to manufacturers. While some are common throughout the industry, others are unique problems that require focused solutions. Through collaboration with innovative start-ups, our innovation teams explore the dynamics of micro-solutions—targeted solutions that enable key elements of smart manufacturing such as scalability, agile deployment, and contextualization. Offered as SaaS solutions based on high-level architecture, micro-solutions help manufacturers respond to needs in the field and achieve manufacturing excellence.

Reduce false calls (pseudo errors) at automated optical inspection (AOI)

Automated optical inspection (AOI) machines generate a high percentage of false calls/pseudo errors (called NFF, no fault found)–between 20%-80%. Due to the high rate of false positives, the errors had to be manually verified, a process that wasted significant time and resources. The webinar will cover a micro-solution to the problem using machine learning models on quality data using Valor technology. The micro-solution improved accuracy in predicting faulty units by 50%, reducing the need for manual verification and shortening time to market. No data science background is needed to benefit—the solution shifts error prediction left to production domain experts who understand how the production line works.

Increase throughput with early detection analytics

The cost of error increases significantly as the manufacturing process progresses. To achieve a better time to market with top quality and cost-effectiveness, manufacturers need to detect errors as early as possible. Using Valor technology, Siemens developed a micro-solution that shifts left and reduces the cost of error, saving valuable materials and resources while increasing throughput, productivity, and quality. It also reduces cycle time while improving the first-pass yield and eliminating bottlenecks in critical stations.

Supply chain improvement with digital component inspection

Experts estimate that 10% of the components used in manufacturing are counterfeit. This is a significant problem, as counterfeit components can cause product defects and damaged brand reputation. In this webinar, the speakers will explain how Cybord AI and Valor developed a micro-solution to identify counterfeit components by connecting to SMT machines, extracting images of all placed components, and comparing them to information in the comprehensive component directory. The micro-solution can authenticate each component and issue a complete component-level traceability report. The report contains reel component authentication, solderability, and tampering data as well as board “as-made” data used to verify compliance with the BOM. The scanning process is completely transparent and does not require any changes in the operator workflow and existing work practices.

Meet your micro solutions webinar speakers:

Smadar David, CEO & co-founder of Vanti Analytics, has over a decade of experience in leading engineering teams through full life cycles of opto-electro-mechanical systems. In the past, she established and managed the MEMS group at Innoviz Technologies, responsible for a cutting-edge MEMS scanning module from concept to serial -production.

Zeev Efrat, CEO at Cybord, is an entrepreneur with a passion for disruptive technologies. In the past, he led the market penetration of PDM integration into ERP for BaaN, leading to multi-million-dollar sales. He also managed Frost & Sullivan’s presence in Israel and launched and managed IoT sensor startups for cleantech.

Sagi Reuven, Business development manager for the electronics industry, at Siemens Digital Industries Software. He is a mechanical engineer with experience in layout design and simulation products, in the past and has previously served as the CEO of a medical device company in retinal imaging.

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This course will be held Online