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Sciemetric Insights

How to stay competitive in a connected revolution

  • Article in Machine Design
April 18, 2018

In this feature published by Machine Design Magazine, Derek Kuhn, Sciemetric’s senior vice-president, argues why machine builders can’t ignore the growing use of process data by manufacturers to drive quality assurance, greater automation, efficiency and profitability. Manufacturers want this intelligence incorporated into a line as it is being built, rather than incur the time and expense of procuring equipment, hardware, and software from different vendors and trying to integrate it all together.

Collecting data: Why machine vision matters as part of your IIoT strategy

  • Article in Industrial Machinery Digest
March 1, 2018

Whether you’re a machine shop, a job shop, or a contract manufacturer in the supply chain of a major OEM, your success today rests with how machine vision data comes together with all the rest of your production data. In this article, Sciemetric's Manufacturing IT Manager, Patrick Chabot discusses how to be more competitive and break machine vision out of its silo.

Eight reasons why you should manage your machine vision data

  • Article in Vision Systems Design
February 7, 2018

In this feature with Vision Systems Design, Mathew Daniel, Sciemetric’s VP of Operations, explores how bringing images and image data into the serialized birth history record for each part in production opens the door to advanced analytics for process improvement and traceability for defect containment. It just takes careful planning to determine how best to collect, store and manage this data.

Harvesting data for Industry 4.0 profitability

  • Article in APMA Magazine
January 29, 2018

Derek Kuhn, Sciemetric’s senior vice-president, takes to the pages of APMA Lead, Reach, Connect to explain why auto manufacturers must look beyond data related to the performance of machines and business processes if they wish to succeed in a sector that is rapidly being redefined by Industry 4.0 and Manufacturing 4.0 principles. They must also collect, serialize and analyze the reams of production data generated by the process and test stations on the line.

Gaining The Manufacturing 4.0 Advantage With Data-Driven In-Process Testing

  • Article in Truck & Off-Highway Engineering
January 15, 2018

Manufacturing is changing thanks to the increasingly sophisticated and intelligent use of data to make a production line smarter and more efficient. For off-highway and specialty vehicle manufacturers, the 4.0 revolution offers great opportunity to achieve significant cost reductions and grow revenue. In this article, Sciemetric's Product Manager, Dave Mannila, discusses the benefits of data-driven in-process testing.

You need to go deeper than MES

  • Article in Manufacturing Technology Insights
October 16, 2017

Mathew Daniel, Sciemetric’s VP of Operations, is featured in Manufacturing Technology Insights Magazine where he discusses how manufacturers must go deeper than conventional manufacturing execution systems (MES) to achieve the quality and efficiency benchmarks demanded by Industry 4.0. This is about taking data collection and analysis to a new level with what Frost and Sullivan calls Manufacturing Performance and Quality Management (MP&QM).

How not to fear the Gage R of your leak test

  • Article in Quality Magazine
October 1, 2017

In this feature with Quality Magazine, Rob Plumridge, Sciemetric leak application engineer, discusses how to tackle the first “R” of Gage R&R for leak testing – repeatability. By focusing on repeatability first (before the other “R” – reproducibility), manufacturers can be certain they have addressed all the controllable variables that can impact the leak test, regardless of the equipment.

Bridging the machine vision data gap for Manufacturing 4.0

  • Article in IMV Europe
July 14, 2017

In this feature published by Imaging and Machine Vision Europe, Mathew Daniel, Sciemetric’s VP of Operations, discusses how vendors of machine vision systems and manufacturers must take a holistic approach to make more effective use of machine vision data as a tool to help raise production quality. He explores the value of integrating vision data into the serialized birth history record for each part in production.