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What can go wrong will

  • Article on
August 30, 2018

Even on a modern manufacturing line equipped to collect and analyze process data, something can always slip through the cracks to derail quality and efficiency. This is only to be expected. But what if a key assembly process still rests entirely with a human being manually completing a task? There is no obvious data trail, no process monitoring in real-time. What then? In this article, Sciemetric's Manufacturing IT Manager, Patrick Chabot discusses the importance of having a strategy for continuous improvement on the line.

Case study: Shaping data, ensuring uptime

  • Article in Smart Industry
May 31, 2018

Industry 4.0 technologies are not merely nice to have. Business pressures are forcing these kinds of investments for many manufacturers. ROI can be rapid when you consider the cumulative cost of downtime, warranty claims, and scrap and rework that can be avoided with the right technology investment. Learn how Sciemetric helps manufacturers embrace Industry 4.0 in this article by Sciemetric's Aaron Alberts for the Smart Industry Forum.

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.

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).

What production data is necessary to drive your Industry 4.0 agenda?

  • Article in Automation Magazine
April 25, 2017

What production data is necessary to drive your Industry 4.0 agenda? “Industry 4.0”, “big data” and “data analytics” are not futuristic “hope to achieve some day” concepts. In this feature with Mathew Daniel, Sciemetric’s VP of Operations, discusses how they are redefining the competitive landscape of global manufacturing today, and the kind of digital architecture manufacturers must adopt to collect, manage and analyze their data to achieve the actionable insight they need.

Quality: Starting with Effective Data Management

  • Article in Industrial Machinery Digest
November 1, 2016

Richard Brine, Sciemetric’s CTO, discusses with Industrial Machinery Digest how manufacturers must evolve beyond scalar data collection and analysis alone to rise to the big data challenge posed by Industry 4.0. He outlines the need for centralized collection and analysis of the digital process signatures, or waveforms, generated by every cycle of the process and test stations on the line.