Revolutionizing Paper & Tissue Production
Now you can solve some of your most pressing challenges…
…with insights hidden inside
your production data…
…to achieve real business impact.
Unlock Business Insights Hidden in Your Data. With the One Platform that Can Actually Do it.
The Sight Machine platform is an analytics breakthrough that harnesses AI and machine learning to acquire, blend, and contextualize all your data — and model it into an in-depth digital twin of your entire production process. Sophisticated analytics are then applied to deliver actionable insights.
Sight Machine’s, Martin Rempel, walks through how data can transform Paper and Tissue operations
Martin is skilled in developing and implementing strategy to generate business results in new markets, with incredible knowledge of and interest in using technology to improve manufacturing, engineering and sales processes for the Paper and Tissue industries.
Watch this video to learn how Sight Machine can provide an integrated view of production cycle times, downtimes and reels and defects for an entire production line.
Changing Changeovers for Maximum Efficiency, Minimal Scrap.
The Sight Machine platform puts an end to on-the-fly guesstimates and excessive changeover-related scrap.
For each paper grade, our AI-driven software analyzes historical run data, correlates relevant environmental and materials input, and supplies recommended settings to ensure the highest quality, complete with upper and lower control limits.
Preventing Downtime Before It Happens.
Sight Machine algorithms assess recent sheet-break event data for your selected paper grade — figuratively every input from every relevant machine.
Parameters are automatically ranked in order of variance ten minutes before the break, pinpointing downtime causation plus actionable measures to prevent recurrence.
All without guesswork, manual data pulls, or the need for meticulous, time-intensive human analysis.
Eliminating Problems at the Root.
The Sight Machine platform analyzes workflow events such as dryer steam pressure swings for defined time intervals in the context of recorded outages, to identify parameters correlated with downtime.
No data wrangling required: the platform presents all data in whatever view, matrix, and context you specify.
Days or weeks of tedious numerical analysis are condensed into a few minutes of straightforward visual scrutiny.