Revolutionize your paper manufacturing with AI-driven optimization. Enhance efficiency, cut costs, and achieve autonomous operations like industry leaders Arjowiggins and Oji Paper. Embrace the future of smarter production!
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The day-to-day work of manufacturing engineers is growing more complex every day. Traditional analytics tools—like PowerBI and Excel—have limitations when it comes to comprehensive analyses. These traditional tools (and the human brain) can’t holistically analyze the layered relationships that exist within manufacturing data sets.
This is where multivariate analysis comes into play. By automating key steps within the analytical process—for example, data contextualization and solving for multiobjective outcomes—engineers can rapidly reduce the effort they spend conducting analyses. Engineers can use multivariate analysis to get more precise results, which in turn drive more impactful outcomes.
By unraveling the complex relationships that exist between manufacturing variables, engineers (and operations teams!) can use multivariate analysis to break free from assumptions and biases, moving the entire organization toward data-driven efficiencies.
This white paper outlines key ways—and proven tools—to uncover deep process optimizations effortlessly. Download this white paper if you want to drive better, more precise results with less effort.
Complete the form to download the white paper now.
With contextualized production data readily available in a usable format, teams can tackle any business challenge and optimize production precisely. Learn why Braincube-powered Digital Twins are different—and why these differences will differentiate your company
Braincube was built for manufacturing experts by manufacturing experts. Learn how our product suite helps engineers gain time back every day (roughly two hours per engineer) by streamlining the “grunt work” so you can do your most valuable work.
A global building materials manufacturer used Braincube’s tools to understand the interrelated impact of different production variables on a key recipe. Their findings resulted in a $200,000 annual savings. See what else they achieved in this case study.