INTRODUCTION What would the impact be to your mill if you had real-time finished quality measurements (i.e., STFI, Mullen, tensile, wet tear, etc.) versus a periodic lab test? What if operational recommendations could also be provided to machine operators in real time? With the help of artificial intelligence and machine learning, OPTIX™ Applied Intelligence is blazing a trail toward complete process visibility with an analytics platform that uncovers mill improvement opportunities not previously possible. Mill owners and operators need to respond to market trends that are pushing the limits of machine operation. The growing trend in e-commerce has forced packaging companies to produce lightweight paper while maintaining or exceeding strength quality requirements and reducing costs through smarter fibre utilisation, chemical optimisation, energy reduction and more. At the same time, higher demands for packaging requires increased productivity and conscious resource management. Additionally, water scarcity and water quality concerns demand improved water management. Big data is emerging as a solution to these challenges. With innovative By Beth Ann Zarko, Product Marketer – Digital Solutions, Cydney Rechtin, Product Launch Specialist, Paul Valeck, Commercial Director and Global Marketing Leader – Predictive Analytics, and Anthony Lewis, Global Product Development Manager – Digital Solutions, Solenis LLC OPTIX™ Applied Intelligence — The Only Real- time Quality Measurement for Finished Product Mill owners and operators need to respond to market trends that are pushing the limits of machine operation. instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever-increasing visibility into their processes. However, the current data analytic tools are mostly backward looking and have specific limitations in highly variable, continuous-process manufacturing like the paper industry. The typical mill has as many as 5,000 data historian tags that can be associated through highly complex, multidimensional variable relationships. Traditional tools simply cannot handle the computations required to evaluate this level of complexity and nonlinearity in real time. Even the most experienced machine operators can struggle to understand all of the interrelated dynamics impacting production and how to respond to process variability. Furthermore, by the year 2029, all baby boomers will be 65 years old or older, presenting a major challenge for manufacturers WORLD PULP&PAPER 95 Figure 1. Overview of data analytics evolution. Improving Operator and Production Efficiency Via Artificial Intelligence (AI) and Machine Learning BENEFITS OF ARTIFICAL INTELLIGENCE