• Mercer Celgar prides itself on its large-scale, modern facilities that primarily produce softwood and hardwood pulp. In addition, the customer generate green energy based on biomass and operate one of the largest softwood lumber mills.  
• Sustainability and self-sufficiency act as key driving factors for this organisation. Material production is carried out through the generation of electrical and thermal energy from biomass by-products which are created from the in-house process of pulping and sawmilling, resulting in overall waste reduction and maximization of forest resources.  
• In 2018, the product specialists were facing cultural and technical disruption when the automated machine health diagnostics provider discontinued the use of the system which led Mercer Celgar to begin the search for a new provider.
• Mercer Celgar aimed to enhance asset performance through data-driven machine learning, increasing productivity and profitability. The company sought a modern, scalable Predictive Maintenance solution capable of analyzing large datasets to predict failures and optimize performance. The customer required a provider with cultural fit, top-tier AI capabilities, and seamless system integration to align with their operational and sustainability goals.
• It was clear the customer required a scalable predictive maintenance solution that could efficiently integrate and process large volumes of data from multiple sources, enhance machine learning and forecast failures whilst monitoring asset performance. 

• Senseye Predictive Maintenance, using advanced AI, emerged as a leading solution, offering a unified platform for overseeing multiple production lines.
• Partnering through pilot programs since 2018, the integration with Mercer Celgar’s new CMMS system replaced traditional diagnostics with real-time insights. Senseye facilitated bespoke reporting across various machine types, providing early warnings and pre-emptive solutions to potential breakdowns.
• The partnership was a result of three pilot deployments that were primarily based on a combination of structured and unstructured learnings. Senseye Predictive Maintenance allowed to monitor multiple production lines and provide individual reporting across every machine type in one platform. This meant the performance could be tracked and compared, while also giving the maintenance teams early insight into potential breakdowns and failures.

Built in 1961, Mercer Celgar (MC)  was the first pulp and paper mill in the interior of British Columbia. Strategically located within one of British Columbia’s most valuable fibre baskets, Mercer Celgar’s close proximity to the BC coast facilitates sales to Asia where the majority of our pulp is sold.