Shell’s Manufacturing Margin Optimization team uses AspenTech solutions to optimize its energy and petrochemicals business, including planning and scheduling applications. In a highly collaborative global project with AspenTech, Shell successfully deployed Aspen PIMS-AO in the Cloud resulting in margin uplift, improved work-life balance and reduced IT costs. Read this case study to learn how you can improve margins with Aspen PIMS-AO in the Cloud.

Shell’s Manufacturing Margin Optimization team uses AspenTech solutions to optimize its energy and petrochemicals business, including planning and scheduling applications. In a highly collaborative global project with AspenTech, Shell successfully deployed Aspen PIMS-AO in the Cloud resulting in margin uplift, improved work-life balance and reduced IT costs. Read this case study to learn how you can improve margins with Aspen PIMS-AO in the Cloud.

壳牌的制造业利润优化团队使用AspenTech解决方案优化其能源和石化业务，包括规划和调度应用程序。 在与AspenTech高度合作的全球项目中，壳牌成功地在云上部署了Aspen PIMS-AO，从而提高了利润率，改善了工作与生活的平衡，降低了IT成本。阅读本案例研究，了解如何在云中使用Aspen PIMS-AO提高利润率。

Las compañías mineras invierten fuertemente en activos para todas las etapas del proceso minero: extracción, procesamiento, refinería y distribución del mineral. Al monitorear la condición y el comportamiento de los activos y desarrollar perfiles de operaciones normales, anomalías y fallas, las herramientas de mantenimiento predictivo pueden notificar al personal sobre los problemas del equipo antes de que ocurra falla. Este documento describe cómo el mantenimiento predictivo proporciona a las organizaciones mineras la inteligencia necesaria para:

Mining companies invest heavily in equipment for all stages of mining, mineral processing, refining and distribution. By monitoring asset condition and behavior and developing profiles of normal operations, anomalies and failures, predictive maintenance tools can notify staff of equipment problems prior to failure. This paper outlines how predictive maintenance provides mining organizations the intelligence needed to:

Aprenda como puede usar el mantenimiento prescriptivo (RxM) para dar un valor real a su organización. En este webinar el experto de AspenTech va a demostrar como los conocimientos basados en datos de AI y de Machine Learning mitigan los problemas de mantenimiento y mejoran la productividad.

In order to grow its business in a highly competitive market, Daicel, a chemicals manufacturer headquartered in Osaka, needed to develop polymers with narrow molecular weight distributions to meet a specific market demand. Read how Daicel used Aspen Polymers to develop a custom RAFT polymerization model. This model enabled our customer to optimize process conditions and reduce experimental costs, which provided the following benefits:

日本に本社を置き、創業から140年の歴史を誇るグローバル製薬会社がAspen Plusを導入して新規原薬（API）の合成を改善しました。同社は、創薬に重点を置きながら医薬品や医療機器の研究開発から製造、販売まで行っています。

With market conditions volatile and uncertain, it’s challenging to manage your business and still plan for future opportunities. Prices bounce. Trade patterns shift. Product slates evolve. Digitalization enables adaptability through this uncertain time and is in fact becoming a strategic imperative to achieving—and sustaining—new levels of profitability.

Genere el máximo impacto en su negocio al elegir el tipo de Machine Learning adecuado para su organización