Upstream companies today must achieve operational excellence by reducing emissions and utility demands, improving production at existing assets and replacing and expanding reserves while exhibiting capital discipline.

Capital project owners need to optimize their CAPEX spending throughout the lifecycle of their projects, from design to operations.

Leading asset-intensive companies are using prescriptive maintenance—powered by AI and machine learning—to unlock the value and productivity lying uncaptured in assets. Using a scalable, easy to implement prescriptive maintenance solution, companies can improve the accuracy of failure detection, increase the advance notification period of asset downtime events and reduce maintenance spend.

As sustainability targets drive the energy industry towards a net zero future and energy security concerns intensify, companies are challenged to meet these competing demands within financial and workforce restraints. Owners and EPCs must cater to short-term energy demands while simultaneously positioning themselves for the long-term energy transition projects gaining traction.

Discover how to predict equipment breakdowns and perform prescriptive maintenance using a new approach to asset performance management. In this white paper, learn how nine early adopters of prescriptive analytics have reduced unplanned downtime and improved asset reliability. Download the paper to read real stories about the bottom-line results that have been achieved—in as little as 2 ½ weeks.

世界中で、主要な組織が高度なデジタル技術を採用および実装しています。デジタルトランスフォーメーションの旅は、資産集約型産業、特にエネルギーおよび化学薬品ビジネスの性質を変えるでしょう。こうした状況下では、デジタルツイン（物理的な資産の仮想化されたコピーとその運用上の動作）が重要な役割を果たします。今日アスペンテックが描くデジタルツインの重要なコンセプトは、仮想データに対して洞察とアドバイスを提供するAIの力です。本ホワイトペーパーでは、これからのデジタルツイン戦略で重要になる鍵をご覧いただけます。

Making big capital project management decisions shouldn’t be left to subjective perceptions or over-simplified analysis. Decision-makers need quantifiable, trustworthy answers to make the most profitable decisions possible. Aspen Fidelis Reliability is a robust RAM analysis tool that can handle the real-world challenges of today's process industries. In this paper, learn how Fidelis enables you to maximize the economics of business decisions and accurately predict future asset performance of the whole system.

Are you wondering how your site stacks up against those of your peers? Do you know what your Advanced Process Control (APC) Score is? Gain key insights into your APC program and find out how best to measure it against others by reading this white paper. Determining where you stand compared to your competitors is a key step toward closing your APC GAPs and improving your site’s profitability. Download this white paper and learn how you can also participate in a benchmarking survey and be a part of an exciting study!

Traditional preventive maintenance alone cannot solve the problems of unexpected breakdowns. With asset performance management powered by low-touch machine learning, it’s now possible to extract value from decades of process, asset and maintenance data to optimize asset performance. In this white paper, learn how this disruptive technology deploys precise failure pattern recognition with very high accuracy to predict equipment breakdowns months in advance and advise on prescriptive maintenance. The paper also outlines five best practices for driving state-of-the-art reliability management to increase production and profitability.

従来の対処的メンテナンスだけでは、不測の事態に対応できません。手軽な機械学習による資産パフォーマンス管理(APM) により、今や製造工場のスタッフが、何十年にもわたって蓄積してきた設計や運用データから容易に価値を引き出し、資産（主に装置などのハードウェア）のパフォーマンスをより適切に管理して最適化することが可能になりました。本書では、常識を覆すような画期的なテクノロジーがどのように精密な故障パターン認識を使い、高精度に装置の故障を数ヶ月も前に予測し、処方的なメンテナンスをガイダンスするかを説明しています。また、５つのベストプラクティス（運用方法）をご紹介し、最先端の信頼性管理による、さらなる生産効率および利益率の改善手法について述べています。