At first glance, many parts of the metals, mining and manufacturing industry may not seem the most obvious candidates for digital technology-driven efficiency gains. For one, many of the manufacturing processes are generations old. Steelmakers have been using electric arc furnaces and Linz-Donawitz processes since shortly after World War II, while aluminium producers have been smelting the light metal using the Hall–Héroult process since its discovery late in the nineteenth century. And it is difficult to apply modern, industrial data-driven pricing methods to markets where one’s own prices are not in one’s own control, are commonly set on exchanges, and have themselves become speculative financial instruments for lightning fast algorithmic traders.
Year: 2018
Mining and metals companies could achieve a step change in their safety records while also significantly improving productivity by incorporating inexpensive sensors and analytics tools into their existing systems, an executive at privately-held US software company OSIsoft says.
Year: 2018
In the first part of this paper, we explored how TransCanada, Petronas, and ADNOC Onshore have successfully embarked on a digital transformation with real-time operational data. Now, we’d like to share the success stories from such leading industry players as Shell, Transocean and SBM Offshore who are achieving similar transformational gains from real-time data using the OSIsoft’s PI System.
Year: 2018
OSIsoft is the most widely deployed solution for capturing data from sensors and smart device across manufacturing, energy, utilities, pharma, life sciences, transportation, and facility management. Solutions are centered around the PI System that stores time series data in its original fidelity and assembles into digital twins within an asset framework that also provides context and can blend operational data with business and environmental information for IoT analytics insights and information management.
Year: 2018
Business analytics, machine learning and Internet of Things (IoT) technologies will transform virtually every aspect of the economy in the coming decades. One of the biggest stumbling blocks, however, remains capturing, organizing and delivering the vast amounts of data from sensors, industrial equipment, power supplies and other operational technologies (OT) in a timely fashion to achieve genuine, actionable insights through information technology (IT). Too often, this data — the raw material of business analytics — remains landlocked in disparate systems as getting it out requires inordinate amounts of time and money.
Year: 2016