For utility companies, end-of-month reporting periods can be stressful. Management waits impatiently to receive the closing reports with KPIs from the previous month’s accounting cycle. The data is often incomplete and not up-to-date, and those responsible for the reports spend days preparing labor-intensive, manual reports. Now imagine the added pressure of being the world’s number one independent power producer, with over 115 gigawatts of installed capacity and millions of customers relying on your business to run reliably and efficiently. That’s precisely the kind of frustrating situation ENGIE, a French multinational electric utility company, faced each month before it automated its business management process with the help of OSIsoft’s PI System.
Year: 2019

OSIsoft Recognized by 451 Research as a ‘451 Firestarter’ The maker of the PI System recognized by leading analyst firm for innovation and vision in the technology industry General 2019-03-25 San Leandro, CA, March 25, 2019 - OSIsoft LLC, a leader in operational intelligence, today announced that it has ...

This white paper contains Navigant Research’s view of key trends within the digital transformation market, case studies from PI System customers, and Navigant Research’s recommendations for industrial customers considering digital transformation platforms.
Year: 2019

Most utility executives and upper level management cannot go a day without hearing from a vendor about how machine learning and artificial intelligence (AI) will revolutionize the utility industry. The truth is, these technologies are already revolutionizing operations and services at many power utilities. Utility personnel at all levels are eager to get access to the right data and tools to optimize their work and transform their businesses. Many utilities still struggle with the functional and strategic elements needed to succeed, even though they want to use data to improve the business. Dealing with data at an enterprise level is one big obstacle that holds back even the most advanced companies. This whitepaper will provide a snapshot of some of the most common data issues facing utilities, with insights on how they can be avoided to ensure long-term success in analytics.
Year: 2019

A pharmaceutical company with global operations recently received a warning letter for failing to properly investigate a batch failure. Among the potential consequences: any pending drug applications listing the affected facility may be stalled. Of the worries that keep pharmaceutical executives up at night, receiving such a letter is high on the list. With the clock ticking, company personnel took stock of what they faced. Their blockbuster medication was manufactured in an ecosystem involving three countries. Was the problem really at that facility, or upstream at one of the others? The answer, of course, was in the data, which was disparate and came from different parts of the world. Moreover, this company’s data was mostly in paper form, and the few electronic files on hand were not contextualized. The organization faced a daunting task, with its billion-dollar-a-year product — and other products and business — at risk. But the story did not have to play out this way. This company could have identified the source of variation in real-time with the right data infrastructure.
Year: 2018