New book helps manufacturers digitally transform their operations to generate substantial cost savings and increased productivity.

By Jim O’Rourke, Academic Industry Principal at OSIsoft

The manufacturing industry is at a crossroads with many challenges to overcome. Deloitte predicts that the skill gap in advanced manufacturing may leave an estimated 2.4 million jobs unfilled between 2018 and 2028. In addition, an aging, retiring workforce with substantial institutional knowledge is the No. 1 concern for many manufacturing organizations.

The time is now for manufacturing organizations to find solutions for these challenges or lose any competitive advantage they’ve built; and technology, including a real-time software information system, should be at the top of the list.

While the concept of digitally transforming your manufacturing facilities may be daunting, the incredible value of having access to all data across your entire operation provides manufacturers with ample motivation for embarking upon this journey.

No matter how old your infrastructure is, or how familiar you are with real-time data solutions, the power of modern real-time data analytics and predictive maintenance allows organizations to achieve substantial value and returns in the form of better work processes, increased productivity, lower controllable costs, and more efficient collaboration inside and outside an organization.

But how does one start on the journey to digital transformation? How do you train your staff to overcome obstacles and analyze the right data that will impact the bottom line?

These can be hard questions to answer for many. That’s why my former colleague, Osvaldo A. Bascur, and I wrote the book, “Digital Transformation for the Process Industries: A Roadmap.”

digital transformation for process industries

The book provides insights into how to justify and deploy software that will provide significant return on investment; as well as how to contextualize operations and production data to be easily consumed by your corporate analytics and models.

If digital transformation isn’t a new concept to you, you’ll find resources to integrate operational data into significant event data, and how to use this information for advanced analytics, such as machine learning (ML) and big data analytics.

Once a company deploys a real-time software information system (referred to as an enterprise industrial data infrastructure, or EIDI in this book) as the time-series system of record, the value begins to manifest:

The sensor data captured from plant/refinery control systems becomes contextualized and transformed into valuable information, to be used by operations, engineering and business personnel alike, bridging these worlds.

The real-time and historical data can be reused many times for numerous use cases, such as asset health, process improvement, root cause analysis, regulatory compliance, and as input to big data analytics.

Most importantly, the company can transform into an efficient, data-driven culture, enhancing profitability, customer satisfaction and environmental stewardship.

The book illustrates many real-world experiences with companies across the process industries that are going through their own digital transformations. It is full of invaluable insights as manufacturers strive to convert operations data into tangible business results.

For example, when forest and paper products manufacturers experienced a downturn in demand during the 2000s, a few resourceful companies discovered they could sell their excess electricity back to the local power company. Not only did they use operating data to make their operations more cost-effective, they also uncovered a new revenue source.

Another example is when a former colleague of ours developed some algorithms that monitored the health of 3,800 electronic submersible pumps (ESPs) in real time and identified bad behavior. In the first year, the company increased ESP uptime by 63%, which resulted in a first-year savings of US$40 million, thanks to a reduction in the number of spare parts and maintenance labor needed to service failed ESPs. Plus, the US$40 million savings did not include lost production revenue at a time when a barrel of oil cost more than US$100.

The book is available now on Amazon and the publisher’s website.

Jim O’Rourke
Jim O’Rourke

Jim O’Rourke is Academic Industry Principal at OSIsoft. In this role, Jim provides OSIsoft technology to universities and academic institutions to help their students learn how to analyze real-time data in their curriculum using leading industry tools and best practices. He also provides universities with PI System software to enable innovative research initiatives that solve tomorrow’s problems through effective data analysis.

www.osisoft.com