The Ghost in the Machine

Six Steps to Automating Knowledge

October 31, 2017

Martin Ford’s book Rise of the Robots — Technology and the Threat of a Jobless Future sheds light on an unsettling view of technology and its impact on the human race. Professor Stephen Hawking has further spooked imaginative minds by saying, “A rogue AI could be difficult to stop.” In automating knowledge, will we eventually hand over the keys of control to artificial intelligence?


The Need for Innovation

The process manufacturing industry is at an inflection point. Companies need to be astute and capitalize on powerful trends impacting this multi-billion-dollar industry. In the developed world, experienced chemical engineers are retiring in droves. However, the replacement pool of new engineers remains undersized. On the contrary, emerging countries such as China and India are graduating an impressive number of young — but inexperienced — engineers.


This necessitates the automation of knowledge to ensure that decades of experience are not lost. The captured knowledge helps new engineers ramp up quickly in their work. Global industry volatility is also creating tremendous industry tailwinds, and companies will need to capitalize on technology to maintain effective environmental, health and safety (EH&S) practices.


Six Smart Steps to Automate Knowledge

With smart manufacturing in mind, there are six key steps organizations can take to automate knowledge.


First, manufacturers should deploy smart flowsheets to capitalize on feedstock opportunities by providing visual and intuitive feedback. Manufacturers then become more agile, and they’re able to make better and faster decisions during volatile times.


Second, robust design optimization solves complex problems by enabling all dimensions of optimization to be considered across multiple cases. Automating knowledge is a safer bet than manual computation.


Third, it is crucial to connect the lifecycle. Manufacturers can reduce rework and save time by compressing the front-end engineering design (FEED) stage to achieve a more stable design in detailed engineering. We envision a single, consistent data model of the asset.


Fourth, the adoption of unified production optimization ensures models stay consistent and common. This streamlined thinking mitigates a complex environment where multiple organizations spanning across time and project necessitate profit maximization.


Fifth, it is crucial to implement maintenance optimization. If reliability can be considered upfront in conceptual design, process engineers can optimize the design for maintenance and reduce total lifecycle costs.


Sixth, prescriptive analytics help transform data into actionable decision support. In fact, prescriptive analytics in a self-maintained mode at the enterprise level is a pinnacle goal.


There Is No Ghost in the Machine

While it is literally impossible to eliminate all external challenges, process manufacturers can ramp up operational excellence to mitigate industry risks. Global management consulting firm McKinsey & Company concurs that by aggressively standardizing and simplifying processes, companies can react to unforeseen events quickly, and they are better enabled to improve safety and productivity. Besides reducing the risk of human error, the automation of repetitive technical decisions also gives time back to engineers to focus on more difficult analyses in their work.


Understandably, many of us still feel a combination of fear and excitement when it comes to these technological advances. Martin Feldstein, professor of economics at Harvard University, puts all of it into perspective: “Rapid technical change is not something new. We have experienced technological change that substitutes machines and computers for individual workers for many years. And yet, despite the ups and downs of the business cycle, the U.S. economy continues to return to full employment.”


Learn how AspenTech is leveraging machine learning, big data and analytics to help companies optimize their asset performance.

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