The Self-Optimizing Plant Is Within Reach

If you want to understand how the Covid-19 pandemic has begun to impact the digital-industrial revolution, one of the first things you should do is check in with AspenTech: the company’s mission is to “embed Artificial Intelligence throughout industrial manufacturing environments,” notably in the oil and gas, chemical and engineering fields. Their vision is to help customers achieve the “self-optimizing plant”, where AI helps to continuously adapt operations to maximize efficiency. They must have their finger on the pulse of the unfolding digital-industrial revolution – it’s their bread and butter.

Ron Beck, AspenTech’s Director of Marketing Strategy, points to the highlights of a recent survey they conducted with energy consultant Crystol Energy. Three insights jump out:

The first is the immensely increased sense of urgency: for all ten strategic priorities listed in the survey (which include diversity and inclusion, digitalization and other new technologies, operational and employee safety, optimizing operational efficiency, defending market share, expansion and investment, diversification of assets and activities, raising capital, improving competitiveness, and environmental, social and corporate governance), the share of industry respondents who rank them as “very important” has risen since the pandemic began – companies feel that the environment has become so much more challenging that they need to redouble their efforts across the board.

The second is that the rise in the perceived urgency of digitalization dwarfs that for all other priorities; it is the single biggest swing, with a 14 percentage point increase in the “very important” responses.

The third is that a shortage of skills ranks high among the companies’ top concerns for the next five years, and even more so for the longer term; in fact, for the longer term it ranks higher than trade wars, cyber security or more stringent regulations.

The skills shortage challenge and the digitalization imperative are closely intertwined.   An aging workforce implies that companies now suffer an accelerating hemorrhage of experienced workers with strong traditional industrial skills. At the same time, digitalization is transforming roles in the industrial world, so that companies need to acquire more software and data science skills but also build new skills combinations in their workforce. To give a sense of the speed of this adjustment, Ron Beck points out that across major energy and chemical companies, today nearly 80% of data scientists have been on the job for no more than three years; more than half have been on the job for no more than two years.

In other words, if you are an industrial company today, you have likely brought on board a number of data-savvy new workers in the last 2-3 years; they play an increasingly important role in your business and you must help them grow into their roles and learn the industrial side as fast as possible. At the same time, you need to make the rest of your workforce conversant with the digital side which is becoming pervasive.

Whether you can meet the dual challenge of digitizing your operations and quickly transforming your workforce skills will determine not just whether or not you succeed, but in all likelihood whether or not you survive.

How can you do it?

According to Beck, with a double-pronged strategy.

First, you have to leverage technology to help your workers learn fast and keep learning; and to improve your company’s own foresight. AspenTech has developed a stable of expert-led training and “eLearning” resources, including virtual classroom courses and “bite-sized” classes, with a corresponding set of certifications. It has also been helping customers develop new economic analysis software models that can deliver better-quality projections starting with a limited set of data. These models look across the enterprise, and they are best employed by generalists, rather than specialists – that is by people who have a broader understanding of the company’s technologies and operations, rather than extremely deep expertise within a limited segment of it. Which takes us to the second part of the strategy:

Second, you need to realize that through digitalization and data you are flattening the organization. This means you need to let go of your traditional hierarchical decision-making setup. You need to encourage and help workers at all levels to be more aware of what is happening through the rest of the company, beyond their own unit and division. You need to set up cross-functional teams to work together, so that people can systematically be exposed to other parts of the organization and exchange information, exchange idea, stimulate and cross-fertilize innovation.  

And that is perhaps the most important takeaway: the self-optimizing plant is within reach. To achieve it, you have to find the right ways and tools to embed digital technologies and AI throughout your organization; but you also have to transform and upgrade the skills and learning abilities of your workforce. The self-optimizing plant, in other words, starts with a self-optimizing workforce.