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Much has already been said and written about the digitalization of the chemicals industry. Even previous blog articles on this website have discussed the power of artificial intelligence, cloud computing, advanced digital algorithms, and quantum computing.

But what exactly is digitalization? How does it work? And in what way will it transform?

What is the Digitalization of the Chemical Industry?

Computers are playing an increasingly large role in our lives. How we interact with family and friends, how we shop, how we work, and even how we find love. But how do you digitalize an industry?

According to Dr Frank Jenner, a global chemical industry consultant at EY, much of it has already happened. All chemical companies have begun digitalization. Using computers to support or run processes, streamlining productivity, and improving efficiency. In the same way that cars are constructed by robots on a production line or supermarket stock levels and ordering systems are regulated by digital processes, so too has most chemical production been computerised. Production, supply chains, and plant maintenance are invariably all controlled or assisted by micro-chip.

But now the final part of digitalization is taking place as computers begin to take control of business models. Using digital power not just for production and supply chain, but for overall company management; from business strategy to customer interaction, product design to market development. Business areas that were once led by human thinking will increasingly be governed by computers.

As Jenner made clear in a recent interview with the strategic management and investment journal Financier Worldwide, “Digitalization is taking place in two-thirds of process re-engineering activities these days. But a digital transformation will take place across the entire company, and that is something different – bringing in new or adapted business models for modified or completely new revenue streams.”

How does Digitalization Work?

This is a view supported by chemical industry consultants at McKinsey, who state that there are three main ways in which digitalization will affect the chemical industry. The first, as outlined earlier, is in production, “… using digital-enabled approaches to improve companies’ business processes.”

The second is the use of digital capabilities to impact, “demand patterns in end markets.” This means the impact that technological advances will have on other industries and how that will influence the chemical industry and its products.

For example, the use of drones in precision farming will have a major impact on the agrichemical industry. As will the spread of online chemical sales, both wholesale and retail, or the way that self-driving cars will lessen the number of traffic accidents, and so reduce demand for car paint.

Even more directly for the chemical industry is the way computers are changing the plastics industry. As the McKinsey report notes, “One further digital-enabled area … is 3-D printing, also referred to as additive manufacturing. The market for polymers and chemicals used in additive manufacturing is growing at 30 percent a year and is set to rise from $0.7 billion in 2015 to $2.5 billion in 2020.” Furthermore, “It is possible the market will evolve toward tailored polymers and chemicals for different additive manufacturing systems, which could open up innovation and commercial opportunities for companies making photopolymers, high-performance thermoplastics, and other chemicals used in these processes.”

But crucially, the third way that digitalzation will impact the chemical industry is in overall chemical business management. Something that McKinsey notes, will be a near-future industry, “… where digital developments lead to changes in business models through which chemical companies capture and create value for customers.”

Far from being a sudden shock, the implementation of digital in the business models and strategic planning of chemical firms will be a natural progression, and one that has already begun.

As Jenner observes, “In the last four years, chemical companies started to develop their digital strategies by looking across their value chains and functions and coming up with a lot of smart ideas, including interesting pilot projects. This was a gradual path. However, the problems became evident when they started to integrate all of these pilots into their current IT landscapes and process infrastructure.”

This is where the third level of digitalization steps in. Or as Jenner puts it, “Overcoming these traps requires new thinking in next-generation business and IT infrastructure.”

In fact, Jenner believes that the chemical industry is long overdue major restructuring.

Why do We Need Digitialization?

The classic top-down management of yesteryear is struggling to keep pace with the rapid changes in modern business. Additionally, the amount of data available for processing, and the growing size of companies seeking economies of scale, means that management of chemical businesses will require more and more digital support.

As Jenner states, “Overall, business process architecture and digitally enabled backbone infrastructure are lacking right now. This slows down the innovation process. We need to provide the foundation first to get all these transition projects connected, interlinked, administrated, and managed. Only then we can explore real-time vertical and horizontal integration over entire value chains – internally, across all operating divisions and subdivisions, and externally, to suppliers and, more critically, into the customer back-ends.”

But chemical industry professionals should not be down-hearted by the size of the task ahead. With growing markets, and growing demands being made for safety, security, and ecological considerations, digitalization can provide a safer, more secure, and environmentally sound chemical industry.

This is a point highlighted by Jenner, when he said that, “Digitalisation creates opportunities to increase transparency across the chemical supply chain. Blockchain and the Internet of Things will enhance how materials are identified and the audit trail of how they are used, such as in certificates of origin and green credits of final products made from environmentally friendly raw material in upstream processes.”

How will Digitalization Happen?

Ultimately, shifting business model design away from the boardroom towards a computer influenced strategy will need to come from the company chiefs. The changes in structure to how the company works will be enormous, and so the impetus must come from those at the top.

While the scale of this task may be daunting, the rewards are clear. Companies who embrace digitalization, such as Amazon (sales) and Facebook (social interaction) will find success. Those who fail to adapt, such as Kodak (non-digital photos), face extinction.

Photo credit: Betachemical, Spotchemi, Frost&Sullivan, WhiteCase, & Capgemini

The chemical industry is known as a world leader in product research and design. Innovation is a core feature of a science-based industry that has made fortunes out of the development of cutting-edge materials. Gore-Tex, Nylon, and Kevlar are clear examples of chemical product innovation that has transformed our world.

At present, the chemical industry invests heavily in researching new products. This year, the US chemical industry alone is predicted to spend more than $70 billion on research; almost 10% of the value of total US chemical industry output.

US chemical industry R&D expenditure (and prediction) in billion US Dollars

However, some chemical industry heads are beginning to question the value behind such massive investment.

As chemical industry consultants at Bain and Company report, “The nature of innovation has changed, and there are fewer breakthrough chemicals and compounds.” Adding that, “… research finds that while two-thirds of executives say innovation is a top priority, less than 25% believe their companies are successful innovators.”

In fact, Bain’s analysis of chemical industry professionals’ thinking was that, “… many senior executives see R&D as something of a black box and don’t understand why returns from innovation are not higher.”

Instead, chemical industry leaders are turning to business model innovation, finding a better return for their investment in reorganising their companies than in searching for miracle chemical products.

As early as 2008, the Boston Consulting Group found that, “business model innovators have been found to be more profitable by an average of 6% compared to pure product or process innovators.”

Meanwhile, business models are becoming outdated at an ever-increasing rate. “In the past 50 years, the average business model lifespan has fallen from about 15 years to less than five.”

This is evident in the number of chemical industry M&A’s witnessed during the past decade. It is also indicative of the value seen in major business model overhaul in the chemical industry, with the $130 billion merger of DowDuPont soon being followed by a restructuring program that divides the business into three parts. This, according to the investment journal MotleyFool, is “… projected to save $3.3 billion in cost synergies.”

This places a chemical company’s business model as a core location for investment. But is major company re-structuring just for large chemical corporations?

Chemical Industry Business Model Innovation

Maybe not, as a recently published report in the online Journal of Business Chemistry, believes that there is also value in smaller chemical companies re-evaluating their business models.

The study’s authors, Martin Geissdoerfer (a doctoral researcher at Cambridge University) and Ron Weerdmeester (a management consultant at PNO), focused on developing business model theories that put flexibility and location at the company’s core. Both of which are easily applied to smaller chemical companies, finding value in adaptability and increased productivity through shorter supply chains.

The analysis was based on proposals by the European Commission Horizon 2020 project, which outlines the advantages of, “Business models for flexible and de-localized approaches for intensified processing.”

As a result, the study developed, “… four business model archetypes (BMA) that facilitate this re-localization: decentralization and modularization; mass customization; servitization and product service systems (PSS); circular business model, by name Re-use, Recycle and Sustainability (RR&S).”

The outcome was a framework for the dynamic evaluation of business models, rather than a static approach that limited business model innovators to set time-frames. This framework has been called INSPIRE, and it contains two core aims for chemical companies and other processing businesses;

1. “Paving the way for dynamic monitoring of key supply chain parameters and factors (e.g. labour costs, production costs, raw material availability, market attractiveness, financial stability of suppliers, etc.) and analysing the long-term impact of the novel business model proposed;
2. Considering the possibility of switching from one business model to an alternative in the medium term.”

The report also outlines how in rapidly changing and volatile markets, flexibility is a key factor to strengthen a chemical processing business.

Adaptability is key

Specifically, they state that, “In order to react to fluctuations in terms of demand or feedstock/energy prices, companies should be able to adapt production accordingly while being cost efficient at the same time (capacity flexibility). Likewise, companies should be able to switch to another product (product flexibility). In this context the innovation flexibility denotes the ability to carry out R&D and pilot settings at production sites. Another aspect relates to the location. Either the place of the production or the production plant itself should be easily moveable (location flexibility). Furthermore, companies should be able to handle different kinds of feedstock (feedstock flexibility).”

If a chemical company is to remain competitive, it can no longer hold firm on any single business approach. Modern chemical businesses must be far more agile and adaptive to ever-changing situations, and therefore their business models must also be flexible and adaptive.

Innovation will always be central to chemical industry growth, yet it is incredible that such large sums of money are being invested in chemical product R&D while investment in business model development is so often overlooked. In fact, when it comes to innovation, is the chemical industry simply doing it wrong?

Photo credit: Statista, JonesdayITCblog, Sitael, Hasbro, Kisspng, Chemistryworld, RVO, & ECEuropa

Since 1970, computer processing speeds have doubled roughly every two years. Moore’s Law predicts that this rate will continue meaning that the ‘brain power’ of most CPU’s will soon out think the human mind.

Considering other factors, such as shared data through cloud computing, quantum computing, the Internet of Things, robotic process automation (RPA), the invention of ever smaller transistors (even as small as a single atom), and artificial intelligence, it is likely that computers will play an increasingly large role in everything.

While this can remind us all of the Terminator movies and thoughts of asking SkyNet to run your defence system, we should totally recall that this is science fiction. Science fact dictates that the practical uses for industry will be enormous and highly profitable for those that grasp the opportunity.

Nowhere will this be more true than in the chemical industry. With its highly technical manufacturing processes, complex logistics, current high use of computers, and advanced R&D programs, the chemical industry will be a clear beneficiary of advanced computers and AI.

But is this the future or the present, because at many chemical companies AI is already making an impact.

One such advocate of investment into artificial intelligence is polymer scientist Dr. Ata Zad. As founder of the Canadian plastics company AxiPolymer he is convinced of the competitive edge that his business has through its implementation of AI. An opinion he made clear in a recent interview with the industry journal Canadian Plastics, “Every plastics manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production,” he said. “Machine learning’s core technologies align well with the complex problems manufacturers face daily, especially large manufacturers that have the largest amount of raw data.”

Sandeep Sreekumar, global head of Adhesive Digital Operations at Henkel, also sees the benefits. “We use AI to run efficient analyses of complex data arrays for achieving higher production performance, quick product innovation and scaleup for our self-adjusting production systems,” he explained. “Our focus is not only on collecting internal manufacturing data, but also on actively working with customers on data collection opportunities during product usage to make improvements and adjust to changing customer needs.”

Pushing the Boundaries of AI in the Chemical Industry Even Further

In Japan, SDK (a Toyko-based chemical and technology business) and the hi-tech manufacturer Hitachi have collaborated in the development of an AI-assisted predictive-maintenance platform.

As the online industry journal Chemical Engineering explains, “SDK’s Oita Complex ethylene plant served as the trial facility for demonstrating the commercial practicality of the new AI service, which utilizes adaptive resonance theory (ART) to analyse and classify plant operational data in real-time and identify anomalies that could lead to equipment failure. In trials at the Oita plant, the technology successfully predicted the occurrence of coking. According to Hitachi, this method is able to detect patterns and abnormalities that would not be detected by conventional predictive-maintenance models. Now, SDK plans to roll out the technology into additional plants, while also further refining the AI model for determining different coking mechanisms.”

Meanwhile, in Huelva, Spain, the energy and chemicals producer Cepsa, has employed AI at its phenol production plant resulting in a 2.5% increase in output (an additional 5,500 metric tons per year).

Phenol plant III, Huelva, Spain

This was achieved through machine learning and real-time predictive models that offer plant personnel recommendations on how to improve production every 15 minutes. The analysis involves the AI considering over 3,000 process variables. It even considers the weather.

And in Madrid, the energy and chemicals producer Repsol has collaborated with Google Cloud to apply AI and advanced data analytics towards optimizing resources at its 186,000 bbl/d petroleum refinery in Tarragona.

The Repsol petroleum refinery in Tarragona

As the company’s press release notes, the process will manage, “… around 400 variables, which demands a high level of computational capacity and a vast amount of data control.” This is something that represents, “an unprecedented challenge in the refining world [because] until now, the highest number of functions integrated digitally in an industrial plant is around 30 variables.”

Crucially, the company also highlights the economic gain in using such advanced computers to analyse its refining process, stating that, “The project has the potential to add 30 cents on the dollar to Repsol’s refined barrel margin, which could translate to 20 million dollars annually for the Tarragona refinery, with significant upward growth if all optimization objectives are achieved.”

Evidently, AI computing’s presence in the chemical industry goes beyond mere potential. Leading manufacturers are already implementing machine learning and real-time ‘big data’ analysis.

While predicting the future has always been the task of fools, it would be unwise not to believe that AI has a future in the chemicals industry as it already has a presence.

If nothing else, its potential is truly astounding, such that what was said in the 1990’s about the Internet, when no one fully understood it, now people talk the same way about AI.

As the online journal Chemical Engineering notes, “While advanced technologies like quantum computing are still very new to the chemical processing industry, new applications will certainly continue to arise as more users begin to understand the capabilities of AI.”

If you would like to learn more about AI and its impact on the chemical industry, then you might enjoy reading ‘How Artificial Intelligence is Making Smarter Chemical Products‘. Or take a look at other articles on the SPOTCHEMI blog page.

Photo credit: Nividous, FlynetViewer, GEA, Ohlindustrial, WashingtonUniversity, Repsol, & NKD