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Meanwhile, business models are becoming outdated at an ever-increasing rate.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;
- “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;
- 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?
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
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.
People aren’t as clever as they think they are; and that includes you and me.
This is because of the all the information we don’t know we don’t know.
Computers are also not as clever as we think they are. Only a few years ago was one able to beat a human at chess. While anyone who has ever used Google translate will know that it could easily be renamed Gobbledygook translate.
This is because, up till now, computers have only ever known what we have told them. Artificial intelligence means that they are about to get a lot, lot smarter. Smart enough to analyse complex problems with more data than the human brain can handle, and yet reason an answer through logic as a human would.
This will have a profound impact on the chemical industry; a raw material sourcing, production, and sales industry packed full of data, processes, logistics and price margins that requires high-powered analytical thinking.
While still relatively new, AI is already being applied by a number of leading companies to make better chemical products.
One such company is the Tokyo-based firm Showa Denka (SDK), which by applying cutting-edge computing in their R&D projects has found a way to increase the efficiency of its polymer design operation.
As the company press release explains, “Researchers from Showa Denka (SDK), [in cooperation with the National Institute of Advanced Industrial Science and Technology (AIST), and the Research Association of High-Throughput Design and Development for Advanced Functional Materials (ADMAT)] conducted AI-based searches for polymers with desired properties. Aiming to demonstrate the effectiveness of AI technology in the process of polymer design, they focused on glass transition temperature, an index of heat resistance. Using 417 different types of structural data on polymers with known structures and glass transition temperatures, they conducted an AI-based search for a polymer with the highest glass transition temperature to see whether it is possible to shorten the development cycle.”
The result was that, “The researchers succeeded in discovering a target polymer with the highest glass transition temperature with an extremely small number of trials, namely, [an average of] 4.6 trials. This figure is about one-fortieth of the number of trials required under random selection of polymers, confirming the effectiveness of AI-based polymer design.”
The results surprised everyone, as the computer was able to predict an optimal polymer design for a specific task with fewer trials, in less time, and with less data than was previously thought possible.
Another project that has successfully applied AI for material development is based at Osaka University. This research focused on using AI computing for the automated selection of materials for organic photovoltaic (OPV) solar cells. These cells are made up of an organic component and a semiconducting polymer.
As the online industry journal Chemical Engineering explains, “The work sought to maximize the power-conversion efficiency (PCE) of OPV cells by determining the optimal combination of organic and polymer materials, a process that typically requires a great deal of time-consuming trial-and-error experimentation. Using AI and machine learning, the team was able to evaluate data from 1,200 different OPV cells to target the optimal set of properties — in this case, band gap, molecular weight and chemical structure — to quickly determine which ones would be most efficient, and then screen polymers for their predicted PCE.”
Once the AI had made its choices, the team was able to evaluate which of the resulting materials were most practical for manufacturing.
This particular form of AI application is called ‘random forest’ machine learning, as it requires the computer to form a network of decision trees for data classification and regression.
As the study’s co-author, author Akinori Saeki says, “Machine learning could hugely accelerate solar cell development, since it instantaneously predicts results that would take months in the lab. It’s not a straightforward replacement for the human factor – but it could provide crucial support when molecular designers have to choose which pathways to explore.”
Beyond the lab, AI is also helping chemical producers to optimise their current product ranges. By using AI to help consumers get the most out of their chemical products, Henkel has found a way to utilize high-powered computing and computer reasoning to aid chemical sales. To this end, Henkel employed AI for boosting hair product sales for its Schwarzkopf Professional range.
Dr Nils Daecke, Head of Digital Marketing at Henkel Beauty Care, explained how the system works, “With SalonLab, we are redefining the way both hairdressers and their clients are experiencing beauty in the hair salon. At the same time, we are laying the foundation for disruptive, data-driven business models, which build upon consumer insights and hair properties.”
As the Henkel website explains, “Around 10,000 hair samples were scanned, colored with different Schwarzkopf Professional products in different shades, and scanned again. Thanks to machine learning and the vast amount of scanned input, the computer is able to predict how the customer’s hair will look when a certain product is used. With augmented reality, the SalonLab Consultant App then brings the ‘output’ to life, showing the customer what he or she will look like after using a certain coloration.”
Smaller chemical companies may point out that an operation the size of Henkel can afford the resources to develop smart computing. Additionally, even if ‘off-the-shelf’ AI computing was affordable, then the time and manpower needed to collect sufficient data would still be prohibitive. It takes a lot of time to collect and process 10,000 hair samples.
This is a challenge highlighted by Tim Gudszend, global head of Adhesive Technologies and Investment at Henkel, when he noted that, “One of the biggest issues is generating all of the relevant data for a process and its influencing environment, and making this information available for a ‘big data’ solution.”
Certainly, at the beginning of any AI project, data collection may be a considerable undertaking. However, Dr Ata Zad, a polymer scientist and founder of the Canadian plastics company AxiPolymer, believes that while the amount of data needed to collect maybe daunting, it is the data’s size that makes using AI essential.
“When a company expands, the amount of new data it has to deal with grows exponentially — data relating to new clients, new formulations and products, new suppliers, new employees, and more — and it’s almost impossible to interpret these huge loads of data and get actionable intelligence from it with a traditional IT system.”
Adding that, “The only way to do this is with AI, which uses algorithms to find hidden patterns in data. These algorithms are iterative and will learn continually and seek optimized outcomes; they also iterate in milliseconds, which lets manufacturers find optimized outcomes in minutes instead of months.”
The true power of AI is its ability to handle so many data points. The chemical industry has a huge quantity of data which at present remains an untapped resource.
As Zad points out, “The main input for AI algorithms is historical data. In the polymer industry, the amount and variety of unprocessed data is incredibly high. It is clearly a matter of time until these tools become more commonly used to realize the full benefits of these data.”
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 Processes‘. Or take a look at other articles on the SPOTCHEMI blog page.