If chemical companies want to stay competitive and move forward in a changing world, they need to rapidly adopt innovative technologies. Incorporating IoT within these companies can provide important benefits. Combining IoT with machine learning can move this industry forward to work more efficiently and create better results.
How could IoT improve chemical production? How can chemical companies use IoT and machine learning with their processes?
Improve Chemical Production With IoT
While many industries are embracing IoT, it might not seem clear how it could relate to chemicals businesses. But Andy Chatha, the president of ARC, made it clear that IOT applies to the chemical industry too, in his keynote presentation for the ARC Advisory Group Industry Forum a few years ago. A review of this presentation was written for Chemical Processing by Paul Miller. Chatha explained that IoT can streamline many parts of industrial companies, including providing smart machines, offering better capacity for big data storage, and helping optimize systems and assets. The benefits of IoT within this industry are far-reaching. They include better productivity, improved asset utilization, and higher revenue.
Fostering Innovation
Significant opportunities exist in R&D to create higher value and higher margin products at a faster pace, particularly in specialty and crop protection chemicals. Advanced analytics and machine learning enable high-throughput optimization of molecules as well as simulation of lab test and experiments for systematic optimization of formulations for performance and costs (“from test tube to tablet”).
In addition, advanced analytics and machine learning can drive allocation of best available resources to research projects in line with portfolio priorities.
Also screening of internal knowledge and patent data bases becomes possible to maximize use of intellectual property and fill gaps therein.
Machine learning can also help chemical manufacturers to run simulations on sustainability and environmental impact across a products life cycle.
Changing the Game in Plant Operations
IOT builds the foundation for Machine Learning in Manufacturing and Asset management as it allows to capture real-time data on asset status and performance, process parameters, product quality, production costs, storage capacity and inventory (telemetry), inbound/outbound logistics, worker’s safety, pairing products with services etc.
With today’s advanced capabilities in capturing, storing, processing and analyzing data a vast amount of plant, asset, and operational data can now be used in conjunction with advanced algorithms to simulate, predict and prescribe maintenance needs for assets to, increase availability, optimize uptime, improve operational performance and extend the assets lifetime.
In this context, digital twins play a major role in managing asset performance and maintenance. Once plants and processes have been designed and engineered, digital twins can be used to train operators by simulating special plant and process conditions related to safety and/or performance (like flight simulators). Digital asset twins can be used in maintenance to predict the impact of certain process parameters on asset performance, asset lifecycle and maintenance needs. A Deloitte University Press document, “Industry 4.0 and the Chemicals Industry”, explains the concept of Digital Twins in such a way that “organizations create value from information via the movement from physical to digital, and back to physical.”
Completely new opportunities for the chemical industry arise from Distributed Manufacturing/3D-Printing in terms of developing innovative feedstock and driving new revenue streams. While more than 3,000 materials are used in conventional component manufacturing, only about 30 are available for 3D printing. To put this into perspective, the market for chemical powder materials is predicted to be more than $630 million annually by 2020.
Worker safety can be enhanced by the addition of smart tags on wearables which could help to alert workers on exposure to dangerous substances (like e.g. toxic gases),p to help locate employees and contractual workers in cases of emergency. Moreover, alerts could be triggered if employees work out of their designated or authorized working area (“connected worker”).
Taking your Supply Chain to another Level
In Supply Chain a lot of untapped potential exists for new technologies of IoT and Machine Learning. Just think about using advanced analytics to increase forecast accuracy leading into improvements along the entire Sales and Operations Planning Process and related KPIs.
Advanced analytics and machine learning could be used for mitigating risks of supply chain disruptions. For example, in case of natural disasters shipments could be automatically re-routed to meet on-time-delivery goals and customer commitments at minimum costs.
Another opportunity resides in optimizing the use of transportation assets and related costs. Moving chemicals in many cases means considering special equipment and complex compliance requirement so that empty backhauls are the norm rather than the exception, resulting in increased costs and suboptimal asset utilization. Here machine learning could help to better leverage transportation assets and drive waste out of the logistics function.
Innovate by getting closer to your Customer
Over the last years the chemical industry as an “asset intensive” industry was focusing its efforts towards optimizing plant and asset operations. However, there is huge untapped potential to develop innovative, customer centric business models and services. Here are a few examples how chemical companies could benefit from better leverage of IoT and Machine Learning at the customer front end:
Leverage sensors and telemetry to implement Vendor/Supplier Managed Inventory concepts and completely automate the replenishment process (“no” or “low touch” order to delivery).
Monitoring your customers’ manufacturing process parameters in real-time via sensor technology, leveraging advanced algorithms to correlate process parameters with quality of (semi-) finished products, start selling first pass quality as business outcome, instead of selling products. Opportunity to offer benchmark data as a service.
Use advanced algorithms to better understand buying behavior/patterns of customers, adjust product and service portfolio correspondingly, identify cross-selling opportunities to increase customer loyalty and share of wallet.
Get visibility into customer/market sentiment via capturing and processing unstructured data from social media, respond with appropriate marketing campaigns and innovative service offerings.
Moving Forward With IoT
By using IoT with machine learning, chemical companies can move forward and gain positive business results. How do chemical companies use IoT technology? Chatha said that industrial businesses already have or are just building the foundations for incorporating IoT and Machine Learning. Overall, IoT can act as a solution that helps the chemical industry keep up with changing times and better meet the needs of shareholders and customers. However, having clean and abundant data available to train algorithms and build high quality models which predict high quality results are pivotal to success. Another critical success factor are highly skilled data scientists. Lack of those could be a severe constraint for rapid adoption of IoT and Machine Learning in the chemical industry.
About Your Guest Blogger
Stefan Guertzgen has worked for 8 years as Senior Director for Industry Solution Marketing Chemicals at SAP. In this function he is globally responsible for driving industry thought leadership, strategic portfolio decisions and overall positioning & messaging as well as executive messaging programs for key stakeholders along SAP’s entire Chemical solution portfolio.
Prior to this assignment he has worked for 11 years in the chemical Industry at Chemtura in various positions comprising R&D, Global Business Development, Business Process Management and Sales & Operations Planning. On top he has a 7 year experience in Pre Sales and Management Consulting for the process industry with focus on business operations, working for companies like AspenTech, AT Kearney and SAP Business Transformation Consulting.
He holds a PhD degree in Chemicals from the Max-Planck-Institute for Coal Research at Mülheim/Germany and has been granted a 1-year post-doctoral fellowship for the University of Berkeley/California from the Alexander von Humboldt Foundation.
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