If chemical companies want to stay competitive in a changing world, they need to rapidly adopt innovative technologies. Incorporating IoT – especially combining IoT with machine learning – can move the chemicals industry forward to work more efficiently and create better results.
Improving the chemical industry with IoT
Andy Chatha, president of ARC, explained in a presentation for the ARC Advisory Group 2014 Industry Forum how the IoT is as important to the chemical industry as it is for other industries. Chatha said that the 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.
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 tests 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 the allocation of best-available resources to research projects in line with portfolio priorities.
They also enable screening of internal knowledge and patent databases to maximize use of intellectual property and fill gaps.
Machine learning can also help chemical manufacturers run simulations on sustainability and environmental impact across a product’s lifecycle.
Changing the game in plant operations
IoT builds the foundation for machine learning in manufacturing and asset management, as it can capture real-time data on asset status and performance, process parameters, product quality, production costs, storage capacity and inventory (telemetry), inbound/outbound logistics, workers’ safety, pairing products with services, and more.
With today’s advanced capabilities in capturing, storing, processing, and analyzing data, a vast amount of plant, asset, and operational data can be used in conjunction with advanced algorithms to simulate, predict, and prescribe maintenance to increase assets’ availability, optimize uptime, improve operational performance, and extend their life.
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,” says with digital twins, “organizations create value from information via the movement from physical to digital, and back to physical.” An IDC whitepaper, “The IoT Imperative for Energy and Natural Resource Companies,” notes that a petrochemical company that used a digital twin model created a 20% improvement in product transitions.
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. 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 alert workers on exposure to dangerous substances (like toxic gases) or help locate 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
There is a lot of untapped potential for new IoT and machine learning technologies in supply chain. Think about using advanced analytics to increase forecast accuracy leading to 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 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 often means considering special equipment and complex compliance requirements so empty backhauls are the norm rather than the exception. Machine learning could better leverage transportation assets to drive waste out of the logistics function, decreasing costs and optimizing asset utilization.
Innovate by getting closer to your customer
Over the last few years, the “asset-intensive” chemical industry has focused 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 of how chemical companies could benefit by better leveraging IoT and machine learning at the customer frontend:
- Leverage sensors and telemetry to implement vendor/supplier managed inventory concepts and completely automate the replenishment process (“no” or “low-touch” order-to-delivery).
- Monitor customers’ manufacturing process parameters in real-time via sensor technology, leverage advanced algorithms to correlate process parameters with the quality of (semi-) finished products, sell first-pass quality as a business outcome rather than selling products, and offer benchmark data as a service.
- Use advanced algorithms to better understand customers’ buying behavior and patterns, adjust product and service portfolio, and identify cross-selling opportunities to increase customer loyalty and purchases.
- Get visibility into customer/market sentiment via capturing and processing unstructured data from social media, then 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. Chatha said industrial businesses already have or are building the foundations for incorporating IoT and machine learning. Overall, IoT can help 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 that predict high-quality results is pivotal to success. Another critical success factor is having highly skilled data scientists; they are key to rapid adoption of IoT and machine learning in the chemical industry.
Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value by reading “Accelerating Digital Transformation in Chemicals.” Explore how to bring Industry 4.0 insights into your business today by reading “Industry 4.0: What’s Next?”