If chemical companies want to remain competitive in a changing world, they need to use innovative technology quickly to perform digital transformation. Combining IoT – especially combining IoT with machine learning – can move the chemical industry forward to work more efficiently and produce better results. Below is how IoT can give impact to chemical industry:
Fostering Innovation
An important opportunity exists in R & D to produce higher value and higher margin products at a faster pace, especially in specialized chemicals and crop protection. Advanced analysis and machine learning enables optimization of high molecular capabilities as well as laboratory and experimental testing simulations for systematic optimization for performance and cost (“from test tube to tablet”).
Besides that, advanced analysis and machine learning can drive the best resource allocation for research projects in line with portfolio priorities.
They also enable the filing of internal knowledge and patent databases to maximize the use of intellectual property and fill the gap.
Machine learning can also help chemical manufacturers conduct simulations on the sustainability and environmental impact across product life cycles.
Changing the game in plant operation
IoT builds the foundation for machine learning in manufacturing and asset management, as it captures real-time data on asset status and performance, process parameters, quality products, cost production, capacity storage and inventory (telemetry), logistics / logistics, employee safety, paired products with services, and more.
With the latest capabilities in capturing, storing, processing, and analyzing data, a large number of plants, assets, and operating data can be used in conjunction with advanced algorithms to simulate, predict, and set up maintenance to increase asset availability, optimize up time, improve operational performance, and extend their life.
Taking your supply chain to another level
There are many unexplored potentials for new IOT learning technologies and machines in the supply chain. Think about using advanced analytics to improve the accuracy of predictions leading to improvements throughout the sales and operations planning processes and related KPIs.
Advanced analysis and machine learning can be used to reduce the risk of supply chain disruption. For example, natural disaster transmission can be automatically resubmitted to meet timely delivery goals and customer commitment at minimum cost.
Another opportunity to optimize the use of transport assets and related costs. Transferring chemicals often means considering specific equipment and complex compliance requirements so that empty backhauls are the norm and not the exception. Machine learning can leverage better transport assets to transfer waste out of logistics functions, reduce costs and optimize asset utilization.
Feel free to contact E-SPIN for your IoT and Machine Learning infrastructure and system performance monitoring, application security testing and continuous protection.
