The digital transformation of industry
Recycling is one of the fundamental concepts of the circular economy, but so is efficiency in the use of raw materials and production facilities. The digitalization of the industry makes it possible to keep these two parameters under control and to significantly improve performance and energy consumption, reducing waste and therefore CO2 consumption.
At a structural level, the most important challenge for the chemical industry is to use data science to increase the overall effectiveness of a plant (Overall Equipment Effectiveness or OEE). The entire value chain is involved in the digital transformation:
- production: digital operations exploit the data collected to improve safety and increase the efficiency of the industry
- customer experience: it is possible to support dynamic customer decisions on multiple digital touchpoints such as websites, social networks, and chats
- business models: web technology is used to offer new advantages to customers through digital marketplaces and online customer services.
The challenges of data science in the chemical industry
At the large Baytown Texas site, Covestro is pursuing advanced digitalization that has the ambitious goal of reducing unplanned outages by 50%. With the use of all available technologies, the aim is to increase safety at work and the environment, improve energy efficiency, reduce maintenance costs with the consequent achievement of business objectives in terms of increased revenue.
The adoption of the digital systems of the Californian company OSIsoft has allowed the US headquarters in Covestro to create a large-scale digital monitoring and data science platform in the space of a few months. A global hierarchy of assets has been organized to monitor the health of company resources in real- time.
The collected data will be used in a predictive way to guide future choices. Digitization does not only mean monitoring, with sensors connected to computers, breaks ,and interruptions as they occur but above all collecting data over time to have a general look at the company's operation. The calculated models can estimate maintenance before failures occur and provide the right information at the right time so that technicians can make quick and informed decisions.