Behind the jargon

Technological progress demands a new skillset and people that carry out and manage the technologies tied to it. Around half of chemical enterprises report a lack of qualified personnel on top of an increasingly aging population of chemical workers. So it’s easy to fall for the promises of high-tech solutions, such as smart manufacturing and AI-based production planning. But following this and trying to implement such a solution, one would quickly face the complexity of implementation and the profound requirements of such, that only a handful can meet. So before we get into all buzzwords, any enterprise needs to ask itself fundamental questions. Rather than just starting to search for solutions, it is important to look at where the value of your operation lies. What kind of processes are at work? What information is passing through the organisation and derived from these processes? How are we structured from a technological standpoint? This may sound trivial, yet a fundamental look at the information, process and deliverables and a detailed understanding of the technical set-up of your company is key. One thing cannot be stressed enough: at its core, digitisation is not merely about digital systems, but shaping the information flow in order to drive business value. To put it bluntly: it’s all about the data. So structural questions about the data already available and how it can be applied allows you to map your digital journey and then make important decisions about the goals of your business.

A basic example: To successfully manufacture custom chemical compounds for customers, you need to also synthesise a wealth of information – from the various properties of chemical elements themselves to sourcing information, special requirements for handling and storage or regulatory requirements. In effect, you have a multitude of data points and are analysing the relationships between them. What then, if this information was also made available to the customer directly in the form of custom product configurators? Imagine the effect this would have on the future of formulating new materials. This idea is by no means new and, in theory, relies on a set of relational databases, but it is extremely complex to implement – and relies completely on the fact that data is available and processable without fail.

Other types of data structures, such as graph databases and network analysis tools, can be useful for analysing data about the supply chain and logistics and allow chemical companies to identify ways to improve the efficiency and sustainability of their operations.

So we shouldn’t focus on one solution and be carried away by buzzwords. A shift towards a structural and data-driven understanding of technological progress needs to set in. Technological change is rapid; technological decisions have an ever shorter shelf life. We must build structures that allow for flexibility and are expandable and rebuildable.

After all, no one wants to implement a specific solution and rebuild entire systems completely from scratch every two years. To set out on the right course of innovation you could begin, for example, by consolidating data management across all areas in order to simplify the connection of new components.

On the odyssey of digitising your enterprise and navigating the sea of decision-making when it comes to your value chain, these key data points will help to guide you:

• Market data:
  In the chemical industry, most data can drive innovation within a number of ways. Particularly useful here is market data, e.g. information on consumer trends and supply shortages.
• Production data:
  Information on the efficiency and productivity of different manufacturing processes is vital for a competitive business. Data from sensors and other sources that can provide real-time information about the performance of equipment and processes. By effectively analysing and using this data, chemical companies can gain valuable insights that help improve their products and processes, increase efficiency and drive innovation. Those are the base to implement technological megatrends such as predictive maintenance or failure probabilities.
• Formulation:
  Data on formulations consists of information about the chemical composition of materials and the properties of different substances. This supports product development, ideally close to the customer and use case. In other words, product generators/configurators are conceivable, where customers can directly put together their own suitable product/composite. This is particularly relevant for associated industries such as those involved in food or cosmetics.
• Supply chain/logistics:
  Collecting and analysing data on the performance of different suppliers, chemical companies can, for example, identify opportunities to improve the efficiency and reliability of their supply chains. This can help them to reduce costs, improve their responsiveness to changing market conditions and increase their ability to deliver high-quality products to their customers.

The trend is clearly towards multisourcing rather than single sourcing to ensure sustained availability. This also requires connections to manufacturer or supplier data.

The focus is also on traceability with the aim of keeping the carbon footprint as low as possible. This requires a high level of transparency in sourcing via data on origin, production processes and transport. Through analysis, you can make informed decisions that can drive more innovative products and improve sustainability.

To conclude, keep your head above water and don’t try to ride every trend and buzz you encounter. It’s important to stay close to your core and make strategic decisions with strong technological partners that allow you to retrieve value from your data and create enough flexibility to adapt what’s really needed.