In this blog series, we will explore the key challenges facing the chemical manufacturing industry. In previous posts, we covered an overview of trends in the chemical industry and how to beat the scarcity challenge. We look forward to providing you with valuable insights and best practices to address these challenges.
In today’s culture of instant gratification and next-day deliveries, consumers have heightened expectations. They demand improved products, and they want them faster. That is true whether they are buying socks and cellphones or paint, plastics, or cleaning products.
CURRENT AND EVOLVING TECHNOLOGY PROVIDES SEVERAL ALTERNATIVES FOR SPEEDING UP THE PROCESS OF BRINGING NEW AND IMPROVED PRODUCTS TO MARKET
Consequently, the chemical industry is facing unprecedented pressure to produce more advanced and innovative products, more quickly. To keep up, chemical companies must bring products to market faster. Fortunately, current and evolving technology provides several alternatives for speeding up the process of bringing new and improved products to market.
One of the most promising means for speeding up innovation is dry-lab testing, which uses high-powered computers combined with artificial intelligence (AI) to simulate experiments to enable swift, efficient innovation.
Dry-lab testing takes the physical world into account in experiments, allowing chemists to reproduce real-world results in a fully simulated environment.
Dry-lab testing accelerates product development by enabling chemists to run hundreds of thousands of experiments rapidly at the same time, then use machine learning to analyze the results to determine success rates and suggest ways to optimize conditions of the experiments for better outcomes.
As the experiments run, the AI collects and analyzes all the data, making the experiments more and more precise over time. These innovative simulated testing methods allow new products to be researched and developed in months instead of years. This practice is also good for sustainability, as simulations reduce waste products.
Still, the most successful chemical companies are those that don’t simply speed up testing and product development. To thrive in the current marketplace, companies need to accurately forecast tomorrow’s customer demands.
Here again, technology can provide companies with what they need to remain ahead of demand. For example, Microsoft Dynamics 365 machine learning uses complex algorithms to interpret sales data to identify product demand patterns. Using this data, machine learning can predict future demand. As technology grows more sophisticated, predictions become increasingly accurate, and can even incorporate data from third-party sources and publicly available information such as patent data.
Future refinements in machine learning will allow modeling and will consider things like economic changes, regional variances, political developments, and weather patterns. Using the information provided by these calculations, chemical companies can determine which products to develop next. In the coming years, the information provided by machine learning and other evolving and emerging technologies will likely provide the chemical industry with invaluable insights that will allow them to not only keep with product demand but also anticipate market shifts and lead the way in responding to changing demands.
Upskilling Employees
To fully gain from technological developments, chemical companies likely will need to invest in their people and their capabilities. But this could be a challenge for the industry: PwC’s 2020 Global CEO Survey reports that 33% of CEOs in the chemicals sector are extremely concerned about the availability of key skills.
Upskilling employees will be critical – recognizing skills gaps and mismatches and creating programs to develop necessary capabilities. This will require a collaborative approach across an organization, including HR, IT, finance and operations.
Would you like to learn more about the challenges facing chemical manufacturing today? Download our free eBook now:
