Info Sheet: Digital transformation drives innovation in the chemicals industry
Innovation in the chemicals sector requires data, but also a platform to manage that data effectively; and current methods are falling short. Companies wouldn’t think twice about investing in hardware to support 20% efficiency gains, if it meant getting more products to their customers faster and with higher confidence in the quality. Yet, true digital transformation can easily yield 20 – 75% efficiencies depending on the process being performed.
While digital transformation is a growing trend in many companies in the chemicals sector, it’s the right approach that will take companies to the next level of digitization and innovation. Let’s see why it’s important and how it can bring them closer to their goals.
Think about the level at which your business could transform, both short term and longer term. Identify a platform that can take you on that transformation today. Global research and innovation (R&I) spending in the chemicals industry reached $50.2 billion in 2018, almost double that in 2008. Innovation in the industry not only secures its future, but its products and technologies also enable innovation in many downstream value chains. Considering the role of innovation in this growing sector, investing in R&I and a digital data management platform to help can have enormous benefits, including minimizing costs and streamlining product development processes.
A novel problem: there is too much data
One of the blockers to innovation is researchers are dealing with too much information. Investment in hardware and technology to automate processes has guaranteed a constant stream of data from lab equipment and connected sensors. But what happens then? Can the data be used? With the amount of information produced, there is neither the time nor the manpower to analyze it all, let alone gain insight for new product development.
The problem is further compounded by information coming into the workflows in different formats. A survey by CrowdFlower shows a data scientist can spend 80% of their time untangling the information: cleaning it up, integrating and formatting it, just so that it can be used. So, companies are left with more data than ever, and no way to sort through it.
Today, companies in the chemicals sector are investing in digital transformation to help organize and analyze the overload of data. But there is still a way to go to maximize analyzing and linking data for groundbreaking insight and innovation. If companies want to get the most value from data, they need to advance their data management capabilities to support the analysis of early stage products undergoing development and compare this to previously created products. Imagine you developed a new product with a more sustainable ingredient that during the development process had been identified as a suitable replacement for a non-sustainable alternative.
Now imagine identifying all the products across your organization that used the older ingredient, and the opportunity this could present your business to drive towards improved sustainability across your product range!
Step one is an R&D management platform to store information with context. Step two is implementing an infrastructure that can link the information, breaking down silos to make it accessible and
move data between applications while guaranteeing data integrity. With a centralized database, data can be accessed whenever and wherever a researcher needs it, encouraging worldwide open collaboration on projects. As well as removing silos and facilitating cross-departmental analyses, it can support a network of institutions and universities innovating across the world, increasing insight.
For example, understanding the performance of an inorganic catalyst means knowing the compositional data as well as the processes utilized to develop the catalyst. If these two pieces of information are held in different institutions, across separate countries, it takes time and effort for a researcher to find the answer. But with a single, integrated platform, the information can be accessed and viewed with context instantly, saving time that can be spent analyzing the data instead of searching for the information.
Outdated technology hinders innovation and accumulates cost
To get a safety and performance certificate takes years and accrues costs. In addition to the lengthy production times, manufacturers must address the risks that come with the industry. Liability issues are expensive and the risk that the product will fail deters from the path of innovation. To protect against liability, manufacturers run extensive tests on their products, driving up costs of development. In fact, liability protection is one of the biggest costs in manufacturing a new product.
Something as simple as illegible notes can have a cascading effect down the line – if the notes can’t be read, it can lead to data integrity issues, which, in turn, could result in liability issues and rework. Re doing tests delays the project and using more materials adds to the overall cost. In fact, it’s estimated that rework accounts for up to 40% of project costs. Getting it right the first time is another major motivator for companies as these efficiency gains lower the cost per study. Digital R&D data capture ensures that it is captured first time, is readable and error-checked, can be searched, analyzed and securely shared. It is stored with rich context, which enables insight in downstream data lake and AI applications.
Digital transformation brings enterprise insight
Many companies have begun steering their organization in the direction of digital transformation, but there is still an uneven spread across different sectors of the industry. With a fast-changing political and consumer landscape, the pressure to innovate faster continues to increase. Companies using outdated ‘analogue’ R&D data management techniques are doomed to fail. Those who win will be leveraging the efficiency of a digital platform.
Those companies that have already implemented a more holistic approach to data management have seen tangible benefits – faster R&D by a minimum of 20%. Revamping digital initiatives by implementing a single, integrated layer of technology is a good place to start.
By capturing searchable, structured data, you’ll already see benefits. Saving data with context and stored against your business terminology means there’s no room for transcription errors, any deviations are flagged, and you can search for information with context. Initially, you can expect to savebetween 2-4 hours per scientist, per week.
Now imagine going another step – implementing process execution enables system integration and eliminating errors and so saving your organization on average 5-7 hours per scientist per week. The further you go in your digital transformation journey, the more return on investment. Soon, you’ll be ready to take the next step, with a data lake, machine learning and AI. But we’ll cover these topics another time.
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