Data Analytics in Chemical Production and Maintenance
How the Chemicals Industries Can Use Statistics to Create more Value more Quickly
CHEManager: Mr. Higgins, innovation is one of the key factors for any organization to be successful. What drives innovation in the chemicals industry and its related sectors?
Stan Higgins: The drivers for innovation in the chemical and wider process industry have until the current millennium mostly been about profitability. Over the last 20 years there has been a change with more and more focus on sustainability. This is compounded by society’s demand for a better environment and a clearer future as articulated in the “Grand Challenges” that we face as a species. Working towards how to house, feed and keep healthy so many more people; how to provide them with energy, transport and healthcare. Climate change issues have also added a new dimension to those challenged to provide effective and innovate new solutions.
Which are the specific internal and external pressures to innovate in the process-enabling industries?
Stan Higgins: Internally within the process industries, those responsible for innovation are driven to speed up research, development, and the related decision-making processes. There is a cost element to this, keeping down R&D and development time; but the real driver is capturing more value, by being first to market.
Such developers need to be ever more responsive particularly towards their downstream customers. They must quickly identify development routes, complete bench testing and scale up, while responding to customer demands for performance information today rather than tomorrow. Such pressures can create internal tensions between business development and R&D managers.
At a more fundamental level: What are the challenges in chemical R&D, production and maintenance?
Stan Higgins: The R&D managers have to constantly balance resources towards internal and external demands, like supporting manufacturing to resolve problems with scale up and introduction of new products as well as providing the business development team timely responses to customer demands and providing solutions to new opportunities.
In all these areas there is a growing recognition that data management is key to drive performance.Which role does analytics play in R&D and innovation? How
Stan Higgins: New chemicals, intermediates, processes and formulations are being developed in these industries on a daily basis and getting them to market more quickly means more value could be realized earlier. The key challenges are always to minimize laboratory and pilot plant time whilst gaining the most information from the work undertaken. Establishing a new product or capturing sales because you have responded best to an enquiry or an opportunity is a crucial commercial driver. This is where analytics can have such a valuable impact on innovation workstreams.
What are the opportunities for analytics in production and maintenance?
Stan Higgins: There is complexity within the data produced in the modern factory and indeed it is easy to be lost in a sea of data. More recently the inability to use the information locked within such data has become known as the “hidden factory”.
How can companies extract valuable insight from the flood of manufacturing data they generate? Which tools are available?
Stan Higgins: Many managers in industry will be uncomfortable with statistical analysis techniques. Most chemical factories will not have a professional statistician in the management team. There may be one at the head office, but that person probably isn’t analyzing the way that machines are working in a particular factory and relating the analysis to the product output, nor studying if there is any connectivity between the two.
How can data be used to improve operations through data-driven monitoring and predictive modeling?
Stan Higgins: The key to the use of statistical software is to have data normalized in some way. A date or time marker, batch number even in continuous processes data can usually be associated with a periodicity. With a limited amount of effort, managers, scientists and engineers will be able to identify relationships and show real statistical evidence of those connections. With that knowledge they will be more encouraged and motivated to take a look into the depths of their data historians and make the effort to analyze the data within. Data can be input from many formats. There can be no doubt that by improving the understanding of the valuable insights that can be gained by using analytical tools, this should be enough of a driver for most to put in the effort to normalize their data. Especially when it will enable improved testing, monitoring and delivery of the performance of their processes, plant and equipment and giving them access to their hidden factory.
Why is design of experiments critical if you want confidence in meeting development project milestones?
Stan Higgins: There have been advances in the understanding of the statistics of the design of experiments. With a quite limited data set, using analytical software, statistical relationships can be identified such that it will determine the number of experiments needed to provide some certainty in the outcome. This enables R&D mangers to be much more accurate in their planning and response times. Reducing the tension between R&D and business development mangers and improving internal or external customer relationships. Being responsive in this way is more likely to result in better value capture for the business concerned.