List of questions
What approach would you recommend to build capability in accessing machine level data and then to turn this information into real time actionable insights?
We have a large manufacturing footprint with a complex mix of equipment of varying ages. Currently we have varying degrees of access and connectivity to the PLC data from this equipment and therefore limited insights are generated from it. We would like to build our strategy in this space to improve our machine level connectivity, generate more data, understand the best way to store this data and then subsequently visualise and analyse it to help us generate insights which we can action in real time. We’d like to move beyond descriptive analytics and into the predictive and prescriptive space. This could be to support performance improvements on the lines or areas like predictive maintenance for example.
How do you build digital capability and culture across a large, diverse, manufacturing and supply chain business?
We have a large number of employees working in a number of functions in our business, covering areas such as innovation, procurement, production, planning and logistics. The workforce is multi-generational with varying capability and needs for digital skills. As we look forward, we see that new technology and digital will be a key enabler in driving our business forward. We would like to understand how best to approach building the capability of our workforce in digital to ready them for the introduction of new technology in their roles. This could be from building basic IT skills, through to more advance analytics capability and on to data science and programming skills. We also feel that it’s key we build the right culture in the organisation where employees embrace digital and the change this may bring. This could mean creating a data driven culture for example, where everyone understands the importance and power of data and the insights we could generate from it and places this at the heart of our operation.
How do you consolidate data provided in different formats to make it analytics ready?
Instrumentation from differing vendors provides data in varying formats which has to be assembled and transformed into a common format that can be interrogated by model machine learning techniques. This is often a slow and arduous process and results in an Excel spreadsheet. How can this process be streamlined to removed the manual intervention and possibility for error.
How can we replace two on-line Gas Chromatograph analysers with a system that can infer the results that the analysers provide by using continuous process data?
Methyl methacrylate (MMA) is a monomer used in the manufacture of poly methyl methacrylate (PMMA), or acrylic polymer products as they are more commonly known. Acrylic products are common and used for a very diverse range of applications including paints and coatings, optical systems, signage, kitchen worktops, prosthetics, etc. Lucite International, wholly owned by the Mitsubishi Chemicals Corporation, is a global leader in the design, development and manufacture of acrylic based products. Lucite is always striving to improve its process technology. One improvement area relates to the control of a reaction step, where currently the chemical components are measured in the feed and exit to the reactor using an on-line Gas Chromatograph. The GC application is difficult because of the composition of the process streams and the need for fairly complex sample conditioning systems. Lucite would like to explore the possibility of replacing on-line analysers in this duty with an alternative means of providing the same results. The project is at an early stage, and as yet we have not come up with many solutions. We would like to investigate the potential to correlate multiple process data inputs with the composition of the output streams and would like to collaborate with others who have up-to-date knowledge and experience in this area.
How might we use Artificial Intelligence to increase the speed of business case analysis so that our clients may quickly make decisions to get products to market?
Business cases require similar data to be gathered for each new project and then manipulated in a relatively unique way to provide forecasts. The manipulation of data is time consuming using spreadsheets and difficult to check for errors. Is it possible to automate some or all of this analysis? Similarly, automated data gathering from operational plant would improve accuracy but this requires an IoT system that many companies don’t have in place. If there is a middle ground that could enable this, that would be highly valuable as well.