Skills for the Future: Data literacy in practice – Alec

Data literacy looks different in every role, but its impact is felt right across the food industry. In this Q&A, Alec Woolner (Group 55) reflects on his first MDS secondment at JDM, where he went from having little experience with data to using it to understand downtime, improve efficiency and support continuous improvement on the factory floor.

Q: Can you talk us through one of the key projects you worked on during your time on the MDS scheme?
I was given a data project to manage during my first placement with MDS, at JDM, a food manufacturer in Lincolnshire. The project involved gathering and analysing data from the factory’s main production lines to get a better understanding of downtime and machine efficiency. When I joined they had just installed a number of sensors on key pieces of equipment but needed somebody to learn how to use the data collection software (known as an OEE software), to gather and present this data so that it was intelligible, and to suggest places where improvements could be made.

Q: How did you feel going into the project, given your background?
As a history grad this was a daunting task for me. I hadn’t done any maths since secondary school and I had very little experience with excel and absolutely none with power BI. I started with the basics: crash courses on excel and BI, in depth training on the OEE software, and one-on-ones with factory ops who understood how the machines worked. On top of this, I was greatly helped by the factory’s continuous improvement guru – who led courses on Kaizen and Lean Manufacturing. These approaches helped me to get my head around what the point of the project was – how incremental improvements across the board could make big differences, and how everyone could get involved in boosting efficiency.

Q. What did you find most challenging about using data in a live factory environment?
The challenge for me was to try and get the data to tell a story and to guide decisions for the factory. The first step was fairly easy: pulling data from the software and putting together graphics which showed downtime causes, machine rankings, problem products etc. With a bit of excel/ BI knowledge these things could be done without much hassle and I was able to put in place live dashboards and presentations for the team.

What was harder was trying to use this data to actually drive improvements in the factory. This involved moving away from the spreadsheets and speaking to people who really knew about the operation.

Q: How did working with others help turn data into real improvements?
What worked most effectively was focusing on specific problems and drilling down on root causes. For example, we found that ‘set-up’ downtime time on a certain line was much longer than we expected. Through deeper analysis we found that a certain ops team could do this process faster than others. When speaking to the operatives involved we got a better understanding of why this was. Using this ops team as the ‘benchmark’, we did a series of observations, wrote an SOP and helped train out all the learnings to the other teams. In such a way, the combination of in-depth data analysis and real-world collaboration allowed us to solve a problem. We could then transfer this process to solve lots of other small problems in the factory.

Q: What did this experience teach you about working with data?
I found this project a great introduction to data – it’s given me a handle on several analysis tools and has allowed me to see the power of data analysis in practice. In the world of manufacturing, tiny details can make huge differences. There’s a reason so many manufacturers invest so heavily in OEE software and other continuous improvement tools.

Alec’s experience shows how data literacy isn’t just about spreadsheets or dashboards, but about combining insight with real-world understanding and collaboration.

You can also read Huw’s story, which explores how data was used to investigate product quality during his secondment – another perspective on why data skills are such an important part of Skills for the Future.