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‘From geek to chic’: Supply chain opportunities for data analysts

‘Boring is the new sexy’: Supply chain opportunities for data analysts
So when I think back to how I became involved in the supply chain and worked in supply chain analytics, it’s been quite a long path for me. I started off by reading physics at university. And from there, I joined BOC Gases on the general management trainee programme. That gave me a good grounding. They moved me a lot around the country, and also around the functions, until I finally found a home in supply chain, and specifically logistics, doing something that I loved, using my maths, but not my physics. And I spent some time there before I then moved on to join Petrofina, the Belgian oil company, specifically as a distribution analyst.
And I worked there doing some network modelling, network optimisation, distribution KPIs, and some outsourcing. I then felt that to broaden my horizons further, I needed to get some consulting experience, so I moved and joined Accenture. And I worked there for about five years in their supply chain function, where I worked mainly in FMCG and retail across a broad variety of supply chain projects. After having a family, I then joined BAT. There, I’ve been involved in a variety of different supply chain roles, developing target operating models, outsourcing. And then a pivotal role, really, in my career was when I moved into the supply planning area, and got involved in the roll out of a global planning software.
And from that, really, my eyes were opened to the whole data, information, and insight from putting in the global platform, where we had a sort of standardised operating model and standardised data for the first time in BAT. We were then able to develop a suite of just reports, initially, which gave us some information about how the business was running. And we could, for the first time, compare apples with apples. But then we really looked at that and thought about what we could really do with this data, and how we could use it to drive insight and analysis into what we could improve and do better in the supply chain. And that led me onto where I am today.
I joined Concentra Analytics in June 2015, where I’m the Head of Client Engagement, and I’m working to roll out a product and support a product which Concentra have called Supply View, which is a supply chain analytics tool. And my role, really, is to make the process as smooth as possible for the clients, so that they can get the best value and insight from the tool. Moving on from a career point of view, I think being involved in the supply planning department at BAT when they were rolling out the global supply planning solution was absolutely pivotal in my career.
And that really helped me to understand that how having a standard set of ways of working, a standard set of data, a standard set of KPIs, could really help a company understand what it’s doing well, what it’s doing badly, and put in place the right measures and corrective actions to make sure that they grow as a company. If I was talking to somebody who was interested in becoming a supply chain data analyst today, what I would say is keep an open mind. Get as much experience as you can. Take as many different job roles. Talk to as many different people, both in industry and academia. Just absorb as much knowledge as you can.
Really no opportunity could turn out to be a bad opportunity. Just seize everything.

Physics and maths may not be everyone’s cup of tea but if you are looking to be at the forefront of data analytics, they are a great spring board.

I am not sure if it is the advent of the cyber-physical age, an increase in cautiousness post Global Economic Crisis (GEC) but the last 8 – 10 years have seen the rise of the ‘geek’. Historically the term was a slang term used in a slightly derogatory way to describe eccentric or non-mainstream people. Now it is used much more affectionately to refer to someone who is an expert or enthusiast, usually in an intellectually complex topic. Maths and Physics are intellectually demanding subjects, and as the TV series ‘The Big Bang Theory’ illustrates, they are commonly associated with the more traditional view of the ‘geek’.

Not anymore. The data driven economy, and need for data analytic solutions is creating the need for a new breed of data analysts. These influential, well remunerated, and often coveted roles are primarily filled by those with a maths or physics background. The first advances in analytics solutions have been in the financial services and marketing domains. It is algorithms that support financial traders to such an extent, that it is arguable if they will be required in the future. Such data driven approaches are starting to permeate the supply chain, and the last 5 years have seen an increased need for supply chain analytic experts.

In her role profile video, Christine McNeill (Head of Client Engagement, Concentra) provides some insight on her pathway to becoming a supply chain data analyst. With a background in physics, she moved onto a career in general management with BOC. Having moved around the company a lot both in terms of function and location, Chris was able to identify that she had a real love for logistics, building on her mathematical expertise. She used these skills to conduct different types of logistics network modelling, network optimisation, a transferable skill set that enabled her to move to Petrofina.

Wishing to continue to absorb as much knowledge as possible, Chris decided to move to Accenture to get some consultancy experience. This gave her experience in Fast Moving Consumer Goods (FMCG) and retail which provided a segue way into a role at BAT. This was a pivotal moment in her career as Chris moved into the supply chain planning domain. This really opened Chris’s eyes to the art of the possible in terms of more data driven approaches to planning. Given her considerable expertise Chris was headhunted to join Concentra where her combined supply chain and analytics skills are high valued and put to good use.

Talking point

  • What type of skills make a good data analyst?
  • What are the similarities and differences between a SC data analyst and financial data analysts?
  • What other types of degree / training other than maths and physics could be a useful foundation for a career in supply chain analytics?
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