How AI in FMCG is Disrupting Skills Requirements

How AI in FMCG is Disrupting Skills Requirements
Fraser Gibson

3 mins

How AI in FMCG is Disrupting Skills Requirements

The demand for AI in FMCG is evolving the nature of the skills required for success. Find out more with Fraser Gibson, Head of FMCG at SRG.

In the UK, where demand for AI skills has more than tripled over the last decade, the nature of work in FMCG is evolving fast. Whereas in the past, many roles, particularly in manufacturing, revolved around mechanical expertise, a growing need for dual-skilled electrical and mechanical engineers, as well as data scientists and software developers, is disrupting the skills landscape of the wider industry, particularly as academic and vocational pathways are yet to catch up. 

In this article, I’ll explore:

  • What’s driving FMCG companies to invest in AI
  • Why a talent-focused AI strategy is key to success
  • Which skills are in demand for AI in FMCG

What’s driving FMCG companies to invest in AI

Like many other industries, FMCG is focused on harnessing the potential of the fourth industrial revolution, where data, AI, and machine learning merge to improve and streamline manufacturing. Having already revolutionised processes in STEM industries like pharma, embedding AI and machine learning is key to remaining competitive in the notoriously fast-moving consumer goods industry.

But what does this competitivity translate to in real terms?

Let’s take a closer look at Colgate-Palmolive’s work with Augury, a predictive maintenance company. Colgate-Palmolive were facing supply chain interruptions and unplanned downtime of machinery in the production of toothpaste, which severely reduced the availability of capital for strategic growth and damaged profits. 

Working with Augury enabled Colgate-Palmolive to predict mechanical faults before they impacted production lines. In just one instance, Augury’s predictive maintenance flagged a critical fault that saved 192 hours of downtime, £12,000 for a new motor, £27,000 in variable costs and 2.8 million tubes of toothpaste.

Overall, the implementation of predictive AI-led maintenance increased production yields, lowered operating costs, optimised operating spend, mitigated manufacturing risk, and increased visibility into the health of machines and equipment.

And that’s just one example among many. According to global research from Deloitte, 60% of manufacturing executives reported decreased costs when using AI, and 50% reported increased revenues as a direct result of introducing AI within the supply chain. 

Some of the ways AI is being used outside of predictive maintenance in FMCG to strengthen and support the FMCG industry include:

Logistics

Autonomous vehicles are evolving the logistical side of the FMCG supply chain, helping to automatically audit shelves and speed distribution. At Ocado, a UK-supermarket retailer, over 1,000 robots control, from a single location, the shipping of over 200,000 orders every week to online grocery customers. These vehicles help reduce human risk while improving speed and accuracy. 

Demand forecasting and supply planning

Like predictive maintenance, demand forecasting, and supply planning use AI algorithms to continuously learn about a company’s normal supply chain patterns and trade partners before analysing and extracting key trends and insights from this data. These insights can help complement rule-based systems, and traditional optimisation techniques in demand forecasting and supply planning to strengthen organisational strategy.

Why a talent-focused AI strategy is key to success

We’ve established that AI and machine learning can and does actively support the FMCG industry to not only maintain systems but optimise strategy, forecast, and improve logistical efficiency and distribution methods.

However, successfully implementing AI isn’t as simple as merely investing in the technology alone.

According to research led by MIT, the following three factors are critical to success: 

1. Governance

The researchers refer to governance in the sense of senior leaders developing and governing a purpose-driven strategy to deploy technology. Successful AI-enabled organisations have a top-down push from senior management and a continuous effort from senior leaders across the organisation to ensure that the digital programs remain on track.

2. Deployment

Effectively deploying talent to maximise potential is the second critical factor to AI-success. Leading organisations were more likely to purposefully organise talent and resources by establishing a centre of excellence where core resources were focused. Deployment is also used to refer to accessing talent from alternative sources, such as partnerships with academia and start-ups to supplement strategy with expertise.

3. Data

Ensuring that data is not only available and accurate but communicated across to the front line. Senior employees from leading organisations were much more willing to provide data access to the front line, and across the board – this helped ensure that employees across the organisation had the information they needed to succeed.

This research reveals a two-fold approach to considering the impact of talent, both in terms of building skills and training front-line employees.

D’Silva, a senior partner emeritus at McKinsey New York explains the active role leaders in successful organisations took in building much-needed AI skills, “the leaders had thought about roles that others hadn’t even gotten to. For instance, things like machine learning engineers versus simply data scientists and data engineers.” D’Silva says, “There were four or five different categories of people that the leaders were building into the process, thinking three or four steps ahead.”)

D'Silva continues, “The second thing is that there was greater emphasis on training their frontline employees.” He explains that even when organisations were developing AI forecasting technologies that were unrelated to their role, “There was a greater degree of emphasis on training the frontline staff to be able to get the most out of it.”

Which skills are in demand for AI in FMC

While number of AI skills varies across roles in the wider workforce, machine learning, and artificial intelligence skills have had the greatest rise in demand within the UK, where they have more than doubled in demand in the labour market between 2016 and 2020.

Recent research from Lightcast reveals the following AI priorities and skills:   

Top 5 in demand AI skills:

  1. Machine learning
  2. Artificial intelligence
  3. Natural language processing
  4. Deep learning
  5. Computer vision

Top 5 in demand AI soft skills:

  1. Communication skills
  2. Teamwork/Collaboration
  3. Research
  4. Problem Solving
  5. Creativity

Get ready for the future of work in FMCG

Given the changing landscape of work in the wider FMCG industry, a strong talent-focused strategy is essential. Bridging today’s skills gap will take proactive consideration for a future talent pipeline where apprenticeships, upskilling initiatives, and training opportunities are embedded within the organisation. Putting in the time, and effort to empower your people as you technologically advance your organisation will help drive retention, engagement, and overall success as your wider teams realise their value and power their success within your organisation.

For FMCG applicants looking to futureproof, or develop their career in FMCG, take the time to discuss opportunities for learning and development with your current employer. Are there opportunities where you could shadow an electrical engineer and procure additional skills? By putting yourself on the path to further development, you’ll likely stand out to hiring managers as actively interested in updating your experience, rather than passively inclined.

To find out more about expanding your FMCG workforce, please click here to get in touch.

Or, if you’re a candidate looking for your next role in FMCG, click here to find your next opportunity.


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