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It was a tough year for the Consumer Packaged Goods (CPG) sector in 2020. There were the obvious initial spikes in demand on key products – remember the great and totally self-inflicted global toilet roll famine of March/April? But the longer term impact was a shift in behavior as consumers looked at empty store shelves and massive online waiting lines and decided that availability came before brand loyalty.

Combine this with the worldwide shift from physical to digital retail and the sector found itself with a ton of supply chain challenges, as Adrian Smalley, Customer Transformation Senior Director, Consumer Goods at Salesforce, noted at this week’s Salesforce Innovate UK and Ireland event:

Historical models for predicting demand and managing inventory became pretty much irrelevant. Organizations became increasingly reliant on more agile and near real-time data as we switched from offices to working from home. Those organizations who invested in building data-driven, integrated planning processes and an agile resilient supply chain, they were able to capitalize on these kind of opportunities by simplifying their portfolio and creating on-shelf availability in a period where consumers were prioritising availability over brand.

All of that has implications for manufacturing and consumer goods companies going forward, he added:

Those organizations that win will be those who continue to build on those agility foundations, by creating real-time connectivity with the ecosystem, coupled with the culture of data-driven decision making. That means connecting teams and data together, not only inside the business, but externally with partners and ensuring a single source of truth is shared to enable fact-based conversations.

Mars sweet spot

A case in point is Mars, well-known to all of us for its confectionery credentials, but also a supplier of other food products as well as a provider of pet care, including brands such as Pedigree and Royal Canin. The firm has its own global team of data scientists who work with business leaders to address current and future operational issues for Mars, tapping into Tableau data analytics tech from Salesforce to do so.

What’s happening inside Mars is happening across the entire sector, says Deepak Jose, Head of Business Strategy & Advanced Analytics at Mars:

A lot of manufacturers are trying to minimize the complexity of the manufacturing process and are simplifying the portfolio and focusing on some of the strategic areas. You see that across consumer goods companies. Very similarly at Mars, there is an aggressive portfolio simplification strategy and plan in place.

The COVID crisis has had its impact, he says, even if toilet roll hoarding seems to have waned:

I was reading an industry report recently about on-shelf availability which said there was four times more out-of-stock situations in January 2021 compared to the pre-pandemic time.

So portfolio simplification is a priority, he adds:

Within portfolio simplification, SKU (Stock Keeping Unit) rationalization is one of the capabilities that our team is enabling. We also have several experiments underway on improving the on-shelf availability, not only from a traditional bricks-and-mortar sense, but, more importantly, from an e-commerce sense. Think about a bricks-and-clicks provider, which has bricks-and-mortar stores and an e-comm website. How can we improve the on-shelf availability and improve the service level? That is something we are actively working on. Reducing the complexity of the supply chain is going to help not only Mars, but, most importantly, give the right choice to the consumers, without any disruption in supply. That’s something that we are very passionate about and we are working towards.

Data engine

To meet this challenge, the focus is on data analytics. Jose explains that Mars leverages a large digital engine:

The Mars digital engine has four key pillars. The first one is finding the problem using user centricity, and design thinking. We generally find the right problem before solving the problem. Second, solve the problem using AI, Machine Learning and Advanced Analytics, which is the second part of the equation. Once you solve the problem then, three, automate. Automation can be done through Robotic Process Automation or various other kinds of technologies which are available. Four, solve and automate is going to work only when we deliver in an agile manner. To move from point A to point B, we don’t need a Ferrari, we just need a skateboard. That is the foundational thought behind agile delivery – how can we deliver something with a progress-over-perfection mindset? That is very important for us across all of these things.

One goal here is the CRM Holy Grail of a single source of truth, although Jose has his own spin on this idea:

I would call it the current single version of truth, because the truth is continuously evolving. As a manufacturer, we leverage a lot of data sources to build that source of truth.

Simplicity is a key factor, he adds:

Building that data asset in a really simple way, in a sustainable way, is very important for us and that is something that we are focusing on. The sales function, the finance function, can take decisions, looking at the same data source, and more importantly, you look at the same data source using the same methodology.

There is a very fundamental principle here – democratizing data analytics for maximum benefit. Jose says:

Converting data to insights is a relatively easy task for a data scientist, but for insights to [become] action, we need to have very powerful stories. That story is not only for internal telling, but also to talk to the consumers. This is where some of the capabilities like Tableau come in handy. It gives a strong and powerful visualization which everybody can understand. It also gives you the ability to click through for anybody at any level – at executive level to mid-manager or the analyst level, they’re able to go through the visualization and explain the story in a very impactful way. To a great extent, storytelling is very important for Mars and Tableau is a strategic partner in helping us build and deploy these capabilities.

This notion of data democratization brings in the related theme of self-service analytics, again something that is a hot topic at Mars, according to Jose:

Recently, our team has been thinking about this concept of Citizen Data Science. A lot of the Advanced Analytics team’s work is [geared] towards catering to a niche population…How can we move from that and make these capabilities available for the 125,000 associates at Mars? This is the kind of question that we are trying to solve.This is not going to be an easy task. It is going to be a challenging task.

But there is a basic idea in place, he concludes:

The way we want our analytics capability to be looked at is as a digital armor. If you think about Iron Man, we want everybody to have an Iron Man suit of digital armor. That’s how we want analytics to be perceived. Now, one of the important aspects of building self-service analytics is to have the right tools and capabilities, like Tableau, to be available for a larger group of people, to have the right training in place so that they have access to the data assets and they can do their own analytics. That is the vision that Mars has.