FMCG in turbulent times: A vision for using AI to manage uncertainty | The Financial Express

FMCG in turbulent times: A vision for using AI to manage uncertainty

Rising costs, volatile supply, unpredictable demand, evolving consumer expectations, new competition, new channels to market, outdated technology, stifling corporate culture are just some of the reasons why the FMCG sector is facing such turmoil.

FMCG in turbulent times: A vision for using AI to manage uncertainty
To leverage the transformational potential of AI, FMCGs need to build a new layer of tech.

– By Ira Dubinsky

With all the headwinds they face, it’s no surprise that fast moving consumer packaged goods (FMCG) businesses are having a bit of a moment. It’s a time of uncertainty that some are weathering, while for others it means an existential crisis. Rising costs, volatile supply, unpredictable demand, evolving consumer expectations, new competition, new channels to market, outdated technology, stifling corporate culture are just some of the reasons why the FMCG sector is facing such turmoil. Moreover, the dawning realisation that sustainability is no longer nice-to-have, but is actually a requirement for business growth. 

Faced with this affront, FMCGs are at a crossroads. They can become primarily makers and movers of stuff, or they can reaffirm their place at the heart of consumers’ lives. If they choose makers and movers of stuff, FMCGs should focus on optimising manufacturing output, consolidating distribution and reducing waste and energy use. This path might mean increasing revenues from private label products, or manufacturing fewer products at higher volumes. 

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FMCGs applying AI to the manufacturing process

The October 2022 announcement from Mars on its increased use of robotics and automation is a good example of a FMCGs prioritising manufacturing efficiency. For the FMCGs that choose to reaffirm their place as staples in consumers’ lives, this means doubling down on responding to consumer demand; building capabilities to reimagine, reinvent and re-engineer product lines, marketing, and entire business models quickly and profitably. A FMCG that moves in this direction might have many more brands, products and distribution channels catering to myriad consumer needs and desires. 

How AI is facilitating data-driven decision making

The starkness of the choice illustrates the importance of identifying a primary concern and rallying competitive advantage around it. In either scenario, there is an opportunity for data to help define and fuel that competitive advantage. The good news for any business looking to get value from their data is that we now live in an era where artificial intelligence (AI) is transforming the world around us and the way we do business. As both the quantity and complexity of decisions increase, so too does the power of technology to augment and enhance those decisions. AI will be the defining technology of our generation because of its ability to enable data-driven decision-making at scale.

Given every business is unique, they will each ultimately need their own AI. But to be successful in deploying AI, they will need to take the right approach. Ten years the focus was on collecting data with the term “big data” dominating technology conferences.  But it’s no longer about having more data, or even better data. And it’s not about increasingly sophisticated algorithms or hiring more data scientists. Ultimately the way to succeed with AI is to focus on the specific value it can add to a business and the end outcome to be achieved.

How FMCGs can integrate AI in its technology ecosystem

But where to start? How does AI fit into a FMCG’s existing technology ecosystem? First, we have to consider the tech they are using today. The systems that run FMCG businesses may include a myriad of legacy supply chain management and enterprise resource planning systems, such as SAP. And according to Peak’s AI maturity survey, some FMCGs may have invested in modern cloud data infrastructure, for example, migrating their data to Snowflake or AWS. And on top of this, most FMCGs now have a patchwork of point solutions for planning and analytics.

To leverage the transformational potential of AI, FMCGs need to build a new layer of tech: an intelligence sits holistically across the value chain to connect decision-making. Rather than a single monolithic system, the future lies in a composable approach to technology with individual tools and systems working together. This offers FMCGs greater flexibility, and creates the opportunity to infuse artificial intelligence across the entire business.

Gartner’s Top 2022 Tech Provider Trend: Composable Business report states that enterprises are beginning to adopt the principles of composable business. Seven percent of respondents in the 2022 Gartner CIO and Technology Executive Survey indicated that they have already invested in composable enterprise, but another 60 per cent expect to have done so by the end of three years. In a composable architecture, individual AI applications can ingest data and then push decisions back into existing systems, with opportunities across the value chain. 

Each AI application deployed by a CPG augments specific decisions. By deploying multiple applications and connecting them together, the benefits of each application are amplified and the whole becomes greater than the sum of the individual parts. There are three stages to developing connected AI: 

1. Connected Data: Bringing together all of a CPG’s data in one place allows previously disparate datasets to work together. By leveraging common underlying data models, multiple AI applications can ingest data from different parts of the business. Linking demand generation and demand fulfillment is one of the best examples of how to do this. There is real power in using customer data to inform the supply chain and vice versa.

2. Connected Forecasts: As AI models are built and tuned to the business, the models create a layer of intelligence that is tailored to that business’ unique needs with decisions in one application driving the decisions in another. For example demand forecasts can be generated at various levels of aggregation and used to augment each other. A connected layer of artificial intelligence can therefore give a CPG unparalleled visibility of demand, supply, and customers along with access to both cost reduction and revenue growth opportunities.

3. Connected Journeys: The final step is to optimize product and customer journeys. For example, location data can help to plan where to place inventory and optimize transportation and warehouse locations; fulfillment times can be used to manage customer expectations and allocate orders; or trends in customer behavior can help a CPG plan promotions.

As the tides of uncertainty and volatility continue to wash over CPGs, it’s important they think about where to build competitive advantage. For some it may be more about manufacturing efficiencies and for others more about customer-centricity, and for others some combination of both. However, without fundamentally improving efficiency through the use of data and AI, CPGs will fail. Whichever way they turn, there is an unprecedented opportunity to use data and AI to lean in.

(Ira Dubinsky is the Go-to-Market Strategy Director at Peak)

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First published on: 29-11-2022 at 13:25 IST