Using AI to Accelerate Decisions in the Consumer Packaged Goods Industry


Using AI to Accelerate Decisions in the Consumer Packaged Goods Industry


AI helps consumer packaged goods companies grow and thrive in the face of accelerating change

The Consumer Packaged Goods (CPG) industry has seen massive disruption and changes over the past year. Even before the pandemic, business leaders were forced to dramatically accelerate their digital timelines and build new “operating rhythms” to stay competitive, retain customers and overcome challenges.

"1 in 5 consumers switched brands between March & August 2020."

"7 in 10 consumers tried new digital shopping channels. The Retail sector experienced 10 years of growth in digital penetration in a matter of months."

McKinsey, March 25, 2021

How the pandemic affected the consumer packaged goods industry

COVID-19 forced a digital transformation on the CPG space, with a massive shift to online buying. Businesses realized their tools were insufficient to adapt to such extreme changes to buyer behavior. With a lack of precedent around such a global event, subject matter experts (SMEs) rely heavily on data to react, however, many businesses were not agile enough with their data to react quickly to address this major shift.

This recent shift brought to light microtrends in the CPG industry, most notably:

  1. Demand forecasting became critical: demand for some products increased dramatically, while it decreased for others. Business leaders typically lacked the tools or resources to forecast accurately.

  2. Gaps in process efficiency: an increase of online buying coupled with in-store pick-up and in-store shopping made it difficult for retailers to keep up with the volume and variety of transactions while minimizing shortfalls and overstock. Businesses had to re-evaluate their current forecasting methods that could not keep up with the volume of data that was available to them.

  3. Shifts in buyer behavior: with people spending more time living in and working from home, online shopping increased significantly, especially for home related items. This drove the need for deeper supply chain and inventory optimization to meet the ever changing demand.

Artificial Intelligence (AI) presents new opportunities for businesses to grow and thrive in the face of accelerating change. By providing AI tools to the SMEs within your company, you can empower them to make better business decisions within a data-driven economy.

How does AI help business?

New business models, shifting targets and buyer behaviors have turned data into a business ‘survival tool’. To make sense of the vast amounts of data you need the analysis capabilities of AI and Machine Learning (ML). We are seeing consumer brands with deep data and little AI operating experience investing in AI to quickly develop more agility with their data to drive decision making.

CPG leaders want to do more than just survive; they want to thrive in the new environment of online shopping. In order to do that, they must make use of fresh data to better understand product demand and adjust production and supply accordingly. Businesses who have invested in AI across core functions have seen a 10% growth in revenue.

Why do companies need AI and machine learning?

Companies that come to us, are often well positioned with a lot of data, but the fact is, they feel that they are falling behind. They may not have the right tools to help them mine and apply their data. Maybe they are using Excel to capture data from numerous sources that gives them a strategic approach to only react to changes.

For various reasons, they are unable to extract the full predictive insights from their data to help inform their decision making. ML and AI on an intuitive platform like Kepler removes technical barriers so they can put the power of ML prediction into daily business decisions.

Key use cases for AI in the consumer packaged goods industry

As the industry recognizes the inherent benefits and embraces AI, we are seeing a trend towards a few key use cases. Customer experience, merchandising, and operational efficiency are areas that have benefitted, with specific use cases around demand planning, SKU pricing and inventory modeling.

For companies that are new to ML/AI, the key is to focus on one specific use case that addresses one business problem. Really drive the understanding and KPIs around that use case and build those proof points so you can extract the full value of AI for your organization. Once you get started you will quickly see how to leverage the data and the platform for your next use case. The most important thing is to just get started.

Traditional data modeling and analysis isn’t able to capture shifts with the speed and granularity that AI can offer. The only way you will see results from AI is to start using it today.

By providing AI tools to the SMEs within your company, you can empower them to make better business decisions within a data-driven economy.

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