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How CPG Brands Are Using Real-Time Data to Improve Supply Chain Performance

2023 is proving to be a momentous year for advancements in machine learning and artificial intelligence. And while it might not be the first place you’d look, breakthrough technology is already at work to modernize an industry ripe for innovation – the consumer goods supply chain. 

Today, hundreds of consumer-packaged goods (CPG) companies are undergoing digital transformation to capture and analyze data across the supply chain, effectively tracking their products across thousands of brick-and-mortar and digital shelves. This helps suppliers make more informed decisions each day, from inventory management to demand forecasting to marketing optimization. By using this data to collaborate with their distributor and retailer partners, they’re also helping the entire supply chain become more agile and responsive in keeping shelves stocked, meeting consumer demand, and reducing waste.

Breaking Down Data Silos

The global supply chain is not actually a linear chain: it is a complex web of manufacturers, producers, suppliers, and other logistics companies that need to collaborate simultaneously to bring products into the hands of consumers at the right time.

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Throughout the COVID pandemic and its aftermath, the supply chain faced compounding challenges including, labor shortages, rapidly changing consumer behavior, extreme weather events, geopolitical dynamics, and inflation. These disruptions exposed the weaknesses in our supply chain and demonstrated the need for a more agile system that can respond to disruptions and keep products on the shelf even post-pandemic.

For too long, data silos built on outdated technologies have kept the industry from adapting to changes, leading to empty shelves and food waste (up to one-third of all food produced).

By breaking down silos and enabling a real-time exchange of data, CPG companies have begun to drastically improve supply chain operations. They’re now able to automate, ingest, analyze, apply, and share a wealth of data in real-time, offering all supply chain partners greater visibility to make better decisions that benefit their bottom line, consumers, and the planet.

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Applications of Real-Time Data

Automated access to a live feed of data across the supply chain enables a host of promising new applications in retail. For instance, machine learning models can be trained on sales and inventory data to identify trends and patterns that would otherwise take hours or days to analyze. Some brands have used machine learning to detect retail voids: instances when a product is out of stock, misplaced, damaged, or otherwise unavailable on shelves when it should be. When manual oversight is unable to account for these out of stocks, they are missed by retailers and suppliers alike and can amount to significant lost revenue – the combined cost of overstocks and out of stocks totaled nearly $350B in lost sales in North America in 2022.

SunButter, a nut-butter alternative made from sunflower seeds, has used ML models within our platform to detect voids in real-time and forecast demand more accurately, ultimately saving $250K in sales. Automated data software is key to spotting and resolving issues in retail without an army of analysts or on-the-ground sales teams.

Beloved Italian coffee brand Illy uses retail analytics to better predict consumer demand and keep distribution centers stocked – ultimately ensuring that there’s enough product at all steps in the supply chain to replenish retail shelves. By using data to compare inventory levels, retailer purchase orders, and shopper behavior, the brand’s overall business has grown 10% in the last year and a half. “With data, we can manage inventory on hand to ensure there is enough product to meet demand and hit our volume targets for the year,” explains Taelor Conley-Marselle, Illy’s Director of Sales.

Envisioning the Future of Supply Chains

As today’s supply chain becomes more globalized and complex, it’s more important than ever for CPGs to gain a clear understanding of how all the pieces fit together in real-time. With real-time data for sales, inventory levels, and distribution patterns, retailers and suppliers can coordinate their efforts and ensure that products are available when and where they are needed. This can lead to reduced costs, healthier revenue streams, and a more sustainable, resilient supply chain.

With connectivity in place and data automation enabled, we’re just beginning to see the many ways that AI and machine learning can analyze and interpret data from across the supply chain, providing a promising path forward for the global economy. 

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