Analyst Insight: Consumer packaged goods brands should embrace data-sharing across supply chains and generative artificial intelligence to predict needed inventory, meet demand, reduce waste and improve profitability in an increasingly competitive retail environment.  

Continued supply chain disruptions, ongoing inflation, a rise in extreme weather events, and rapidly changing consumer behaviors are all part of the new normal for the retail industry. Retailers, CPG brands and distributors need to manage against growing pressures and seek out new ways of optimizing their supply chain operations to increase efficiencies, reduce waste, and cut costs, while remaining agile to navigate supply chain disruptions when they occur.

Complicating these challenges is a common pain point that many CPG brands face: attempting to translate siloed data from thousands of retail and distributor locations into usable insights.

Businesses are turning to AI to quickly access information and address ongoing supply chain pressures. “Dynamism,” or the ability to efficiently use updates across supply chains to make adjustments when needed, is the new imperative. When supply chain data is effectively combined with AI capabilities, retail supply chains can achieve dynamism and adapt to an increasingly competitive retail landscape.

Overcoming data silos is central to manufacturers’ ability to manage the fragmented landscape. But data foundations are nuanced, so the solution isn’t just about harnessing as much data as possible. Investing in an expensive systems integrator doesn’t guarantee that companies will see the desired return on investment.

If AI is applied to data that doesn’t have the right format and protocol, the results are likely to be disorganized and require significant manual effort to revise and interpret. To provide CPG brands with insights for navigating the “new normal,” data must first be normalized, harmonized and contextualized so that AI can perform effective analytics. 

With clean data, AI can streamline operations and help companies shift to a proactive approach where retail supply chains can prepare for events before they occur. For example, if a product has been on a truck for too long, a shelf is empty, or inventory is accumulating on pallets at a distribution center, companies don’t have to wait for delayed aggregation reports, trend analysis, shortage or overstock situations to occur.

Aggregated, clean, normalized data paired with AI can immediately identify these signals, and alert them as anomalies so that decision-makers can prevent potential losses and reduce latency in response times. This creates dynamism, where supply chains can quickly adjust replenishment cycles in real time, optimize pricing strategies, and analyze market trends to reduce waste and ensure that products are available where they are most needed. 

Value capture and creation are both possible for businesses that effectively apply AI to their retail supply chain operations. Waste reduction and achieving zero-waste supply chains can become a reality for CPG manufacturers. In addition to achieving operational and supply chain efficiencies, companies can drive top-line growth by adjusting product assortments to anticipate changes to consumer preferences through data integration and AI-powered predictive analytics.   

Outlook: The retail industry will continue to face growing supply chain pressures, requiring companies to embrace a more dynamic environment and new technologies to manage a continuously changing landscape. Companies that integrate real-time data and AI can build effective strategies that enable them to adapt and thrive in a complex landscape. 

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