Transforming Consumer Industries in the Age of AI 2025

3.5 Operations and supply Soon, companies will be able to operate fully autonomous, AI-driven supply chains that predict demand and proactively reconfigure in real time to meet customer and consumer needs. They will link manufacturing, sourcing, logistics and retail with interconnected AI-powered systems, creating a seamless, self-optimizing value chain. That means their supply chains will be anticipatory as well as reactive. They will self-adjust to ensure optimal stock levels, transparent sourcing and zero downtime. Projected impact from AI transformation of operations and supply mega process FIGURE 17 Source: Impact analysis of genAI from over 1,800 Accenture client engagements, including companies in the consumer industries. For example, by harnessing genAI with traditional advanced analytics and machine learning, supply chains will analyse both structured data (such as available inventory) and unstructured data (such as social media insights) simultaneously to prescribe orders directly to partners in real time. Further upstream, by integrating genAI and predictive analytics, agribusinesses can work closely with suppliers to optimize sourcing decisions and increase transparency throughout the value chain. In turn, suppliers can provide consumer packaged goods companies with real-time insights into raw material quality, sustainability practices and ingredient availability, enabling smoother transitions to production. Lastly, self-adaptive manufacturing means keeping pace with fluctuating demand, especially for companies managing brownfield manufacturing estates, filled with lots of sites, equipment and personnel. By harnessing AI, robotics and computer vision, manufacturing processes can automatically adjust in real time – fine-tuning machine settings, ingredient ratios and processing parameters as needed. The projected impact of such activities includes a 25-31% improvement in labour efficiency, a 10-15% reduction in inventory carrying costs, a 15-25% reduction in costs of goods sold and a 15-25% improvement in on-shelf availability.34 Picture a supply manager who used to spend most of their time dealing with product shortages and operational issues during high-demand seasons. Next year, that same manager may be able to oversee a system that predicts demand surges, adjusts production schedules accordingly and reroutes shipments as needed. When the system identifies a spike in demand, the supply manager can collaborate with other suppliers to ensure they are all on the same page, even to the point of alerting farmers to adjust harvest schedules to reduce waste and optimize production. Figure 18 illustrates how an AI-augmented approach can make operations and supply more automated, agile and resilient. By integrating genAI and predictive analytics, agribusinesses can work closely with suppliers to optimize sourcing decisions and increase transparency throughout the value chain. 25-31% improvement in labour efficiency 15-25% lower cost of goods sold10-15% reduced inventory carrying costs 15-25% improvement in on-shelf availability 22 Transforming Consumer Industries in the Age of AI