The FMCG industry is facing unprecedented uncertainty amidst multiple pressures, including accelerated global supply chain restructuring, soaring energy costs, and increasingly stringent industry regulations. As a typical essential consumer goods industry, food, beverage, daily chemical, and life science companies are caught in a dilemma: on the one hand, they must ensure stable supply and quality safety; on the other hand, they must efficiently address the challenges of flexible production brought about by small batches, frequent deliveries, and high customization.

Traditional manufacturing models centered on "scale and cost efficiency" are no longer sustainable. Flexibility, agility, sustainability, and profitability are becoming new hard indicators for industry upgrading.

To break through this transformation dilemma, Schneider Electric, in conjunction with its subsidiary AVEVA, released a new white paper—"From Hype to Implementation: Building Core Competitiveness in FMCG Manufacturing with Practical AI." This report, based on a survey of nearly 1,500 FMCG industry decision-makers worldwide, reveals the practical path for industrial AI to move from conceptual exploration to value realization.

The harsh reality: 15% of revenue is being eroded by "efficiency losses"

Hard-hitting research data has sounded an alarm for the industry. "Preventable losses" such as production delays, unplanned equipment downtime, rework and repairs, quality deviations, and insufficient asset utilization are continuously eroding the profitability of FMCG companies.

Data shows that these efficiency losses currently account for 15% of companies' production revenue and are included in over 20% of finished product costs. Worse still, this proportion continues to rise, and is expected to approach 30% by 2030.

Under immense pressure to improve efficiency, global FMCG companies are ramping up their AI investments. It is projected that by 2030, over 37% of companies will have AI as a core part of their operations, representing a roughly three-fold increase in application penetration compared to current levels.

However, the white paper also points out an awkward contrast: while AI hype is soaring, large-scale implementation is severely lagging.

1.Insufficient End-to-End Applications: Currently, only 13% of FMCG manufacturing companies have implemented end-to-end AI applications in their core operational processes, with the vast majority still at the stage of localized pilot projects.

2.Low Return on Investment (ROI): Over 70% of companies that have deployed AI projects have an actual ROI of less than 20%.

This stark reality of "expectations leading, reality lagging" indicates that the industry is not questioning the value of AI, but rather has not yet found a feasible path to move from "isolated pilot projects" to "large-scale replication."

Bottleneck of Value: The problem lies not in technology, but in basic capabilities and organizational readiness

Why is it so difficult to implement AI in industrial settings? The white paper incisively points out that the key constraint on releasing the value of industrial AI is not the technology itself, but rather the basic capabilities and organizational readiness of enterprises.

Talent shortages, outdated automated equipment, data fragmentation, resistance from frontline employees to transformation, and concerns about cybersecurity and compliance collectively constitute the deep-seated obstacles hindering the implementation of AI.

In this context, enterprises must clearly define the core positioning of industrial AI: the value of AI lies not in replacing humans, but in empowering them.

Especially in highly regulated industries such as food, beverage, and life sciences, it is essential to combine "Human in the Loop" with "Agentic AI." AI must not only provide optimization conclusions but also clearly explain "why," ensuring that every adjustment has a traceable, auditable, and accountable basis.

To this end, enterprises need to simultaneously solidify three foundational transformation pillars:

1.Strengthening the Data Foundation: Breaking down IT and OT barriers, building a consistent, cross-system, and efficient data foundation, and eliminating information silos.

2.Enhancing Transformation Empowerment: Industrial AI is a comprehensive business transformation that requires strategic leadership from senior management and deep collaboration among IT, OT, and business departments, reshaping the culture of collaboration.

3.Building Modular Infrastructure: Creating a next-generation AI-ready architecture that is flexible, scalable, and supports "edge-cloud collaboration."

Schneider Electric's Solution: Building a Four-Dimensional, Effective Industrial AI

Addressing the urgent needs of FMCG companies, Schneider Electric unveiled its industrial AI solution. This solution is grounded in reality, exhibiting four core characteristics: openness, practicality, security, and sustainability.

1.Open and Flexible Architecture: Built around the EcoStruxure Open Automation Platform (EAE), based on open standards, it supports smooth expansion from single-point pilots to multi-factory deployments, enabling flexible deployment of AI at multiple levels, including the field and control room.

2.Effective Implementation Path: Rejecting grand narratives, it directly addresses high-value scenarios such as energy management, quality control, process optimization, and predictive maintenance. It employs "small-scale, dedicated models" trained on real industrial data to achieve ROI within a short timeframe, and then leverages digital twin technology for rapid reuse in similar scenarios.

3.Secure and Reliable Assurance System: Building end-to-end protection from the design stage ensures that AI decision-making logic is explainable, operation is auditable, and accountability is traceable, eliminating compliance concerns for frontline deployment.

4.The foundation of green and low-carbon development: Small, dedicated models are prioritized for edge deployment, naturally possessing lower computing power and energy consumption requirements. They are directly applied at the edge for energy optimization and peak management, helping enterprises achieve the dual goals of cost reduction and carbon reduction.

Effective Implementation: Practical Verification from Industrial Sites

The quality of industrial technology ultimately depends on the data in the workshop. Schneider Electric not only owns its own "Lighthouse Factory" in Le Vaud-Leuth, France, recognized by the World Economic Forum, but also applies these mature experiences to a wide range of FMCG sub-sectors:

Case 1: A Multinational Beer Company

In the traditional beer filtration process, the addition of diatomaceous earth often relies on manual experience or fixed formulas. Schneider Electric uses AI to intelligently control the opening of the diatomaceous earth addition pump, with the model outputting the globally optimal addition strategy. While ensuring safety and quality, this directly achieves 20% material savings and a 15% increase in production efficiency.

Case 2: A Large-Scale Dairy Project in Asia

Unplanned downtime of key equipment such as homogenizers and centrifuges is a nightmare for dairy companies. This project introduced the EcoStruxure™ PMA predictive maintenance consultant solution, integrating big data and AI technologies, and ultimately delivered impressive results:

Equipment utilization increased by 17%

Maintenance costs decreased by 35%

Unexpected downtime rate decreased by 80%

Production efficiency increased by 15%

Case Study 3: A Large Pharmaceutical Factory

By adopting a comprehensive solution including the AVEVA PI System operational big data platform, the system can automatically determine batch status and intelligently release products. While strictly controlling human error and strengthening production compliance and process transparency, it significantly improves automation efficiency and release speed.

Conclusion: Towards 2030, Bridging the Transformation Gap of Industrial AI

Industrial intelligence naturally serves lean operations. Wang Peidong, head of the Industrial Automation Business Development Team at Schneider Electric, emphasized:

"The key to AI's ability to create value lies in whether a company already possesses a solid foundation in digital intelligence and automation. Only when data, architecture, and organizational mechanisms are in place can AI truly integrate into business scenarios and continuously create value." Looking ahead, 2026 to 2030 will be the golden period for the large-scale implementation of AI applications in the fast-moving consumer goods (FMCG) industry. Companies can no longer blindly follow trends; they must base their efforts on real-world business scenarios. In this race to bridge the transformation gap, only by solidifying the intelligent foundation, building a learning organization, and deeply integrating AI with real-time data and modern automation technologies can companies steadily move towards a more efficient, green, and sustainable intelligent manufacturing future.

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