Waste Reduction in Action: Insights from the Food & Beverage Industry

• Supply Chain

As demand grows less predictable, sustainability goals get tougher, and margins continue to tighten, waste reduction has become a boardroom priority. Nowhere is this more pressing than in food and beverage, where shelf life and product quality directly shape profitability. Relying on old ways of managing waste after the fact simply does not work anymore.

The leaders making progress understand that reducing waste is about more than cost savings; it is about running smarter, more resilient operations. With connected intelligence driving visibility, causality, and actions, waste becomes something you can spot early, control with confidence, and steadily reduce – improving both the bottom line and sustainability performance.

Building the Foundation: Data, AI, and Role-Specific Design

Before results like lower waste or faster action can happen, the groundwork has to be right. Organizations that succeed start by connecting their data, layering AI for foresight, and designing insights around how people actually work. C5i’s Connected Intelligence framework brings these pieces together to turn information into early action.

Data Integration and Connected Intelligence

The breakthrough comes when enterprises unify data from warehouses, mixing centers, transportation, and sales into a single insights consumption layer powered by C5i Discovery, our AI-powered augmented analytics platform. Automated updates replace manual spreadsheets, giving managers a real-time, single source of truth. For the first time, they can view inventory risks across the network and act before issues escalate.

AI-Powered Predictive Analytics and Digital Twins

The real engine of change is models that automate Root Cause Analysis and orchestrating specific actions with the warehouse and supply chain teams. C5i’s machine learning models go beyond reporting what has already gone wrong to identify the true drivers of waste—mismatched supply and demand, logistics delays, or quality issues. Using digital twin simulations, site managers can test what-if scenarios, anticipate inventory at risk weeks in advance, and select the most effective corrective action. With each cycle, the system learns, refines its recommendations, and delivers sharper, persona-based insights across the organization.

Role-Based Dashboards

Insights and actions are delivered through dashboards tailored for each role. Executives view high-level summaries of total waste by site and product category, while site managers receive alerts specific to their location. Built-in “what-if” tools help them test scenarios before making decisions. This role-based approach ensures that every user sees only what matters to them—nothing more, nothing less.

Let’s see how these principles came to life in the journey of one food and beverage conglomerate.

The Waste Challenge: Understanding the $100+ Million Problem

Behind every expired product and damaged pallet was a deeper issue: disconnected data and reactive processes costing millions of waste.

The Problem

A global food and beverage conglomerate was grappling with an inventory waste problem worth nearly $100+ million annually. Most of this waste came from expired products (stales), in-transit or warehouse damages, and customer returns. With data fragmented across planning, warehousing, transportation, and sales systems, site managers lacked a unified view to anticipate and act on waste risks in time.

The Approach

C5i implemented a Connected Intelligence and Supply Chain Analytics solution to unify waste-related datasets and move from backward-looking reports to predictive insights and specific actions. By building a single data platform, applying machine learning for waste prediction, and generating persona-based recommendations, the system gave managers proactive alerts and a clear set of recommendations. Adoption was carefully phased, with strong executive support, user training, and continuous measurement of tool usage tied to outcomes.

Here’s the business impact achieved:

• Finished goods waste reduced by ~20% year-on-year, with potential to increase to 30% as adoption scales.
• Adoption by site managers as the single source of truth for viewing and analyzing inventory waste metrics.
• Insights generation and action orchestration time cut, giving site managers more time for analysis and action. 
• Waste management moved from reactive firefighting to proactive, measurable business impact with predictive visibility and actionable insights.

The Real Cost of Waste

For this F&B leader, waste was more than a financial drain. Every spoilage incident added logistics costs, created inventory shortages, and clashed with sustainability goals. Over time, these breakdowns eroded customer trust and brand reliability. By turning data into foresight, the company not only protected its bottom line but also advanced its sustainability and customer-centric commitments.

With ~$20 million saved in the first year, the company is now extending the same approach to demand sensing, inventory optimization, and other AI-driven use cases. This story proves a simple truth: you don’t have to accept waste as the cost of doing business. The key is to connect your data, predict problems before they happen, build tools people actually want to use, and support your teams through change.

Looking to take control of waste before it cuts into your bottom line?

See how C5i’s AI-powered solution can help you reduce waste, boost efficiency, and drive measurable results—just as it did for leading global brands.

FAQ Section

Q: How long does it take to see results from a waste reduction analytics implementation?
A: Organizations typically start seeing measurable improvements within the first few months of deployment. In this case, early results showed a steady reduction in waste, eventually achieving ~20% improvement and $20 million in annual savings as adoption scaled across sites. For different sectors, the timeline may vary based on data readiness, process maturity, and how quickly teams can integrate new insights into their daily operations.

Q: What data sources are typically needed for waste analytics?
A: The implementation of integrated data from warehouse management systems, planning platforms, transportation systems, sales data, and shipment information to create comprehensive visibility across the supply chain.

Q: Can this approach work for companies without advanced IT infrastructure?
A: Yes, the solution utilizes cloud-based architecture and automated data pipelines, making it accessible for companies with varying levels of IT maturity. The key is having access to core operational data, which can be integrated through APIs and connectors.

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