A mid-tier global beverage company with a portfolio of regional brands in carbonated soft drinks, flavored sparkling waters, and ready-to-drink teas. The organization operated 14 production facilities and served more than 10,000 retail outlets across North America and Latin America.
This case study is based on a real client engagement. Specific identifying details have been removed or generalized to protect confidentiality, which is standard practice in strategy and transformation work.
Client name withheld under NDA.
The Challenge
Rising operational costs were eroding profitability. Costs as a percentage of revenue had climbed from 38% to 43%, driven by energy price volatility, labor constraints, unplanned downtime, and approximately $48 million in annual inventory write-offs. Profit margins had fallen below internal and industry benchmarks, creating pressure to improve efficiency without compromising service levels or product quality.
The Approach
Neo Artificial Intelligence partnered with the client to design and execute a sequenced, three-wave Applied AI program focused on high-ROI operational use cases. The work combined predictive analytics, computer vision, and demand-sensing models with a new cross-functional operating model, data governance standards, and change management support.
Wave 1 – Manufacturing
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- Augmented existing sensors with predictive maintenance models (85% accuracy, providing 7–14 days of advance notice).
- Deployed computer-vision systems for real-time defect detection.
- Outcomes in this wave: unplanned downtime reduced 34%, overall equipment effectiveness improved by 9 percentage points, energy consumption per unit declined 12%, and approximately $61 million in annual savings were realized.
Wave 2 – Planning & Inventory
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- Implemented machine-learning demand-sensing models that incorporated internal and external signals and refreshed forecasts daily.
- Outcomes: inventory waste reduced 41% and approximately $37 million in annual savings generated through improved production planning and reduced changeovers.
Wave 3 – Logistics & Distribution
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- Applied AI-driven route optimization and dynamic loading.
- Extended predictive maintenance to the fleet.
- Outcomes: truck fill rates improved from 78% to 91%, breakdowns fell 28%, warehouse picking labor decreased 15%, and approximately $29 million in annual savings were achieved.
Supporting elements across all waves included a cross-functional AI hub for reviewing recommendations, structured training for operators and planners, clear data governance, and model explainability to build trust and adoption.
Results (18 Months)
| Metric | Results |
|---|---|
| Operational cost reduction | 22% overall (18% in the first year) |
| Profit growth | 17% |
| Return on invested capital | +2.4 percentage points |
| On-time delivery | 94.2% → 97.6% |
| Quality complaints | –19% |
| Scope 1 & 2 emissions intensity | –11% |
The program delivered measurable financial and operational gains while improving service levels and reducing environmental impact. No material degradation in product quality or customer experience was observed.
Key Success Factors
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- Clear focus on cost reduction and quantifiable value as the primary objective.
- Sequenced rollout of use cases rather than a broad, simultaneous deployment.
- Parallel investment in people, processes, and governance alongside the technology.
- Rigorous baseline measurement and ongoing value tracking against the client’s own financial and operational systems.
Implications
This engagement demonstrates that targeted, well-governed Applied AI initiatives can produce rapid and substantial improvements in manufacturing, planning, and logistics performance. Organizations facing similar margin pressure can benefit from starting with a limited set of high-ROI use cases, establishing transparent measurement, and embedding AI recommendations into existing operating rhythms rather than treating AI as a standalone experiment.
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