$8.7M Saved Through AI‑Driven Predictive Factory Operations
A North American manufacturer partnered with CES to achieve smart‑factory impact. CES delivered real‑time operational insights, automated logistics, and intelligent LGVs, reducing downtime and accelerating throughput.
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The Challenge
Security and offline-access gaps
Inefficient logistics and queueing
High downtime, costly maintenance, and slow fixes
the client
Beverage Manufacturing
Technology Stack
- Python
- Microsoft Azure
- Machine Learning
- Predictive Analytics
- Data Aggregation
- Image Recognition
- HoloLens
- Unity 3D
- RAG Knowledge Base
Solution Area:
- Smart Factory Modernization | Automation & Analytics
the impact
$8.7M
saved in operational costs
10x
faster data retrieval
56%
loading-time improvement
$1.7M
quarterly savings through predictive maintenance
The shift was intelligence‑led. The result?
Faster, safer, connected plant operations.
The Need
Plant operations needed a smarter, faster operating model that could strengthen security and access, reduce logistics bottlenecks, improve monitoring, and shorten time-to-remediation for equipment and process issues. The goal was clear: minimize downtime and maintenance costs while improving throughput and operational visibility.
Challenges
- Security and access limitations: Operational users needed stronger controls for secure access, including scenarios where offline access and continuity mattered.
- Logistics congestion and weak queue control: Truck check-in/out, docking flow, and yard movement required faster coordination to reduce delays and improve throughput.
- Downtime risk with limited monitoring and slow remediation: Lack of smart monitoring and actionable plant data increased maintenance cost, slowed dispute handling, and extended remediation time for machine failures.
- Real-time bottling health monitoring with predictive maintenance: Delivered real-time monitoring for automated bottling by combining data aggregation, machine learning, and predictive analytics to reduce delays, improve maintenance planning, and avoid production disruption.
- Driver authentication and logistics automation: Implemented an automated solution using image recognition for driver authentication, truck check-in/out, docking, and LGV traffic—reducing processing time from 1.5 hours to under 45 minutes using Python and Azure.
- Mixed-reality remote assistance using HoloLens + Unity 3D: Implemented a mixed-reality solution that enabled remote assistance and reduced downtime by improving issue response and accelerating onboarding for plant teams.
- LGV safety and efficiency workflows: Enabled trailer inspection and safety checks by scanning trailers and analyzing images for GO/NO-GO signals, with fallback to manual operators when required to reduce risk, delays, and maintenance cost.
- Plant technician RAG knowledge base: Integrated technician knowledge, past remediation plans, and OEM manual data into a RAG-based knowledge base to support training and day-to-day troubleshooting for maintenance teams.
- $8.7M saved in operational costs through modernization and process automation
- $1.7M savings per quarter enabled through predictive maintenance capabilities
- Logistics processing cut from 1.5 hours to under 45 minutes through automation
- 10x faster data retrieval supporting faster operational decisions and dispute handling
- 56% improvement in loading time for better usability and responsiveness
- Remote assistance improved field execution: 30% lower travel costs, 50% faster issue resolution, and 30% faster onboarding
- Automated processing and scalable resource allocation improved availability and reduced infrastructure cost
strategic client partner
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If your factory operations are constrained by downtime, logistics congestion, or limited monitoring, we can design an automation and analytics stack that improves throughput, response time, and operational control. Talk to our Strategic Client Partner
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