The Stakes: When Success at One Site Unlocks Opportunity Everywhere
Most pilot programs answer a simple question: Does this work?
Ranpak's pilot deployment of IoTFlows at a single facility was designed to answer something far more ambitious: Can we build a model that transforms how we operate globally and how we serve customers worldwide?
The answer needed to be definitive. Because if this worked, it wouldn't just optimize one factory. It would become the blueprint for operational excellence across every Ranpak facility on the planet. And beyond that, it would unlock an entirely new value proposition: proactive health monitoring for the thousands of machines Ranpak deploys in customer facilities around the world.
This wasn't a test. It was the foundation for a strategic transformation.
The Challenge: Excellence at Scale Requires Data, Not Assumptions
Ranpak manufactures sustainable protective packaging solutions, machines and materials that replace plastic bubble wrap and foam with paper-based alternatives. Their equipment runs in their own factories and in customer facilities globally, converting paper into protective packaging at high speeds.
But like any manufacturer operating complex machinery across diverse environments, Ranpak faced fundamental operational blind spots:
Machine Utilization: The Optimization Gap
- Which machines were running at peak efficiency? Which were underperforming?
- Was material usage aligned with production output, or were machines consuming resources inefficiently?
- Without granular machine-level data, optimizing the entire facility to maximum capacity was guesswork.
Downtime: The Recurring Mystery
- Machines stopped for various reasons: mechanical failures, material jams, operator errors, scheduled maintenance.
- Each downtime event represented lost production, but without systematic tracking, patterns remained invisible.
- Were certain machine types more failure-prone? Were specific issues recurring preventably?
Material Consumption: The Hidden Variable
- Total material usage could be tracked at the facility level, but connecting consumption to specific machine behavior required inference, not instrumentation.
- Was excessive material usage due to operator technique, machine calibration, or process inefficiency?
The question wasn't whether Ranpak could run their facility effectively, they were already doing that. The question was: Could they achieve systematic, data-driven optimization that could be replicated everywhere?
The Solution: Unified Machine Intelligence Through Dual-Product Deployment
Ranpak deployed both SenseAi and BeamTracker in a tightly integrated configuration, creating a unified intelligence layer across the entire facility.
This wasn't just monitoring. It was building a complete operational nervous system for every machine on the floor.
SenseAi: The Vibration Intelligence Engine
SenseAi transformed Ranpak's approach to understanding machine behavior and material consumption:
Vibration Analytics for Operational Insight
- Advanced sensor arrays capture vibration signatures from each machine
- Continuous monitoring of machine operating states and behavior patterns
- Foundation for future predictive health monitoring capabilities
Feed Rate Analysis for Material Optimization
- Real-time tracking of material consumption through vibration-based feed rate monitoring
- Correlation between machine operating states and material usage patterns
- Identification of machines consuming excessive material due to calibration drift or mechanical issues
Downtime Root Cause Intelligence
- Every stoppage captured with precise duration and contextual machine state data
- Categorization of failures by type: mechanical, material-related, operator intervention, quality hold
- Pattern analysis across machine types to identify systematic issues vs. isolated incidents
BeamTracker: The Production Accountability System
BeamTracker brought precision tracking to production output across the facility:
High-Accuracy Production Monitoring
- Real-time counts per machine, per shift, per day
- Production velocity tracking to identify underperforming equipment
- Cumulative output trends over time to validate improvement initiatives
Utilization Optimization Through Transparency
- Complete visibility into which machines are running, idle, or in maintenance states
- Identification of capacity bottlenecks and underutilized assets
- Data-driven rebalancing of workloads to maximize facility throughput
Goal Attainment Through Balanced Performance
- Facility-wide production targets decomposed to machine-level expectations
- Real-time variance analysis: which machines are exceeding targets, which are falling behind
- Immediate visibility when aggregated performance trends toward shortfall
The Power of Integration: Two Systems, One Intelligence Layer
The breakthrough wasn't deploying two products. It was the synergy between them.
- SenseAi explains why a machine is underperforming (vibration anomaly, excessive material consumption, recurring failure pattern)
- BeamTracker quantifies how much that underperformance costs in lost production and identifies which machines need optimization first
Together, they create a closed-loop optimization system:
- BeamTracker identifies a machine running below capacity
- SenseAi diagnoses the root cause through vibration and feed rate analysis
- Maintenance or calibration intervention targets the specific issue
- BeamTracker validates that the fix restored optimal performance
- Both systems track whether the issue recurs
Business Outcomes: From Reactive Management to Systematic Excellence
The pilot deployment delivered three transformational capabilities that became the foundation for global scalability.
1. Complete Machine Utilization Visibility
For the first time, Ranpak had granular, real-time data on every machine in the facility.
Before IoTFlows:
- Aggregate facility output was tracked, but machine-level performance was inferred from shift reports
- Utilization assumptions were based on planned uptime, not instrumented reality
- Identifying which machines needed optimization required manual observation and anecdotal feedback
After IoTFlows:
- Every machine's uptime, downtime, production output, and material consumption tracked continuously
- Immediate identification of underperforming equipment with quantified impact on facility capacity
- Data-driven path to balance workloads and optimize all machines to maximum sustainable utilization
From Guessing to Knowing: "Before, we'd plan production based on assumptions about machine capacity. Now we know exactly what each machine is capable of and where the gaps are. That visibility is the foundation for systematic optimizationyou can't improve what you can't measure."
Chiran JBR, Operations Manager
2. Systematic Downtime Tracking and Root Cause Analysis
Downtime shifted from an unavoidable disruption to a measurable problem with identifiable patterns.
The Foundation for Future Optimization:
Comprehensive Capture
- SenseAi logs every stoppage with duration, machine ID, and contextual state data
- Operators categorize events: mechanical failure, material jam, quality issue, scheduled maintenance
- No downtime event goes unrecorded or unexplained
Pattern Recognition Across Machine Types
- Aggregate downtime by machine type: Do certain models fail more frequently?
- Identify recurring failure modes: Are the same issues happening repeatedly across different machines?
- Quantify cumulative impact: Which failure categories cost the most production time?
Data-Driven Maintenance Planning
- Historical failure data enables targeted maintenance schedules
- Identification of high-impact failure patterns guides resource allocation
- Track whether maintenance interventions reduce failure frequency and duration
The Result: From Reactive Fixes to Systematic Improvement
This comprehensive downtime tracking creates the foundation for moving beyond reactive maintenance—understanding which failures occur most frequently and implementing targeted solutions to reduce their recurrence.
Downtime Pattern Analysis in Action
Recurring Issue Identified:
- Multiple machines of the same model experiencing material feed jams
- Pattern analysis reveals these jams account for significant cumulative downtime
- Vibration data collected during normal operation vs. jam events
Data-Driven Response:
- Focused maintenance attention on machines with highest jam frequency
- Scheduled inspections during planned downtime rather than reactive emergency stops
- Documentation of failure patterns to guide future predictive strategies
Measured Impact:
- Systematic tracking enables prioritized maintenance scheduling
- Shift from unplanned emergency stops to planned interventions
- Foundation established for future predictive maintenance capabilities
3. Material Usage Optimization Through Feed Rate Intelligence
Material consumption became a controllable variable, not an accepted cost.
SenseAi's vibration-based feed rate analysis revealed insights previously hidden:
- Machine-specific consumption patterns: Some machines were using 12-15% more material than others for identical output due to calibration drift
- Operator technique impact: Feed rate variability correlated with different operator approaches, enabling targeted training
- Process efficiency opportunities: Certain production configurations consumed more material without corresponding quality or speed benefits
The Optimization Loop:
- BeamTracker tracks production output per machine
- SenseAi monitors material feed rates through vibration analytics
- Ratio analysis identifies machines with inefficient material-to-output performance
- Calibration adjustments or operator training targets specific inefficiencies
- Continuous monitoring validates improvement and flags new deviations
Result: Data-driven path to minimize material waste while maintaining or improving production quality and speed.
The Blueprint: What Makes This Scalable Globally
The pilot wasn't just successful , it was replicable.
Ranpak now has a proven deployment model for rolling out IoTFlows across all facilities worldwide:
Standardized Instrumentation
- Defined sensor placement and configuration per machine type
- Calibrated vibration analysis baselines for normal operation
- Consistent downtime categorization taxonomy across facilities
Proven ROI Metrics
- Quantified value from utilization optimization
- Measured downtime reduction through systematic tracking and pattern analysis
- Documented material usage efficiency gains
Repeatable Implementation Process
- Installation playbook tested and refined at pilot facility
- Training curriculum for operators and maintenance teams
- Integration with existing production management systems validated
Phase 2 is already mapped: Deploy to additional Ranpak manufacturing facilities using the exact blueprint proven at the pilot site.
Beyond Internal Operations: The New Customer Value Proposition
Here's where the vision gets transformative.
Ranpak doesn't just manufacture packaging materials. They deploy thousands of machines in customer facilities worldwide: food distributors, e-commerce fulfillment centers, manufacturers who need protective packaging on-demand.
Currently, those customer-deployed machines are serviced reactively. A machine fails, the customer calls for support, a technician dispatches, repairs are made, production resumes.
What if Ranpak could shift from reactive service to predictive intervention?
The Vision: Global Fleet Health Monitoring
Imagine deploying the same SenseAi and BeamTracker intelligence proven at Ranpak's own facility across their entire customer-deployed machine fleet:
Proactive Health Diagnostics Across Thousands of Machines
- Deploy continuous vibration monitoring to customer machines globally
- Enable early detection of degradation patterns before failures occur
- Future capability: Predictive alerts to Ranpak service teams: "Machine #4721 at Customer Site XYZ showing bearing wear signature—intervention recommended within 7 days"
From Break-Fix to Prevention
- Shift from reactive emergency calls to scheduled maintenance windows
- Enable proactive parts shipment based on usage patterns and failure history
- Prevent catastrophic failures that cause extended customer downtime
Guaranteed Maximum Uptime as a Service
- New value proposition: Machines aren't just sold—they're continuously monitored and maintained for peak performance
- Transform customer SLA from "we'll fix it when it breaks" to "we monitor it continuously and intervene proactively"
- Market differentiation: Ranpak machines come with intelligent fleet management, not just warranty support
Data-Driven Customer Success
- Aggregate performance analytics across customer base
- Benchmarking: "Your facility's machines are operating at 94% of optimal efficiency, here's how to close the gap"
- Continuous improvement recommendations based on global fleet intelligence
The Business Model Transformation
This isn't just operational excellence. It's a strategic business model evolution:
- From product sales to service relationships: Machines become the platform for ongoing value delivery
- From reactive support to predictive service: Reduce emergency dispatch costs while increasing customer satisfaction
- From individual transactions to fleet optimization: Serve customers with the same intelligence Ranpak uses internally
The pilot facility proved the technology works. The global deployment will prove the operational model scales. The customer fleet rollout will prove the business model transforms.
Why This Matters: The Difference Between Monitoring and Intelligence
Many manufacturers deploy sensors and call it "smart manufacturing." They track production. They log downtime. They generate reports.
Ranpak built something fundamentally different: operational intelligence through unified machine learning.
Monitoring tells you what happened. Intelligence tells you why it happened, identifies patterns that indicate when it might happen again, and guides you to prevent it.
Monitoring counts production. Intelligence optimizes utilization, balances workloads, and identifies capacity bottlenecks.
Monitoring logs downtime. Intelligence categorizes root causes, identifies patterns, and enables systematic elimination.
Monitoring is descriptive. Intelligence is analytical and prescriptive.
The integration of SenseAi and BeamTracker created more than visibility, it created a closed-loop optimization engine that continuously drives performance improvement.
Looking Ahead: The Roadmap from Pilot to Global Standard
Ranpak's journey is unfolding in three strategic phases:
Phase 1: Pilot Success: Complete
- Single-facility deployment
- Validation of technology, integration, and ROI
- Development of replicable deployment blueprint
- Outcome: Proof of concept becomes operational standard
Phase 2: Global Manufacturing Rollout: In Progress
- Deployment across all Ranpak factories worldwide
- Standardization of operational intelligence across facilities
- Cross-facility benchmarking and best practice sharing
- Target Outcome: Operational excellence as a competitive advantage
Phase 3: Customer Fleet Intelligence: Strategic Vision
- Deployment to thousands of customer-deployed machines
- Proactive health monitoring leveraging patterns learned from internal operations
- Predictive maintenance capabilities to prevent failures before they impact customers
- Guaranteed maximum uptime as a service offering
- Target Outcome: Business model transformation from product sales to intelligent service delivery
Each phase builds on the previous one. The pilot proved the foundation. Global deployment scales the capability. Customer fleet deployment transforms the business.
The Competitive Advantage: When Operations Become Strategy
Most manufacturers view operational improvements as cost reduction initiatives. Ranpak recognized something deeper: operational intelligence becomes strategic differentiation.
When you can:
- Optimize every machine to maximum sustainable utilization
- Identify failure patterns and implement targeted solutions to reduce recurrence
- Build the foundation to monitor and maintain thousands of customer machines proactively
- Create performance visibility that competitors lack
You're not just running a better factory. You're competing on a different level entirely.
The pilot facility didn't just prove ROI on a technology investment. It validated a vision for how manufacturing excellence creates customer value that's impossible to replicate without the same foundational intelligence.
From Proof of Concept to Global Blueprint
It's not often that a single facility deployment becomes the template for worldwide transformation.
But Ranpak didn't implement IoTFlows to solve a local problem. They implemented it to prove a global solution.
The data is definitive. The blueprint is ready. The vision is clear.
What started as one facility's optimization journey is becoming the operational standard for every Ranpak factory worldwide, and the foundation for an entirely new way to serve customers.
Complete machine visibility. Systematic downtime analysis. The foundation for predictive maintenance and proactive fleet monitoring.
This is manufacturing excellence that scales from one machine to thousands. From one facility to every facility. From internal operations to customer success.
The pilot proved it works. Now the real transformation begins.
