How Coveris Transformed Production with Real-Time Insights from Shoplogix

Coveris, a leading European packaging manufacturer, faced challenges with setup inconsistencies and limited visibility into production issues at their Louth plant. By implementing Shoplogix’s OneSignal, they unlocked powerful real-time insights that quickly drove operational improvements.

Key Challenges:

  • Inconsistent setups across 5 printing presses and 17 job changes per day
  • Increased scrap, reduced run speeds, and missed production targets
  • Lack of visibility into root causes of inefficiencies

The Shoplogix Solution:

  • Connected machines in just 1 day (vs. over a month with a competitor)
  • Delivered real-time data on key metrics like scrap, setup time, and run speed
  • Identified inefficiencies in:
    • Mechanical setup
    • Registration
    • Final adjustments

Results & Business Impact:

  • 10% reduction in setup waste, saving £800,000 annually
  • 16% increase in run speed, boosting revenue by £340,000
  • 9% improvement in OEE, raising plant-wide productivity

Why It Matters for Manufacturers:

This case shows how fast, cost-effective smart connectivity can help manufacturers:

  • Gain instant visibility into performance issues
  • Standardize processes and reduce variability
  • Make data-driven decisions that improve efficiency, reduce waste, and increase profitability

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How Magna Uncovered Hidden Inefficiencies and Saved $520K with Shoplogix

Even in facilities with mature continuous improvement (CI) practices, hidden inefficiencies can go undetected, costing time and money. Magna International, a leading global automotive parts manufacturer, faced a puzzling productivity gap despite optimized machine performance and seemingly minimal downtime.

The Challenge:

Magna’s CI team suspected inefficiencies during shift transitions and breaks but lacked the granular data to verify it.

The Solution:

After implementing Shoplogix, detailed loss analytics revealed an unexpected cause: employees waiting to use microwaves during breaks, leading to production delays.

Key Results for Magna:

  • 70% reduction in break and shift transition delays
  • $520,000 in annual savings
  • Enhanced visibility into previously undetectable production losses
  • Empowered CI teams to take targeted, data-driven action
Takeaway for Manufacturers:

Even the most minor inefficiencies can have a major financial impact. With Shoplogix’s real-time analytics, manufacturers can identify hidden production barriers, drive engagement, and turn overlooked issues into significant savings.

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How Greif Recovered 20% of Lost Production Time with Accurate Roll Change Tracking

Greif, a global leader in industrial packaging, faced persistent challenges in reducing roll change times despite investing in operational excellence initiatives. Roll changes, which accounted for 20% of lost production time, continued to exceed the targeted two-minute window, often taking 15 minutes or more. Manual tracking methods failed to capture the root of the issue—until Shoplogix introduced a smart, simple solution.

The Challenge:

  • Roll changes were consuming up to 15 minutes, well beyond the two-minute target.
  • Manual data collection didn’t capture the true scope of the inefficiencies.
  • Despite consulting support, the issue remained unresolved, affecting production goals and leading to costly overtime.

The Solution:

  • A simple switch was added to the roll mandrel to track roll change times automatically.
  • Real-time alerts were set up to notify the team when the two-minute target was exceeded.
  • Data integration into existing systems enabled continuous monitoring and analytics.

The Results:

  • Roll change times were reduced to under two minutes consistently.
  • 20% of previously lost production time was recovered.
  • Operational visibility improved, enabling the team to manage and sustain performance gains.
  • Greif was finally able to hold the gains, not just get them.

Key Takeaways for Manufacturers:

  • Simple, smart connectivity can uncover and resolve hidden inefficiencies.
  • Accurate, real-time data is essential for sustaining improvements.
  • Small hardware enhancements, when paired with integrated analytics, can lead to significant operational gains.

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How Coveris Transformed Production with Real-Time Insights from Shoplogix

Coveris, a leading European packaging manufacturer, faced challenges with setup inconsistencies and limited visibility into production issues at their Louth plant. By implementing Shoplogix’s OneSignal, they unlocked powerful real-time insights that quickly drove operational improvements.

Key Challenges:

  • Inconsistent setups across 5 printing presses and 17 job changes per day
  • Increased scrap, reduced run speeds, and missed production targets
  • Lack of visibility into root causes of inefficiencies

The Shoplogix Solution:

  • Connected machines in just 1 day (vs. over a month with a competitor)
  • Delivered real-time data on key metrics like scrap, setup time, and run speed
  • Identified inefficiencies in:
    • Mechanical setup
    • Registration
    • Final adjustments

Results & Business Impact:

  • 10% reduction in setup waste, saving £800,000 annually
  • 16% increase in run speed, boosting revenue by £340,000
  • 9% improvement in OEE, raising plant-wide productivity

Why It Matters for Manufacturers:

This case shows how fast, cost-effective smart connectivity can help manufacturers:

  • Gain instant visibility into performance issues
  • Standardize processes and reduce variability
  • Make data-driven decisions that improve efficiency, reduce waste, and increase profitability

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CI Playbook for Modern Manufacturers Unlocking Hidden Opportunities to Accelerate Continuous Improvement

Continuous Improvement (CI) Managers in manufacturing are under constant pressure to optimize performance, reduce costs, and increase throughput. However, many improvement efforts fall short—not because of a lack of effort, but because they focus on the wrong issues, rely on outdated data, or struggle to engage frontline teams.

This playbook outlines how manufacturers can overcome these barriers by using real-time, machine-level insights to identify true root causes, engage operations, and unlock hidden capacity.

Key Takeaways for Manufacturers:

Solve What Really Matters

  • Avoid wasting resources on the most visible—but not necessarily the most impactful—problems
  • Identify hidden performance drains like speed loss and micro-stoppages
  • Prioritize initiatives based on real-time impact, not assumptions

Replace Assumptions with Accurate Data:

  • ERP targets and spreadsheets often mask inefficiencies
  • Use Top Historical Speed (THS) to uncover true machine potential
  • Set smarter standards and detect speed variability early

Engage Operators in the CI Journey

  • Lack of frontline engagement can stall even the best strategies
  • Empower operators with visual tools, feedback loops, and performance visibility
  • Make CI a collaborative effort between leadership and operations

Results You Can Expect:

Reclaim thousands of production hours

  • Optimize job speeds and eliminate unnecessary slowdowns
  • Increase output—without investing in new equipment
  • Build a continuous improvement culture grounded in transparency and accountability

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How Coveris Transformed Production with Real-Time Insights from Shoplogix

Coveris, a leading European packaging manufacturer, faced challenges with setup inconsistencies and limited visibility into production issues at their Louth plant. By implementing Shoplogix’s OneSignal, they unlocked powerful real-time insights that quickly drove operational improvements.

Key Challenges:

  • Inconsistent setups across 5 printing presses and 17 job changes per day
  • Increased scrap, reduced run speeds, and missed production targets
  • Lack of visibility into root causes of inefficiencies

The Shoplogix Solution:

  • Connected machines in just 1 day (vs. over a month with a competitor)
  • Delivered real-time data on key metrics like scrap, setup time, and run speed
  • Identified inefficiencies in:
    • Mechanical setup
    • Registration
    • Final adjustments

Results & Business Impact:

  • 10% reduction in setup waste, saving £800,000 annually
  • 16% increase in run speed, boosting revenue by £340,000
  • 9% improvement in OEE, raising plant-wide productivity

Why It Matters for Manufacturers:

This case shows how fast, cost-effective smart connectivity can help manufacturers:

  • Gain instant visibility into performance issues
  • Standardize processes and reduce variability
  • Make data-driven decisions that improve efficiency, reduce waste, and increase profitability

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From Frustration to High-Performance: How Genpak Gained 19 Hours of Weekly Efficiency in 90 Days

Packaging manufacturers like Genpak often face a familiar set of challenges: late machine starts, inconsistent shift transitions, high rework, and a lack of visibility into what’s actually happening on the floor. Despite having systems in place, Genpak needed a way to surface hidden inefficiencies and act on them in real time.

Through a focused 90-day pilot with Shoplogix, Genpak achieved measurable, high-impact results:

  • A +11% increase in OEE
  • 8 hours/week saved from roll change optimization
  • 6 hours/week recovered by reducing rework on the night shift
  • 5 hours/week gained by improving shift handovers

In total, the plant unlocked 19 hours of operational time per week, equivalent to nearly 1,000 hours per year.

This success wasn’t about adding another layer of technology—it was about making performance visible, empowering teams, and building the habits that lead to consistent improvement.

Key Benefits for Manufacturers:

  • Real-time visibility into production losses
  • Faster changeovers and more efficient shift transitions
  • Data-driven accountability between shifts
  • A clear path to operational improvement in just 90 days

If your plant is struggling with hidden inefficiencies or underperforming shifts, this case shows what’s possible with the right visibility and a focused action plan.

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Maximize Packaging Output: A Plant Manager’s Guide to Eliminating Downtime & Boosting OEE

In today’s fast-paced packaging environment, plant managers face mounting pressure to hit production targets, reduce waste, and improve shift performance — all while managing complex changeovers and tight customer requirements. This guide offers a practical, non-salesy roadmap to help manufacturers uncover and eliminate hidden operational losses in just 90 days.

Based on real-world results from packaging plants using Shoplogix, the guide highlights common challenges — from unplanned downtime and shift creep to inconsistent performance and rework — and outlines a clear 4-step action plan to overcome them. With real-time visibility into your operations, you'll gain actionable insights that lead to measurable improvements.

Key Benefits for Packaging Manufacturers:

  • Identify and eliminate up to 19 hours/week of hidden inefficiencies
  • Improve OEE with better visibility into downtime, rework, and shift performance
  • Standardize changeovers and reduce rework through real-time data
  • Empower frontline teams with the right information at the right time
  • Achieve tangible results within 90 days

Whether you're battling daily production frustrations or looking to scale best practices, this playbook will help your plant take smarter, faster steps toward high-performance manufacturing.

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Role of Data Solutions for AI Deployment

Great AI products need to solve the data woes of enterprises reliably and consistently. Sudeep George, VP of Engineering at iMerit, deep dives into the importance of high-quality data in building effective machine learning models on the I M Possible podcast with Eddie Avil.

Watch iMerit’s VP of Engineering, Sudeep George, discuss:

  • How to ensure that data is high-quality
  • The role of ML DataOps and how it helps with AI model performance
  • How enterprises can accelerate their AI development and deployment
  • How AI can be made more accessible
  • Cutting-edge AI innovation & its social impact

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Vision-Language-Action Model For Autonomous Mobility

To support a cutting-edge demo in autonomous mobility, a leading AI company partnered with iMerit to power its Vision-Language-Action (VLM) model with high-quality, expertly curated training data. iMerit’s team of domain specialists rapidly annotated real and synthetic driving scenarios, improving model accuracy, explainability, and safety—while delivering a 50% boost in task efficiency.

This case study highlights how iMerit’s tailored data annotation and VLM expertise enabled the client to surpass demo goals, enhance model transparency, and accelerate innovation in autonomous vehicle technology.

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Improving Retrieval-Augmented Generation For Healthcare Chatbot

To ensure accurate and safe generative outputs from its healthcare chatbot, a leading U.S. tech company partnered with iMerit to evaluate and refine a new medical dataset using Retrieval-Augmented Generation (RAG). Leveraging a team of nurses and a board-certified physician, iMerit developed a scalable, expert-driven workflow to audit medical definitions for accuracy, safety, and usefulness—achieving 99% consensus and over 72% in project cost savings.

This case study highlights how iMerit's domain-specific RLHF and human-in-the-loop evaluation helped the client reduce risk, improve quality, and scale responsibly in a high-stakes healthcare environment.

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RLHF For AI Co-Pilot

A leading professional social networking platform partnered with iMerit to enhance its AI-powered co-pilot through Reinforcement Learning from Human Feedback (RLHF). The goal was to ensure that the co-pilot delivers accurate, coherent, and responsible responses tailored to users' job-related queries. iMerit provided expert annotation and response evaluation services, helping align the assistant’s output with the platform’s values and professional tone.

By leveraging a skilled team and customizable tools, iMerit improved the assistant’s conversational quality, boosted user satisfaction, and upheld ethical standards. The collaboration led to measurable gains in responsiveness, relevance, and user engagement—making the AI co-pilot a more effective and trusted digital assistant.

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Leveraging Consensus Logic and Escalations to Improve RLHF

In the evolving world of AI development, ensuring alignment between machine behavior and human intent is critical. This blog explores how consensus logic and escalation workflows can significantly enhance the effectiveness of Reinforcement Learning from Human Feedback (RLHF). By aggregating multiple human judgments and escalating ambiguous cases to expert reviewers, these strategies reduce bias, increase reliability, and improve the overall trustworthiness of AI systems.

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Unlocking the Potential of In-cabin Solutions: Emerging Use Cases & Evolving Data Needs

As vehicles advance toward autonomy, in-cabin monitoring systems are becoming essential for enhancing safety, comfort, and compliance. These AI-driven systems track driver alertness, passenger behaviour, and environmental factors—transforming the driving experience and meeting growing regulatory demands. With the market projected to soar past $3.2B by 2031, the need for smart, reliable monitoring has never been greater.

iMerit supports this evolution with high-quality data annotation via its Ango Hub platform, combining predictive AI with expert oversight. This ensures automotive companies can train accurate, bias-free models for use cases ranging from driver fatigue detection to child safety—driving innovation in next-generation vehicle intelligence.

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Generative AI Chatbots in Healthcare

Generative AI chatbots are reshaping the healthcare industry by streamlining operations, enhancing patient care, and offering round-the-clock support. From symptom checking and appointment scheduling to chronic disease management and mental health assistance, these AI-powered assistants provide scalable, accessible solutions for patients and professionals alike. As technology evolves, so does their potential—integrating with wearables, delivering personalised care, and supporting complex diagnoses with impressive accuracy.

Despite the promise, challenges like data privacy, regulatory compliance, and the need for expertly annotated medical datasets remain. iMerit addresses these issues head-on with secure, HIPAA-compliant data solutions, expert-driven annotation services, and domain-specific reinforcement learning support. With its proven track record and deep expertise in healthcare AI, iMerit is empowering organisations to build accurate, ethical, and scalable AI chatbots that are shaping the future of patient-centred care.

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