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Designing Sustainability: The Solupax Platform for
AI-Driven Packaging in FMCG
Abstract
This study explores how Solupax—an AI-powered, cloud-based platform—can enhance sustainable packaging design processes in the FMCG sector. The central research question is: To what extent can intelligent design automation and real-time sustainability metrics improve design quality, production efficiency, and environmental outcomes in packaging workflows? The research identifies major pain points in traditional packaging development, including lack of lifecycle insight, disconnected stakeholders, and manual iteration loops. A qualitative multiple case study approach was employed to analyze three real-world projects—Hernuta Day Nuts, Proshot Coffee, and Hive Bee Honey—using predefined metrics such as recyclability score, estimated carbon footprint reduction, number of revision cycles, and material efficiency. These case studies were evaluated in the context of Solupax’s AI-generated dieline proposals, environmental dashboards, and collaborative design environment. Results indicate that early access to lifecycle metrics and smart material suggestions accelerated decision-making and reduced unnecessary iterations. Furthermore, the visual feedback provided by the platform improved client-designer alignment. The findings support the potential of Solupax as a scalable, data-driven design ecosystem that bridges creative exploration with measurable sustainability outcomes in FMCG packaging.
Full Paper
Introduction:
Packaging design in the FMCG sector faces increasing pressure to meet sustainability goals while maintaining speed and creativity. This paper introduces Solupax, a platform that blends artificial intelligence with collaborative design to improve environmental performance and efficiency in packaging development.
Problem Statement:
Traditional packaging design workflows are fragmented and lack visibility into environmental consequences. Designers often work without real-time metrics, leading to inefficiencies, excess material use, and missed opportunities for circular design.
Proposed Solution:
Solupax combines five key modules: (1) AI Dieline Generator, (2) Material Recommendation Engine, (3) Lifecycle Simulation Dashboard, (4) Real-Time Collaboration Interface, and (5) Production Specification Output. AI is leveraged to predict optimal dielines and materials based on a product’s category and sustainability goals, and to offer real-time feedback through environmental indicators.
Research Question and Methodology:
This study asks: Can AI-driven packaging tools embedded in a collaborative platform significantly enhance design speed, reduce waste, and improve sustainability metrics in FMCG packaging? A multiple case study methodology was applied, evaluating three client-based design projects—Hernuta Day Nuts, Proshot Coffee, and Hive Bee Honey. Each case was examined for material impact, carbon footprint reduction, revision cycles, and time-to-production.
Case Study Results:
• Hernuta Day Nuts: AI proposed a compact dieline that reduced surface area by 12%, paired with compostable paperboard.
• Proshot Coffee: Sustainability scoring influenced a shift to plant-based ink, decreasing estimated emissions by 18%.
• Hive Bee Honey: Optimized hexagonal structure achieved 98% material utilization, minimizing production scrap.
Discussion:
Findings suggest that Solupax’s use of AI not only accelerates the design process but helps designers and brands make informed decisions based on quantifiable impact. By visualizing outcomes early, unnecessary iterations are reduced and collaboration becomes more aligned. The novelty of the platform lies in merging predictive intelligence with practical design tools in a user-centric system.
Conclusion:
Solupax represents a new generation of packaging design tools that merge creativity, intelligence, and accountability. Its unique integration of AI, sustainability analytics, and user interaction creates scalable value across FMCG sectors. Future development includes multilingual interfaces, blockchain-based material verification, and design competitions to foster community growth.
AI-Driven Packaging in FMCG
Abstract
This study explores how Solupax—an AI-powered, cloud-based platform—can enhance sustainable packaging design processes in the FMCG sector. The central research question is: To what extent can intelligent design automation and real-time sustainability metrics improve design quality, production efficiency, and environmental outcomes in packaging workflows? The research identifies major pain points in traditional packaging development, including lack of lifecycle insight, disconnected stakeholders, and manual iteration loops. A qualitative multiple case study approach was employed to analyze three real-world projects—Hernuta Day Nuts, Proshot Coffee, and Hive Bee Honey—using predefined metrics such as recyclability score, estimated carbon footprint reduction, number of revision cycles, and material efficiency. These case studies were evaluated in the context of Solupax’s AI-generated dieline proposals, environmental dashboards, and collaborative design environment. Results indicate that early access to lifecycle metrics and smart material suggestions accelerated decision-making and reduced unnecessary iterations. Furthermore, the visual feedback provided by the platform improved client-designer alignment. The findings support the potential of Solupax as a scalable, data-driven design ecosystem that bridges creative exploration with measurable sustainability outcomes in FMCG packaging.
Full Paper
Introduction:
Packaging design in the FMCG sector faces increasing pressure to meet sustainability goals while maintaining speed and creativity. This paper introduces Solupax, a platform that blends artificial intelligence with collaborative design to improve environmental performance and efficiency in packaging development.
Problem Statement:
Traditional packaging design workflows are fragmented and lack visibility into environmental consequences. Designers often work without real-time metrics, leading to inefficiencies, excess material use, and missed opportunities for circular design.
Proposed Solution:
Solupax combines five key modules: (1) AI Dieline Generator, (2) Material Recommendation Engine, (3) Lifecycle Simulation Dashboard, (4) Real-Time Collaboration Interface, and (5) Production Specification Output. AI is leveraged to predict optimal dielines and materials based on a product’s category and sustainability goals, and to offer real-time feedback through environmental indicators.
Research Question and Methodology:
This study asks: Can AI-driven packaging tools embedded in a collaborative platform significantly enhance design speed, reduce waste, and improve sustainability metrics in FMCG packaging? A multiple case study methodology was applied, evaluating three client-based design projects—Hernuta Day Nuts, Proshot Coffee, and Hive Bee Honey. Each case was examined for material impact, carbon footprint reduction, revision cycles, and time-to-production.
Case Study Results:
• Hernuta Day Nuts: AI proposed a compact dieline that reduced surface area by 12%, paired with compostable paperboard.
• Proshot Coffee: Sustainability scoring influenced a shift to plant-based ink, decreasing estimated emissions by 18%.
• Hive Bee Honey: Optimized hexagonal structure achieved 98% material utilization, minimizing production scrap.
Discussion:
Findings suggest that Solupax’s use of AI not only accelerates the design process but helps designers and brands make informed decisions based on quantifiable impact. By visualizing outcomes early, unnecessary iterations are reduced and collaboration becomes more aligned. The novelty of the platform lies in merging predictive intelligence with practical design tools in a user-centric system.
Conclusion:
Solupax represents a new generation of packaging design tools that merge creativity, intelligence, and accountability. Its unique integration of AI, sustainability analytics, and user interaction creates scalable value across FMCG sectors. Future development includes multilingual interfaces, blockchain-based material verification, and design competitions to foster community growth.