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Context

The Fashion Retail industry faces a structural challenge in managing product packaging and folding, which impacts the entire supply chain, operational efficiency, and ultimately, the customer experience.

This issue manifests across several dimensions:

• Reliance on manual and inaccurate data: Critical information on supplier compliance is collected manually and in fragmented ways, leading to errors, delays, and limited ability to respond effectively. This hampers data-driven corrective or preventive actions.
• Hidden operational costs: Lack of standardization in packaging and folding leads to rework at distribution centers, repacking, refolding, relabeling, resulting in extra material costs, labor hours, delivery delays, and in some cases, contractual penalties.
• Inconsistent standards across suppliers: Variability in how suppliers interpret and execute packaging and folding guidelines creates an unreliable quality control system, impacting the operational efficiency
• Reactive supplier behavior: Suppliers often respond only after issues arise or penalties are applied, rather than following clear, automated, data-based instructions.
• High operational complexity: A wide variety of SKUs, formats and sizes, turns each day into a unique case, increasing the risk of errors and complicating the development of scalable efficiencies.
• Limited visibility and delayed decision-making: Critical operational information remains scattered across systems and teams, making it difficult to consolidate in a timely manner.

Together, these issues result in a reactive operation with limited systemic visibility, where control and improvement rely heavily on manual intervention. This leads to inefficiencies, higher costs, and reduced scalability in the face of increasing consumer and market demands.

Proposed Solution

The proposed solution is to build around the integrated components designed to improve packaging and folding processes across the supply chain:

• Data-Driven Packaging and Folding Recommendations: Automatically generates optimal packaging and folding instructions based on product type and historical performance. These recommendations aim to reduce variability and improve standardization across suppliers.
• Intelligent Risk Matrix (Supplier-Product Level): Classifies suppliers by their historical performance and commercial relevance, assigning each a risk level (high, medium, low). This determines the appropriate audit frequency and intensity, optimizing resource allocation for quality control.
• Evolution of Audit Processes: Transitions from fully manual audits to a hybrid and eventually digital-first model. Incorporates mobile tools for capturing deviations, supports real-time tracking, and enables automated triggers for post-audit actions.
• Post-Audit Decision Framework: Based on audit results, the system supports automated decisions such as whether to proceed with repackaging, initiate a return process, or escalate issues. This reduces delays and promotes consistent responses.
• Reporting Dashboard: Provides a centralized view of key indicators related to compliance and adherence to the packaging and folding recommendations. Includes metrics such as guideline adherence, rework rates, audit scores and analytics insights from the machine learning models

By unifying historically fragmented data, operationalizing four machine-learning models, and visualizing their outputs in two complementary dashboard views, the project replaces ad-hoc inspections and costly rework with an end-to-end, evidence-based decision system.