AI-Powered Data Validation and Standardization in Supply Chain
Phase 4- Report: Final Analysis and Insights
College Name: Karnatak Law Society Vishwanathrao Deshpande Institute of
Technology
Group Members Name:
1. Naveenkumar Hosamani
2. Siddharoodha R Kanaka
3. R Supreeth Habbu
4. Prashant Hindasageri
Phase 1 – Problem Analysis
Overview
Supply chain data is often inconsistent, containing errors and format differences across
various systems. Manual validation is slow and prone to human error, leading to
inefficiencies and increased costs. This phase identifies these challenges and defines an
AI-driven solution to automate data validation and standardization.
Key Challenges Identified
• Inconsistencies in inventory, logistics, and supplier systems data, leading to
miscommunication and operational delays.
• Duplicate entries, missing values, and incorrect formats causing redundancy and
inefficiencies in supply chain workflows.
• Challenges in integrating siloed data for accurate forecasting, planning, and
real-time decision-making.
• Limitations of traditional manual validation methods, which are resource-
intensive and non-scalable for large datasets.
Proposed AI-Powered Solution
• Automate data validation and standardization to minimize human
intervention and reduce operational errors.
• Real-time data processing and anomaly detection to enhance the
responsiveness of supply chain systems.
• Cloud-based architecture with scalable storage and computation capabilities
to support high-volume transactions.
• Expected Outcomes: Improved data accuracy, streamlined decision-making,
cost reduction, and seamless data interoperability across different platforms.
Phase 2 – Solution Architecture
Overview
This phase details the end-to-end technical architecture for AI-powered product
catalog validation, covering data ingestion, preprocessing, AI validation, storage,
and real-time serving.
Architecture Layers
1. Data Sources: Collects data from supplier feeds, ERP systems, APIs, IoT
sensors, and third-party logistics providers.
2. Data Ingestion: Uses ETL tools (Apache NiFi, Talend, Airflow) and real-
time streaming (Kafka, Google Pub/Sub) for high-velocity data processing.
3. Storage: Raw data is stored in AWS S3, Azure Data Lake, while processed
and structured data resides in Snowflake, Google BigQuery, and MongoDB
for optimized querying.
4. Preprocessing: Implements NLP-based text normalization, unit conversion,
and categorical mapping to maintain data consistency.
5. AI Validation: Applies Machine Learning (Isolation Forest, Autoencoders)
and rule-based validation to detect anomalies, validate SKU formats, and
check compliance with industry standards.
6. Serving Layer: Offers validated data through REST APIs, GraphQL
endpoints, and interactive dashboards using Power BI and Tableau.
7. Monitoring & Feedback: Uses Grafana, Datadog, and logging frameworks
to ensure continuous tracking of data quality metrics and model performance.
Key Features Implemented
• Automated data ingestion pipelines to fetch and process supply chain records
efficiently.
• Adaptive AI models that dynamically refine validation rules based on historical
data trends.
• Real-time dashboards that provide interactive visualization of supply chain
KPIs, alerting stakeholders about critical data discrepancies.
Phase 3 – Model Development and Evaluation
Overview
This phase involves building AI models, training them on historical supply chain
data, performing validation, and assessing their effectiveness using robust
evaluation metrics.
Implementation Steps
1. Data Simulation: Generates synthetic supply chain datasets with attributes like
SKU, price, quantity, supplier ID, and shelf life to train AI models
effectively.
2. Data Storage: Uses MongoDB, PostgreSQL, and Elasticsearch for scalable
data storage and retrieval.
3. Data Validation: Ensures completeness, correctness, uniqueness, and
consistency through AI-driven rules.
4. Price Adjustment Algorithms: Dynamically adjusts Electronics & Furniture
prices (+15%) and Food prices based on market fluctuations to optimize
pricing strategies.
5. Visualization: Implements matplotlib, seaborn, and Plotly to generate
insightful graphs for performance tracking.
Model Performance & Insights
Performance Metrics
• Accuracy: 0.67 – Measures the proportion of correctly classified instances
among total instances.
• Precision: 0.57 – Represents the proportion of correctly predicted positive cases
out of all predicted positive cases.
• Recall: 0.67 – Indicates the proportion of actual positive cases correctly
identified by the model.
• F1 Score: 0.62 – A harmonic mean of precision and recall, balancing both
metrics for an overall performance measure.
Classification Report
Class Precision Recall F1-Score Support
0 0.75 0.67 0.71 18
1 0.57 0.67 0.62 12
Accuracy 0.67 30
Macro Avg 0.66 0.67 0.66 30
Weighted Avg 0.68 0.67 0.67 30
Deep Dive Analysis
• AI-driven validation significantly enhances the system’s capability to detect
anomalies, thereby reducing false positives and operational risks.
• The precision score (0.57) suggests scope for threshold tuning, aiming to
lower false positive rates while maintaining anomaly detection robustness.
• Recall score (0.67) confirms strong fraud detection capability, indicating
that most fraudulent or erroneous records are successfully flagged.
• F1 Score evaluation highlights the need for hyperparameter optimization
and feature engineering to balance between false positives and false negatives.
• Dashboards facilitate real-time tracking of validation trends, enabling
supply chain managers to make data-driven decisions efficiently.
Visualizations:
Future Enhancements
• Deploy UI on Vercel for enhanced accessibility and scalability.
• Enable CSV uploads and API integrations for external users to validate custom
datasets.
• Expand database support by integrating distributed storage for real-time
transaction logging.
• Automate report generation with AI-powered insights to provide advanced
predictive analytics.
• Incorporate blockchain technology for enhanced data security and immutability
in supply chain transactions.
• Enhance AI model explainability by integrating SHAP (SHapley Additive
exPlanations) to improve trust in AI-driven decisions.
• Introduce reinforcement learning techniques to optimize supply chain
operations dynamically.
• Develop real-time alert mechanisms using AI-based anomaly detection to flag
inconsistencies proactively.
• Leverage federated learning to enable data validation across multiple suppliers
while ensuring data privacy.
• Deploy UI on Vercel for enhanced accessibility and scalability.
• Enable CSV uploads and API integrations for external users to validate custom
datasets.
• Expand database support by integrating distributed storage for real-time
transaction logging.
• Automate report generation with AI-powered insights to provide advanced
predictive analytics.
Conclusion
Through these iterative phases, we have successfully designed, implemented, and
refined an AI-powered supply chain data validation system that ensures high
accuracy, automation, and scalability. Moving forward, the focus will be on
enhancing user accessibility, integrating deep learning for better anomaly
detection, and scaling the solution for enterprise-level deployments to ensure long-
term sustainability and efficiency in supply chain operations.
