AI Solution

Supply Chain Optimization

Supply chains deal with massive amounts of data. By effectively managing this data, businesses can optimize operations, reduce costs, and improve decision-making.

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Use Cases of AI/ML IN SUPPLY CHAIN OPTIMIZATION

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Demand Forecasting

This involves predicting future customer demand using historical data, analytics, and market insights to optimize inventory and ensure timely delivery. The challenges of demand forecasting include managing volatile consumer behavior influenced by economic uncertainties, accurately predicting demand for new products, integrating diverse data sources for comprehensive analysis, adapting to rapidly changing market trends, and minimizing forecasting errors that can lead to overstocking or stockouts.

Role of AI

Through machine learning, AI can analyze the volumes of data, to enhance the precision of forecasts. This will enable companies to,

  • optimize inventory levels
  • reduce costs,
  • and improve customer satisfaction by ensuring the right products are available at the right time

AI-driven demand forecasting also allows for real-time adjustments, making supply chains more responsive and resilient to fluctuations.

Technologies we use in Supply Chain Optimization Solution

AI/ML
Data Ingestion

Snowflake(Collect/Store data)

Applications
Data Transform

NVIDIA RAPIDS(Accelerate Transformation Layer)

Data Lake
Data Process

AWS(Store Processed data back to Snowflake)

Unistore
Data Analytics Modeling

Google Cloud(Save data sources to cloud ecosystem)

Unistore
Data Visualization and Reporting

Power BI(Azure Power BI to visualize forecasted data)

Workloads

Implementation Strategy