Product
Preferred partner and a powerhouse for AWS SC Implementation
AWS Supply Chain Product
AWS Supply Chain implementation skills
AWS Supply Chain training skills
AWS Supply Chain, Amazon Forecast customization skills.
Product AWS Supply Chain
AWS SC Workshop
AWS SC Implementation
AWS SC, Forecast customization
Kick-off Meeting
Application Overview | Application Setup
Inventory Health Analysis
Demand Planning Analysis
Review and transition
Insights and Collaboration
Business Evaluation
Lesson Learned
Customer Sign off
Satisfaction Survey
AWS SC 10 REASONS
Unified Data Model with ML-driven Insights:
Unified data model using machine learning (ML) to unify disparate data from various sources, enabling actionable insights for supply chain management.
Seamless Integration with Existing Systems:
Connects effortlessly to existing enterprise resource planning (ERP) and supply chain management systems, eliminating the need for replatforming, upfront licensing fees, or long-term contracts.
Data Lake for Comprehensive Data Management
Sets up a data lake to understand, extract, and transform diverse data into a unified model, utilizing ML and natural language processing (NLP) for association, ensuring comprehensive data management.
Real-time Visual Mapping
Contextualizes data in a real-time visual map, allowing users to interactively view current inventory, its health, and potential risks across locations, providing valuable insights for inventory managers.
Automated Insights and Risk Detection
Automatically generates insights into potential supply chain risks using ML models, minimizing overstock or stock-out risks. Alerts are generated for detected risks, ensuring proactive risk mitigation.
ML-powered Vendor Lead Time Predictions
Applies ML models similar to those used by Amazon to generate accurate vendor lead time predictions, enabling supply planners to update assumptions and reduce stock-out or excess inventory risks.
Personalized Insight Watchlists
Enables inventory managers, demand planners, and supply chain leaders to create personalized insight watchlists, allowing customization based on location, risk type, stock threshold, and team members as watchers.
Recommended Actions with Sustainability Impact:
Automatically evaluates, ranks, and shares rebalancing options with inventory managers, scoring options by risk resolution, facility distance, and sustainability impact, improving decision-making over time.
Built-in Contextual Collaboration
Facilitates collaboration by providing built-in contextual collaboration capabilities, allowing teams to chat and message about risks and recommended options, reducing errors and delays caused by poor communication.
ML-driven Demand Planning
Enhances demand planning accuracy by generating more accurate demand forecasts, adjusting to market conditions, and enabling collaboration across teams to avoid excess inventory costs and waste, all powered by ML analysis of historical and real-time data.