Supply Chain Analytics Software and Logistics Analytics Solutions

Turn Supply Chain Data Into Action

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Supply Chain Analytics Software Use Cases

Embedded analytics within supply chain software empower companies to gain insight and extract real value from the large amounts of data you’re creating around the procurement, processing, distribution and transportation of goods.

The Importance of AI

AI and machine learning is changing the face of supply chain analytics platforms. It can automate the analysis of large volumes of historical data and use it to provide real-time insights for forward-looking decision making.

Logistics analytics software can quickly surface patterns and trends and offer embedded decision logic to improve efficiency, increase productivity and dramatically lower costs in everything you do.

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Supply chain analytics from start to finish

Embedded analytics for SaaS applications empowers users of a logistics analytics software application to analyze data in way that fits each business.

Qrvey offers the most complete embedded analytics solution for supply chain companies that includes a native data management layer, workflow automation and dashboards with custom dashboard builders.

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Key Capabilities of Qrvey for Supply Chain Analytics

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Analytics for Those Who Want More

Build Less Software. Deliver More Value.

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Frequently Asked Questions

What is supply chain analytics?

Supply chain analytics involves collecting and analyzing data across the supply chain to gain visibility, identify insights, and optimize planning and execution.

  • Supply chain analysts/managers
  • Logistics and distribution managers
  • Procurement and sourcing teams
  • Inventory managers
  • Operations managers
  • Executive leadership
  • Demand forecasting and planning – Predict future demand to optimize inventory and production.
  • Supplier & risk analytics – Evaluate suppliers on cost, quality, risk factors to improve sourcing.
  • Logistics optimization – Analyze shipping routes, modes, costs to enhance delivery efficiency.
  • Supply chain visibility – Provide real-time tracking of inventory, orders, shipments.
  • Production analytics – Identify bottlenecks, waste, downtime to boost throughput.
  • Inventory optimization – Improve levels, locations based on demand trends and forecasts.
  • Anomaly detection – Identify abnormalities or outliers to mitigate supply chain disruptions.
  • What-if scenario modeling – Assess action scenarios to manage risks and plan continuity.