Manufacturing Analytics

An American Biotechnology Company had implemented analytics solutions that provided visibility into actual and historical data on finance, supply chain, and purchasing business processes. However, these solutions did not provide visibility into manufacturing data to enable the manufacturing team to identify bottlenecks and improve processes.

This led to various challenges for the manufacturing team such as a lack of visibility into production data, obtaining insight into planned cost vs actual cost, difficulty in measuring planned vs actual material, no measurement of batch aging and batch cycle time, lack of reliable information on store levels, aging, and expiration dates.

DiLytics implemented a solution on Oracle Analytics that involved:

  • Phase I: Oracle Manufacturing Analytics was installed, set up, and configured and out-of-the-box data was loaded from Oracle E-Business Suite (EBS) and Oracle Advanced Supply Chain Planning (ASCP).
  • Phase II: Out-of-the-box Discovery and Requirements Gathering – capture business requirements for use of Manufacturing Analytics by the manufacturing team. Detailed design, development, and testing of Oracle Manufacturing Analytics to implement and deploy the product based on users’ business requirements.
  • Phase III: Subsequent rollouts to address requirements not implemented in Phase II.

DiLytics implemented a solution on Oracle Analytics that involved:

  • Loading planned data from Oracle Advanced Supply Chain Planning (ASCP) and actual manufacturing orders execution, and inventory data from EBS into Data Warehouse.
  • Building metrics, KPIs, dimensions and hierarchies on the operational data, planning data, inventory data, and manufacturing execution data in Oracle BI Server.
  • Building reports, dashboards, conditional alerts, and guided navigations in Oracle BI Presentation Server.

DiLytics solution provided business benefits to the client such as

  • Spot business process improvement opportunities.
  • Stakeholders have access to real-time production data through shared dashboards.
  • Consistent definitions and calculations for metrics and data points align multiple organizational functions.
  • Manufacturing supervisors can optimize production operations more efficiently.
  • Automatic alerts for changes in manufacturing operations.

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