Project –Integrated Controlling Application & Integrated Sales Planning                               Jan 16– Jun 17

• Responsible for BAU Design,Development and Support.
• Implementation of end to end transfer price P&L.Includes data loads from FDMEE to ORADB,PL-SQL, Procedures to apply complex transformation logics , ODI Package implementation to load from SQL DB to Hyperion applications.
• Implementation of ODI logics from SAP BW ,HFM & ISA  to ICONA , ICONA to ISA ,HFM & SAP BW.Functional changes include new customer dimension inclusion, new plant roll outs.
• Maintenance of Security changes to all the consolidated and region specific Hyperion Planning application.
• Integrated Sales Planning application migration between different environments.
• Automation of application backups for daily, monthly and yearly.
• Tuning of PLSQL Procedures to reduce the time of data loads from SAP BW to ODI Source environment.

Environment: Hyperion Essbase 11.x,Hyperion Planning, ODI ,FDMEE , EPMA,Unix, Batch scripting, Python scripting

●       Responsible for BAU Design,Development and Support.

●       Implementation of ODI logics from SAP BW ,HFM & ISA  to ICONA , ICONA to ISA ,HFM & SAP BW.Functional changes include new customer dimension inclusion, new plant roll outs.

●       Maintenance of Security changes to all the consolidated and region specific Hyperion Planning application.

●       Integrated Sales Planning application migration between different environments.

●       Automation of application backups for daily, monthly and yearly.

Tuning of PLSQL Procedures to reduce the time of data loads from SAP BW to ODI Source environment

●       Responsible for development of FX rates extract and Load Interfaces from HFM to Planning and Version Console Areas for respective scenarios and versions

●       Responsible for efficient transition and onsite knowledge transfer about the Hyperion applications to the end users

●       Conducted workshops and onshore training with process documentation. 

Prediction of plant throughput at final station using time series and deep learning algorithms