Nagappan Periakaruppan

age ~47

from Burlington, MA

Also known as:
  • Nagappan Periakaruppa
  • Nagappan N
  • N N
Phone and address:
29 Rocky Hill Rd, Burlington, MA 01803

Nagappan Periakaruppan Phones & Addresses

  • 29 Rocky Hill Rd, Burlington, MA 01803
  • Frisco, TX
  • Marina del Rey, CA
  • Medford, MA
  • Wichita, KS

Work

  • Position:
    Senior principal product manager

Resumes

Nagappan Periakaruppan Photo 1

Senior Principal Product Manager

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Location:
Burlington, MA
Work:

Senior Principal Product Manager

Us Patents

  • Consumption-Driven Forecasting Using Multi-Level Heterogeneous Input Data

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  • US Patent:
    20150379536, Dec 31, 2015
  • Filed:
    Jun 25, 2014
  • Appl. No.:
    14/314373
  • Inventors:
    - Redwood Shores CA, US
    Kiran SAINDANE - Dublin CA, US
    Nadav ZIVELIN - Menlo Park CA, US
    Bart FELDMAN - Lexington MA, US
    Michael LIEBSON - Cambridge MA, US
    Vikash GOYAL - Newark CA, US
    Nagappan PERIAKARUPPAN - Burlington MA, US
    Eytan E. ARKIN - Chestnut Hill MA, US
  • Assignee:
    Oracle International Corporation - Redwood Shores CA
  • International Classification:
    G06Q 30/02
  • Abstract:
    A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.

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