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Paul R Sapenaro

from Overland Park, KS
Age ~67
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Also known as:
Paul Sapenaro, Cindy Sapenaro, Cynthia Sapenaro, Jessica Sapenaro, Paul A, Paul O
Phone and address:
13828 Hemlock, Overland Park, KS 66223
Related to:
Zachary Parrett, 39Cindy Pagenkopf, 62Robert J Pagenkopf, 33Jill Weber, 67Jon Guilford, 61
Connected to:
Jill H Sapenoff, 34Hensley W Sapenter, 83Abraham Saper, 73Benjamin M Saper, 37Karen S Saper, 78Victoria C Saper, 72Patricia M Sapere, 63Nathan A Saperia, 39Phillip A Saperia, 76William D Sapero, 70

Paul Sapenaro Phones & Addresses

  • 13828 Hemlock, Overland Park, KS 66223
  • 13828 Hemlock St, Shawnee Mission, KS 66223
  • 15228 84Th St, Lenexa, KS 66219
  • Stanley, KS
  • Kansas City, MO
  • Olathe, KS
  • Raleigh, NC

Resumes

Resumes

Paul Sapenaro Photo 1

Paul Sapenaro

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Position:
Executive Consulting at ****
Location:
Kansas City, Missouri Area
Industry:
Management Consulting
Work:
**** since Mar 2008
Executive Consulting
Sprint Nextel 2002 - 2008
Vice President
Sprint Consumer Long Distance Division Jan 1999 - Jan 2002
Vice President
Sprint Business Long Distance Division Jan 1988 - Jan 1999
Director
Sprint Local Division Jan 1987 - Jan 1988
Product Manager
Education:
Product Managment & Development Association Certification 2005
NPDP, New Product Development Professional Project Managment Institute Certification 2005
PMP, Project Management Professional Rockhurst University 1991
MBA, Leadership & General Management Missouri State University 1973 - 1977
B.S., Physics
Skills:
Strategic Planning
Project/Program Management
PMO
Pipeline Managment
Product Management & Development
Leadership Development
Paul Sapenaro Photo 2

Vice President At Sprint Nextel

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Location:
13828 Hemlock St, Overland Park, KS 66223
Industry:
Telecommunications
Work:
Sprint
Vice President at Sprint Nextel
Education:
Missouri State University 1973 - 1977
Interests:
Exercise
Sweepstakes
Home Improvement
Shooting
Reading
Sports
Golf
Home Decoration
Cooking
Gardening
Outdoors
Electronics
Crafts
Music
Movies
Collecting
Kids
Automobiles
Woodwork
Travel
Investing

Publications

Us Patents

Enhanced Project Predictor

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US Patent:
7949663, May 24, 2011
Filed:
Feb 8, 2007
Appl. No.:
11/672905
Inventors:
Deandra T. Cassone - Overland Park KS, US
Joseph E. Dudley - Overland Park KS, US
George R. Kather - Leavenworth KS, US
Paul R. Sapenaro - Overland Park KS, US
Jason N. Ward - Lee's Summit MO, US
Assignee:
Sprint Communications Company L.P. - Overland Park KS
International Classification:
G06F 17/30
G06F 15/16
US Classification:
707750, 705306, 705400
Abstract:
A computer implemented system for project prediction is provided. The system includes a data manager to obtain historical project data. The system also includes an analyzer to analyze the historical project data and an analysis cycle time to generate models for a proposed project cycle time. Additionally, the system includes a user interface to select one model for the proposed project cycle time, wherein the selected model includes linear sub-models corresponding to a historical data range, and apply proposed project data and analysis cycle time to one linear sub-model corresponding to a proposed data range to predict the proposed project cycle time. Furthermore, the system captures proposed project data and obtains additional project data to update the selected model. The models provide for the accurate prediction of cycle times, or project costs, in an enterprise development environment.

Systems And Methods For Identifying High Complexity Projects

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US Patent:
7983946, Jul 19, 2011
Filed:
Nov 12, 2007
Appl. No.:
11/938778
Inventors:
Deandra T. Cassone - Overland Park KS, US
Noel R. Marchbanks - Overland Park KS, US
Paul R. Sapenaro - Overland Park KS, US
Assignee:
Sprint Communications Company L.P. - Overland Park KS
International Classification:
G06F 17/50
US Classification:
705 736
Abstract:
Computer implemented methods and systems are provided for identifying high to high complexity projects. Some method embodiments include defining proposed project data for identified factors, calculating a planning team index for a project associated with the proposed project data, and identifying the project as complex when the planning team indicator falls on one side of a breaking point. The factors are those identified as determinative of project complexity.

Flexible Project Governance Based On Predictive Analysis

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US Patent:
8108238, Jan 31, 2012
Filed:
May 1, 2007
Appl. No.:
11/742994
Inventors:
Deandra T. Cassone - Overland Park KS, US
Paul R. Sapenaro - Overland Park KS, US
Assignee:
Sprint Communications Company L.P. - Overland Park KS
International Classification:
G06Q 10/00
US Classification:
705 713, 705 711, 705 712, 705 722, 705 723
Abstract:
A computer implemented method is provided for flexible project governance based on predictive analysis. Project complexity factors are analyzed to determine a project complexity index. Initial development gates are selected for a project based on the project complexity index. Project governance factors are periodically analyzed to determine a project governance index. Actions for project reporting and for the initial development gates are selected based on the project governance index.

Project Predictor

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US Patent:
7676490, Mar 9, 2010
Filed:
Aug 25, 2006
Appl. No.:
11/467226
Inventors:
Deandra T. Cassone - Overland Park KS, US
Joseph E. Dudley - Overland Park KS, US
George R. Kather - Leavenworth KS, US
Paul R. Sapenaro - Overland Park KS, US
Jason N. Ward - Lee's Summit MO, US
Assignee:
Sprint Communications Company L.P. - Overland Park KS
International Classification:
G06F 17/00
G06F 15/16
US Classification:
707102, 7071041, 705 7, 705 10, 705 37
Abstract:
A computer implemented system for project prediction is provided. The system includes a data manager to obtain historical project data. The system also includes an analyzer to analyze the historical project data to generate models for a proposed project cycle time. Additionally, the system includes a user interface to select one model for the proposed project cycle time, wherein the selected model includes linear sub-models corresponding to a historical data range, and apply proposed project data to one linear sub-model corresponding to a proposed data range to predict the proposed project cycle time. Furthermore, the system captures proposed project data and obtains additional project data to update the selected model. The models provide for the accurate prediction of cycle times, or project costs, in an enterprise development environment.
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Paul R Sapenaro from Overland Park, KS, age ~67 Get Report