IFMAFMCC Eastern Live Webinar – “Un-Sensored! Analytics for Lower-Tech Facilities” by Nitin Bangera & Dwayne Henclewood

Wednesday, May 4th, 2016

 NitinBangera DwayneHenclewood

Nitin Bangera, Lead Associate, Booz Allen Hamilton
Dwayne Henclewood, Lead Associate, Booz Allen Hamilton

Date: Thursday May 26, 2016
Time: 6 am Central Time Zone (USA)
Register today @  here

IFMAFMCC Eastern Live Webinar – “Un-Sensored! Analytics for Lower-Tech Facilities”

by Nitin Bangera & Dwayne Henclewood

Many commercial enterprises are highly dependent on predictive analytics to save money, improve performance, and provide value to customers. Facility management (FM), too, has begun to leverage vast amounts of data collected by sensors to make intelligent decisions. However, this capability generally requires software sophistication and real-time data which are mostly seen in urban and specialized settings. The overall poor state of public infrastructure and facilities across the country underscores that FM has not yet fully benefited from the information revolution. Public and mission-based organizations, even with their large, diverse facility portfolios, tend to lag their for-profit counterparts in using advanced data analytics to make real-time or strategic decisions. Older infrastructure, a lack of intelligent systems, and the fear of costly solutions may be holding them back.

A recent project by a large US government agency with more than 28,000 buildings and 50 million square feet of space nationwide showed that a probabilistic approach to portfolio management can inform and improve capital planning and facility operations decisions, even without sensor-based or real-time data. This presentation will highlight macro-level portfolio insights that can be gleaned from limited real property, operational, and maintenance logs from a standard computer maintenance management system. This approach is especially relevant to mission-based and budget-constrained facility portfolio managers. Attendees will come away with a greater understanding of Bayesian machine learning and how it can provide prescriptive or predictive analytics on a case-by-case basis, regardless of whether they manage high-tech facilities or lower-tech, un-“sensored” portfolios.

Learning Objectives:

  1. Understand the types of data analytics available
  2. Recognize the limitations of data analytics in the facility world
  3. Learn about probabilistic modeling and its application in our use case
  4. Apply these lessons to your “unsensored” facility portfolio


Presenter(s) Biography:


Nitin Bangera, Lead Associate, Booz Allen Hamilton

Mr. Bangera is a portfolio decisions analyst within Booz Allen Hamilton’s Infrastructure Energy and Environment team serving U.S. public sector clients in the areas of business intelligence, data analytics, portfolio planning, and facility management strategies.

He has assisted government executives with implementing elegant data analytics, portfolio visualizations, capital planning solutions, and organizational change management strategies. His team employs predictive models and visualization techniques to assist portfolio managers with improved decision-making and data quality. His clients include the U.S. National Park Service, US Environmental Protection Agency, NASA, and U.S. Federal Aviation Administration.

Mr. Bangera is a certified Lean Six Sigma Green Belt (ASQ), Cost Estimator/Analyst (ICEAA), and Change Management Advanced Practitioner (Georgetown University). He holds a Masters in Engineering Management from Cornell University and a Bachelors in Civil Engineering from Texas A&M University.



Dwayne Henclewood, Lead Associate, Booz Allen Hamilton

Dr. Henclewood is Transportation System Analyst supporting Booz Allen Hamilton’s Justice, Homeland Security and Transportation line of business. He primarily supports the US Department of Transportation with their connected vehicle, intelligent transportation system and data initiatives. His expertise include mathematical modeling, simulation, and data analytics for informed decision making processes.

He has deployed his expertise in supporting the full data lifecycle, from generation and collection to analysis and visualization. He has built a number of decision support tools, including dynamic dashboards – for data exploration and insight gathering, to support his clients’ mission critical work flow. His clients include the Federal Highway Administration, Pipeline and Hazardous Material Safety Administration, National Highway Traffic Safety Administration, Intelligent Transportation Systems Joint Program Office, and Commercial Airline.

Dr. Henclewood hold a doctorate in Transportation Systems Engineering from the Georgia Institute of Technology, a Masters in Civil Engineering from University of Massachusetts Amherst and a Bachelors of Arts in Physics from the College of the Holy Cross.


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