Management Analytics Workshop

Organizer

Dan Adelman, JP Dube and Rob Gertner

Schedule

Thursday 11:45-1:00 p.m.

Location

Harper Center 3B

For information concerning this workshop, please email Tina Haraf, christina.haraf@ChicagoBooth.edu

2012

 
Date Topic Speaker, Institution
April 5 "Enabling analytics for performance based industrial systems management" Chris Johnson, GE Global Research
Bio: Chris Johnson leads GE Global Research’s Management Science Lab.  His and the interests of MSL are to mathematically describe and optimize complex business-physical systems to unlock customer value in asset design, operational decision support and performance based business models.  Current research programs include healthcare capacity and safety, aviation and rail network optimization, smart grid and various valuation and optimization endeavors.  Credited with generating in excess of $1billion dollars of value for GE, MSL is a recipient of the INFORMS Prize, which is awarded to an organization that has repeatedly applied the principles of Management Science in pioneering, varied, novel, and lasting ways. Chris is a 27 year GE veteran, entering the company through GE’s Engineering Development Program.  He holds over 30 US and foreign patents, is a recipient of GE’s Whitney and Dushman technology awards and the Alexander Hamilton Gold Award for Corporate Risk Management.  He is a graduate of the Executive MBA program at the University of Chicago Booth School of Business, MBA '95.
Abstract: Commercial forces point to an ever increasing need for firms such as GE to uncover trapped value in customer operations, engineering designs of assets and systems, services and active decision support for the operations management of complex industrial systems - and providing performance based contractual assurances for these complex solutions. This ability aligns GE with its customer’s intent, differentiates a solution and reduces the financial risk of these complex, long lived investments. Accounting for and describing variation as exogenous to the ability to change risk and returns within complex business systems and the assets these systems use and consume is insufficient to shape economically viable solutions for industry’s core infrastructures.  Using specific case studies from GE, we discuss a quantitative method of approaching industrial systems design and management that optimizes financial risk and return while taking into account real-world factors not typically handled by traditional finance and accounting methods.
 
Date Topic Speaker, Institution
 
May 17 "Data, Data Everywhere: Not enough consumption?" Mukund Raghunath, Mu Sigma
 
Bio: Mukund Raghunath is part of the senior leadership team at Mu Sigma. He has been responsible for all aspects of company building at the firm. He has been based in the US and has had experience scaling Mu Sigma operations and business, in the process managing client services across a spectrum of companies in different verticals. He currently heads client services for the East and Mid-west geographies handling several fortune 100 clients in pharmaceuticals, pharmacy, consumer products, retail, insurance, banking etc. Mukund has over 15 years of Engineering and consulting experience with Motorola and a leading Sales and Marketing Strategy firm in the US. He also has extensive experience in product planning, development and management in the telecom sector. Mukund has a Masters Degree in Computer Science from the University of Illinois and an MBA with honors from the University of Chicago, Graduate School of Business.
Abstract: The Decision Sciences and Analytics industry has grown dramatically in the past 5-6 years.  This is evinced by the big bets made by big players (like IBM) as well as the emergence of pure-play companies (like Mu Sigma) that are scaling and industrializing the use of decision sciences across enterprises in multiple industries. Advances in computing technologies (e.g. Big data, Cloud Computing and Real time Intelligence) coupled with new sources of Data (e.g. Social data,  Location Data, Machine data) have opened up possibilities for creating new sources of competitive advantage for existing businesses as well as the creation of new disruptive business models. At the same time, it is not surprising that many organizations are unable to completely leverage these opportunities for a host of reasons.
In the workshop, Mu Sigma will draw upon its experience to talk about specific examples on the gaps and challenges that are preventing realization of this vision at organizations. These challenges span  the categories of Business Problems, Data Hurdles, Mathematical Challenges and Organizational Culture. Recognition of these gaps will help direct R&D efforts both within corporate as well as academic environments.
     

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2011

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