Supply Chain Scenario Modeler: A Holistic Executive Decision Support Solution

Kaan Katircioglu (), Robert Gooby (), Mary Helander (), Youssef Drissi (), Pawan Chowdhary (), Matt Johnson () and Takashi Yonezawa ()
Additional contact information
Kaan Katircioglu: IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598
Robert Gooby: McKesson Corporation, Dallas, Texas 75006
Mary Helander: IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598
Youssef Drissi: IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598
Pawan Chowdhary: IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598
Matt Johnson: McKesson Corporation, San Francisco, California 94104
Takashi Yonezawa: IBM Global Services, Chuo-Ku, Tokyo 103-8510, Japan

Interfaces, 2014, vol. 44, issue 1, 85-104

Abstract: McKesson is America’s oldest and largest healthcare services company. IBM Research developed an innovative scenario modeling and analysis tool, supply chain scenario modeler (SCSM), for McKesson to optimize its end-to-end pharmaceutical supply chain policies. Through integrated operations research (OR) models, SCSM optimizes the distribution network, supply flow, inventory, and transportation policies, and quantifies the impacts of changes on financial, operational, and environmental metrics. The modeling work spawned a roadmap of projects with quantified opportunities, including a new air freight supply chain path, and provided new insights that have been critical to improving McKesson’s performance as a pharmaceutical industry leader. A structured data model supporting the OR models has provided a basis for additional improvement projects. The model directly links OR modeling results to a detailed profit-and-loss statement by product category for the different supply chain paths that McKesson uses. Since this effort began in 2009, McKesson Pharmaceutical division has reduced its committed capital by more than $1 billion.

Keywords: supply chain; inventory optimization; network optimization; vehicle routing problem (VRP); business analytics; common data model; enterprise planning; sustainability; profit-and-loss (P&L) (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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