Application Case: HP Applies Management Science Modeling to Optimize Its Supply Chain and Wins a Major Award

HP’s ground-breaking use of operations research not only enabled the-tech giant to successfully transform its portfolio program and return $500 million over a 3-year period to the bottom line, it also earned HP the coveted 2009 Edelman Award from INFORMS for outstanding achievement in operations research. “This is not the success of just one person or one team,” said Kathy Chou, Vice President of Worldwide Commercial Sales at HP, in accepting the award on behalf of the winning team. “It’s the success of many people across HP who made this a reality, beginning several years ago with mathematics and imagination and what it might do for HP.”

Q & A: Modeling & Analysis

1.  Q: List three lessons learned from modeling.
     A: This question refers to lessons found in the examples in this section, not to lessons learned from modeling in general. Many examples could be cited, among them these four:
•    DuPont learned how different approaches to rail transportation would work out.
•    The University of Virginia Health Science Center (the institution studied in the cited Rossetti and Selandari paper) learned how human couriers and mobile robots compare in making deliveries within a hospital.
•    Procter & Gamble learned the best shipping options from product sources to distribution centers.
•    American Airlines learned the optimum ascent and descent profiles for its aircraft.

Q & A Review on DSS Concepts. Methodologies, and Technologies: An Overview

1.    Q: List and describe the three major components of DSS.
       A: The three major components are: data, models, and user interface.

Data refers to the information needed to make a decision, typically stored in a database, and to how these data are organized and managed by a DBMS.

Models refer to the models used to analyze the data and predict the results of a decision, as well as to the software used to manage the use of the models in a DSS.

User interface refers to the way a manager or knowledge worker can use the system to support his or her decision making needs without having to become an expert in its technology.