Co-Chairs: Patrick Leach, Decision Strategies Inc.; Eva K. Lee, Georgia Institute of Technology
The Decision Analysis track holds a variety of papers dealing with the application of decision science to practical business situations. We will hear about “soft” issues like how a company’s culture – like a country’s culture – affects its ability to adapt to change, as well as more quantitative problems like optimizing operations at a retail store. A novel way to apply decision analytics to personnel evaluations will be introduced, insights into what makes for a successful investment strategy will be revealed, and we will finish with a discussion about an innovative approach to achieving first-mover advantage.
DA & Culture: Finally the Twain Shall Meet
Rob Kleinbaum, PhD, CEO, RAK & Co.
Most of us are in the business of making the right thing happen. Every experienced practitioner knows that culture matters, we see it in many of our projects, and know it can contribute to our success and failure. But existing theories of business culture have been inadequate for helping decision analysts understand or manage it. Extending the work of scholars (Larry Harrison and Sam Huntington, Culture Matters) who have studied the role of culture in the prosperity nations, Kleinbaum has developed a new model of culture in business that is credible and decision relevant. In this talk, he will describe the overall theory, show how corporate culture affects decision making, and how to manage cultural issues that surface in a DA project. He also will discuss how to use culture to "supercharge" problem solving, therefore making decision analysis much more effective. DA practitioners are well positioned to improve a company’s culture and be more effective problem solvers, if they are willing learn a new set of tools.
Total Remodeling of a Do-it-Yourself Chain Store Business using Analytics--From the Strategic to the Daily Operational Level
Atsuo Suzuki, PhD, Professor and Dean, Faculty of Information Sciences and Engineering, Nanzan University
Recent developments in optimization and data mining software prompt midsize retailers to utilize analytics for improving their business. In Japan, the consumer market has been trending down for the past two decades and the retail business has become highly competitive. All retailers are remodeling their businesses to win a place in the market and have now identified analytics as the most promising tool for remodeling. As a pioneering work, Suzuki and his group have been collaborating with a home center chain store in Japan for six years to remodel their business totally. In this talk, he will introduce selected case studies, including a decision problem of optimal inventory policy, optimal assortment of items in a shop, optimal arrangement of items on shelves, and making an automatic daily shift scheduling system of shop stuffs. The results of these case studies have brought essential improvements to the home center business.
WholeSoldier and WholeSurgeon: Multiattribute Performance Appraisal Systems to Support Personnel Decisions and Predictions
Robert A. Dees, MS, Major, U.S. Army; Research Collaborator, Dept. of Surgery, Mayo Clinic
In nearly any organization, quality people are the most important resource, which dictates the importance of recruiting, retaining, promoting, and separating people. It is important, but also difficult and often emotional, to answer questions like “Who do we want?” Despite overwhelming amounts of information regarding personnel, often the desired “intangibles” are not captured. Quantifiably structuring performance appraisals within a Value-Focused multiattribute decision framework, a step beyond what is currently done in many organizations (i.e. military, healthcare, academia, business, sports, etc.), provides leaders a useful measure to support a broad class of personnel decisions. Using the WholeSoldier model for the U.S. Army and the initial WholeSurgeon model for the Mayo Clinic, this presentation will highlight:
- The difficulty/importance of personnel decisions
- What organizations are saying they want in people
- Building multiattribute models of organizational preference
- Casting vision inside the organization and marketing to others
- Performance appraisal design and implementation
- Incentivizing truth-telling in performance appraisals
- Mentoring and making decisions regarding current personnel
- Predicting performance of candidates
Will My Risk Parity Strategy Outperform?
Robert Anderson, PhD, Professor of Economics & Mathematics, University of California Berkeley, and Director, Coleman Fung Risk Management Research Center
In a study on quantitative metrics in selection of investment strategies, Anderson and his co-authors gauged the return-generating potential and risk inherent in four investment strategies: value weighted, fixed mix, and levered and unlevered risk parity, over an 85-year horizon. There are three essential conclusions from the study. First, even over periods lasting decades, the specific start and end dates of a backtest can have a material effect on the results; second, transaction costs can negate apparent out-
performance; third, statistical significance of findings needs to be assessed. In this session, Anderson will describe the study and explore its results. Co-authors: Lisa Goldberg Stephen Bianchi
The Imperative for First Mover Advantage: Inventive Problem Solving of Complex Business Challenges and Risks
Hristo Trenkov, MS, Senior Manager, Center for Risk Modeling and Simulation, Deloitte & Touche, LLP ; Jason R. W. Merrick, PhD, Professor, Statistical Sciences and Operations Research, Virginia Commonwealth University
In pursuit of first mover advantage, organizations have developed a variety of sophisticated models and methodologies that attempt to predict emerging events in order to get ahead of the market, gain competitive advantage, or mitigate a rising adversarial risk. Despite these advances, traditional prediction models contain flaws in that they are predominantly based on the organization’s capabilities rather than on clear understanding of the requirements and rely on static snapshot of structured information. This talk describes an innovative, systematic, and self-learning approach that leverages technology to gather, analyze, and disseminate critical, relevant intelligence to decision-makers most capable of using it to gain visibility into both known and unknown complex business challenges and risks. A key insight is that problems arise and evolve in generally repeatable patterns irrespective of organization type or field of science; therefore developed solutions and knowledge can be leveraged and shared across industries, freeing up time and resources to focus on innovation in the areas where it is really required.