Managerial Decision Making


Companies routinely collect large volumes of data on customer profiles, point of sales transactions, and operating performance at different units. How can management use this data to make effective decisions? How do managers covert this data into business insights and action? The goal of this course is to help the participants build operational models from raw data, and apply these models in taking effective business decisions. Specifically, we will study how decision-making is shaped by uncertainty, complexity, and ambiguity.

  • Learning Goals

This course is to develop the fundamental knowledge and skills for leveraging the decision sciences in dealing with current management challenges.  Conference and workshop topics include the role of managerial decision-making in leadership, organizational learning, business process improvement, and decision support systems. Using case studies drawn from variety of industries and markets, the course will focus on the models, tools, techniques, and realities of data-driven decision-making.

  • Learning Outcomes

On successful completion of the course, the students shall be able to :

  • Understand how business analytics can enhance decision making by converting data into actionable insights
  • Apply the models and tools for creating, collecting, codifying and sharing information
  • Provide insights on how to choose and use appropriate  statistical tools based on the challenges  at hand
  • Select and utilize appropriate formats for the presentation and/or analysis of data.
  • Analyze the ethical issues and problems inherent in knowledge management, information sharing, and decision-making
  • Concepts and theories to which students are exposed during the course
  • Abductive, deductive, inductive reasoning
  • Anchoring
  • Data distributions
  • Decision trees
  • Digital storytelling
  • Discrete probablity
  • Hyperbolic Discounting
  • Knowledge bias
  • Logistical regressions
  • Probability