BUS 653 - Operations Strategy

This Operations Strategy course explores how operations can create and sustain competitive advantage. The first step in the development of an effective operations strategy is to ask: what value proposition does the firm want to offer its customers, how does it seek to compete? The course considers a variety of possible bases of competition, including lowest price, highest quality, flexibility, or speed of customer response, and innovativeness.

The choice of competitive differentiation, or industry position, will then suggest choices along critical dimensions of operating system design and management. The course provides exposure to how such an operating system must be configured so that it does what is required to deliver the chosen value proposition, or business model, most appropriately and effectively. The course also examines the complexities associated with global operating systems, including the hidden costs of outsourcing and offshoring.

This course aims to frame key strategic operations issues and provide tools to resolve them. The cases, readings, and frameworks covered in the course are designed to serve two audiences: students who plan a career specifically in operations, and others with broader interests, but who may in the future need to analyze and improve operations for strategic purposes. The latter group might include students envisioning a future in consulting or general management, as well as others interested in marketing, finance, or accounting.


In BUS 550 we developed models to predict the expected dollars contributed by a town and then use that information to decide which towns to canvas first. This is a problem that is analogous to many business situations that follow the path of:

  1. need to make a decision; 
  2. collect data; 
  3. develop model; 
  4. predict values; 
  5. make decision; 
  6. repeat often or do only once.

This course is a very "hands on" working with data course, working with sample data sets or even better bringing problems from work. Through sharing of experience and discussion of MANY data sets and problems we gain years of experience in a few months. Grading is based on 4 projects and a presentation that, normally group work. The course is structured to challenge the very good quantitative people while providing a path to success for the numerically challenged. Along the way, subjects we will cover include

  1. Decision Support Systems; 
  2. Collecting data; 
  3. Data sources; 
  4. Data-Bases ( as tools ); 
  5. Review regression; 
  6. Expanded regression; 
  7. Time dependent models and analysis; 
  8. Advanced Solver; 
  9. Time series techniques, 
  10. Exponential smoothing, 
  11. Moving averages, 
  12. Box-Jenkins ( ARIMA); 
  13. Synthetic Neural Networks; 
  14. Chaos Theory; 
  15. Genetic Algorithms

Examples I will cover will include: Financial models - Stock prices, Risk, including bond ratings, Cash flow; Behavior models - Customer attrition, Customer likes/dislikes; Buying patterns - Propensity to buy; Politics - Identify swing voters; and Sales.

We will also work on any data set that student choose to share with the class and get help on. We often yield instantly applicable results for students at work. We will also maintain privacy as needed by your company.

Cross-Listed

  • BUS 653P