Sethi is Eugene McDermott Chair Professor of Operations Management and Director of the Center for Intelligent Supply Networks at The University of Texas at Dallas. He has also taught at Rice University, University of Toronto, and Carnegie Mellon University. At University of Toronto, he was General Motors Research Professor and Connaught Senior Research Fellow.
Honors and awards
Alumni Achievement Award, Tepper School, Carnegie Mellon University
Conferences organized in his honor in 2005 and 2006
Books edited in his honor in 2005 and 2006
Fellow of Production and Operations Management Society
Wickham-Skinner Best Paper Award at The 2nd World Conference on POM, 15th Annual Production and Operations Management Conference held in Cancun, Mexico
The Sethi advertising model or simply the Sethi model was developed by Suresh P. Sethi in 1981, and it describes the process of how sales evolve over time in response to advertising. The rate of change in sales depend on three effects: response to advertising that acts positively on the unsold portion of the market, the loss due to forgetting or possibly due to competitive factors that act negatively on the sold portion of the market, and a random effect that can go either way. The model and some extensions have been empirically tested and are widely used in the marketing literature.
DNSS points
arise in optimal control problems that exhibit multiple optimal solutions. A DNSS point is an indifference point in an optimal control problem such that starting from such a point, the problem has more than one different optimal solutions. Suresh P. Sethi identified such indifference points for the first time in 1977.
Hierarchical manufacturing systems
Most manufacturing systems are large, complex, and subject to uncertainty. The problem of the efficient management of such systems is of critical importance to a nation's economic competitiveness. However, obtaining optimal feedback policies to run these systems is usually impossible. Hierarchical feedback control policies, on the other hand, offer the promise of being able to handle realistically complex manufacturing systems in a tractable fashion to make their management more efficient. Suresh Sethi and his co-authors have articulated a profound theory that shows that hierarchical decision making in the context of a goal-seeking manufacturing system can lead to near optimization of its objective. They consider manufacturing systems in which events occur at different time scales. In such systems, longer term decisions such as those dealing with capital expansion can be based on the average existing production capacity and can be expected to be nearly optimal even though the shorter term capacity fluctuations are ignored. Having the long-term decisions in hand, one can then solve the simpler problem of obtaining production rates. Multilevel decisions constructed in this manner are shown to be asymptotically optimal as the average time between successive short-term events becomes much smaller than that between successive long-term events. Much attention is given to establish that the order of deviation of the cost of the hierarchical solution from the optimal cost is small. The striking novelty of their approach is that this is done without the insurmountable task of solving for the optimal solution. The approach represents a new paradigm in convex production planning and a new research direction in control theory. The research presented cuts across the disciplines of Operations Management, Operations Research, System and Control Theory, Industrial Engineering, Probability and Statistic, and Applied Mathematics. The research is detailed in two books.
Inventory problems with incomplete information
For the first time since the beginning of the inventory theory nearly hundred years ago, Sethi and co-authors have extended the filtering theory in the electrical engineering literature to treat inventory models with incomplete information. It is shown that ignoring this realistic feature comes at a significant cost. Also, the existence of optimal feedback ordering policies is proved and these policies are partially characterized.
Inventory problems with Markovian demand and forecast updates
Sethi and co-authors have made sustained contributions to the study of inventory problems with Markovian demands with discounted as well as average-cost criteria. Also, they have generalized the standard assumptions to include unbounded demands and cost functions having polynomial growth. Their work is detailed in a book titled Markovian Demand Inventory Models. Sethi and co-authors have studied the optimality of base stock and type policies in case of forecast updates and multiple delivery modes. They introduce a general forecast updating scheme, termed peeling layers of an onion, and show the optimality of forecast-dependent base stock and policies with two delivery modes. They show further that the base stock policy is no longer optimal for other than the first two consecutive modes. These results are collected in a 2005 book by Sethi, Yan, and Zhang titled Inventory and Supply Chain Management with Forecast Updates.
Forecast and decision horizons
In 1978, Sethi began to look into the fundamental problem of how long-term planning influences immediate decisions. His work on decision and forecast horizons has provided a logical framework for the practice of finite horizon assumptions and the choice of horizon. This framework has been widely adopted by researchers in the area.
Supply chain contracts with risk-averse agents
Much of the research in operations management assumes that the agents in supply chain are expected profit maximizers. However, it is well known in the finance literature that individuals are usually risk averse. Gan and Sethi generalized the existing supply contracts to allow for the agents to be risk averse. They developed a definition of coordination in this case, based on the Nash Bargaining Solution, and obtain coordinating contracts in a variety of supply chains with agents observing different risk-averse objectives.
Flexibility in manufacturing
Developed the widely used framework of “Flexibility in Manufacturing”.
Scheduling in robotic cells
Optimum operations in robotic cells: scheduling of parts and re-sequencing of robot moves.
Investment/consumption problems with bankruptcy
The problem of optimal consumption and investment is concerned with the decisions of a single agent endowed with some initial wealth who seeks to maximize total expected discounted utility of consumption. The decisions are the rate of consumption and the allocation of their wealth directed to risky and risk-free investments over time. The problem was first studied by Paul Samuelson and Robert Merton in 1969 and 1971; however none of their formulations took into account the possibility that an agent might go bankrupt in the process. In a set of articles published during the period from 1979 to 1996, Suresh Sethi and various co-authors explicitly introduced a bankruptcy value/penalty in the consumption/investment model. They also introduced a nonzero subsistence consumption level, which makes the consideration of bankruptcy even more important. This provided the ability to deal mathematically with the problems of bankruptcy in the study of consumption and investment. This research provides a useful frame for deepening our understanding of the consumption and portfolio selection behavior of individuals and households, and it is collected in a book titled Optimal Consumption and Investment with Bankruptcy.
Optimal control formulations and solutions of a variety of dynamic operations management and economics problems
Suresh Sethi is the key figure in the development and use of optimal control theory to address dynamic and stochastic problems in management science. Sethi wrote his 1972 doctoral thesis on optimal control and its applications. Sethi extended the theory to deal with the special characteristics of management problems, such as the nonnegativity constraints and time lags. His thesis and the subsequent work eventually led to the classic 1981 Sethi-Thompson book that brought the theory of optimal control to management schools. The second edition of this classic text became available in Fall 2000. Central to the book is its extraordinarily wide range of optimal control theory applications. These cover finance, production and inventory problems, marketing, machine maintenance and replacement, optimal consumption of natural resources, and a number of applications to economics.
Causality detection in multivariate stochastic processes
Caines, Keng and Sethi developed the theory of multivariable causality in the time series analysis, and also applied it to study the determinants of the sales in the Toronto supermarkets. Prior to this, causality studies were used to detect whether a variable such as advertising causes sales over time or whether advertising expenditures are in practice determined as some percentage of sales. The authors extended the concept of causality when more than two variables are involved.
Optimal Economic Growth with Value of Population and Genuine Savings
et al. study a model of economic growth with population growing in an arbitrary manner. This requires population as well as capital as state variables. In a later paper, they consider population dynamics to be endogenously determined and derive the expression of genuine savings and evaluate the sustainability of the economic system.
