Dr. Esther Mohr, Assistant Professor at the University of Mannheim, Business School contributes to the institution’s May-June hosting for the Council on Business & Society.
The challenge of deciding “in the dark”
“Oftentimes we know only little about the future demands of customers or imminent developments in prices. But managers still have to make a decision and they should be as good as possible”, explains Dr. Esther Mohr who is Assistant Professor at the Chair for Service Operations Management at the University of Mannheim, Business School.
Traditional research models often rely on several restrictive and possibly unrealistic assumptions. For example, in the field of inventory management and/or revenue management, it is commonly assumed that demand is independent and stationary. The outcome of such models is appropriate when probability distributions, e.g. of the market demands, are fully known. But in many real-world situations, for a variety of reasons, distributions are often unknown, in particular for products with short life-cycles. A telling example is that of retailers: most are not able to forecast their customer’s demand with accuracy due to scanty historical data or volatility. Subsequently, optimal decision-making becomes slightly more challenging: managers seek for alternative solutions that work with limited, inaccurate or unavailable demand information.
The solution: deliver provable statements on the quality of a potential decision
The application of traditional models and problem-solving techniques is problematic when data patterns deviate substantially from past history. Thus, Dr. Mohr’s intention is to establish an alternative form of optimal decision-making called online optimization. Her approach relies on competitive analysis, and provides algorithms that are robust because they guarantee a certain performance level under all possible scenarios. Its most important feature is the complete independence from statistical assumptions. In particular, information relevant for taking an optimal decision is processed online, or, to put it in another way, piece-by-piece in a serial fashion. The goal is to construct algorithms to take optimal decisions under uncertainty. The key idea is to give worst-case performance guarantees on algorithms for sequential decision making that hold no matter how unfortunate the future will be. The concept is applicable both to continuous and discrete problems, and it allows for provable statements about the quality of a decision taken.
Dr. Esther Mohr is convinced that online optimization complements existing, established decision-making approaches. Since April 2014 she has been working at the Chair for Service Operations Management headed by Professor Dr. Cornelia Schön.The Chair engages in research and teachings with the development and application of Management concepts, with the focus lying in decision support for practice-oriented problems in the areas of service operations management.
Dr. Esther Mohr, Assistant professor at the University of Mannheim, Business School
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