Data-Driven Decisions in Service Engineering and Management

Abstract

Today, the frontier for using data to make business decisions has shifted, and high-performing service companies are building their competitive strategies around data-driven insights that produce impressive business results. In principle, the ever-growing amount of available data would allow for deriving increasingly precise forecasts and optimised input for planning and decision models. However, the complexity resulting from considering large volumes of high-dimensional, fine-grained, and noisy data in mathematical models leads to the fact that dependencies and developments are not found, algorithms do not scale, and traditional statistics as well as data-mining techniques collapse because of the well-known curse of dimensionality. Hence, in order to make big data actionable, the intelligent reduction of vast amounts of data to problemrelevant features is necessary and advances are required at the intersection of economic theories, service management, dimensionality reduction, advanced analytics, robust prediction, and computational methods to solve managerial decisions and planning problems.