Organizational Flexibility for Hypercompetitive Markets

This research project, which builds on the conceptual work of Henk Volberda on the flexible firm, empirically investigates four aspects of organizational flexibility. Our analysis of data of over 1900 firms and over 3000 respondents shows (1) that several increasing levels of organizational flexibility can be distinguished, from operational to strategic flexibility, and these are formed by increasingly complex components of organizations. (2) Flexibility pays off particularly in unpredictable and dynamic markets. In less turbulent markets it pays not to invest in the highest order type of flexibility; operational flexibility will be more efficient, compared to strategic flexibility, in predictable markets. (3) The assumption that smaller firms by definition are better able to develop strategic flexibility than larger firms are, appears not to hold completely. Large firms are able to develop strategic flexibility as well, be it through different means. Once sufficiently flexible, large firms are better positioned to reap the benefits. The study further, and finally, shows (4) that firms can apply two different criteria to adjust the organization to the environment and create strategic fit: by adjusting to the requirements of their unique task environment or by adjusting to more generic institutional norms and best practices in the market. Both ‘ways of learning by organisations’ affect each other and will in business reality exist next to each other

Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming

Global optimization of decision trees has shown to be promising in terms of accuracy, size, and consequently human comprehensibility. However, many of the methods used rely on general-purpose solvers for which scalability remains an issue. Dynamic programming methods have been shown to scale much better because they exploit the tree structure by solving subtrees as independent subproblems. However, this only works when an objective can be optimized separately for subtrees. We explore this relationship in detail and show necessary and sufficient conditions for such separability and generalize previous dynamic programming approaches into a framework that can optimize any combination of separable objectives and constraints. Experiments on five application domains show the general applicability of this framework, while outperforming the scalability of general-purpose solvers by a large margin

A recursive paradigm for aligning observed behavior of large structured process models

The alignment of observed and modeled behavior is a crucial problem in process mining, since it opens the door for conformance checking and enhancement of process models. The state of the art techniques for the computation of alignments rely on a full exploration of the combination of the model state space and the observed behavior (an event log), which hampers their applicability for large instances. This paper presents a fresh view to the alignment problem: the computation of alignments is casted as the resolution of Integer Linear Programming models, where the user can decide the granularity of the alignment steps. Moreover, a novel recursive strategy is used to split the problem into small pieces, exponentially reducing the complexity of the ILP models to be solved. The contributions of this paper represent a promising alternative to fight the inherent complexity of computing alignments for large instances.Peer ReviewedPostprint (author's final draft