TY - RPRT A1 - Seidlmeier, Heinrich T1 - Hidden networks in business processes - theoretical considerations and experimental verification N2 - The discipline of process mining was the first to demonstrate the construction of social networks from business processes. Collaboration in the process flow creates relationships between the participants. The objective of this article is to further develop this basic idea of “hidden process-induced networks” (in short: process networks) on the basis of the findings of social network research and to make a new contribution to the management and design of business processes. A contingency network theory of processes is presented for the first time as the methodological foundation for the article. In essence, it is demonstrated how the characteristics of a process network (contingency factors) can be utilized as design variables for achieving desirable process improvements. This is followed by an experimental verification and confirmation of this theory of business process design. Finally, use cases demonstrate the practical applicability of the findings. Based on experimentally obtained data, regression analyses show that there are strong correlations between certain network measures (quantitative network characteristics) and the process measures processing time and cycle time. Processes therefore not only form networks. Networks can also support the optimization of processes. Process networks can be regarded as novel, theoretically sound and empirically verified explanatory and design models for business process management. KW - business process management KW - business process network KW - social network analysis KW - process mining KW - experimental organizational research Y1 - 2024 U6 - https://doi.org/10.13140/RG.2.2.24967.00161 ER - TY - RPRT A1 - Seidlmeier, Heinrich T1 - Process organization design by networks N2 - Organizational mining, a sub-discipline of process mining, introduced social networks based on business processes. However, process mining has only half-heartedly pursued this line of development. In particular, the well-founded results of interdisciplinary social network analysis have hardly been taken up. This article shows how the explanation and design of the phenomenon "organization" can benefit from the further development of this network view of business processes. To this end, the basic idea of social process networks is presented and the current state of research is outlined. Furthermore, social networks are presented as a new component of organization design. An interesting, far-reaching new field of research is expected. This paper presents three novel findings that could be the starting point for further research, but can also be used in practice today. KW - process network KW - social network analysis KW - business process management KW - process design Y1 - 2024 U6 - https://doi.org/10.13140/RG.2.2.12369.13920 ER - TY - RPRT A1 - Ott, Holly C. T1 - Germany’s Mittelstand Companies: Can Asia Share their Success? KW - Germany’s Mittelstand Companies Y1 - 2020 ER - TY - RPRT A1 - Ott, Holly C. T1 - Building Foundations for Talent Development KW - Talent Development Y1 - 2020 VL - 2013 IS - 7 SP - 46 EP - 48 ER - TY - RPRT A1 - Ott, Holly C. T1 - Electronics Supply Chain Specialization: survey of electronics companies to identify critical areas in SCM specific to the electronics industry KW - Supply Chains Y1 - 2020 ER - TY - RPRT A1 - Ott, Holly C. T1 - Semiconductor Industry Training & Development Survey: survey of semiconductor companies with offices in Singapore covering the entire value chain to identify gaps in existing training programs and to identify areas of interest KW - Semiconductor Industry Y1 - 2020 ER - TY - RPRT A1 - Bischof, Wolfgang T1 - M/G/1-queues with Setup Times and Vacations under Various Service Disciplines T2 - Report No. 417 des Instituts für Mathematik, Universität Augsburg KW - M/G/1-queues KW - Setup times Y1 - 1999 VL - 1999 ER - TY - RPRT A1 - Bischof, Wolfgang T1 - On the Stability of Polling-Systems for General Arrival Processes T2 - Report No. 384 des Instituts für Mathematik, Universität Augsburg KW - Polling Systems KW - Arrival Processes KW - Server modelling Y1 - 1998 ER - TY - RPRT A1 - Pruschka, H. A1 - Wellisch, Ulrich T1 - Asymptotic behaviour of estimation equations with functional nuisance or working parameter. N2 - We are concerned with the asymptotic theory of semiparametric estimation equations. We are dealing with estimation equations which have a parametric component of interest and a functional (nonparametric) nuisance component. We give sufficient conditions for the existence and the asymptotic normality of a consistent estimation equation estimator for the parameter of interest. These conditions concern the asymptotic distribution of the estimation function and of its derivative as well as the effect of the functional nuisance part in the estimation equation. In order to treat the nonparametric component we introduce a general differential calculus and a general mean value theorem. For the nonparametric part in the estimation equation we distinguish two cases: the situation of a (classical) nuisance parameter and the case of a so called working parameter. As a special case we get regularity conditions for estimation equations with finite dimensional nuisance or working parameter. As an example we present the semiparametric linear regression model. KW - Estimating equations KW - Asymptotic distribution KW - semiparametric linear regression model Y1 - 1997 ER - TY - RPRT A1 - Küchenhoff, H. A1 - Wellisch, Ulrich T1 - Asymptotics for generalized linear segmented regression models with an unknown breakpoint N2 - We consider asymptotic theory for the maximum likelihood estimator in the generalized linear model with an unknown breakpoint. A proof for the asymptotic normality is given. The methods are based on the work of Huber (1967). The main problem is the non--differentiability of the likelihood and the score function, which requires non--standard methods. An example from epidemiology is presented, where confidence intervals for the parameters are calculated with the asymptotic results. KW - Asymptotic theory KW - Linear segmentet regression models Y1 - 1997 ER -