Petersen, K. ., Gencel, C. ., Asghari, N. ., Baca, D. ., & Betz, S. . (2014). Action research as a model for industry-academia collaboration in the software engineering context. In Proceedings of the 2014 international workshop on Long-term industrial collaboration on software engineering (S. 55–62).
Petersen, K. ., Khurum, M. ., & Angelis, L. . (2014). Reasons for bottlenecks in very large-scale system of systems development. Information and Software Technology, 56, 1403–1420.
Brandenburg, M. ., Govindan, K. ., Sarkis, J. ., & Seuring, S. . (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233, 299–312. http://doi.org/10.1016/j.ejor.2013.09.032
Abstract
Sustainability, the consideration of environmental factors and social aspects, in supply chain management (SCM) has become a highly relevant topic for researchers and practitioners. The application of operations research methods and related models, i.e. formal modeling, for closed-loop SCM and reverse logistics has been effectively reviewed in previously published research. This situation is in contrast to the understanding and review of mathematical models that focus on environmental or social factors in forward supply chains (SC), which has seen less investigation. To evaluate developments and directions of this research area, this paper provides a content analysis of 134 carefully identified papers on quantitative, formal models that address sustainability aspects in the forward SC. It was found that a preponderance of the publications and models appeared in a limited set of six journals, and most were analytically based with a focus on multiple criteria decision making. The tools most often used comprise the analytical hierarchy process or its close relative, the analytical network process, as well as life cycle analysis. Conclusions are drawn showing that numerous possibilities and insights can be gained from expanding the types of tools and factors considered in formal modeling efforts.
Petersen, K. ., Roos, P. ., Nyström, S. ., & Runeson, P. . (2014). Early identification of bottlenecks in very large scale system of systems software development. Journal of Software: Evolution and Process, 26, 1150–1171.
Gencel, C. ., Holmgren, J. ., & Petersen, K. . (2014). The Relationship between Immediacy, Trust and Students’ Choice of Supervisors in the Software Engineering Context. In European Conference of Software Engineering Education.
Yilmaz, P. ., Glöckner, F. O., Imhoff, J. F., Labes, A. ., Panzer, K. ., Schmaljohann, R. ., … Reich, M. . (2014). A complete view on the aquatic-derived fungal phylogeny and ecology. In 15. International Symposium on Microbiology Ecology. Abgerufen von http://oceanrep.geomar.de/26376/
Bin Ali, N. ., Petersen, K. ., & Wohlin, C. . (2014). A systematic literature review on the industrial use of software process simulation. Journal of Systems and Software, 97, 65–85.
Brandenburg, M. ., Kuhn, H. ., Schilling, R. ., & Seuring, S. . (2014). Performance- and value-oriented decision support for supply chain configuration. Logistics Research, 7(1), 16. http://doi.org/10.1007/s12159-014-0118-8
Abstract
The task of supply chain (SC) configuration is to establish a product-specific SC for new products. Apart from cross-regional network aspects and inter-disciplinary factors, the problem complexity is driven by dynamics and uncertainties of short product life cycles. SC configuration affects SC performance and value creation. Hence, resulting impacts have to be measured and compared under consideration of dynamics and uncertainties. In the conceptual part of this paper, dynamics and uncertainties of SC configuration are integrated into a conceptual framework for value-based supply chain management. In the model-based part of this paper, this framework is linked to a discrete-event simulation model. The performance outcomes and value impacts of SC configuration options are empirically assessed in a case study of a fast-moving consumer goods manufacturer. The shortfalls of cost-focused decision-making without consideration of capital-related factors and non-financial aspects are illustrated.