Gerd J. Hahn
The Catholic University of America
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Gerd J. Hahn.
decision support systems | 2012
Gerd J. Hahn; Heinrich Kuhn
Value-based Management (VBM) concepts are prevalent in theory and practice since shareholder value creation is commonly considered the paramount business goal. However, VBM mainly applies data-driven concepts to support decision-making, disregarding model-driven approaches. This paper develops a comprehensive approach to designing model-driven DSS for VBM. First, we derive a conceptual architecture for Integrated Business Planning (IBP) as the foundation for a model-driven approach to VBM. Second, we present a unified modeling approach for value-based performance and risk optimization that implements Value Added (xVA) performance metrics and applies robust optimization methods to mitigate risk impact.
Journal of the Operational Research Society | 2011
Gerd J. Hahn; Heinrich Kuhn
Economic Value Added (EVA®) and corresponding value driver trees are prevalent frameworks of value-based management to measure and analyse shareholder value creation. However, they are explanatory models from an operations research perspective and do not provide decision support for performance optimisation. In this paper, we develop a comprehensive value-based decision framework for mid-term sales and operations planning (S&OP) in the supply chain implementing EVA as the objective function. The pivotal element of our framework is a decision-oriented extension of EVA-based value driver trees bridging the gap to the decision variables of S&OP as the operational performance levers. We utilise a numerical example to highlight the significant improvement potential due to the value-based optimisation approach. Working capital management emerges as the major mid- to short-term value driver in the supply chain.
decision support systems | 2015
Gerd J. Hahn; Josef Packowski
Big data, advanced analytics, and in-memory database technology are on the agenda of top management since they are seen as key enablers for enhanced business decision-making. In this paper, we provide a comprehensive perspective on applications of in-memory analytics in the field of supply chain management (SCM) that use the aforementioned concepts. Our contribution is threefold: First, we develop a top-down framework to position in-memory analytics applications against extant IT systems in SCM. Second, we conduct a bottom-up categorization of 41 in-memory analytics applications in SCM to provide supporting empirical evidence of the efficacy of the framework. Third, by contrasting top-down and bottom-up perspectives we derive implications for research and industrial practice. In-memory analytics applications in SCM can be structured along four use cases.Real-time analytics is the predominant focus of emerging in-memory applications.Integrated data models further support functional integration in adjacent domains.Emerging applications do not substitute but complement current APS systems.A stochastic planning approach in APS systems still remains open for research.
Computers & Industrial Engineering | 2016
Gerd J. Hahn; Torben Sens; Catherine Decouttere; Nico Vandaele
Robust DEA-based approach for multi-criteria decision-making developed.Partial views of DEA envelope curves provide instructive decision support.Enhanced aggregate planning approach for stochastic environments developed.Demand variability drives outsourcing volumes and reduces internal batch sizes.Higher setup variability increases insourcing volumes and average batch sizes. Manufacturing outsourcing is a key industry trend towards greater operations effectiveness and is related to the discussion of strategic core competencies. We study the issue of contract manufacturing at the strategic-tactical level aiming for robust decisions to accommodate stochastic manufacturing environments and immanent uncertainty of planning parameters. The topic is approached from a multi-criteria decision-making perspective, since service, cost, quality, and more long-term value-related aspects need to be considered to arrive at well-balanced decisions. Our contribution is twofold: first, we develop a scenario-based non-parametric ranking approach to determine beneficial outsourcing options at the strategic level. The ranking method uses both model-based Key Performance Indicators (KPIs), which are obtained from a tactical planning model, and non-model-based KPIs that are derived in an independent assessment from multiple stakeholders. Second, we provide an enhanced aggregate planning approach at the tactical level in order to evaluate the performance implications of the strategic outsourcing decisions which in turn serve as the model-based KPIs for the ranking method. A queuing network-based approach is incorporated in the aggregate planning model to anticipate the stochastic behavior of manufacturing systems. An industry-derived case example with distinct outsourcing options is used to highlight the benefits of the approach and to investigate tactical trade-offs when coordinating internal and external manufacturing decisions.
A Quarterly Journal of Operations Research | 2012
Gerd J. Hahn; Chris Kaiser; Heinrich Kuhn; Lien Perdu; Nico Vandaele
Mathematical models for Aggregate Production Planning (APP) typically omit the dynamics of the underlying production system due to variable workload levels since they assume fixed capacity buffers and predetermined lead times. Pertinent approaches to overcome these drawbacks are either restrictive in their modeling capabilities or prohibitive in their computational effort. In this paper, we introduce an Aggregate Stochastic Queuing (ASQ) model to anticipate capacity buffers and lead time offsets for each time bucket of the APP model. The ASQ model allows for flexible modeling of the underlying production system and the corresponding optimization algorithm is computationally very well tractable. The APP and the ASQ model are integrated into a hierarchical framework and are solved iteratively. A numerical example is used to highlight the benefits of this novel approach.
Computers & Operations Research | 2017
Gerd J. Hahn; Marcus Brandenburg
Abstract Process industries typically involve complex manufacturing operations and thus require adequate decision support for aggregate production planning (APP). In this paper, we focus on two relevant features of APP in process industry operations: (i) sustainable operations planning involving multiple alternative production modes/routings with specific production-related carbon emission and the social dimension of varying operating rates, (ii) integrated campaign planning with the operational level in order to anticipate production mix/volume/routing decisions on campaign lead times and WIP inventories as well as the impact of variability originating from a stochastic manufacturing environment. We focus on the issue of multi-level chemical production processes and highlight the mutual trade-offs along the triple bottom line concerning economic, environmental and social factors. To this end, production-related carbon emission and overtime working hours are considered as externalized factors as well as internalized ones in terms of resulting costs. A hierarchical decision support tool is presented that combines a deterministic linear programming model and an aggregate stochastic queuing network model. The approach is exemplified at a case example from the chemical industry to illustrate managerial insights and methodological benefits of our approach.
Archive | 2018
Marcus Brandenburg; Gerd J. Hahn; Tobias Rebs
Sustainable supply chain management (SSCM) has become a highly relevant topic in scientific research as well as in managerial practice. This chapter summarizes the findings of several reviews of SSCM literature. In addition, propositions and guidelines for future SSCM research are given. Based on these insights, the structure of the book at hand and the coherence of the book chapters are outlined.
Data in Brief | 2018
Marcus Brandenburg; Gerd J. Hahn
Process industries typically involve complex manufacturing operations and thus require adequate decision support for aggregate production planning (APP). The need for powerful and efficient approaches to solve complex APP problems persists. Problem-specific solution approaches are advantageous compared to standardized approaches that are designed to provide basic decision support for a broad range of planning problems but inadequate to optimize under consideration of specific settings. This in turn calls for methods to compare different approaches regarding their computational performance and solution quality. In this paper, we present a benchmarking problem for APP in the chemical process industry. The presented problem focuses on (i) sustainable operations planning involving multiple alternative production modes/routings with specific production-related carbon emission and the social dimension of varying operating rates and (ii) integrated campaign planning with production mix/volume on the operational level. The mutual trade-offs between economic, environmental and social factors can be considered as externalized factors (production-related carbon emission and overtime working hours) as well as internalized ones (resulting costs). We provide data for all problem parameters in addition to a detailed verbal problem statement. We refer to Hahn and Brandenburg [1] for a first numerical analysis based on and for future research perspectives arising from this benchmarking problem.
International Journal of Production Economics | 2012
Gerd J. Hahn; Heinrich Kuhn
International Journal of Production Economics | 2012
Gerd J. Hahn; Heinrich Kuhn