@article{ZengKellnerLienland2016, author = {Zeng, Li and Kellner, Florian and Lienland, Bernhard}, title = {Rucksackprobleme in der Praxis - schnelle Entscheidungen schwer gemacht}, series = {HMD - Praxis der Wirtschaftsinformatik}, volume = {53}, journal = {HMD - Praxis der Wirtschaftsinformatik}, number = {3}, doi = {10.1365/s40702-016-0207-z}, pages = {310 -- 322}, year = {2016}, abstract = {Unternehmen sind h{\"a}ufig mit Situationen konfrontiert, in denen schnell Entscheidungen bez{\"u}glich der Auswahl mehrerer Handlungsalternativen gefunden werden m{\"u}ssen. Mathematische Verfahren k{\"o}nnen hierbei unterst{\"u}tzen, z. B. f{\"u}r die Ermittlung einer ersten Diskussionsbasis. Verf{\"u}gbare Softwarel{\"o}sungen errechnen zwar h{\"a}ufig optimale Ergebnisse, zeigen jedoch Schw{\"a}chen bei der praktischen Anwendbarkeit. So ist eine Einarbeitung in komplexe Optimierungssoftware f{\"u}r die teilweise sporadisch auftretenden Probleme in der Regel f{\"u}r Unternehmen nicht m{\"o}glich, unter anderem auch unter Anbetracht der teilweise hohen Kosten der Standardsoftware und dem ben{\"o}tigten hohen Einarbeitungsaufwand. Gerade Problemstellungen in Fachbereichen, die nicht auf mathematische Probleml{\"o}sung spezialisiert sind, m{\"u}nden daher regelm{\"a}ßig in Ineffizienzen. Basierend auf den in der Literatur diskutierten L{\"o}sungsans{\"a}tzen wurde ein praxisorientierter Ansatz zur Entscheidungsunterst{\"u}tzung f{\"u}r Rucksackprobleme bzw. 0-1 Probleme mithilfe von Genetischen Algorithmen (GA) entwickelt und technisch in Microsoft Excel® umgesetzt. Ein Praxistest bei einem chinesischen Textilunternehmen belegt erh{\"o}hte Effektivit{\"a}t und Effizienz. Die Software kann kostenfrei nach MIT Lizenz unter http://www.solvega.de/ heruntergeladen werden.}, language = {de} } @article{KellnerOttoLienland2014, author = {Kellner, Florian and Otto, Andreas and Lienland, Bernhard}, title = {Cost Assignment Paradox: Indirect Tooling Costs and Production Orders}, series = {Advances in Management Accounting}, volume = {23}, journal = {Advances in Management Accounting}, doi = {10.1108/S1474-787120140000023007}, pages = {211 -- 251}, year = {2014}, abstract = {Purpose Tooling is a common component of an industrial product's manufacture. Specific tooling is devised to serve the fabrication of a particular product, while generic tooling can be used in the manufacture of multiple products. In the latter case, companies are confronted with the problem of fairly allocating the indirect costs of the tooling. This article studies how to allocate costs of generic tooling to single production orders. Methodology Ten allocation methods (AMs) are described that are in principle suited to the distribution of generic tooling costs to production orders. Since the presented methods have for the most part been discussed in differing contexts, we apply them to a specified generic tooling problem for comparison. Evaluation of the various methods is based on 16 criteria. Reasoning is supported by a computational Monte Carlo simulation. Furthermore, we suggest using the Analytical Hierarchy Process (AHP) to elaborate one final proposition concerning the most preferable allocation scheme. Findings The article reports the single allocation rules' performances for different allocation scenarios. The described characteristics refer to fairness, efficiency, and simplicity as well as to empty-core performance. Using AHP analysis allows for the aggregation of the rules' criteria ratings. Thus, especially suitable allocation schemes for the problem at hand are identified. Practical implications An allocation is required for budgeting reasons and also for the definition of projects' bottom-up sales prices. Selecting the "right" AM is important, as a suboptimal AM can result in unfair allocation vectors, which will act as incentives to stop using the common resource, potentially leading to higher total costs. Originality/value of the article Research on the comparison of AMs is typically performed for certain purposes, such as enterprise networks, horizontal cooperative purchasing scenarios, or municipal service units. This article will augment the research evaluating AMs by introducing a novel set of evaluation criteria and by providing an in-depth comparison of AMs suited for the allocation of generic tooling costs.}, language = {en} } @article{LienlandKellner2012, author = {Lienland, Bernhard and Kellner, Florian}, title = {Projektauswahl mit der linearen Optimierung im Mittelstand}, series = {HMD - Praxis der Wirtschaftsinformatik}, volume = {49}, journal = {HMD - Praxis der Wirtschaftsinformatik}, number = {3}, doi = {10.1007/BF03340699}, pages = {52 -- 62}, year = {2012}, abstract = {Die Projektauswahl stellt eine Herausforderung insbesondere f{\"u}r mittelst{\"a}ndische Industrieunternehmen dar. Abh{\"a}ngigkeiten verschiedener Alternativen und vorgegebene Investitionsbudgets f{\"u}hren bei Anwendung von in der Praxis standardm{\"a}ßig eingesetzten Verfahren regelm{\"a}ßig zu suboptimalen Projektkombinationen. In enger Abstimmung mit einem Automobilzulieferer wurde ein Ansatz zur Projektauswahl unter Ber{\"u}cksichtigung von Ressourcen und laufenden Projekten entwickelt sowie technisch umgesetzt. Implementierte Praxistests belegen Verbesserungen bei der Projektauswahl, aber auch bei der Budgeteinhaltung, der Fixkostenreduzierung sowie der Angebotspreisbestimmung.}, language = {de} } @article{KellnerChristinaGoerkLienland2017, author = {Kellner, Florian and Christina Goerk, Anna and Lienland, Bernhard}, title = {Mit CO₂-Kennzahlen die {\"O}kobilanz verbessern}, series = {Controlling \& Management Review}, volume = {61}, journal = {Controlling \& Management Review}, number = {9}, doi = {10.1007/s12176-017-0118-z}, pages = {58 -- 64}, year = {2017}, language = {de} } @article{LienlandZengMayrocketal.2017, author = {Lienland, Bernhard and Zeng, Li and Mayrock, Michael and Kellner, Florian}, title = {Operations Research im Controlling}, series = {Controlling: Zeitschrift f{\"u}r erfolgsorientierte Unternehmenssteuerung}, volume = {29}, journal = {Controlling: Zeitschrift f{\"u}r erfolgsorientierte Unternehmenssteuerung}, number = {2}, doi = {10.15358/0935-0381-2017-2}, pages = {55 -- 57}, year = {2017}, language = {de} } @article{KellnerLienlandUtz2021, author = {Kellner, Florian and Lienland, Bernhard and Utz, Sebastian}, title = {A multi-criteria decision-making approach for assembling optimal powertrain technology portfolios in low GHG emission environments}, series = {Journal of Industrial Ecology}, volume = {25}, journal = {Journal of Industrial Ecology}, number = {6}, doi = {10.1111/jiec.13148}, pages = {1412 -- 1429}, year = {2021}, abstract = {Environmental regulations force car manufacturers to renew the powertrain technology portfolio offered to the customer to comply with greenhouse gas (GHG) emission targets. In turn, automotive companies face the task of identifying the "right" powertrain technology portfolio consisting of, for example, internal combustion engines and electric vehicles, because the selection of a particular powertrain technology portfolio affects different company targets simultaneously. What makes this decision even more challenging is that future market shares of the different technologies are uncertain. Our research presents a new decision-support approach for assembling optimal powertrain technology portfolios while making decision-makers aware of the trade-offs between the achievable profit, the achievable market share, the market share risk, and the GHG emissions generated by the selected vehicle fleet. The proposed approach combines "a posteriori" decision-making with multi-objective optimization. In an application case, we feed the outlooks of selected market studies into the proposed decision-support system. The result is a visualization and analysis of the current real-world decision-making problem faced by many automotive companies. Our findings indicate that for the proposed GHG restriction at work in 2030 in the European Union, no optimal powertrain technology portfolio with less than 35\% of vehicles equipped with an electric motor exists.}, language = {en} } @inproceedings{KellnerLienlandUtz2020, author = {Kellner, Florian and Lienland, Bernhard and Utz, Sebastian}, title = {Optimal engine technology mix in a low carbon economy}, series = {2020 International Conference on Decision Aid Sciences and Application (DASA)}, booktitle = {2020 International Conference on Decision Aid Sciences and Application (DASA)}, doi = {10.1109/DASA51403.2020.9317094}, pages = {95 -- 98}, year = {2020}, abstract = {Environmental regulations force automotive companies to modify the powertrain technology portfolio offered to the customer to comply with greenhouse gas (GHG) emission targets. Automotive companies, in turn, are faced with the decision of finding the right powertrain technology portfolio because the selection of a particular technology portfolio affects different company targets at the same time. What makes this decision even more interesting is the fact that future market shares of the different technologies are uncertain. With its numerous objectives, this challenge requires multi-criteria decision-making techniques to identify the optimal powertrain technology portfolio. The objective of this research is to present a new decision support approach for assembling optimal powertrain technology portfolios while making decision-makers aware of the trade-offs between the achievable market share, the market share risk, and the GHG emissions generated by the selected vehicle fleet. The proposed approach combines `a posteriori' decision-making, multi-objective optimization, and the Markowitz portfolio theory. In an application case, the outlooks of selected market studies are fed into the proposed decision support system. The result is a visualization and analysis of the current real-world decision-making problem faced by many automotive companies. Interesting findings of this research include that for the assumed GHG restrictions in place in 2030, there exists no optimal powertrain technology portfolio that is not composed of at least 20\% of electric vehicles.}, language = {en} } @book{KellnerLienlandLukesch2022, author = {Kellner, Florian and Lienland, Bernhard and Lukesch, Maximilian}, title = {Produktionswirtschaft: Planung, Steuerung und Industrie 4.0}, edition = {3}, publisher = {Springer Gabler}, address = {Berlin, Heidelberg}, doi = {10.1007/978-3-662-65803-1}, year = {2022}, abstract = {Dieses Buch bietet eine einsteigerfreundliche Einf{\"u}hrung in die f{\"u}r industrielle Betriebe maßgebliche Aufgabe der Produktionswirtschaft. Im Zentrum steht dabei das operative Produktionsmanagement. Hierzu geh{\"o}ren die erfolgswirksame Bewirtschaftung der Produktionsfaktoren Mensch, Betriebsmittel und Material, der Einsatz von Information sowie die Planung und Steuerung der industriellen Produktion. Mithilfe zahlreicher Unternehmensbeispiele und {\"U}bungsaufgaben wird der Leser durch produktionswirtschaftliche Grundlagen sowie durch Ablauf und Inhalt zentraler Prozesse in der Industrie geleitet. Auch die aktuelle Diskussion um das Thema Industrie 4.0 und cyber-physische Produktionssysteme werden lesefreundlich und mit konkretem Anwendungsbezug pr{\"a}sentiert.}, language = {de} } @article{FuchsBeckLienlandetal.2018, author = {Fuchs, Christoph and Beck, Daniel and Lienland, Bernhard and Kellner, Florian}, title = {The role of IT in automotive supplier supply chains}, series = {Journal of Enterprise Information Management}, volume = {31}, journal = {Journal of Enterprise Information Management}, number = {1}, doi = {10.1108/JEIM-03-2017-0038}, pages = {64 -- 88}, year = {2018}, abstract = {Purpose The purpose of this paper is to explore the impact of information technology (IT) on supply chain performance in the automotive industry. Prior studies that analyzed the impact of IT on supply chain performance report results representing the situation of the "average industry." This research focuses on the automotive industry because of its major importance in many national economies and due to the fact that automotive supply chains do not represent the supply chain of the average industry. Design/methodology/approach A research model is proposed to examine the relationships between IT capabilities, supply chain capabilities, and supplier performance. The model divides IT capabilities into functional and data capabilities, and supply chain capabilities into internal process excellence and information sharing. Data have been collected from 343 automotive first-tier suppliers. Structural equation modeling with partial least squares is used to analyze the data. Findings The results suggest that functional capabilities have the greatest impact on internal process excellence, which in turn enhances supplier performance. However, frequent and adequate information sharing also contributes significantly to supplier performance. Data capabilities enable supply chain capabilities through their positive impact on functional capabilities. Practical implications The findings will help managers to understand the effect of IT implementation on company performance and to decide whether to invest in the expansion of IT capacities. Originality/value This research reports the impact of IT on supply chain performance in one of the most important industries in many industrialized countries, and it provides a new perspective on evaluating the contribution of IT on firm performance.}, language = {en} } @article{KellnerLienlandUtz2019, author = {Kellner, Florian and Lienland, Bernhard and Utz, Sebastian}, title = {An a posteriori decision support methodology for solving the multi-criteria supplier selection problem}, series = {European Journal of Operational Research}, volume = {272}, journal = {European Journal of Operational Research}, number = {2}, doi = {10.1016/j.ejor.2018.06.044}, pages = {505 -- 522}, year = {2019}, abstract = {This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate 'risk' into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.}, language = {en} }