Abstract
While in the traditional understanding managers are single individuals that takes decisions as fractal parts of macro economy, the situation within supply chains is more complex. With the rise of sustainability considerations as well as with smart concepts, classical approaches of supply chain management were transformed from the “dilemma of operational planning”, described as the “magic square” into “magic cubes” integrating sustainability dimensions as well as flexibility. Beside these aspects, future supply chains have to consider that not only human stakeholder are involved in the interactions, but also artificial participants comprising weak Artificial Intelligence and Machine Learning based solutions. These artificial approaches force the reshaping of traditional supply chain structures together with their underlying supporting and management processes.
The aim of the research is to outline a framework of design parameters for relevant decision situations in supply chain management, so that they can be designed and processed digitally and implemented into human-task-technology systems, with outline the way how managerial decision-making will be changed. First, the study creates a model of strategic and operative management decisions within supply chains by using approaches that are suitable for realizing as Artificial Intelligence (AI) solutions together with methods from conjoint analysis, logistics curve theory and distributed investment appraisal to safeguard stakeholder-orientation. Since the authors were involved in several green transportation projects with a special focus on autonomous vehicles and related management solutions for the last-mile transportation, the developed model is empirically validated in the context of smart supply chains.
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Kitzmann, H., Prause, G. (2023). Stakeholder-Oriented Investment Activities for Sustainable Supply Chain Management. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2022. Lecture Notes in Networks and Systems, vol 640. Springer, Cham. https://doi.org/10.1007/978-3-031-26655-3_12
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