Sustainable Mobility Scenario Modeling : Evaluating Future Resilience of Modular Concepts for Electrified Trucks

Detta är en Master-uppsats från KTH/Industriell ekonomi och organisation (Inst.); KTH/Industriell ekonomi och organisation (Inst.)

Sammanfattning: Today, one of the greatest concerns for companies is how well their business will fit their future markets. However, predicting how the future will unfold is almost impossible for many industries, but companies that fail to prepare their products for future markets will most likely face substantial problems. Consequently, many companies have drawn their interest to product development strategies that cope with an unpredictable future, and research has highlighted Modularization as one such strategy. Nevertheless, there are no current methods that integrate future studies into the modularization process. Besides, there are no methods that evaluate the resilience of modular configurations against future scenarios. In the absence of such methods, this study targets the gap between future studies and modularization. The objective is to explore how scenario modeling can be used in the modularization process to evaluate the fitness of modular configurations against future conditions. The study scope is a simplified inter-urban transport mission with a particular focus on battery-electric and fuel-cell electric trucks. To meet the objective, this study builds upon a scenario framework from previous research that provides possible but yet distinctive futures within the transportation industry. Further, the future scenarios are bridged to the modularization process by transitioning the most important customer values from the scenarios to measurable design variables. Subsequently, by assigning weights to the customer values in accordance with scenario narratives, the overall efficiency of 42 unique modular configurations could be evaluated against the presumed importance of future customer values. Those findings were used to assess the relative performance of modules with respect to multiple futures and to provide reflections on the most and least robust modular design and configuration choices across multiple futures. In summary, the contribution from this method is shown to be two-fold. On the one hand, the model can provide insights and directions on the future resilience of modular concepts in the early stages of product development processes. On the other hand, it can be used in recurring performance assessments of modular configurations and guide optimization of module variants to prepare modular product configurations for multiple scenarios. 

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