The fulfillment of individual customer affective needs may award the producer extra premium in gaining a competitive edge. This entails a number of technical challenges to be addressed, such as the elicitation, evaluation, and fulfillment of affective needs, as well as the evaluation of affordability of producers to launch the planned products. Mass customization and personalization have been recognized as an effective means to enhance front-end customer satisfaction while maintaining backend production efficiency. This paper proposes an affective design framework to facilitate decision-making in designing customized product ecosystems. In particular, ambient intelligence techniques are applied to elicit affective customer needs. An analytical model is proposed to support affective design analysis. Utility measure and conjoint analysis are employed to quantify affective satisfaction, while the producer affordability is evaluated using an affordability index. Association rule mining techniques are applied to model the mapping of affective needs to design elements. Configuration design of product ecosystems is optimized with a heuristic genetic algorithm. A case study of Volvo truck cab design is reported with a focus on the customization of affective features. It is demonstrated that the analytical affective design framework can effectively manage the elicitation, analysis, and fulfillment of affective customer needs. Meanwhile, it can account for the manufacturer's capabilities, which is vital for ensuring a profit margin in the mass customization and personalization endeavor.
Modern industrial production logistics systems in a modern are very complicated, which generally includes many continuous variables and discrete events. For these complicated industrial production logistics systems, this paper has set up a new hybrid Petri network model based on the common hybrid Petri network and with a combination of differential Petri net and controlled Petri net. This paper made simulation calculation with Java language and studied the modelling object through an industrial DFM solvent recovery process, which has revealed that this model is suitable for the modelling and simulation of hybrid production logistics system in industrial enterprises and can unify the simulation and analysis under the framework of one model. Targeted control strategies can be proposed based on simulation results, which is of great significance in instructing and improving production efficiency.
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