Volume 7 - 2016

Customer Perceived Value for Self-designed Personalised Products Made Using Additive Manufacturing

Syahibudil Ikhwan Abdul Kudus, R. Ian Campbell, Richard Bibb

As end-users become more involved in personalising designs, Additive Manufacturing (AM also known as 3D printing) has become an enabler to deliver this service through the manipulation of three-dimensional designs using easy-to-use design toolkits. Consequently, end-users are able to fabricate their personalised designs through various types of AM systems. This study employs an experimental method to investigate end-users’ reflections on the value of 3D Printed personalised product designs based on Product Value and Experiential Value. The results suggest that end-users gave higher value to all measurements for the 3D printed personalised products. This indicates that 3D printed personalised products have increased perceived value when compared to standard mass-production counterparts.

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Finding Suitable Amount of Variety for Product Platforms

Slavomir Bednar, Vladimir Modrak

The development of methods to identify the optimal product variety of a product platform is an important research issue in mass customization. A product platform which includes a wide portfolio of modules or components allows customers to customize their product by expressing a lot of different requirements. However, certain requirements may be constrained each other thus bringing customers to be disappointed by unfeasible product configurations. The present article explores the possibility of using entropy-based measures for quantifying the complexity induced by product variety in the context of constrained product configuration. More specifically, this article proposes a method which uses entropy-based measures to decide the optimal variety for product platforms. This method characterises a given product platform comparing the entropy associated to the feasible product configurations with the entropy associated to the unfeasible product configurations. Computational experiments performed on two case applications show that the proposed method can be effectively used to quantify variety-induced complexity and to assist product managers to choose optimal product variety.

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