Empowering customers and democratizing manufacture for product value co-creation are the main driving forces shaping the future of manufacturing. In this trend, personalization has received extensive attention as a new product realization strategy in the last decade where one-of-a-kind products are delivered to meet individual needs through customer participation in design, fabrication, and/or assembly. Personalization of products requires an appropriate assembly architecture, which describes the layout of personalized functions and support their cost-effective realization. An open, modular product architecture that can integrate personalized modules with other manufacturer-prescribed modules is a key enabler for personalization. Developing personalized products requires the coordination of the design of product architecture with manufacturing process and supplier selection. A concurrent design framework, built upon the intrinsic dependency of product architecture design, and process and supplier selection, can help manufacturers to optimize product development decisions for designated objectives, including shortened product development time and higher profitability. The development of such a concurrent design framework must address some critical challenges, such as modeling heterogeneous customer preferences, integrating personalized module configuration in an integrated decision model, achieving robustness of product architecture, and developing a nexus of product architecture, process, and supplier. In this dissertation, three tasks are carried out in developing a concurrent design framework for personalized product architecture design, and integration with manufacturing process and supplier selection: Task 1. Integrated design model for personalized product architecture. A decision hierarchy is developed for product variety determination, module variant selection, and personalized module configuration. A profit model is formulated as an architecture performance metric, incorporating customer preferences and manufacturing cost. Product utilities for individual customers are evaluated by a customer preference modeling method combing conjoint analysis, market segmentation, and Monte-Carlo simulation. Customer purchase probabilities are estimated by a logit choice model for profit calculation. Manufacturing constraints about process and material are included as they influence a manufacturer’s planning of candidate module variants and production strategies of personalized modules. These models are used to determine a product family architecture that maximizes potential profit. Task 2. Robustness optimization of personalized product architecture. Design uncertainty will cause performance fluctuation of a product architecture. The variation of profit due to uncertain customer preferences and cost estimates is formulated using a sensitivity analysis. A robustness index is introduced by combining the objectives of maximizing profit and minimizing variation. A robustness optimization model is established to optimize product architecture by maximizing the robustness index. Task 3. Integrating optimal process and supplier selection in personalized product architecture design. The architecture decision hierarchy in Task 1 is extended by including the manufacturing process and supplier selection decisions. A cost structure model is formulated to link the decisions about product architecture, processes, and suppliers. Thus, a concurrent optimization method is developed for integrating process and supplier selection in product architecture design. This dissertation research leads to three outcomes: (1) it provides a practical solution to leverage customer personalization preferences in personalized product architecture development; (2) it builds a foundation for the design integration of product, process and supply chain in personalized product development; and (3) it offers a tool for concurrent design of personalized product architecture, and process and supplier selection, which can help manufacturers to optimize product development decisions to increase product profit and reduce profit variation under market and manufacture uncertainties.