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Design freedom, and particularly the freedom to incorporate innovative designs and strategies, is greatest at the very beginning of vehicle conceptual design. Conversely, this is when the least knowledge of the product exists. As product content decisions are made the level of freedom in the design decreases and the design becomes "locked in." The majority of vehicle lifecycle cost is also set by the end of vehicle conceptual design. This makes it critical to make well-informed and validated decisions in the concept design phase to avoid expensive iterations and redesign in the detailed design phase. Parametric vehicle modeling permits rapid iteration and optimization of vehicles in the conceptual design phase. A significant portion of vehicle design can be optimized parametrically without knowing specific Computer Aided Design (CAD) based details. Many overall vehicle characteristics such as curb mass, center of gravity location, key dimensions, occupant packaging and cargo volume can all be assessed and improved at the parametric level. Key vehicle performance measures can also be determined to a high level of confidence. In developing vehicle dimensions for a parametric model it is recommended to build up a vehicle using an "inside-out" approach centered on effective, knowledge-based occupant packaging. This work develops a continuum of dimensional parameters which tie vehicle internal and external dimensions together; it employs a combination of industry standard and author-defined component dimensions which make up overall vehicle outside dimensions. An effective continuum of functional parameters is also developed. In order to develop and optimize models for a desired vehicle type and size class, a knowledge base of vehicle typical values for key dimensional parameters has been compiled using a combination of data sources and field measurements. These values provide a useful starting point for the vehicle design optimization process. They also increase optimization effectiveness and ensure that the optimization begins within a valid design space. This work also develops a parametric modeling, scenario builder and optimization software framework which provides a design and optimization tool for vehicle design with trade-off evaluation tools. These parametric design methods improve design maturity prior to beginning vehicle detailed design.

How should a company best allocate its spending between acquisition and retention? Under what condition should a company devote resources and money to analytics? The above questions are just examples of more general issues concerning many companies when managing their customer acquisition and retention programs. To answer the above questions, I will conduct a study on the allocation of financial resources between incentives that target different types of customers, and the allocation of resources between incentives and analytics spending. This research first distinguishes between customers and acquisition, between incentive and price discount, and between acquisition and retention. It then proposes a new concept, “free rider”, in a customer acquisition and retention context. Building on the free-rider concept, two mathematical models are formulated to examine the optimal allocation between acquisition incentive, retention incentive, and analytics spending. Closed-form solutions are reached for both models and the results are interpreted in the context of marketing practice. The conditions leading to different patterns of optimal solutions of analytics spending, acquisition incentives, and retention incentives are discussed. Specifically, the detailed conditions under which the optimal acquisition incentives is zero or non-zero, the optimal retention incentives is zero or non-zero, and the optimal analytics spending is zero or non-zero, are provided. Factors determining the ceiling for acceptable level of cost of analytics are also examined.