In designing consumer durables such as appliances and power tools, it is important to account for variations in product performance across different usage situations and conditions. Since the specific usage of the product and the usage conditions can vary, the resultant variations in product performance also can impact consumer preferences for the product. Therefore, any new product that is designed should be robust to these variations-both in product performances and consumer preferences. This article refers to a robust product design as a design that has (1) the best possible (engineering and market) performance under the worst-case variations and (2) the least possible sensitivity in its performance under the variations. Achieving these robustness criteria, however, implies consideration of a large number of design factors across multiple functions. This article's objectives are (1) to provide a tutorial on how variations in product performance and consumer preferences can be incorporated in the generation and comparison of design alternatives and (2) to apply a multi-objective genetic algorithm (MOGA) that incorporates multifunction criteria in order to identify better designs while incorporating the robustness criteria in the selection process. Since the robustness criteria is based on variations in engineering performance as well as consumer preferences, the identified designs are robust and optimal from different functional perspectives, a significant advantage over extant approaches that do not consider robustness issues from multifunction perspectives. This study's approach is particularly useful for product managers and product development teams, who are charged with developing prototypes. They may find the approach helpful for obtaining customers' buy-in as well as internal buy-in early on in the product development cycle and thereby for reducing the cost and time involved in developing prototypes. This study's approach and its usefulness are illustrated using a case-study application of prototype development for a handheld power tool.
We present an integrated design and marketing approach to facilitate the generation of an optimal robust set of product design alternatives to carry forward to the prototyping stage. The approach considers variability in both (i) engineering design domain, and (ii) customer preferences in marketing domain. In the design domain, the approach evaluates performance and robustness of a design alternative due to variations in its uncontrollable parameters. In the marketing domain, in addition to considering competitive product offerings, the approach considers designs that are robust in customer preferences with respect to: (1) the variations in the design domain, and (2) the inherent variations in the estimates of preferences given the fit of the preference model to the sampled data. Our overall goal is to obtain design alternatives that are multi-objectively robust and optimal, i.e., (1) are optimal for nominal values of parameters, and (2) are within a known acceptable range in their multi-objective performance, and (3) maintain feasibility even when they are subject to applications and environments that are different from nominal or standard laboratory conditions. We illustrate the highlights of our approach with the design of a corded power tool example.
The ability to select a design alternative, from a set of feasible alternatives, that is likely to meet customers’ and designer’s preferences and also account for uncertainties is vital to the success of a product design process. This paper presents a new metric, a Customer-based Expected Utility (CEU) metric, for product design selection that accounts for a range of attribute levels (i.e., the customer range) within which customers make purchase decisions. The metric also accounts for designer’s preferences and uncertainty in achieving a desired attribute level (or a combination of attribute levels). The application of the CEU metric is demonstrated by rank-ordering a set of design alternatives for a cordless power tool. Using this metric, design alternatives that fall outside the customer range will yield a relatively low CEU value, while among those that fall in the customer range, the alternatives with a higher value of the designer’s utility yield a higher value of the CEU metric.
We present an integrated engineering design and marketing approach to facilitate the selection of a robust set of product designs to carry forward to the prototype stage. Our approach considers variability (i.e., noise or uncertainty) in both (i) engineering design domain, and (ii) customer preferences in marketing domain, to prune a set of design alternatives to a manageable size. In the design domain, our approach evaluates performance and feasibility robustness of a design alternative when there are variations in uncontrollable parameters. The goal of our approach in the design domain is to obtain a set of design alternatives that shows the best possible performance while maintaining feasibility even if the alternatives are subject to applications and environments that are different from their standard laboratory conditions (i.e., nominal parameter values). In the marketing domain, our approach considers the impact of performance variations in different usage situations and conditions on customer preferences and the uncertainties and sampling errors in estimating customer preferences. In addition, competitive products and their positions are considered in pruning the set of design alternatives. We illustrate our approach in the context of the design of a cordless power tool example, which highlights the advantages of using our approach.
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