A new model and solution method for product line design with pricing
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A new model and solution method for product line design with pricing LS Thakur1*, SK Nair1, K-W Wen2 and P Tarasewich3 1
University of Connecticut, USA; 2University of Milwaukee, USA; 3University of Maine, USA
Existing customer preference based product design models do not consider product prices and consumer budgets. These models assume that a purchase is based only on the satisfaction obtained from the product, irrespective of the product price and customer budget. However, when products are expensive relative to buyers' budgets, the effect of prices and budgets must be considered in addition to customer satisfaction. Most current models, moreover, assume that a low preference for one product characteristic is compensated by high preference for another, which may not hold for unacceptable levels of characteristics. For such products, we incorporate prices, budget constraints, and minimum acceptable thresholds in our model. To solve the model we develop a highly accurate, robust and ef®cient Beam Search (BS) based heuristic that identi®es optimal or near optimal product lines. The heuristic is tested on 300 simulated problems and an application. It is also compared to a Genetic Algorithms (GA) based heuristic. We found that our heuristic worked better than the GA heuristic in identifying optimal and near optimal solutions quickly. We also give detailed examples that illustrate the heuristic and demonstrate a pricing analysis application of the model. Keywords: product design; modeling; combinatorial analysis; heuristics
Introduction The problem of product design using customer preference data can be stated as follows. Given a data set containing preference values for a group of individuals for each characteristic level, the problem is to select one level from each characteristic to de®ne a product which optimises an objective. For example, in automobiles a characteristic may be `type of transmission,' with two levels `automatic' and `stick-shift,' and another one `number of doors,' with levels `4-door' and `2-door.' The objective may maximise number of customers or total preference value across all customers. The problem of product line design is to determine several products that will optimise the chosen objective. These products in the product line may appeal to different individuals or market segments and hence may be complementary. It is well recognised that the solution of the above problems that help develop product designs and product lines are critical decisions for a company. With increasing globalisation and competition, greater attention is being paid to customer preferences in such approaches as total quality management, continuous improvement, market driven quality, and customer service. The trend underscores the importance of models, such as the one presented here, that are based on conjoint analysis, and directly utilise customer preference data in product design and selection *Co
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