Heuristics for pricing and positioning a product-line using conjoint and cost data
Heuristics for pricing and positioning a product-line using conjoint and cost data
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DOI：
10.1287/mnsc.39.2.160
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发表时间：
1993-02
影响因子：
5.4
通讯作者：
G. Dobson;S. Kalish
中科院分区：
文献类型：
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作者：
G. Dobson;S. Kalish
产品线的设计和定价是每个企业的本质。近年来，帮助管理者完成这一任务的定量方法越来越受欢迎。联合分析已经被广泛用于衡量不同产品的偏好，并建立市场模拟模型。在过去的几年里，已经发表了几篇论文，建议如何根据这些数据最佳地选择产品线。我们将这个问题形式化为一个数学规划，其中企业的目标是利润或总福利。与其他已公布的方法不同，我们为每个产品配置文件引入固定和可变成本。要引进的产品的数量是根据它们的可取性、固定和可变成本以及在利润的情况下它们对其他产品的蚕食效应内生地确定的。虽然这个问题是困难的NP-完全，我们表明，最大福利问题是相当于无能力的工厂选址问题，可以非常有效地解决使用贪婪的互换启发式。根据过去发表的经验，这个问题，我们进行模拟，我们表明，最佳或接近最佳的解决方案，在几秒钟内获得的大问题。我们开发了一个新的贪婪启发式的利润问题，它的应用程序模拟问题表明，它运行得很快，并具有更好的性能比各种替代品和以前发表的启发式。我们还展示了如何应用该方法，考虑到现有的产品的公司和竞争。
Designing and pricing a product-line is the very essence of every business. In recent years quantitative methods to assist managers in this task have been gaining in popularity. Conjoint analysis is already widely used to measure preferences for different product profiles, and build market simulation models. In the last few years several papers have been published that suggest how to optimally choose a product-line based on such data. We formalize this problem as a mathematical program where the objective of the firm is either profit or total welfare. Unlike alternative published approaches, we introduce fixed and variable costs for each product profile. The number of products to be introduced is endogenously determined on the basis of their desirability, fixed and variable costs, and in the case of profits, their cannibalization effect on other products. While the problem is difficult NP-complete, we show that the maximum welfare problem is equivalent to the uncapacitated plant location problem, which can be solved very efficiently using the greedy interchange heuristic. Based on past published experience with this problem, and on simulations we perform, we show that optimal or near optimal solutions are obtained in seconds for large problems. We develop a new greedy heuristic for the profit problem, and its application to simulated problems shows that it too runs quickly, and with better performance than various alternatives and previously published heuristics. We also show how the methodology can be applied, taking existing products of both the firm and the competition into account.