【作者】 金茂竹；

【导师】 陈荣秋；

【作者基本信息】 华中科技大学 ， 管理科学与工程， 2008， 博士

【摘要】 基于产品平台的产品族设计和生产是实现MC的有效策略之一,被广泛应用于创建产品族以保持规模经济的同时向市场提供多样性的产品。适宜的产品族构架和产品平台配置需要对预期经济利润做出估计,其中包括由于通用性带来的成本节省以及市场上成功的产品绩效带来的收益。产品平台范围,即合适的产品平台数量、对应的产品平台配置以及产品族市场策略,处理从产品构架、制造、成本、工程性能、价值、需求到市场竞争各元素之间相互影响的基本认识问题,本文从产品平台配置、多产品平台配置和产品平台战略评价三种视角对产品平台范围进行定量和定性的考察和求证。主要获得以下创新成果。首先,基于产品平台的产品族配置理念的演变、产品平台评价的研究现状和发展趋势、产品平台及产品族理论与应用研究现状等问题及相关领域成果之上,提出产品族的产品平台配置方法,即,利用产品族通用零部件实现产品平台范围。该方法考虑产品需求以及每种产品类型结构以寻求制造成本最小化为目标,成本包括零部件成本,产品平台上大规模装配成本以及对个体变型产品上增加或删除零部件的成本。利用产品平台配置,高效地实现产品多样性以提高满足顾客需求的效率。产品平台配置处理关于产品平台工程域的问题,将配置问题规划为一般优化问题,特别是在精确解计算量巨大时通过启发算法高效求解问题。其次,对上述方法引入需求的不确定性,提出不确定需求环境下产品族的产品平台配置方法。使用该方法,产品族中每一变型产品通过自身零部件直接装配或从具有相似零部件组的产品平台组装生成。该方法考虑在每种产品类型的随机需求情况下,寻求产品族的产品平台配置的总成本最小,包括制造成本,产品平台库存成本,以及由于缺货造成的需求缺损成本。通过随机规划的方法对产品平台配置中产品需求不确定性进行说明,并通过精确或启发的方法对模型进行求解。该方法从产品平台支持生产配置的视角分析顾客需求不确定性对装配复杂度以及成本的影响,为分析更复杂的需求分布和成本构成对产品平台配置影响奠定了基础,并为更多不确定因素对产品平台配置影响的定量分析提供了借鉴依据。然后,提出产品族制造的多产品平台配置方法,该方法考虑个体产品需求以及每种产品类型结构以寻求制造成本最小化为目标,成本包括零部件成本,产品平台大规模装配成本以及对个体变型产品上增加或删除零部件的成本。利用该方法,产品族中每一变型产品或者直接由零部件装配获得或者从产品平台装配而成。本文利用混合整数规划对多产品平台配置方法进行建模通过精确或启发的方法对问题进行求解。结果证明在给定的准备成本情况下,利用多产品平台配置产品族比利用单一平台生产更能节省成本。多产品平台较单产品平台的优势在于变型产品和对应产品平台的匹配程度更高。研究结论为利用启发式算法对从独立零部件到模块到同产品平台产品族到多产品平台的配置问题的进一步综合分析研究奠定了基础,并对产品平台范围在产品价值链的综合定量描述做了铺垫。最后,提出基于产品平台的产品族战略综合评价框架。该框架针对受未来不确定性影响的产品平台战略,结合实物期权理论和博弈论,寻找基于产品平台的产品族投资策略的最优规则,构造受到竞争威胁企业的产品平台战略投资期权价值的评估方法。本文利用实物期权评估方法充分考虑产品平台战略中的期权价值。竞争的存在改变了产品平台战略决策的灵活性及未来投资机会的价值,本文分析竞争因素对实物期权价值的影响,并利用期权博弈方法,分析在不同信息和不同竞争条件下产品平台战略的最优投资时机选择及其对期权价值的影响。在此基础上,本文给出了竞争条件下产品平台战略投资价值的评定步骤及模型,为多种不确定性存在情况下的决策分析提供了借鉴。产品族战略综合评价框架通过识别技术性和经济性产品族实物期权及其定价模型,为建立综合产品平台柔性规划模型,最终为MC的战略实施应用提供理论基础和技术支持。更多还原

【Abstract】 The platform-based design and production is one of widely implemented MC strategies to create product families that provide sufficient variety to the market while maintaining economies of scale.Appropriate product platform configuration and architecture for product family requires estimating expected financial benefits both in terms of savings due to commonality and revenues due to successful product performance in the market.The question of finding the optimal platform extent,that is,the right number of platforms,the corresponding product platform configuration for a product family and product family strategy is to achieve a rudimentary understanding of the interplay among product architecture,manufacturing,cost,engineering performance, value,demand and the role of competition.The dissertation explored and demonstrated the platform extent problem rigorously addressed both quantitatively and qualitatively in the perspectives of product platform configuration,multiple product platform configuration and product platform strategy valuation.Firstly,based on the background and related literature review on research areas such as evolution of product platform concept and theory,various platform strategies and platform techniques,product platform based configuration,and heuristic approach to solve large scale optimization problems.An approach for platform-based configuration of product family production is put forward.Considering the problem of determining a product platform configuration for the production of a product family while minimizing the overall production cost,the problem is formulated as a general optimization problem of minimizing the production cost using product platform architecture while satisfying the part assembly constraints.Both an optimal formulation and an evolutionary strategy based on Genetic Algorithm are presented and the approache is illustrated with an example of a family.Secondly,given the aforementioned approach and taking the stochastic demands into account,a single platform configuration approach for product family is presented.The problem is formulated as a two stage stochastic programming model with recourse.The objective is to minimize the total production cost that includes the cost of production of platforms,cost of production of products using the platforms,holding cost for unused platforms and stockout cost for lost demands.This problem is solved using two approaches,exact method and a genetic algorithm based heuristic that combines integer programming to solve the problem.The two approaches are investigated and their importance for various problem instances is presented.The approaches are illustrated and validated by using the same example of a product family.Thirdly,considering the problem of proposing multiple platforms for the production of a given product family while minimizing the overall production cost,the methodology considered the given demand for each product variant,with the decision variables of the optimal number of platforms,optimal configuration of each platform,and assignment of the products to the platforms.The advantage for using multiple platforms over a single platform is ability to optimally match products to a particular platform.The problem is formulated as an integer program,and both the optimal formulation and an evolutionary strategy based on genetic algorithm are presented.The approach is illustrated by using an example of a procut family.Finally,the dissertation elaborated a comprehensive methodology for evaluating the platform-based product family strategy roadmaps.The proposed methodology addressed the issues concerned with strategy embedded with future uncertainties and combined real options valuation and game analysis using a game theoretic approach at different nodes of the decision tree by introducing games that represent competitive strategic moves which will help decision-makers to make intelligent decisions during the course of product platform strategy.The investment opportunities with important competitive or strategic dimensions change the decision-making flexibility and thus the value of real options.The proposed real option-game approach addressed the influence of competitor’s move on evaluating real options and decistion-making timing in the implementing process of platform strategy in the different information accession and competitive senarioes.Given the above-mentioned analysis,the steps and models to evaluate multistage product platform strategy are discussed and demonstrated in the end.更多还原