3.8 Proceedings Paper

A Preliminary Model for Delayed Product Differentiation Towards Mass Customization

期刊

出版社

SPRINGER-VERLAG SINGAPORE PTE LTD
DOI: 10.1007/978-981-19-9205-6_31

关键词

Product variety; Mass customization; Delayed product differentiation; Product platforms

向作者/读者索取更多资源

In recent years, industrial companies have been moving from mass production to mass customization to meet the diverse customer requirements. Traditional production strategies have limitations, leading to the emergence of new hybrid production strategies such as Delayed Product Differentiation (DPD). This paper proposes a preliminary indicator to assess the similarity among product variants, which can help companies determine whether to implement DPD or use traditional production strategies.
In the recent years, the diversity in customer requirements asks industrial companies to move from mass production to mass customization, overcoming the traditional strategy of realizing a large volume of a single product in favor of the manufacturing of multiple product variants matching different customer needs. In such a scenario, traditional production strategies such as Make to Stock (MTS) and Make to Order (MTO) show some limitations, leading to the advent of new hybrid production strategies. The Delayed Product Differentiation (DPD) is one of the most relevant, which attempts to join the dual needs of high variety and quick customer response time by using the so-called product platforms. This working paper proposes a preliminary indicator to assess the similarity among a set of parts, i.e., product variants, according to their production cycle, acting as a first criterion to assess the feasibility of implementing the DPD strategy. The application of the proposed indicator to an operative industrial instance showcases its effectiveness to suggesting to the company whether to implement the DPD or to using traditional production strategies.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

3.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据