TY - JOUR
T1 - Managing the complexity of new product development project from the perspectives of customer needs and entropy
AU - Yang, Qing
AU - Shan, Chen
AU - Jiang, Bin
AU - Yang, Na
AU - Yao, Tao
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This study was supported by the National Natural Science Foundation of China (Grant Nos.: 71472013, 71528005 and 71872011).
Publisher Copyright:
© The Author(s) 2018.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - To successfully develop a complex product, a firm must answer two critical questions: how to “develop the right product” and how to “develop the product right.” Motivated by the real practice of Xiaomi’s new product development (NPD) projects, this article responds to these calls in the following ways. To design the right product, using an enhanced PageRank algorithm to investigate customer needs, NPD managers can select appropriate function modules of the new product to meet customers’ demand. To develop the new product in the right way, NPD managers should optimize the NPD organization. This article applies the multi-domain matrix (MDM) to identify the technical coordination dependency strength among different teams and then to measure the NPD organization’s complexity according to its entropy. By proposing the External Entropy of Cluster (EEC) and Internal Entropy of Cluster (IEC), we develop an entropy-based two-stage clustering criterion of design structure matrix (DSM) to optimize the NPD organization. The first-stage clustering criterion maximizes the added average dependency strength of DSM, and the second-stage clustering criterion minimizes the Weighted Total Entropy, including the IEC and EEC. An industrial example is provided to illustrate the proposed model. The results indicate that the clustered DSM can reduce the organization’s complexity.
AB - To successfully develop a complex product, a firm must answer two critical questions: how to “develop the right product” and how to “develop the product right.” Motivated by the real practice of Xiaomi’s new product development (NPD) projects, this article responds to these calls in the following ways. To design the right product, using an enhanced PageRank algorithm to investigate customer needs, NPD managers can select appropriate function modules of the new product to meet customers’ demand. To develop the new product in the right way, NPD managers should optimize the NPD organization. This article applies the multi-domain matrix (MDM) to identify the technical coordination dependency strength among different teams and then to measure the NPD organization’s complexity according to its entropy. By proposing the External Entropy of Cluster (EEC) and Internal Entropy of Cluster (IEC), we develop an entropy-based two-stage clustering criterion of design structure matrix (DSM) to optimize the NPD organization. The first-stage clustering criterion maximizes the added average dependency strength of DSM, and the second-stage clustering criterion minimizes the Weighted Total Entropy, including the IEC and EEC. An industrial example is provided to illustrate the proposed model. The results indicate that the clustered DSM can reduce the organization’s complexity.
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U2 - 10.1177/1063293X18798001
DO - 10.1177/1063293X18798001
M3 - Article
AN - SCOPUS:85058672234
SN - 1063-293X
VL - 26
SP - 328
EP - 340
JO - Concurrent Engineering Research and Applications
JF - Concurrent Engineering Research and Applications
IS - 4
ER -