A framework for Big Data driven product lifecycle management. (15th August 2017)

• Record Type: Journal Article
• Authors: Zhang, Yingfeng; Ren, Shan; Liu, Yang; Sakao, Tomohiko; Huisingh, Donald
• Is Part Of: Journal of cleaner production. Volume 159(2017)
• Page Start: 229
• Page End: 240
• Publication Date: 2017-08-15
• Subjects: Maintenance -- Service -- Macro level analysis -- Micro level analysis -- Economic impact; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2017.04.172
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Optimization of the process of product lifecycle management is an increasingly important objective for manufacturing enterprises to improve their sustainable competitive advantage. Originally, this approach was developed to integrate the business processes of an organization and more effectively manage and utilize the data generated during lifecycle studies. With emerging technologies, product embedded information devices such as radio frequency identification tags and smart sensors are widely used to improve the efficiency of enterprises' routine management on an operational level. Manufacturing enterprises need a more advanced analysis approach to develop a solution on a strategic level from using such lifecycle Big Data. However, the application of Big Data in lifecycle faces several challenges, such as the lack of reliable data and valuable knowledge that can be employed to support the optimized decision-making of product lifecycle management. In this paper, a framework for Big Data driven product lifecycle management was proposed to address these challenges. Within the proposed framework, the availability and accessibility of data and knowledge related to lifecycle can be achieved. A case study was presented to demonstrate the proof-of-concept of the proposed framework. The results showed that the proposed framework was feasible to be adopted in industry, and can provide an overall solution for optimizing the decision-making processes in different phases of the whole lifecycle. The key findings and insights from the case study were summarized as managerial implications, which can guide manufacturers to ensure improvements in energy saving and fault diagnosis related decisions in the whole lifecycle. Highlights: A framework for Big Data driven product lifecycle management was proposed. Macro and micro level analysis of business model and economic impact were conducted. The proposed framework verified by case study was feasible to be adopted in industry. Managerial implications in energy saving and fault diagnosis were summarized.