# A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry
## 4 Data Mining with Big Data Applications in the Electronics Industry
### Data Mining with Big Data Frameworks for the Electronics Industry  
On the basis of the aforementioned review, a framework of DM with Big Data applications in the electronics industry is presented in Figure 10 in which the stage of design and production corresponds to the beginning of lifecycle, and the sale and service can be taken as the middle of lifecycle, while recycling is at the end of lifecycle, respectively [1,8]. Figure 10: The framework of DM with Big Data towards IT applications in the electronics industry. Each stage of the lifecycle corresponds to different application scenes. The DM application of product and manufacturing process design mainly includes the product design, manufacturing process design, and marketing with relevant knowledge areas, such as quality improvement, development cost and time prediction, product customization, manufacturing parameter optimization, and SCM. The equipment management, production management, and procurement are the main application areas of DM with Big Data in the production stage with typical knowledge areas such as AEC/APC, PMO, QI, and SCM. The application of the DM with Big Data for sale and service cannot only be support for quality improvement and customization design but also optimize logistics and facilitate customer service and maintenance. The recycling attracted less attention from DM with Big Data application in the electronics industry, which could be used in remanufacturing, reuse, and environment protection, considering the knowledge areas of product recovery, remaining life prediction, and reverse logistics optimization. The details of knowledge areas of different stages have been summarized in Section 4.2. The quality improvement for design and production can be further divided into quality (yield) prediction, classification, description, and parameter optimization. Post hoc diagnosis can be taken as the quality description at the production stage with the purpose of process parameters adjustment to ensure future quality. The tasks of AEC/APC that consists of FDC, R2R control, SPC, VM, and so forth are also for quality enhancement, and therefore, the quality improvement at the production stage and AEC/APC are not a disjoint division here. The DM and Big Data application in PMO is a collection of scheduling optimizations, cost/time prediction, and so on. SCM is used to optimize the logistics for material supply at the beginning stage of the lifecycle and it can also be used to achieve greater efficiencies and effectiveness in delivering customer value at the end of the lifecycle. The application of DM or Big Data tools in CRM is an emerging trend in the global economy. Analyzing and understanding customer behaviors and characteristics is the foundation of the development of a competitive CRM strategy so as to acquire and retain potential customers and maximize customer value [3]. The tasks of customer identification, attraction, retention, and development of CRM can be realized through Big Data-based marketing prediction, personalized service, predictive maintenance, remote online diagnosis and so on. Data preparation such as data acquisition, accumulation, and storage for different knowledge areas and applications can be guided by the diagram of data content for different knowledge areas given in Section 5.1. The commonly used data preprocessing techniques including data cleaning, transformation, reduction, and discretization that can utilize the preprocessing approaches summarized in Sections 2.2 and 4.1, based on the requirement of application areas and the quality of data. DM, in a narrow sense, for each function, can be implemented based on some pervasive DMTs summarized in Section 4.2.4. The interpretation, evaluation, and implementation software can be conducted by combing experts' knowledge with performance indicators given in Section 2.4, which is not given in the framework because it has many selections in practice. The final purpose of the DM application has been proved by many researchers and practitioners.