On applying big data to transform the inspection lines

Jrjung Lyu, Chia Wen Chen, Hong Yu Chen

Research output: Contribution to journalConference article

Abstract

Inspection lines are a heavy duty yet expensive part in many industries such as wireless communication industry, auto industry. While inspection is always a necessary evil in a very complicated and competitive industry, there is a strong need to reduce the unnecessary inspection processes and resources to improve the competitiveness. This work develops a framework to apply big data analytics to improve the inspection processes based on case study method. A world class case company is selected and the data-warehouse is established for a specific lot of products. After the data pre-processing work, the relationships among the detection stations and the detection results could be found. Various meta-models are used, which including association rule method to find the correlation between different test items and to reduce the number of required tests. Decision tree is also applied to find the optimal controllable factor. Finally, the developed rules are further verified by empirical data. Based on the empirical results, the number of detection items could be reduced by around 16.2% - a huge cost saving. It is also found out that the critical control factors, recommended by the decision tree, are temperature and humidity, which is a way to improve its quality without extra cost. The proposed big data application framework is therefore feasible in this case and machine learning or other models could be further extended, which is the future research direction.

Original languageEnglish
Pages (from-to)1964-1970
Number of pages7
JournalProceedings of the International Conference on Industrial Engineering and Operations Management
Volume2019
Issue numberMAR
Publication statusPublished - 2019 Jan 1
Event9th International Conference on Industrial Engineering and Operations Management, IEOM 2019 - Bangkok, Thailand
Duration: 2019 Mar 52019 Mar 7

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Inspection
Industry
Decision trees
Data warehouses
Association rules
Learning systems
Costs
Atmospheric humidity
Big data
Communication
Processing
Decision tree
Factors
Temperature

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

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abstract = "Inspection lines are a heavy duty yet expensive part in many industries such as wireless communication industry, auto industry. While inspection is always a necessary evil in a very complicated and competitive industry, there is a strong need to reduce the unnecessary inspection processes and resources to improve the competitiveness. This work develops a framework to apply big data analytics to improve the inspection processes based on case study method. A world class case company is selected and the data-warehouse is established for a specific lot of products. After the data pre-processing work, the relationships among the detection stations and the detection results could be found. Various meta-models are used, which including association rule method to find the correlation between different test items and to reduce the number of required tests. Decision tree is also applied to find the optimal controllable factor. Finally, the developed rules are further verified by empirical data. Based on the empirical results, the number of detection items could be reduced by around 16.2{\%} - a huge cost saving. It is also found out that the critical control factors, recommended by the decision tree, are temperature and humidity, which is a way to improve its quality without extra cost. The proposed big data application framework is therefore feasible in this case and machine learning or other models could be further extended, which is the future research direction.",
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On applying big data to transform the inspection lines. / Lyu, Jrjung; Chen, Chia Wen; Chen, Hong Yu.

In: Proceedings of the International Conference on Industrial Engineering and Operations Management, Vol. 2019, No. MAR, 01.01.2019, p. 1964-1970.

Research output: Contribution to journalConference article

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