Kai Yang

120 papers receiving 2.7k citations

Peers

Kai Yang
Comparison fields: 5 of 166
  • Statistics, Probability and Uncertainty 424
  • Safety, Risk, Reliability and Quality 498
  • Management of Technology and Innovation 376
  • Management Information Systems 469
  • Industrial and Manufacturing Engineering 487
Replace Ratna Babu Chinnam with:
Ratna Babu Chinnam United States
Ming Dong China
Ajith Kumar Parlikad United Kingdom
Daoud Aı̈t-Kadi Canada
Geert‐Jan van Houtum Netherlands
El‐Houssaine Aghezzaf Belgium
Long Liu China
Edward Pohl United States
Zhen He China
Mohammad Modarres United States
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Citations per year

Countries citing papers authored by Kai Yang

Since Specialization
Citations

This map shows the geographic impact of Kai Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kai Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Yang more than expected).

Fields of papers citing papers by Kai Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kai Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kai Yang. The network helps show where Kai Yang may publish in the future.

Co-authors

The 25 scholars most cited alongside Kai Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kai Yang Line = papers co-authored together Kai Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 126 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Design for Six Sigma: A Roadmap for Product Development
2003258
2 2014207
3
Design for Six Sigma
2005126
4 1995116
5 2014106
6 201999
7 201693
8 202277
9 201177
10 201569
11
199969
12 200460
13 200255
14 201955
15 199052
16 201851
17 201347
18 199245
19 201443
20 201641

About Kai Yang

Kai Yang is a scholar working on Mechanical Engineering, Statistics, Probability and Uncertainty, Management of Technology and Innovation, Management Science and Operations Research and Industrial and Manufacturing Engineering, having authored 126 papers that have together received 2.9k indexed citations. Recurring topics across this work include Manufacturing Process and Optimization (19 papers), Product Development and Customization (18 papers), Design Education and Practice (16 papers), Optimal Experimental Design Methods (14 papers), Reliability and Maintenance Optimization (13 papers), Probabilistic and Robust Engineering Design (12 papers), Advanced Statistical Process Monitoring (10 papers) and Advanced Multi-Objective Optimization Algorithms (9 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (424 citations), Safety, Risk, Reliability and Quality (498 citations), Management of Technology and Innovation (376 citations), Management Information Systems (469 citations) and Industrial and Manufacturing Engineering (487 citations). Kai Yang has collaborated with scholars based in United States, China and Italy. Frequent co-authors include Basem El‐Haik, Satish Tyagi, Xianming Cai, Anoop Verma, Alok Choudhary, Adel Alaeddini, Hossein Badri, Guangbin Yang, Tayebeh Bahreini and Daniel Grosu. Their work appears in journals such as Quality and Reliability Engineering International, International Journal of Production Research, International Journal of Six Sigma and Competitive Advantage, IEEE Transactions on Reliability and Energy.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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