Dan Shunk

1.1k total citations
37 papers, 773 citations indexed

About

Dan Shunk is a scholar working on Management Information Systems, Industrial and Manufacturing Engineering and Management Science and Operations Research. According to data from OpenAlex, Dan Shunk has authored 37 papers receiving a total of 773 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Management Information Systems, 11 papers in Industrial and Manufacturing Engineering and 10 papers in Management Science and Operations Research. Recurrent topics in Dan Shunk's work include Scheduling and Optimization Algorithms (7 papers), Supply Chain and Inventory Management (6 papers) and Technology Assessment and Management (6 papers). Dan Shunk is often cited by papers focused on Scheduling and Optimization Algorithms (7 papers), Supply Chain and Inventory Management (6 papers) and Technology Assessment and Management (6 papers). Dan Shunk collaborates with scholars based in United States, South Korea and Philippines. Dan Shunk's co-authors include John Fowler, Teresa Wu, Gerald T. Mackulak, Susan Ferreira, James Collofello, Jennifer Blackhurst, Michael Greiner, W. Matthew Carlyle, Joseph R. Carter and Richard E. Billo and has published in prestigious journals such as European Journal of Operational Research, Information Sciences and International Journal of Production Research.

In The Last Decade

Dan Shunk

35 papers receiving 709 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dan Shunk United States	16	254	230	226	168	118	37	773
Bernd Hellingrath Germany	15	286 1.1×	207 0.9×	150 0.7×	272 1.6×	81 0.7×	135	815
Yuh‐Jen Chen Taiwan	13	201 0.8×	109 0.5×	251 1.1×	218 1.3×	104 0.9×	37	710
Cevriye Gencer Türkiye	11	234 0.9×	174 0.8×	410 1.8×	245 1.5×	40 0.3×	60	916
İbrahim Doğan Türkiye	9	201 0.8×	132 0.6×	423 1.9×	173 1.0×	40 0.3×	11	808
Yves Ducq France	19	476 1.9×	249 1.1×	215 1.0×	219 1.3×	118 1.0×	61	909
Markus Ettl United States	14	462 1.8×	233 1.0×	149 0.7×	170 1.0×	49 0.4×	39	736
Ip‐Shing Fan United Kingdom	14	219 0.9×	133 0.6×	67 0.3×	109 0.6×	117 1.0×	73	630
A.K. Choudhury United States	16	248 1.0×	61 0.3×	339 1.5×	153 0.9×	112 0.9×	41	1.1k
Murari Lal Mittal India	19	513 2.0×	384 1.7×	346 1.5×	487 2.9×	71 0.6×	46	1.2k
Wan Seon Shin South Korea	9	135 0.5×	150 0.7×	126 0.6×	84 0.5×	36 0.3×	31	489

Countries citing papers authored by Dan Shunk

Since Specialization

Citations

This map shows the geographic impact of Dan Shunk'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 Dan Shunk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Shunk more than expected).

Fields of papers citing papers by Dan Shunk

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dan Shunk. 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 Dan Shunk. The network helps show where Dan Shunk may publish in the future.

Co-authorship network of co-authors of Dan Shunk

This figure shows the co-authorship network connecting the top 25 collaborators of Dan Shunk. A scholar is included among the top collaborators of Dan Shunk based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dan Shunk. Dan Shunk is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Fowler, John, et al.. (2019). Design for customer responsiveness: Decision support system for push–pull supply chains with multiple demand fulfillment points. Decision Support Systems. 123. 113071–113071. 13 indexed citations

Wu, Teresa, et al.. (2014). A stochastic AHP decision making methodology for imprecise preferences. Information Sciences. 270. 192–203. 54 indexed citations

Wu, Teresa, et al.. (2014). An intelligent decomposition of pairwise comparison matrices for large-scale decisions. European Journal of Operational Research. 238(1). 270–280. 16 indexed citations

Shunk, Dan, et al.. (2013). A pairwise comparison matrix framework for large-scale decision making. 2 indexed citations

Shunk, Dan, et al.. (2012). Prioritization of applications for software-As-A-service migration using total life cycle costs and the analytic network process. World Congress on Engineering. 1352–1357.

Kim, Seung-Hwan, John Fowler, Dan Shunk, & Michele E. Pfund. (2012). Improving the push–pull strategy in a serial supply chain by a hybrid push–pull control with multiple pulling points. International Journal of Production Research. 50(19). 5651–5668. 23 indexed citations

Sun, Yang, John Fowler, & Dan Shunk. (2010). Policies for allocating product lots to customer orders in semiconductor manufacturing supply chains. Production Planning & Control. 22(1). 69–80. 14 indexed citations

Ferreira, Susan, Dan Shunk, James Collofello, Gerald T. Mackulak, & Amylou C. Dueck. (2010). Reducing the risk of requirements volatility: findings from an empirical survey. Journal of Software Maintenance and Evolution Research and Practice. 23(5). 375–393. 15 indexed citations

Ferreira, Susan, James Collofello, Dan Shunk, & Gerald T. Mackulak. (2009). Understanding the effects of requirements volatility in software engineering by using analytical modeling and software process simulation. Journal of Systems and Software. 82(10). 1568–1577. 63 indexed citations

10.

Fowler, John, et al.. (2007). A compact abstraction of manufacturing nodes in a supply network. International Journal of Simulation and Process Modelling. 3(3). 115–115. 11 indexed citations

11.

Sun, Yang, et al.. (2007). Decision Paradigms in the Semiconductor Supply Chain: A Survey and Analysis. 106–110. 5 indexed citations

12.

Greiner, Michael, et al.. (2003). A hybrid approach using the analytic hierarchy process and integer programming to screen weapon systems projects. IEEE Transactions on Engineering Management. 50(2). 192–203. 51 indexed citations

13.

Fowler, John, et al.. (2002). Manufacturing supply chain applications 2: parameterization of fast and accurate simulations for complex supply networks. Winter Simulation Conference. 1327–1336. 1 indexed citations

14.

Greiner, Michael, et al.. (2002). An Assessment of Air Force Development Portfolio Management Practices. Defense Technical Information Center (DTIC). 2 indexed citations

15.

Ferreira, Susan, et al.. (2001). Behavioral characterization: finding and using the influential factors in software process simulation models. Journal of Systems and Software. 59(3). 259–270. 15 indexed citations

16.

Roberts, Chell, et al.. (1998). An improved methodology for evaluating the producibility of partially specified part designs. International Journal of Computer Integrated Manufacturing. 11(2). 153–172. 8 indexed citations

17.

Shunk, Dan. (1992). Integrated Process Design and Development. Medical Entomology and Zoology. 10 indexed citations

18.

Billo, Richard E., et al.. (1988). Enhancing group technology modeling with database abstractions. Journal of Manufacturing Systems. 7(2). 95–106. 10 indexed citations

19.

Billo, Richard E., et al.. (1987). Integration of a group technology classification and coding system with an engineering database. Journal of Manufacturing Systems. 6(1). 37–45. 12 indexed citations

20.

Shunk, Dan. (1985). GROUP TECHNOLOGY PROVIDES ORGANIZED APPROACH TO REALIZING BENEFITS OF CIMS.. 21 indexed citations

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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