Jon Bokrantz

1.4k total citations
33 papers, 849 citations indexed

About

Jon Bokrantz is a scholar working on Management Information Systems, Industrial and Manufacturing Engineering and Strategy and Management. According to data from OpenAlex, Jon Bokrantz has authored 33 papers receiving a total of 849 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Management Information Systems, 18 papers in Industrial and Manufacturing Engineering and 9 papers in Strategy and Management. Recurrent topics in Jon Bokrantz's work include Quality and Supply Management (16 papers), Digital Transformation in Industry (13 papers) and Quality and Safety in Healthcare (7 papers). Jon Bokrantz is often cited by papers focused on Quality and Supply Management (16 papers), Digital Transformation in Industry (13 papers) and Quality and Safety in Healthcare (7 papers). Jon Bokrantz collaborates with scholars based in Sweden, United States and Brazil. Jon Bokrantz's co-authors include Anders Skoogh, Johan Stahre, Cecilia Berlin, Thorsten Wuest, Mukund Subramaniyan, Jan Dul, Maheshwaran Gopalakrishnan, Azam Sheikh Muhammad, Björn Johansson and Christoph Roser and has published in prestigious journals such as International Journal of Production Economics, International Journal of Operations & Production Management and Computers & Industrial Engineering.

In The Last Decade

Jon Bokrantz

33 papers receiving 812 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jon Bokrantz Sweden 17 422 280 211 163 114 33 849
Irene Roda Italy 13 305 0.7× 115 0.4× 165 0.8× 95 0.6× 54 0.5× 41 651
Umar M. Al‐Turki Saudi Arabia 15 260 0.6× 214 0.8× 177 0.8× 219 1.3× 51 0.4× 34 712
Peter Söderholm Sweden 14 124 0.3× 155 0.6× 119 0.6× 250 1.5× 94 0.8× 49 715
Georges Abdul-Nour Canada 17 381 0.9× 120 0.4× 158 0.7× 78 0.5× 29 0.3× 66 846
Jos A.C. Bokhorst Netherlands 15 376 0.9× 198 0.7× 131 0.6× 137 0.8× 24 0.2× 34 687
Dorota Stadnicka Poland 17 352 0.8× 257 0.9× 227 1.1× 43 0.3× 26 0.2× 68 796
Laura Swanson United States 6 99 0.2× 242 0.9× 247 1.2× 246 1.5× 160 1.4× 7 730
Katerina Lepenioti Greece 7 218 0.5× 167 0.6× 78 0.4× 58 0.4× 36 0.3× 15 593
Marco Frosolini Italy 20 350 0.8× 495 1.8× 307 1.5× 224 1.4× 83 0.7× 48 1.3k
Tore Markeset Norway 22 93 0.2× 216 0.8× 189 0.9× 387 2.4× 76 0.7× 78 1.3k

Countries citing papers authored by Jon Bokrantz

Since Specialization
Citations

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

Fields of papers citing papers by Jon Bokrantz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jon Bokrantz

This figure shows the co-authorship network connecting the top 25 collaborators of Jon Bokrantz. A scholar is included among the top collaborators of Jon Bokrantz 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 Jon Bokrantz. Jon Bokrantz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chen, Siyuan, et al.. (2024). Enhancing Digital Twins With Deep Reinforcement Learning: A Use Case in Maintenance Prioritization. Lund University Publications (Lund University). 1611–1622. 1 indexed citations
2.
Bekar, Ebru Turanoğlu, et al.. (2023). Understanding Stakeholder Requirements for Digital Twins In Manufacturing Maintenance. Chalmers Research (Chalmers University of Technology). 2008–2019. 4 indexed citations
3.
Bekar, Ebru Turanoğlu, et al.. (2023). Data-Driven Smart Maintenance Decision Analysis: A Drone Factory Demonstrator Combining Digital Twins and Adapted AHP. Chalmers Research (Chalmers University of Technology). 1996–2007. 1 indexed citations
4.
Bekar, Ebru Turanoğlu, et al.. (2023). Domain Knowledge in CRISP-DM: An Application Case in Manufacturing. IFAC-PapersOnLine. 56(2). 7603–7608. 3 indexed citations
5.
Bokrantz, Jon & Jan Dul. (2022). Building and testing necessity theories in supply chain management. Journal of Supply Chain Management. 59(1). 48–65. 51 indexed citations
6.
Bokrantz, Jon, et al.. (2022). Improved root cause analysis supporting resilient production systems. Journal of Manufacturing Systems. 64. 468–478. 16 indexed citations
7.
Bokrantz, Jon & Anders Skoogh. (2022). Adoption patterns and performance implications of Smart Maintenance. International Journal of Production Economics. 256. 108746–108746. 10 indexed citations
8.
Gullander, Per, et al.. (2021). Dealing with resistance to the use of Industry 4.0 technologies in production disturbance management. Journal of Manufacturing Technology Management. 32(9). 285–303. 17 indexed citations
9.
Gullander, Per, et al.. (2021). Prioritisation of root cause analysis in production disturbance management. International Journal of Quality & Reliability Management. 39(5). 1133–1150. 5 indexed citations
10.
Subramaniyan, Mukund, et al.. (2021). Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions. Journal of Manufacturing Systems. 60. 734–751. 39 indexed citations
11.
Subramaniyan, Mukund, Anders Skoogh, Azam Sheikh Muhammad, et al.. (2020). A data-driven approach to diagnosing throughput bottlenecks from a maintenance perspective. Computers & Industrial Engineering. 150. 106851–106851. 21 indexed citations
12.
Bokrantz, Jon, et al.. (2020). Development of digitalised maintenance – a concept. Journal of Quality in Maintenance Engineering. 28(2). 367–390. 15 indexed citations
13.
Bokrantz, Jon, et al.. (2020). Performance indicators for measuring the effects of Smart Maintenance. International Journal of Productivity and Performance Management. 70(6). 1291–1316. 20 indexed citations
14.
Bokrantz, Jon, Anders Skoogh, Cecilia Berlin, Thorsten Wuest, & Johan Stahre. (2019). Smart Maintenance: an empirically grounded conceptualization. International Journal of Production Economics. 223. 107534–107534. 100 indexed citations
15.
Bokrantz, Jon, Anders Skoogh, Cecilia Berlin, Thorsten Wuest, & Johan Stahre. (2019). Smart Maintenance: a research agenda for industrial maintenance management. International Journal of Production Economics. 224. 107547–107547. 83 indexed citations
16.
Skoogh, Anders, et al.. (2017). Identification of maintenance improvement potential using OEE assessment. International Journal of Productivity and Performance Management. 66(1). 126–143. 55 indexed citations
17.
Bokrantz, Jon, et al.. (2017). Data quality problems in discrete event simulation of manufacturing operations. SIMULATION. 94(11). 1009–1025. 28 indexed citations
18.
Bokrantz, Jon. (2017). On the Transformation of Maintenance Organisations in Digitalised Manufacturing. Chalmers Research (Chalmers University of Technology). 3 indexed citations
19.
20.
Gopalakrishnan, Maheshwaran, et al.. (2015). Planning of Maintenance Activities – A Current State Mapping in Industry. Procedia CIRP. 30. 480–485. 16 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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