Daniel Kiefer

420 total citations
8 papers, 220 citations indexed

About

Daniel Kiefer is a scholar working on Industrial and Manufacturing Engineering, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Daniel Kiefer has authored 8 papers receiving a total of 220 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Industrial and Manufacturing Engineering, 3 papers in Artificial Intelligence and 2 papers in Management Science and Operations Research. Recurrent topics in Daniel Kiefer's work include Manufacturing Process and Optimization (3 papers), Industrial Vision Systems and Defect Detection (3 papers) and Forecasting Techniques and Applications (2 papers). Daniel Kiefer is often cited by papers focused on Manufacturing Process and Optimization (3 papers), Industrial Vision Systems and Defect Detection (3 papers) and Forecasting Techniques and Applications (2 papers). Daniel Kiefer collaborates with scholars based in Germany, Netherlands and Finland. Daniel Kiefer's co-authors include Michael Stupperich, Aldo Dagnino, John H R May, Dirk Muthig, Tuomo Kähkönen, Mikael Lindvall and Clemens van Dinther and has published in prestigious journals such as Computer, Journal of the Association for Information Systems and Procedia CIRP.

In The Last Decade

Daniel Kiefer

5 papers receiving 194 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Kiefer Germany	4	166	59	50	36	35	8	220
Michael Stupperich Germany	7	236 1.4×	57 1.0×	78 1.6×	51 1.4×	65 1.9×	7	281
Ömer Uludağ Germany	8	150 0.9×	78 1.3×	46 0.9×	55 1.5×	30 0.9×	16	225
Daniel Karlström Sweden	5	216 1.3×	50 0.8×	55 1.1×	29 0.8×	37 1.1×	7	264
Eva Amdahl Seim Norway	7	184 1.1×	61 1.0×	59 1.2×	48 1.3×	14 0.4×	9	264
Tuomo Kähkönen Finland	2	214 1.3×	51 0.9×	66 1.3×	44 1.2×	43 1.2×	2	234
Maarit Laanti Finland	9	248 1.5×	89 1.5×	108 2.2×	64 1.8×	21 0.6×	15	330
Muhammad Faisal Abrar Pakistan	9	115 0.7×	55 0.9×	25 0.5×	50 1.4×	46 1.3×	22	276
Hiva Alahyari Sweden	8	246 1.5×	69 1.2×	42 0.8×	68 1.9×	47 1.3×	8	316
Panagiota Chatzipetrou Sweden	11	173 1.0×	44 0.7×	30 0.6×	31 0.9×	35 1.0×	30	230
Jennifer Stapleton United Kingdom	5	207 1.2×	66 1.1×	40 0.8×	34 0.9×	44 1.3×	8	266

Countries citing papers authored by Daniel Kiefer

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Kiefer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Kiefer

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

All Works

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8 of 8 papers shown

Kiefer, Daniel, et al.. (2025). A Framework for Explainable Root Cause Analysis in Manufacturing Systems – Combining Machine Learning, Explainable Artificial Intelligence and the Ishikawa Model for Industrial Manufacturing. Proceedings of the ... Annual Hawaii International Conference on System Sciences.

Kiefer, Daniel, et al.. (2024). Speeding Up CNC Tool Manufacturing: Implementing Explainable AI for Setup Time Reduction and Production Agility. Procedia CIRP. 130. 982–987.

Kiefer, Daniel, et al.. (2024). Automatic Gear Tooth Alignment in Vision Based Preventive Maintenance. Procedia Computer Science. 232. 1564–1572. 1 indexed citations

Kiefer, Daniel, et al.. (2024). Anomaly Detection in Hobbing Tool Images: Using An Unsupervised Deep Learning Approach in Manufacturing Industry. Procedia Computer Science. 232. 2396–2405.

Kiefer, Daniel, et al.. (2022). Artificial Intelligence in Supply Chain Management: Investigation of Transfer Learning to Improve Demand Forecasting of Intermittent Time Series with Deep Learning. Proceedings of the ... Annual Hawaii International Conference on System Sciences. 3 indexed citations

Kiefer, Daniel, et al.. (2021). Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods. Proceedings of the ... Annual Hawaii International Conference on System Sciences. 13 indexed citations

Dinther, Clemens van, et al.. (2020). Machine Learning in SME: An Empirical Study on Enablers and Success Factors. Journal of the Association for Information Systems. 26 indexed citations

Lindvall, Mikael, Dirk Muthig, Aldo Dagnino, et al.. (2004). Agile software development in large organizations. Computer. 37(12). 26–34. 177 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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