John E. Boylan

4.9k total citations
79 papers, 3.1k citations indexed

About

John E. Boylan is a scholar working on Management Science and Operations Research, Management Information Systems and Statistics, Probability and Uncertainty. According to data from OpenAlex, John E. Boylan has authored 79 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Management Science and Operations Research, 29 papers in Management Information Systems and 18 papers in Statistics, Probability and Uncertainty. Recurrent topics in John E. Boylan's work include Forecasting Techniques and Applications (58 papers), Supply Chain and Inventory Management (28 papers) and Advanced Statistical Process Monitoring (18 papers). John E. Boylan is often cited by papers focused on Forecasting Techniques and Applications (58 papers), Supply Chain and Inventory Management (28 papers) and Advanced Statistical Process Monitoring (18 papers). John E. Boylan collaborates with scholars based in United Kingdom, France and China. John E. Boylan's co-authors include Aris Syntetos, Freya Johnston, Argyrios Syntetos, Mohammad Ali, Κωνσταντίνος Νικολόπουλος, M. Zied Babaï, Stephan Kolassa, Robert Fildes, Stephen Michael Disney and Estelle Shale and has published in prestigious journals such as Applied Energy, European Journal of Operational Research and International Journal of Production Economics.

In The Last Decade

John E. Boylan

79 papers receiving 2.9k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John E. Boylan United Kingdom	29	2.4k	1.3k	734	445	422	79	3.1k
Aris Syntetos United Kingdom	35	2.5k 1.0×	1.9k 1.4×	758 1.0×	917 2.1×	336 0.8×	109	4.0k
M. Zied Babaï France	29	1.1k 0.5×	1.0k 0.8×	331 0.5×	493 1.1×	164 0.4×	77	2.1k
Zhe George Zhang United States	29	878 0.4×	1.1k 0.8×	183 0.2×	277 0.6×	628 1.5×	103	3.4k
Nikolaos Kourentzes United Kingdom	29	1.6k 0.7×	487 0.4×	237 0.3×	139 0.3×	548 1.3×	78	2.5k
Kaveh Khalili‐Damghani Iran	34	1.4k 0.6×	387 0.3×	190 0.3×	524 1.2×	109 0.3×	149	3.0k
Xiao‐Yue You China	18	1.1k 0.5×	229 0.2×	368 0.5×	529 1.2×	123 0.3×	24	2.3k
Wenyan Song China	30	1.2k 0.5×	302 0.2×	419 0.6×	920 2.1×	81 0.2×	77	2.7k
Jiang Wu China	36	1.6k 0.7×	800 0.6×	92 0.1×	935 2.1×	123 0.3×	116	3.0k
Chung‐Piaw Teo Singapore	37	912 0.4×	1.5k 1.2×	122 0.2×	863 1.9×	204 0.5×	130	4.2k

Countries citing papers authored by John E. Boylan

Since Specialization

Citations

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

Fields of papers citing papers by John E. Boylan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John E. Boylan. 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 John E. Boylan. The network helps show where John E. Boylan may publish in the future.

Co-authorship network of co-authors of John E. Boylan

This figure shows the co-authorship network connecting the top 25 collaborators of John E. Boylan. A scholar is included among the top collaborators of John E. Boylan 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 John E. Boylan. John E. Boylan 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

Kourentzes, Nikolaos, et al.. (2025). The importance of forecast uncertainty in understanding the Bullwhip effect. International Journal of Production Research. 1–22. 1 indexed citations

Xie, Naiming, et al.. (2024). Forecasting seasonal demand for retail: A Fourier time-varying grey model. International Journal of Forecasting. 40(4). 1467–1485. 24 indexed citations

Xie, Naiming, et al.. (2024). Time-varying polynomial grey prediction modeling with integral matching. Knowledge-Based Systems. 290. 111581–111581. 4 indexed citations

Svetunkov, Ivan & John E. Boylan. (2023). Staying positive: challenges and solutions in using pure multiplicative ETS models. IMA Journal of Management Mathematics. 35(3). 403–425. 1 indexed citations

Boylan, John E., et al.. (2023). A time-expanded network design model for staff allocation in mail centres. Journal of the Operational Research Society. 75(10). 1949–1964. 1 indexed citations

Kourentzes, Nikolaos, et al.. (2022). Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation. Omega. 110. 102614–102614. 11 indexed citations

Teunter, Ruud, et al.. (2018). Forecasting and Inventory Control with Compound Poisson Demand Using Periodic Demand Data. University of Groningen research database (University of Groningen / Centre for Information Technology). 2018(10). 1 indexed citations

Syntetos, Aris, et al.. (2015). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research. 252(1). 1–26. 188 indexed citations

Ali, Mohammad & John E. Boylan. (2010). The value of forecast information sharing in the supply chain. Lancaster EPrints (Lancaster University). 5 indexed citations

10.

Νικολόπουλος, Κωνσταντίνος, Aris Syntetos, John E. Boylan, Fotios Petropoulos, & Vassilios Assimakopoulos. (2010). An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis. Journal of the Operational Research Society. 62(3). 544–554. 111 indexed citations

11.

Ali, Mohammad & John E. Boylan. (2010). Feasibility principles for Downstream Demand Inference in supply chains. Journal of the Operational Research Society. 62(3). 474–482. 23 indexed citations

12.

Syntetos, Aris, John E. Boylan, & Stephen Michael Disney. (2009). Forecasting for inventory planning: a 50-year review. Journal of the Operational Research Society. 60(sup1). S149–S160. 103 indexed citations

13.

Syntetos, Aris, Κωνσταντίνος Νικολόπουλος, & John E. Boylan. (2009). Judging the judges through accuracy-implication metrics: The case of inventory forecasting. International Journal of Forecasting. 26(1). 134–143. 81 indexed citations

14.

Boylan, John E., et al.. (2006). Classification for forecasting and stock control: a case study. Journal of the Operational Research Society. 59(4). 473–481. 112 indexed citations

15.

Boylan, John E.. (2005). Intermittent and Lumpy Demand: A Forecasting Challenge. Lancaster EPrints (Lancaster University). 36–42. 6 indexed citations

16.

Syntetos, Aris, et al.. (2004). On the categorization of demand patterns. Journal of the Operational Research Society. 56(5). 495–503. 267 indexed citations

17.

Syntetos, Aris & John E. Boylan. (2004). The accuracy of intermittent demand estimates. International Journal of Forecasting. 21(2). 303–314. 312 indexed citations

18.

Boylan, John E.. (2003). Intermittent demand forecasting : size-interval methods based on averaging and smoothing. Lancaster EPrints (Lancaster University). 7 indexed citations

19.

Syntetos, Argyrios & John E. Boylan. (2001). On the bias of intermittent demand estimates. International Journal of Production Economics. 71(1-3). 457–466. 242 indexed citations

20.

Johnston, Freya & John E. Boylan. (1994). How Far Ahead Can an EWMA Model be Extrapolated?. Journal of the Operational Research Society. 45(6). 710–713. 5 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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