Mahya Seyedan

564 total citations · 1 hit paper
7 papers, 307 citations indexed

About

Mahya Seyedan is a scholar working on Management Information Systems, Management Science and Operations Research and Strategy and Management. According to data from OpenAlex, Mahya Seyedan has authored 7 papers receiving a total of 307 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Management Information Systems, 4 papers in Management Science and Operations Research and 2 papers in Strategy and Management. Recurrent topics in Mahya Seyedan's work include Supply Chain and Inventory Management (4 papers), Forecasting Techniques and Applications (4 papers) and Stock Market Forecasting Methods (3 papers). Mahya Seyedan is often cited by papers focused on Supply Chain and Inventory Management (4 papers), Forecasting Techniques and Applications (4 papers) and Stock Market Forecasting Methods (3 papers). Mahya Seyedan collaborates with scholars based in Iran, Canada and China. Mahya Seyedan's co-authors include Fereshteh Mafakheri, Chun Wang, Amir‐Reza Abtahi and Omid Mahdi Ebadati E. and has published in prestigious journals such as SHILAP Revista de lepidopterología, Annals of Operations Research and Journal Of Big Data.

In The Last Decade

Mahya Seyedan

7 papers receiving 276 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

2020 233 citations Mahya Seyedan, Fereshteh Mafakheri Journal Of Big Data profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mahya Seyedan Iran	5	132	93	74	44	34	7	307
Kajla Basu India	10	77 0.6×	121 1.3×	84 1.1×	44 1.0×	50 1.5×	33	318
Süleyman Ersöz Türkiye	9	51 0.4×	70 0.8×	61 0.8×	39 0.9×	50 1.5×	46	280
Patrick Brandtner Austria	8	66 0.5×	60 0.6×	43 0.6×	36 0.8×	72 2.1×	34	279
Alireza Haji Iran	11	157 1.2×	49 0.5×	130 1.8×	139 3.2×	24 0.7×	29	344
Avi Giloni United States	9	164 1.2×	86 0.9×	98 1.3×	40 0.9×	41 1.2×	26	323
Mazaher Ghorbani Iran	10	125 0.9×	159 1.7×	169 2.3×	52 1.2×	30 0.9×	20	377
Adel Pourghader Chobar Iran	12	64 0.5×	42 0.5×	119 1.6×	57 1.3×	13 0.4×	27	281
Konstantia Litsiou United Kingdom	5	84 0.6×	98 1.1×	58 0.8×	68 1.5×	21 0.6×	7	256
Sumit Sakhuja India	6	59 0.4×	145 1.6×	111 1.5×	30 0.7×	29 0.9×	11	320
Young H. Chun United States	12	143 1.1×	144 1.5×	60 0.8×	94 2.1×	49 1.4×	46	481

Countries citing papers authored by Mahya Seyedan

Since Specialization

Citations

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

Fields of papers citing papers by Mahya Seyedan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mahya Seyedan

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

All Works

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7 of 7 papers shown

1.

Seyedan, Mahya, Fereshteh Mafakheri, & Chun Wang. (2023). Order-up-to-level inventory optimization model using time-series demand forecasting with ensemble deep learning. SHILAP Revista de lepidopterología. 3. 100024–100024. 23 indexed citations

2.

Seyedan, Mahya, Fereshteh Mafakheri, & Chun Wang. (2022). Cluster-based demand forecasting using Bayesian model averaging: An ensemble learning approach. Decision Analytics Journal. 3. 100033–100033. 30 indexed citations

3.

Seyedan, Mahya & Fereshteh Mafakheri. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal Of Big Data. 7(1). 233 indexed citations breakdown →

4.

Abtahi, Amir‐Reza, et al.. (2018). Supply chain performance evaluation using fuzzy network data envelopment analysis: a case study in automotive industry. Annals of Operations Research. 275(2). 461–484. 14 indexed citations

5.

Seyedan, Mahya, et al.. (2016). Urban green spaces assessment approach to health, safety and environment. SHILAP Revista de lepidopterología. 5 indexed citations

6.

Seyedan, Mahya, et al.. (2016). Implementation of child safety and health management system by means of FMEA method. SHILAP Revista de lepidopterología. 1 indexed citations

7.

Seyedan, Mahya, et al.. (2015). Performance analysis and forecasting on Crude Oil: Novel support vector regression application to market demand. 126–130. 1 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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