Methodology & Assumptions

How the directory decides who is included and how the productivity and impact rankings are computed. Written for a technical audience.

Key parameters

Last data pull
2026-06-24
Schools tracked
151
Tenure-track OM/SCM faculty
1603
Disciplines included
Operations, Supply Chain Management, Decision Sciences
Min. OM/SCM TT faculty per school (leaderboard)
5
Min. alumni per PhD program (leaderboard)
6
Min. UTD papers for a faculty impact h-index
3
Min. years since PhD (productivity leaderboard)
4
Leaderboard display cap (medals: top 30)
100

These values are generated from the live build configuration.

Data quality & validation

To validate completeness, we audit our publication data against the primary source of truth — faculty members' own CVs. For every faculty member with a linked CV, we extract the UTD-24 journal papers they list and check how many appear in our directory.

93.3% of the UTD-24 papers listed on faculty CVs are captured here (5,681 papers checked across 623 faculty CVs, as of 2026-06-18).

The small remainder is dominated by papers not yet indexed by our upstream sources, plus a handful of CV/source title mismatches.

Citation counts cross-checked against Scopus

Our impact metric is built on OpenAlex citation counts. To confirm they are sound, we compare them against a second, independent source — Elsevier Scopus — for the 351 OM/SCM faculty indexed in both. The two services rank faculty by total citations almost identically (Spearman ρ = 0.9395; Pearson on log-counts = 0.9175, as of 2026-06-19).

The two sources index different corpora, so absolute totals differ (Scopus typically runs lower); the agreement that matters here is in the ranking of faculty, which is what the impact metric depends on.

Overview

This directory tracks tenure-track Operations & Supply Chain Management (OM/SCM) faculty and ranks schools, PhD programs, and individuals on their research productivity and impact. This page documents the assumptions behind those numbers. Exact threshold values are shown in the Key parameters box above and are read directly from the live configuration.

Who is part of the directory

The directory focuses on business school faculty. Industrial Engineering departments are currently not tracked. The starting point for building the directory was the list of UTD 100 business schools. I subtracted a few schools from the list if they did not have a clearly distinguishable OM/SCM department, and added many schools, particularly outside of the United States.

In terms of areas within a school, I focused on operations management, operations research, supply chain management and decision analysis. Information systems faculty are currently not tracked.

The directory focuses on active faculty members. Emeritus and retired faculty members are not tracked. Only tenure track faculty are included. Faculty that request privacy have their information hidden. Note that whether a faculty member is tenure track or not is determined by their title on their department web page, which can lead to classification errors.

Data sources

  • Publications and citations come primarily from OpenAlex. Where a faculty member is better represented by Google Scholar, that source is used instead. Each record links to a specific author profile; we cache the full per-paper history (title, year, venue, citation count, co-authors).
  • A data pull is a refresh of this publication data from the sources above. The date of the most recent pull is shown in the Key parameters box; all time-based metrics (e.g. citations per year) are measured relative to the pull year.
  • Schools are seeded from the UTD Top-100 Business School Research Rankings set.
  • The ground truth for faculty information are department faculty web pages.

Journals counted

  • Productivity and impact are computed over the UTD-24 list of premier business journals.
  • A broader OM journal list is surfaced on faculty pages for context but is not part of the ranking metrics. Practitioner outlets are excluded from the metrics.
  • Journal names are matched case-insensitively after normalizing punctuation.

Productivity metric

For each faculty member, productivity is:

UTD-24 publications ÷ (data-pull year − PhD year) — i.e. premier-journal output per year since the PhD.

  • A school's productivity is the median of this value across its tenure-track OM/SCM faculty; a PhD program's is the median across its alumni in the directory.
  • The faculty productivity leaderboard requires a minimum number of years since the PhD (see Key parameters) so that very recent graduates are not ranked on a tiny denominator.
  • Productivity does not take into account the number of authors on a paper; it also does not factor in leaves and extensions granted to faculty members.

Impact metric

Impact is built on citations per year (CPY) of a paper:

CPY = citations ÷ max(1, data-pull year − publication year) — current-year papers get a one-year exposure floor.

  • A faculty member's impact is a fractional h-index over the CPY of their UTD-24 papers. The integer part is the classic h-index — the largest h such that h of their papers are each cited at least h times per year — and the fraction interpolates toward the next level (see below). It is only shown once they have at least the minimum number of UTD papers (Key parameters).
  • A school's (or PhD program's) impact is a fractional faculty-level h-index: the largest k such that k of its tenure-track OM/SCM faculty (or alumni) each have a personal impact score of at least k, again interpolated. This rewards genuine depth of high-impact faculty while not being inflated by a large roster of low-impact ones, and it cannot be carried by a single superstar.

Both levels use the same fractional (interpolated) h-index, described next.

Why a fractional h-index

A plain integer h-index produces heavy ties: dozens of faculty all sit at h = 10 and share a single rank, and the same happens to schools. The fractional h-index turns that step function into a continuous value in the range [h, h+1), so entities are ranked individually and the displayed number itself reflects how close they are to reaching the next whole level.

How it is computed

Sort the values — a faculty member's paper CPYs, or a school's per-member impact scores — in descending order as v1 ≥ v2 ≥ … ≥ vn. Take the integer h-index h (the largest h with v_h ≥ h). Then interpolate between the two boundary values: the one that qualifies (rank h) and the one that just misses (rank h+1). Drawing the straight line between those two points and taking where it crosses the diagonal y = x gives:

h_frac = (v_h + d·h) / (1 + d), where d = v_h − v_(h+1) is the gap between the two boundary values.

The result always lies in [h, h+1): if the next value is far below the bar, h_frac stays near h; if it nearly qualifies, h_frac approaches h+1. When every value qualifies (there is no entry beyond rank h), h_frac = h.

Worked example. Suppose a faculty member's paper CPYs, sorted, are 40, 22, 15, 11, 9.5, 8, 4. The integer h-index is 6 (the 6th value, 8, is ≥ 6, but the 7th, 4, is < 7). With v_h = 8 and v_(h+1) = 4, the gap is d = 4, so h_frac = (8 + 4·6) / (1 + 4) = 32 / 5 =6.4. Had that 7th paper instead had a CPY of 6 (closer to qualifying), d = 2 and h_frac = (8 + 2·6) / 3 =6.67 — a higher score, reflecting that the next level is within closer reach.

Leaderboard display & ranking

  • School and PhD leaderboards offer a toggle between productivity and impact.
  • Rankings use competition ranking: tied entities share a rank, and the next distinct value skips ahead. The number of rows displayed is capped (Key parameters); medals are awarded to the top 30 on each metric.

PhD-program attribution

  • Faculty are grouped into PhD programs by their doctorate-granting institution, after canonicalizing name variants (e.g. school-of-management names) to a single institution.
  • A program needs at least the minimum number of alumni in the directory (Key parameters) to appear on the PhD leaderboard.

Reviewer Finder — matching by abstract

The Reviewer Finder helps editors locate conflict-free potential reviewers for a manuscript. Alongside matching by topical/methodological tags, it offers a by abstract mode: paste a manuscript's title and abstract and it returns directory faculty ranked by how closely their own published work resembles it.

  • How the matching works. Every UTD-24 / OM-journal paper with an available abstract is converted once into a sentence embedding — a list of numbers that captures the abstract's meaning — using a compact open language model (a MiniLM sentence transformer). When you submit a manuscript, the same model embeds your abstract and each faculty member is scored by the cosine similarity between your manuscript and their single closest paper. A score near 1 means the manuscript and that paper sit very close together in meaning; the ranked list is sorted by this score.
  • Nothing leaves your browser. The language model runs entirely client-side: the pasted manuscript is embedded locally and compared against precomputed paper vectors that ship with the site. No abstract text is uploaded to any server.
  • Conflicts of interest are excluded exactly as in tag matching — anyone sharing an institution, a co-authorship, or a PhD program with a listed author is removed from the results.
  • Optional restrictions. You can additionally narrow the ranked list to reviewers who work on selected topical/methodological tags and/or who have published in a named journal. These act as filters on top of the similarity ranking.

Limitations. Similarity is computed only over papers in the premier/OM-journal lists that have an abstract on record, so reviewers whose relevant work sits outside those journals, or whose abstracts are missing, may be under-ranked. The score reflects topical and semantic closeness, not a paper's quality or a reviewer's availability, and the embedding model is a small general-purpose one — the ranking is a starting point for editorial judgment, not a substitute for it.

Known limitations

  • OpenAlex sometimes misses publications. I try to catch them through co-authors, but if a paper is missing from your record, claim your profile on OpenAlex, and keep it up to date.
  • I have missed many departments and faculty. If you feel left out, my apologies. Hit the contact button and let me know. I will quickly remedy the situation.

Acknowledgements

I thank my wife, Min, for always supporting me, and my daughter Thalia, for being an amazing teenager during our time in Lisbon (where this site was created). I also thank my colleagues at Nova University for their warm welcome and support. I am grateful to the people of Portugal for welcoming us to their wonderful country for a year.

I would also like to thank Evgeny Kagan, Kostas Stouras, Blair Flicker, Charles Corbett, Jordan Tong, Ioannis Stamatopoulos, Beril Toktay, Elena Katok, Tinglong Dai, Atalay Atasu and the countless others who provided feedback and helped with getting this site up and running.