INFORMS Open Forum

Management Science Updates: July 2022

  • 1.  Management Science Updates: July 2022

    Posted 22 days ago
      |   view attached

    Dear Colleagues and Friends,

    So far, this summer has been incredible, with a few in-person conferences where I had the opportunity to report and discuss the state of the journal. It is in that spirit that I am providing an update on Management Science by focusing on a few developments.

    Journal Statistics and Impact: The number of new submissions in the first two quarters of 2022, including both Regular and Fast Track papers, is slightly below the same period last year and is projected to be around 4,000 submissions this year. The largest departments in terms of submission volume include Finance, Behavioral Economic and Decision Analysis, Operations Management, and Accounting.

    Authors benefit from relatively short cycle times. Indeed, the average time to first decision is 64 days for regular papers and 28 days for Fast Track papers. Excluding papers that are desk rejected, either by a Department Editor or an Associate Editor, the median time to first decision is 83 days and 109 days to final decision, far shorter than previous years.

    Interest in papers published by Management Science has increased dramatically; the number of papers downloaded in the first six months of the year doubled relative to the same period last year. Finally, with the help of INFORMS, we have reduced the publication backlog significantly to less than 9 months; see Figures below.

    The journal 2-year and 5-year Impact Factors have both increased significantly, as you can see in the charts below. Specifically, whereas the 2-Year Impact Factor was 4.883 in 2020, it is 6.172 in 2021. Similarly, the 5-year Impact Factor increased from 6.619 in 2020 to 7.772 in 2021. Improvements are also observed by looking at the 2022 Google Scholar Metrics. Indeed, Management Science's h5-index has increased from 103 last year to 109 this year and the h5-median has increased from 145 to 165. These two metrics count citation for papers published in the last five complete calendar years. Importantly, Management Science still is ranked very high when compared with all 24 journals on the University of Texas Dallas' Naveen Jindal School of Management List of Journals, see Table below.

    The Replication Project: As you may recall, the editorial board initiated a replicability project with the objective to report replicability of laboratory experiments published by Management Science.

    A team of eight academics with significant experience in behavioral operations committed to addressing the replicability challenge. The team includes members from five institutes with established labs, which allowed us to conduct each replication in multiple locations. The faculty involved include Andrew Davis, Cornell University, Blair Flicker, University of South Carolina, Kyle Hyndman and Elena Katok, The University of Texas at Dallas, Samantha Keppler and Stephen Leider, University of Michigan, and Xiaoyang Long and Jordan Tong, University of Wisconsin.

    The team collected survey results from the community in which they asked participants to vote for the papers they would like to see replicated.  The papers were in the following five areas: Inventory Management, Supply Chain Contracts, Queueing, Forecasting, and Sourcing.  The team chose two papers with the highest number of votes from each category, for a total of 10 papers. Each paper was scheduled to be replicated at two different sites.

    I am pleased to report that the results of the replication study are now available on As you can see, out of 10 papers, six were completely replicated, one did not replicate at all, two were partially replicated (only one site was able to confirm the results), and one paper requires additional experiments.

    A New Challenge: As you know, in 2019 Management Science implemented a Data and Code Disclosure policy that requires the disclosure of data and code associated with accepted manuscripts. Since the establishment of the policy, the journal has archived data and code from more than 350 papers. The code and data review process verifies that all necessary information is provided to allow for replication but does not undertake the replications themselves.

    • The editorial board would like to publish a paper, likely a Fast Track paper, that evaluates the impact of the Data and Code Disclosure policy by attempting to replicate a sample of the archived papers. We are looking for volunteers who will attempt to replicate papers and tracking their replication efforts and results.

    If you have suggestions or comments regarding this new replicability project, please feel free to contact me or the journal's Code & Data Editor.

    Finally, I would like to close with a brief story I received from a member of our community, Anton Ovchinnikov, about a recent Fast Track paper experience:

    "In 2020 we submitted a full-length paper titled "Customization and Returns" co-authored with Gokce Esenduran and Paolo Letizia, to the Marketing department of Management Science. Two reviewers were both positive, noting that the paper was well-motivated and contained one nice core result. However, they both were concerned that a paper with just one result does not have enough contribution, and proposed to check/implement multiple things. The AE did a good job summarising the suggestions and commenting on a few other possibilities to improve and extend the paper. The DE, however, gave us two alternatives: (i) do the requested analyses and submit a full-length revision, or (ii) shorten the paper to "6000 words" focusing on that one core result that everyone liked, and submit the paper as a Fast Track. This was an unexpected outcome, but we were intrigued, and chose the second option. The "second round" was successful, took just a few weeks, and we again received extraordinary feedback on how to present our results even better. For instance, instead of presenting a formal proposition we provided the optimal solution in a Table in the main text and then stated and proved the formal proposition in the Appendix. This saved space and made the results easier to digest.

    Further, upon acceptance, the paper also was typeset very quickly which was a nice surprise, see Customization and Returns | Management Science (

    More so, we also published an "HBR version" of the paper: We sent the accepted paper to the HBR editors asking if they'd be interested in publishing a managerial piece on this topic, and they responded positively. Product returns were always a problem, but during COVID, when so much shopping went online, returns skyrocketed and firms were looking for innovative ideas to curb them – our paper provided one such idea. Here is the link to the online version A shorter version should also appear in the print edition, but I've not seen it yet."

    I wish you all a great summer!

    David Simchi-Levi


    Management Science


    Acknowledgement: Thanks go to Tinglong Dai from John Hopkins University for helping with the data on Impact Factor and Google Schooler Metrics. For your convenience, I am also attaching a pdf version of this blog. 


    Table: Google Scholar Metric for Journals on the UTD-List





    Journal of Financial Economics



    The Review of Financial Studies



    Management Science



    Journal of Finance



    Strategic Management Journal



    Academy of Management Journal



    Journal of International Business Studies



    MIS Quarterly



    Academy of Management Review



    The Accounting Review



    Journal of Consumer Research



    Journal of Marketing



    Journal of Accounting and Economics



    Administrative Science Quarterly



    Manufacturing and Service Operations Management



    Journal of Marketing Research



    Production and Operations Management



    Organization Science



    Journal of Accounting Research



    Operations Research



    Information Systems Research



    Marketing Science



    Journal of Operations Management



    Journal on Computing




    Data Source: English - Google Scholar Metrics




    David Simchi-Levi
    Professor of Engineering Systems
    Massachusetts Institute of Technology
    Cambridge MA