So, our paper with Anna Sroginis got rejected from a special issue of the International Journal of Production Research after a second round of revision. And here is what I think about this!

First things first, why am I writing this post? I want to share failures with the community, because I am tired of all the success stories. It is okay not to win, and this happens much more often than it seems.

Now, about the paper. In the first round of revisions, four reviewers looked at it and provided their comments. We expanded the paper accordingly, making it now 46 pages long (ouch!). We introduced inventory simulations and showed how using some basic principles improves forecasting accuracy and can lead to a reduction in inventory costs.

In the second round, the AE added one more reviewer. After careful consideration, two of the reviewers recommended major revisions, while the other two suggested a strong rejection, claiming that the paper does not make new and significant contributions to the production research literature.

Obviously, I disagree with this evaluation. Based on the reviewers’ comments, I have a feeling they didn’t read the paper in full (their main concerns relate to Section 3, and some of these could have been resolved if they had reached Section 5). But this probably also means that the paper in its current state is too big and needs to be rewritten to become more focused. Maybe this is what confused the reviewers.

So, what’s next?

We will amend it to address the reviewers’ comments, shorten it a bit to make it more focused, and then submit to another OR-related journal.

And while we are doing that, I have updated the arXiv version of the paper to show what we did after the first round, and here is a brief summary of the main findings:

• Using a stockout dummy variable and capturing the level of data correctly (removing the effect of stockouts) improves the accuracy of forecasting approaches;
• The stockouts detection should be done for both the training and the test sets. If the series with stockouts are not removed from the test set, the forecasts would be evaluated incorrectly;
• Splitting the demand into demand sizes and demand occurrence, producing forecasts for each of the parts and then combining the result substantially improves the accuracy;
• Using the feature for regular/intermittent demand improves the forecasting accuracy, but does not seem to impact the inventory performance. Note that this separation is straightforward in AID: if after removing the stockouts, there are some zeroes left, the demand is identified as intermittent;
• The further split into smooth/lumpy leads to slight improvements in terms of accuracy, without a substantial impact on the inventory;
• The split into count/fractional demand does not bring value in terms of forecasting accuracy or inventory performance.