Managerial Insight and “Optimal” Algorithms

Work is increasingly being completed by humans and algorithms in collaboration. A relative strength of humans in this partnership is their insight: private information that is relevant to the task but not available to computerized systems. I introduce a flexible model of managerial insight that accepts any distribution of demand, an advantage over alternative models, and apply it to the newsvendor setting. The optimal policy in this setting is theoretically straightforward but difficult for managers to implement directly. I propose a novel method called FIND that leverages historical forecasts to convert a point estimate of demand into a conditional probability distribution. In eight experiments, FIND outperforms all other ordering regimes considered over a broad range of conditions. To model subtle, unstructured demand signals, the last four experiments convey managerial insight nonquantitatively using images, colors, and tones. FIND performs equally well with these perceptual signals as it does with more traditional numerical signals. This paper was accepted by Felipe Caro, Special Issue on the Human-Algorithm Connection. Funding: This research was supported by the National Science Foundation (Award No. 1729837) and the Research Grant Program of the Darla Moore School of Business, University of South Carolina. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03919 .