Lost sales inventory models with large lead times, which arise in many practical settings, are notoriously difficult to optimize due to the curse of dimensionality. In this paper we show that when lead times are large, a very simple constant-order policy, first studied by Reiman [39], performs nearly optimally. The main insight of our work is that when the lead time is very large, such a significant amount of randomness is injected into the system between when an order for more inventory is placed and when the order is received, that "being smart" algorithmically provides almost no benefit. Our main proof technique combines a novel coupling for suprema of random walks with arguments from queueing theory.

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More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of these drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs. In many cases, there is already evidence of efficacy for cancer, but trying to manually extract such evidence from the scientific literature is intractable. In this emerging applications paper, we introduce a system to automate non-cancer generic drug evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language processing techniques and plan to treat them as baseline approaches for future improvement of individual components.

ften there is substantial disparity in sales performance across various units of an organization. It is crucial to model the effects of various drivers/inhibitors on sales performance, particularly those that can be acted upon, since insight into such drivers/inhibitors is essential for determining optimal actions for improving performance. We present a framework for sales performance diagnostics which focuses on: (1) modeling and quantifying the effects of various factors on multiple sales performance metrics to help identify actionable factors that can impact performance; and (2) providing scenario analysis and optimization capabilities to understand the effects of taking various actions on sales performance, as well as to suggest the best possible actions to achieve certain objectives given current constraints. We describe an implementation of this framework at IBM and provide examples of analyses to demonstrate how it can be used to support sales performance initiatives.

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