Using gravity model to make store closing decisions: A data driven approach.

Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.

Data Monitoring for the Chinese Clinical Trials of Remdesivir in Treating Patients with COVID-19 During the Pandemic Crisis.

Two phase-III, double-blind, randomized clinical trials of remdesivir plus SOC (standard of care) versus placebo plus SOC have been conducted in Wuhan hospitals by Chinese investigators during the urgent COVID-19 epidemic [ClincalTrials.gov NCT04257656 and NCT04252664]. These trials have been highly anticipated worldwide. We expect investigators of the trials will soon report the clinical and laboratory findings from the medical perspective. This manuscript provides documentary style information on the process of monitoring key data and making recommendations to the sponsor and investigators based on analytical insights when dealing with the emergent situation from the statistical viewpoint. Having monitored data sequentially from 237 patients, we comment on the strength and weakness of the study design and suggest the treatment effect of remdesivir on severe COVID-19 cases. Our experience with using the Dynamic Data Monitoring (DDM) tool has demonstrated its efficiency and reliability in supporting DSMB's instantaneous review of essential data during the emergent situation. DDM, when used properly by disciplined statisticians, has shown its capability of exploring the trial data flexibly and, in the meantime, protecting the trial's scientific integrity.