Owing to the coronavirus disease 2019 (COVID-19) pandemic, many companies have experienced a rapid and considerable decline in sales, causing a major crisis. Particularly, the effect of the drastic drop in demand was critical for transportation companies, whose performance has been stable for many years. These companies find it difficult to predict a major decline in sales far beyond the historical maximum. To address this issue, Ohori (J. Disaster Res., Vol.18, No.7, 2023) proposed a model for estimating the sales decline risk by applying the quantile method of three-parameter lognormal distribution with a good fit for both tails, and demonstrated the model in a single case. In this study, to elucidate the reliability, limitations, and applicability of the model, we empirically analyze it using actual sales data from three stable performing companies in the airline, railway, and travel industries in Japan. This study has revealed the following: (1) as a common characteristic of three companies, the statistic is lognormally distributed for a time interval of one, two, and three years, regardless of whether pre- or post-pandemic; (2) the time interval for which high estimation accuracy can be obtained depends on the sales stability of each company; (3) the cases in which the model is not applicable or the estimation accuracy is considerably deteriorated are verified using actual data; (4) the excess probabilities of the three companies’ actual sales declines (2020 and 2021) are estimated to be between 0.06% and 1.6%; (5) the estimated sales decline risk curve reflects the sales change trend for each company. These findings would be useful for stable companies and insurance companies to analyze the worst-case scenario of sales decline in the future and to consider specific countermeasures, such as risk financing.

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