A Latent Class Analysis for Item Demand Based on Temperature Difference and Store Characteristics

• Published in: Industrial Engineering & Management Systems Vol. 20; no. 1; pp. 35 - 47
• Main Authors: Seko, Yuto, Shimizu, Ryotaro, Kumoi, Gendo, Yoshikai, Tomohiro, Goto, Masayuki
• Format: Journal Article
• Language: English
• Published: 대한산업공학회 31.03.2021
• Subjects: 산업공학; Weather; Purchase History Data; Demand Prediction; Weather Data; Latent Class Model
• ISSN: 1598-7248; 2234-6473
• DOI: 10.7232/iems.2021.20.1.35

In retail stores, there is an increasing need for predicting item demand using accumulated purchase history data to cope with the fluctuating consumer demands. These fluctuations in item demand are influenced by external factors and consumer preferences. Among these, store characteristics and weather conditions, which are closely related to consumer behavior, have strong effects on item demand. For this reason, it is very important to quantitatively grasp demand fluctuations of items that are influenced by changes in weather conditions for each store by using an integrated analysis of the purchase history data of many stores and weather conditions. In this research, we focus on the temperature difference, which is the average temperature difference from the previous day, as a weather condition affecting item sales. Because consumer feeling about a temperature is dependent on the temperature difference from the previous day, it is meaningful to construct a prediction model using this information. In this research, we propose a latent class model to express the relationship between weather conditions, store characteristics, and item demand fluctuation. Also, through an analysis experiment using an actual data set, we show the usefulness of the proposed model by extracting items that are influenced by weather conditions. Sleep deprivation has been cited as a major factor that plays an important role in many incidents in the transportation sector. Sleep-deprived train drivers is a fairly common phenomenon in Indonesia, with local reports indicating a good percentage of train drivers who are sleep deprived prior to work. The present study was aimed at quantifying the effects of sleep deprivation on alertness and performance during prolonged simulated train driving. A total of 12 subjects participated in this study and were asked to sleep for approximately 2 h (sleep deprived) and 8 h (normal sleep) the night before the experimental day. The experiment consisted of driving a train simulator for 4 h on a monotonous route. Fatigue and sleepiness were assessed using Psychomotor Vigilance Task (PVT) and Sustained Attention Test (SAT), conducted before and after the driving simulation. Subjective levels of fatigue and sleepiness were determined using questionnaires, while driving performance was measured based on the number of speed-limit violations. Results of this study showed that two hours of sleep was characterized with an initial subjective fatigue and sleepiness measures that were up to two to three times greater than the normal sleep condition. This condition also resulted in poorer driving performance (75% increase in the number of speeding error). While the effects of sleep deprivations on the performance of train driving is probably obvious, the quantitative effects have not been addressed extensively in the literature. It is concluded in this study that the effects of excessive sleep deprivation on fatigue and sleepiness varies, depending on the measures used. KCI Citation Count: 0