ABSTRACT
Most of the electric distribution companies in the Philippines are interested in analyzing customer load profile, they are concerned in classifying their customer's profile into different categories based on the energy consumption, Also the user's profile will help to understand how the consumption of energy may affect the electric distribution grid. In the current condition right now, facing the COVID-19 pandemic, most Filipinos are inclined to work at home, thus the consumption of energy increased. In this paper, residential data were collected in one of the electric distribution companies in the Philippines amidst the COVID-19 pandemic conditions. The data consist of 1,048,575 customer profiles from the year 2021. This study aims to use clustering methods such as the K-means algorithm in grouping customers' profiles and validate the suitable amount of clusters using the proposed method, such as the multi-criteria model and elbow method. Results show that 2 and 7 clusters, respectively, were fitted in the data. © 2022 IEEE.
ABSTRACT
The lack of products in the store shelves is a problem that is affecting Latin American nations, and Costa Rica is not an exception. People experience a feeling of dissatisfaction because the product they are looking for is not available in the store, so they will have to go elsewhere to look for it. As a result, the business suffers a loss of both, profitability and business image. The objective of this study consists of determining the factors that mainly influenced the lack of products in the store shelves during 2020 in Costa Rica. The factor analysis by principal components methodology was used in order to identify the main factors that causes the lack of product in gondola. The case study pointed out that inventory management policies, inventory databases inaccuracy, replenishment management at the point sales, and the impact of the pandemic by Covid19 were the main factors for product shortage on the store shelves. It was concluded that the variable "Insufficient Inventory in the distribution center, No Order was delivered, and Product in Special Display" has a significant influence on the inventory management factor. In addition, the variable “Insufficient Order by Local to the distribution center has a significant influence at the inventory inaccuracy databases factor. Additionally, the variable "Inventory Inaccuracy, and Stores Did Not Justify Causal" has a significant influence at the coronavirus pandemic factor. Finally, the variable “Backroom Merchandise, Bad Condition Merchandise, Insufficient Order by the Buyer, and Product without Consumer View” has a significant influence at the supply logistics management factor in the point of sale. © 2021 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.