Visualization of Data Inventory Using Visual Data Mining (VDM) and Exploratory Data Analysis (EDA) Methods

Naavagreen Sriwijaya Skincare Clinic in Semarang encountered difficulties in interpreting inventory data, which led to operational inefficiencies, stock imbalances, and potential sales losses. To address this issue, we aim to transform raw data into comprehensible visual insights for better decision-making. The study employed Visual Data Mining (VDM) and Exploratory Data Analysis (EDA) methods using Tableau software to visualize and analyze inventory records from January 2019 to December 2020. The methods were implemented in three main phases, consisting of project planning, data preparation, and data analysis. In the project planning phase, we conducted justification and a project plan, and identified the top business question. In the data preparation phase, we choose, transform, and verify the dataset. In the data analysis phase, we chose visualization or mining tools, analyzed the visualization or mining model, and verified and presented the visualization or mining model. The results indicated that among ninety-eight products, three were identified as efficient and three as inefficient based on their stock and sales behavior. Product visualizations showed distinct inventory patterns, while sales turnover lacked consistent trends, with the highest increase occurring in January 2020 at 12.86%. The visualizations were reviewed and validated by the clinic’s administrative team, demonstrating their practical value in supporting inventory management improvements. The efficiency dashboard indicates that Ng Facial Wash, Ng Skin Toner, and Ng Moisturizing Sunscreen 1 are deemed inefficient due to the imbalance between sales and incoming stock. Conversely, the top three most efficient products are Ng-Neher Pagi, Ng Badan Pagi, and Naavagreen Moist Aha Cream. This analysis aids in making informed decisions regarding stock management and future sales strategies.