Optimizing inventory management and demand forecasting system using time series algorithm

Abstract

In the rapidly evolving business landscape, effective inventory management and meeting customer demands rely heavily on accurate forecasting. While technology automates parts of inventory control, human expertise remains vital in decision-making for forecasting. Building supplier relationships, monitoring market trends, and adaptable supply chains are crucial too. Accurate demand forecasting reduces costs, streamlines operations, and boosts customer satisfaction. Therefore, companies must carefully review their forecasting methods to stay competitive. Researchers are addressing the lack of data on inventory and forecasting by focusing on implementing time series algorithms, recognizing their crucial role in optimizing these processes. This academic pursuit has led researchers to develop a user-friendly system tailored for improved inventory management, integrating a feature set dedicated to demand forecasting. The project aims to streamline user operations by offering an intuitive platform that facilitates seamless navigation. By encompassing forecasting capabilities, the system empowers businesses to accurately predict their future product requirements. The primary objective of this initiative is to simplify inventory procedures while enabling users to proactively meet upcoming demands effectively. While conducting the study, the researcher considered the first problem in how the user will use the inventory system in a more user-friendly manner. The second problem that the researchers conducted was manual input, and it will cost more when the documents are not organized. Lastly, the highest problem that the inventory management conducted was the overseers of the products by excessive inventory, low stocks, and expired products. The researchers use some of the sub-characteristics of ISO 25010 that are appropriate for evaluating inventory management. After evaluation, the sub-characteristics of functional stability garnered an overall weighted mean of 3.90. The compatibility and usability garnered an overall weight of 3.89. Reliability garnered an overall weight of 3.66. Lastly, maintainability was overall weighted at 3.63. The confusion matrix was used with the help of the tool of Weka Software using the scheme of function. Simple Logistics. The evaluation on the training set has a summary of correctly classified instances of 89.4737% and incorrectly classified instances of 10.5263%, which indicates that the application has an accurate algorithm.