International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024
p-ISSN: 2395-0072
www.irjet.net
Inventory Management tool (SAPpy) Amruta Gurav1 1Amruta Gurav, Dept of Computer Science, Vishwakarma Institute of Information Technology,
Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Proper management of stock is an integral
exploiting data with the use of inventory optimization solutions.
part of any business that involves selling goods, and it has an impact on the company’s profitability and efficiency. In this paper, the authors present SAPpy, a novel web-based information system that tries to change the way inventories are managed using data science and machine learning. SAPpy changes the game by reducing redundant effort in processes such as consumption calculations, duplicate item maintenance, and supplier performance metrics setups, thus enabling retailers to rely on evidence. Furthermore, the application anticipates future amounts of stock required and their associated costs, thus lowering the chances of excess stock and understock. After reporting the results of the analysis of SAPpy potential and actual functions, this paper demonstrates that such systems can help to reduce the costs and improve the effectiveness of inventory management. That case illustrates the place of advanced retail analytics in operation and decision making.
SAPpy is introduced as a novel application aimed at tackling such challenges using data science and machine learning. Using SAPpy, retailers can gain such insights by performing some of the critical inventory functions like consumption calculations, cutting out duplicates and monitoring suppliers’ performance.
1.1 Problem Statement Due to the diverse and dynamic nature of consumption in the retail sector and the conventional manual methods employed, replenishment of the inventory is not done in time or is very inefficient. Thus, it is common in retailing for the business to try and maintain stock levels that are optimal for each item so as to avoid costs associated with excessive holding or running out of products and dissatisfying customers. Even more, many supplier evaluations lack data and analysis in real time, compromising evaluation and offensive decisions. The aim of this paper is to solve some of these problems by investigating how SAPpy with its data aspects can help improve practices of inventory management, decreasing operational inefficiencies and in the long run increasing profitability in the retail sector.
Key Words: Inventory Management, Data Science, Machine Learning, SAPpy, Supply Chain Optimization, Retail Efficiency, Predictive Analytics.
1. INTRODUCTION Due to the growing competition in the retail sector, it is obvious that the customers are not just satisfied with simple supply/logistics; indeed, the inventory functions have become a core of the business, in that, without effective management, the business may not succeed. With the changing trends in the consumers and growing pattern of demand uncertainty, retailers have been put at a section of meeting the set inventory levels and getting rid of the excess costs. In addition, there are other challenges like such as supply chain problems, the variation of lead times and the need for being responsive to trends in the market which all make this balancing exercise harder.
1.2 Motivation The motivation behind this research is fundamental to implementing innovative solutions for retailers that effectively deal with the complexities of modern inventory management. The retail landscape is increasingly characterized by rapid changes in consumer behavior. supply chain disruptions and increased competition This makes traditional inventory practices inadequate. Retailers must leverage data-driven technology to increase operational efficiency and make informed decisions.
Conventional methods of inventory management often involve carrying out physical counting of stocks without any technology or quantitative research that may take days and is usually inaccurate. Such measures do not take into consideration the ambidexterity and aggressiveness which is needed by the retailers in this day and age, hence overstocking, understocking and ineffective suppliers’ management arises. This has seen retailers explore new approaches to inventory management and increasing
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Impact Factor value: 8.315
A key challenge for retailers is the enormous financial impact of inefficient inventory management. With reports suggesting that retailers lose an estimated $1.75 trillion in revenue per year due to these inefficiencies (Business Wire, 2015), this highlights the need for systems that can increase efficiency. Inventory practices Poor inventory management can lead to overstock situations that tie up
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