IJERT-Inventory Management using Machine Learning

In a global market that makes room for more competitors by the day, some companies are turning to AI and machine learning to try to gain an edge. Supply chain and inventory management is a domain that has missed some of the media limelight, but one where industry leaders have been hard at work developing new AI and machine learning technologies over the past decade. Many well-known companies are now using machine learning to optimize business processes in ways that might have been deemed science fiction 30 years ago, from customer service inquiries to planning for next month's shelf supply based on satellite data. Supply chain and inventory management is primed to embody the concept of smart automation over the next five to 10 years. In this paper, we have investigated the research made till date and proposed a way to improve the inventory management so that it can benefit the customer as well as organizations.

European Journal of Business and Management, 2021

The aim of this research is to apply an intelligent technique to predict optimal inventory quantity in small and medium-scale enterprise. This is in view of the fact that the conventional models such as the EOQ model use only deterministic while some decision variables are non-deterministic in nature. Forecasted average demand of items for ten months in a small-scale retail outlet was collected and trained using an Artificial Neural Networks (ANN) of 5 neurons in the input layer with eight neurons in the first hidden layer and four neurons in the second hidden layer. Two feed-forward training algorithms of quasi-newton and quick propagation were employed in the training with the results of fuzzy logic technology found in the literature as the target output. Results obtained show that the quasi-newton algorithm covaries stronger with the fuzzy logic results than the quick propagation results. The objective and subjective feelings of the inventory manager were also captured to optimise the results of the training. The study is at a framework stage and will proceed to implementation level when more datasets are collected. Data collection in a small-scale outlet is a daunting task as record keeping is hardly done. The inclusion of non-deterministic circumstances such as emotional and objective feelings of the inventory manager to predict inventory is novel considering the fact that studies in the available intelligent inventory prediction have not employed such variables in their predictions.

2023

Building an adaptative, flexible, resilient, and reliable inventory management system provides a reliable supply of cross-border e-commerce commodities, enhances supply chain members with a flow of products, fulfills ever-changing customer requirements, and enables e-commerce service automation. This study uses an e-commerce company as a case study to collect intensive inventory data. The key process of the AI approach for an intensive data forecasting framework is constructed. The study shows that the AI model’s optimization process needs to be combined with the problems of specific companies and information for analysis and optimization. The study provides optimization suggestions and highlights the key processes of the AI-predicting inventory model. The XGBoost method demonstrates the best performance in terms of accuracy (RMSE = 46.64%) and reasonable computation time (9 min 13 s). This research can be generalized and used as a useful basis for further implementing algorithms in other e-commerce enterprises. In doing so, this study highlights the current trend of logistics 4.0 solutions via the adoption of robust data-intensive inventory forecasting with artificial intelligence models for cross-border e-commerce service automation. As expected, the research findings improve the alleviation of the bullwhip impact and sustainable supply chain development. E-commerce enterprises may provide a better plan for their inventory management so as to minimize excess inventory or stock-outs, and improve their sales strategies and promotional and marketing activities.

IJRASET, 2021

The idea of a smarter inventory management and business intelligence systems is a challenge in its implementation for various organization and businesses especially small and medium sized retailers. The concept of the paper is based on the business aspects while it merges various technological features to make a Predictive Analytical Model. Very few organizations and companies implement this which amount to loss of business, redundancies and errors that can be largely solved by system that uses data science, machine learning and visualization. This paper aims to present various aspects that can be infused with technology to aid Business Techniques and enable automation. I.

Jurnal Sistem Teknik Industri

This article aims to address the impacts that companies can have with the application of machine learning to carry out their demand forecasts, knowing that a more accurate demand forecast improves the performance of companies, making them more competitive. The methodology used was a literature review through descriptive, qualitative and with bibliographical surveys in International Journal from 2010 – 2022 by different authors. Findings show that the references prove that demand forecasting with the use of machine learning brings many benefits to organizations, for example, since the results are more accurate, there is better inventory management, consequently customer satisfaction for having the product at the right time and place. Further, this article concludes and suggests that the use of machine learning is able to identify variables that affect the demands, with this it makes a forecast closer to reality and helps managers to make more accurate decisions, improving strategic p...

International Journal of Advances in Data and Information Systems, 2020

Samsung Partner Plaza is a company that sells smartphones and serves large-scale sales by adopting the membership and computerized systems. The company’s inventory management activities consist of the normal inventory flow direction and management, ranging from the inventory procurement, storage to sales. The company would often face a problem where it would be running out of finished goods inventory to be sold in coming months. The remaining inventory would be inadequate to meet the customer demands. Such situation, and the fact that the company has competitors both inside and outside Sorong city, might cause customers to find other companies that could meet their demands, thus reducing the company’s capacity to generate profit. Given the situation, this research was conducted to create an information system which could be used by Samsung Partner Plaza for inventory forecasting. The system would provide information regarding the correct amount of inventory which could meet the cust...

Lecture Notes in Computer Science, 2016

Applied Sciences

Challenges related to effective supply and demand planning and inventory management impose critical planning issues for many small and medium-sized enterprises (SMEs). In recent years, data-driven methods in machine learning (ML) algorithms have provided beneficial results for many large-scale enterprises (LSE). However, ML applications have not yet been tested in SMEs, leaving a technological gap. Limited recourse capabilities and financial constraints expose the risk of implementing an insufficient enterprise resource planning (ERP) setup, which amplifies the need for additional support systems for data-driven decision-making. We found the forecasts and determination of inventory management policies in SMEs are often based on subjective decisions, which might fail to capture the complexity of achieving performance goals. Our research aims to utilize the leverage of ML models for SMEs within demand and inventory management by considering various key performance indicators (KPI). Th...

Journal of Informatics Education and Research, 2024

In the rapidly evolving landscape of digital transformation, artificial intelligence (AI) stands at the forefront of enhancing supply chain operations. This paper explores the application of AI-based demand sensing in improving forecast accuracy within supply chains. Demand sensing utilizes real-time data to anticipate market demand with greater precision, and AI enhances this capability by processing vast datasets and uncovering patterns beyond human reach. By employing machine learning models, natural language processing, and advanced analytics, AI-based demand sensing provides deep insights into consumer behavior, market trends, and external factors influencing demand. Various AI techniques, such as neural networks, time-series analysis, and predictive analytics, are examined for their contributions to more accurate demand forecasts. The integration of diverse data sources, including social media, weather patterns, and economic indicators, is highlighted to show how these enrich AI models and offer a comprehensive view of demand drivers. The findings reveal significant benefits of AI-based demand sensing, including enhanced inventory management, reduced stockouts and overstocks, and increased agility in supply chain operations. Additionally, the paper addresses implementation challenges such as data quality, the need for specialized skills, and the importance of continuous model training and refinement. This study provides valuable insights for supply chain professionals and decision-makers, illustrating how AI can be leveraged to achieve superior forecast accuracy and operational efficiency.

MEDIA Gender equality in media is existent and they are being treated as the same individual and are not being discriminated due to their genders it all depends on the idea and articles in media groups to how they portray each individual. Gender Discrimination exists to all genders, whether it'd be men, women and LGBT, but from media towards these genders, it's mainly the LGBT and women, because of women's rights movement, the issues of SJW's that consist mainly by women, the LGBT community where some of them are being diminished by media. Gender inequity towards the 3 genders are all common, there are many articles in the internet regarding "gender inequity" but mostly of it consist about women and LGBT because, of the lack of respect they are treated by men which is not true, because of the over exaggeration of these two genders. FAMILY/HOME Gender equality in family exists because it is one of the important foundation for having a family, this helps build up the relationship between both male and female genders, there are many articles regarding the equality between men and women, both partners work together and spend the same amount of time at home and shared the household tasks equally There are some family' that men are more condescending than women and can be treated the other way around, this depends between both male and female, it mostly depends who has the most high paying job than the other and who does the most job than the other, there are many situations like this where in a family gender discrimination is dominant Studies show that gender inequity is existent and is not really a problem in the family because of the cooperation between the two individuals in the family, where women would only do household chores while men would work from a 9 to 5 job, but if male denies to be considerate to fairness in the family then this can be called gender discrimination