Enhancing the Inventory Management through Demand Forecasting

Integrating Big Data into Demand-Driven Material Requirements Planning (DDMRP) has become a game- changer in supply chain management. This study explores Big Data’s significant impact in the context of DDMRP. Big Data, sourced from various channels such as online transactions, sensor data, social interactions, and more, can transform demand forecasting, inventory management, and overall supply chain optimization. By allowing real-time data analysis and predictive modeling, organizations can make informed and agile decisions that drive cost reduction, inventory optimization, and improved customer service. However, implementing Big Data in DDMRP has its challenges, including data security and the need for advanced analytics capabilities. This research delves into the application, benefits, challenges, and impact of Big Data implementation in DDMRP. The study provides valuable insights for organizations seeking to leverage their potential. The findings underscore the significance of Big Data in reshaping supply chain strategies and enhancing the responsiveness of modern businesses in a dynamic market environment. The uniqueness of this paper lies in examining how Big Data, sourced from various channels, can revolutionize demand forecasting, inventory management, and overall supply chain optimization. The research also highlights the challenges of Big Data adoption in DDMRP, including data security and the need for advanced analytics capabilities.

Contributors

With the acceleration of globalization and the increasingly fierce competition in the market, demand forecasting and inventory management in supply chain management have become particularly important. Traditional supply chain management methods are difficult to cope with the complex and changing market demand, resulting in the frequent occurrence of inaccurate forecasts, excessive or insufficient inventory, and other problems. This paper aims to explore the application of intelligent technology in supply chain demand forecasting and inventory management, focusing on analyzing how technologies such as big data, artificial intelligence, and machine learning can improve forecasting accuracy and inventory management efficiency. Through the analysis of actual cases, this paper demonstrates the implementation effect of intelligent demand forecasting technology and proposes corresponding inventory management optimization strategies. The study shows that intelligent supply chain management can not only effectively reduce operating costs, but also improve service levels, thus enhancing the competitiveness of enterprises in a dynamic market environment

Inventory management plays a very important role in the process of business operation, providing favorable backing for the smooth operation of production and sales. In this paper, LightGBM and PSO-LSTM models in big data technology are combined to improve inventory management and demand forecasting in supply chains. Then, the relationship between inventory, order, and forecast is elaborated, the two-level inventory cost components and the relationship between them are analyzed, the model constraints are formulated, and a mathematical model for two-level multi-cycle inventory control is constructed. Finally, the single demand forecasting model is compared with the improved model to explore the optimization effect of inventory management after the application of the LightGBM-PSO-LSTM model. The LightGBMPSO-LSTM model is the best fit and can be used for actual demand forecasting. After the optimization of inventory management, the inventory turnover ratio of Company H increased from 8.2 in 2022 to the maximum value of 9.2 in 2023, and the OTIF achievement rate of sales orders increased from 97.9% in 2022 to 99.3% in 2023. This paper provides a successful example of optimizing supply chain inventory management using big data analytics.

Inventory management (InvM) is a critical aspect of supply chain management (SCM) and optimizing inventory levels can lead to significant cost savings and improved customer satisfaction. Hence, business organizations have recently worked to increase the value of their operations by utilizing modern and digital technologies as industry 4.0 (Ind 4.0). In the era of Ind 4.0, the technologies as Internet of Every Things (IoET), AI, BDA…etc. Recognizing the significance of InvM in a supply chain (SC), motivated us to volunteer technologies of Ind 4.0 as machine learning (ML) techniques to boosting Decision Making (DM) process to optimize InvM. Subsequently, this study is constructed for providing an intelligent Decision Support ML framework for automating the process of optimizing inventory management. Our constructed framework ensembles powerful ML prediction algorithms for inventory management, such as Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Machine (SVM) for building robust sales regressors. The extensive experimentations on a case study of Walmart suggested that the proposed system has the potential to transform inventory management and improve supply chain performance, but further research is needed to address the challenges of data availability and quality.

Contributors

In local and globalized markets, warehouses are crucial supply chain nodes. This study aimed to investigate the effect of warehousing on supply chain performance: A case of Inyange Industries Ltd. The goals were to evaluate how inventory management, warehouse capacity building, and material handling affects supply chain performance in Inyange Industries Ltd. This research experimented with three different theories: Just-in-time, Lean, and Triple-A supply chain. The population of this study were 105 staff members of Inyange Industries Ltd. Therefore, census was conducted because the population size is affordable and the researcher can be able to contact all the respondents. Quantitative data were analyzed using inferential research methodology in this study. Questionnaires were used to gather primary data, which was then analyzed. Data analysis was carried out using the Statistical Package for the Social Sciences (SPSS), and descriptive and inferential statistics were used to show the study's results. According to the research, supply chain performance is positively and significantly affected by both automated and manual material handling. Mechanical material handling, on the other hand, improves supply chain performance, but not much. Inyange Industries' supply chain performance is influenced by the material handling systems to the tune of 19.3%, as shown by the modified R2 value of 0.193. According to the research, supply chain performance is positively and significantly affected by inventory counts, warehouse management systems, and inventory organization. A 0.967 adjusted R^2 value suggests that 96.7% of the variation in Inyange Industries' supply chain performance is explained by the inventory management techniques. According to the research, supply chain effectiveness is positively and significantly impacted by warehouse demand planning, design, and management. Warehouse capacity planning techniques at Inyange Industries account for 95.4% of the variation in supply chain performance, as shown by the modified R2 value of 0.954. The research concluded that material handling, inventory management, and warehouse capacity planning are all important warehouse management measures that significantly impact supply chain performance for the better. The methods used by Inyange Industries Ltd. for managing their warehouses have resulted in an improvement in supply chain performance of 87.2%. The regression coefficients, supported by their t-test values and Beta coefficients, highlight both the significance and strength of relationships between independent and dependent variables. Material Handling demonstrated the greatest impact (β1=0.532, t=8.556, p=0.000), followed by Inventory Management (β2=0.379, t=6.147, p=0.000), and Warehouse Capacity Planning (β3=0.172, t=3.268, p=0.002), emphasizing their respective contributions to Supply Chain Performance of Inyange Industries Ltd.'s supply chain. This proved that material handling in warehouse management has a major influence on supply chain performance. Because the p-value was lower than 5%, we may say that the association is statistically significant. The research concluded that both automated and human material handling should get additional funding. To maximize efficiency in stock-taking as well. Previous studies have looked at a variety of organizational factors that affect performance of supply chains. To further understand how warehouse management strategies affect overall performance, more research is needed.

This study examines the relationship of e-procurement i.e., e-payment, e-tendering, e-invoicing and e-customer relationship management on the performance of supply chain management. It is observed that previously it is quite a time-consuming and energy absorbing activity as well as it is inefficient too. With the introduction of e-procurement in the organization, the overall process can be less time consuming as well as it is also very much efficient in order to do all procurement related activities in a timely manner. In this research it has been analyzed that e-procurement is a key role in future for companies as there are many companies already switching their supply chain management process into e-procurement activities as it can be proved in the previous studies that shows how important e-procurement is on the supply chain management and its performance. Present studies attempt the best possibilities with the effect of e-procurement steps which are in practices in supply chain process in Pakistan and manufacturing sector. Through the help of SPSS software, we are able to find the impact of supply chain with e-procurement variables which has a positive impact on supply chain management and results are agreed with the findings of research. The results show that the four electronic variables of E-Procurement are essential factor which directly reduce uncertainty in supply chain functions increase supply chain activities and practices, fast moving process, fast moving supply chain order, fulfillment of orders with quick and improve supply chain performance.

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.

Inventory optimization is pivotal in mitigating economic losses and enhancing efficiency in supply chain management. This paper examines the challenges associated with traditional inventory practices, including inefficiencies, demand variability, and supply chain disruptions, which lead to substantial financial and operational setbacks. Building on theoretical foundations and insights from existing research, a comprehensive inventory optimization framework is proposed, integrating advanced technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and cloud-based systems. The framework emphasizes data-driven decision-making, real-time tracking, lean inventory management, and sustainability, offering scalable solutions for diverse industries. Practical recommendations for implementation include investments in advanced technologies, collaborative supply chain practices, and continuous performance monitoring. The proposed framework aims to reduce costs, improve operational resilience, and align inventory management with broader economic and environmental goals. By adopting this innovative approach, organizations can navigate the complexities of modern supply chains and achieve long-term success.. Keywords: Inventory Optimization, Supply Chain Management, Economic Losses, Advanced Technologies, Lean Inventory, Sustainability.

With the rapid development of the digital economy, big data technology has brought unprecedented opportunities for supply chain management. This paper takes Shenzhen Shemanquban Supply Chain Co., Ltd. as a case study to explore the application and effects of big data technology in supply chain management. Through the construction of a big data platform, the paper has optimized key links such as demand forecasting, inventory management, and logistics distribution. The results show that big data-driven optimization strategies have significantly improved the efficiency and competitiveness of the supply chain, with inventory turnover rate increased by 28%, logistics costs reduced by 18%, and overall operating costs decreased by 15%. This paper not only provides a new perspective for supply chain management theory but also offers valuable references and insights for the digital transformation and practical application of enterprises.

This paper presents a systematic review of studies related to Artificial Intelligence (AI) and Machine Learning (ML) applications in Supply Chain Management (SCM). Our systematic search studied 109 journal articles published between 2007 and 2025. It was presented definitions for AI, ML, and SCM and a bibliometric analysis was carried out to examine trends and patterns in the existing literature. The 109 articles were grouped into five clusters based on keywords and abstracts, identifying promising research areas and proposes directions for future investigations. The current literature on AI and ML in SCM is expanding, with an increasing number of publications and growing diversity in applications, although significant gaps remain warranting attention. Various AI and ML techniques, including supervised learning algorithms, neural networks, and optimization techniques, have been studied in scenarios such as demand forecasting, inventory management, and risk analysis. Additionally, AI and ML methods are expected to gain broader acceptance in SCM scenarios, such as, application of Generative AI (GenAI) and Large Multimodal Models (LMMs); Quantum ML techniques to handle with large SCM data; to implement SHAP (SHapley Additive exPlanations) for demand forecasting and fraud detection; and to use Generative AI models (like, GPT-4) to generate synthetic supply chain scenarios.

The research focuses on improving supply chain performance under weather related uncertainties. Weather has a significant impact on the demand for various products in many sectors. Firms dealing in those products often face problems in demand management. Hence, managing supply chains under the weather risk is very important and challenging. To improve the performance of supply chains of weather-sensitive products, the main objective of the research is to design new classes of contract that can outperform the widely used supply chain contracts, such as wholesale, buyback, and revenue sharing contracts, and trade credit contracts. To design those new classes of contract, the research investigates: (1) coordination mechanisms of both risk-neutral and risk-averse supply chain members, (2) implementation of weather rebates based on Cooling Degree Days (CDD)/Heating Degree Days (HDD), which are the underlying assets for the temperature-based weather derivatives traded on the financial market, and (3) investigating how weather rebate and financial hedging strategies can be implemented to improve the supply chain performance. The design of new classes of contract involves three aspects: modeling a firm's objective under risk, modeling the payoff from weather derivatives, and designing the supply chain parameters. A firm's objective under risk is measured by using the Conditional Value at Risk (CVaR) approach. The supply chain parameters are designed such that the contracts coordinate with a Pareto-improving solution for both parties involved in the contract. The study has many contributions. Firstly, it fills the gap of the existing domain of literature on weather risk in supply chains. With an increase in climate variations, weather risk in supply chains is a pertinent issue. Secondly, the designed new classes of contract are beneficial to administer for a wide range of products and perform better compared to the regularly used traditional contracts. The designed contracts are analyzed using actual demand data collected from a large retail company in Canada. Thirdly, with trade credits being one of the most common ways of supply chain financing, the study explains how trade credits with weather rebates and financial hedging can effectively coordinate supply chains under weather risk.

The development of globalization has made digital technology play an important role in increasing the efficiency of supply chain management to ensure that its products are truly halal at the point of purchase for consumers. The technology that plays an active role in supply chain management is artificial intelligence (AI). This study aims to determine the effectiveness of implementing AI in halal product supply chain management. The object of this study is a scientific article that implements AI technology in halal product supply chain management with a qualitative approach through the Systematic Literature Review (SLR) method. Literature studies were conducted using Google Scholar and Science Direct to obtain detailed and up-to-date information on the topics to be studied. This study found 12 AI models that can be used in supply chain management. However, its implementation in the field of halal supply chains has not been widely implemented. This study also found that the implementation of AI in the halal product supply chain can improve operational efficiency and improve supply chain performance. However, successful adoption depends on technological readiness, good collaboration between technology and human expertise, and consumer trust. Investing in AI-based systems can significantly improve supply chain integrity and consumer trust.

With the deepening of globalization and technological innovation, the concept of new quality productivity has gradually come onto the agenda, profoundly affecting the efficiency improvement of logistics supply chains. This study systematically analyzes the application and strategies of emerging technologies such as information technology, artificial intelligence, and big data analysis in enhancing the efficiency of logistics supply chains from the perspective of new quality productivity development. By constructing a Data Envelopment Analysis (DEA) model and a multi-indicator comprehensive evaluation method, this paper quantitatively assesses the role of new quality productivity in improving supply chain efficiency and verifies the effectiveness of related strategies through simulation optimization. The research results show that the Internet of Things (IoT) and big data analysis play a key role in supply chain information sharing, inventory management, and demand forecasting, while artificial intelligence and machine learning technologies significantly improve supply chain responsiveness by optimizing the decision-making process. In addition, this paper explores the potential of blockchain technology in ensuring supply chain transparency and security and proposes precise improvement plans for the supply chain agglomeration effects in different regions. This study provides insights for formulating improvement strategies and guiding supply chain performance management and offers decision-making references for policymakers. Future research can further explore new supply chain management models under competition and cooperation, assess the impact of new economic forms on the stability of supply chain systems, and predict the long-term trends of global supply chains in the context of changes in the global political and economic landscape.