Supply Chain Demand Research Articles

Big data and impact in demand-driven material requirements planning

Big Data integrated with DDMRP has turned a new leaf in managing the supply chain. The research elucidates the deep impact of Big Data in relation to DDMRP. Big Data from online transactions, sensor data, social interactions, and many other areas possesses the potential to bring a paradigm shift in the area of demand forecasting, inventory management, and supply chain optimization as a whole. It allows organizations to make informed, agile decisions for cost reduction, inventory optimization, and improved customer service by enabling real-time data analysis and predictive modelling. However, there are challenges to the adoption of Big Data in DDMRP, including data security and advanced analytics capability. The study examines the application, benefits, challenges, and impact of Big Data implementation in DDMRP. Therefore, the study also provides valuable input for organizations interested in making use of the potential of big data. Its findings point toward a decisive role of big data in reshaping supply chain strategy and enhancing responsiveness to market fluctuations for modern business.

Supply Chain Demand Forecasting Based on Data Mining Algorithm and Seq2Seq

Supply Chain Demand Forecasting Based on Data Mining Algorithm and Seq2Seq

A Review of Machine Learning for Predicting Supply Chain Demand in Retail

A Review of Machine Learning for Predicting Supply Chain Demand in Retail

Tariffs, transportation, and profits in cross-border e-commerce: A dual-channel supply chain decision-making strategy.

The development of cross-border e-commerce platform promotes the new channel model between domestic and international. How to determine the dual-channel pricing decision of manufacturers and retailers under the condition of tariff and transportation heterogeneity has become an important and realistic problem. Based on the perspective of cross-border e-commerce dual-channel supply chain, this paper considers the impact of import tariff, transport heterogeneity and export tax rebate, compares and analyzes the performance difference between decentralized decision-making and centralized decision-making, and analyzes the impact of import tariff, export tax rebate and transport heterogeneity on cross-border e-commerce dual-channel pricing, demand and profit. The results show that the tariff is positively correlated with the manufacturer's direct selling price and the retailer's retail price, while the tariff is negatively correlated with the wholesale price, the demand and profit of direct selling channel and the retail channel. Export tax rebate rate is positively correlated with manufacturers' demand and profit and retailers' demand and profit, and negatively correlated with manufacturers' wholesale price, direct selling price and retail price. The increase of unit freight in direct channel is unfavorable to manufacturers and beneficial to retailers; The increase in unit freight rates in retail channels is bad for both manufacturers and retailers. Centralized decision-making is beneficial to supply chain demand and profits, and can improve the overall performance of the supply chain.

Role of SAP Order Management in Managing Backorders in High-Tech Industries

Effective order management is critical in the high-tech industry, where rapid innovation, fluctuating demand, and global supply chain complexities create unique challenges. SAP Order Management emerges as a robust solution for addressing these challenges, particularly in managing backorders. Backorders, a common issue in high-tech industries, can significantly impact customer satisfaction, operational efficiency, and profitability. This paper explores the role of SAP Order Management in mitigating backorder risks and streamlining the fulfillment process. SAP’s comprehensive tools offer real-time visibility into inventory, orders, and demand forecasts, enabling companies to prioritize high-value orders, reallocate resources effectively, and minimize delays. Its integration with advanced analytics allows businesses to identify root causes of backorders, such as supply chain disruptions or inaccurate demand planning. Furthermore, SAP’s automation capabilities facilitate efficient workflows by automating order prioritization and fulfillment processes, reducing manual errors and operational bottlenecks. In high-tech industries, where product lifecycles are short and customer expectations are high, the ability to manage backorders effectively is vital. SAP Order Management supports seamless collaboration across departments, ensuring that sales, supply chain, and production teams work cohesively to meet demand. This study highlights case examples where SAP Order Management has improved order accuracy, reduced backorder rates, and enhanced customer experiences in high-tech firms. Ultimately, SAP Order Management proves to be a transformative tool for high-tech companies, enabling them to address backorder challenges proactively and maintain a competitive edge in a fast-paced market.

Supply Chain Analytics, Definitions, Characteristics and Applications: A Systematic Literature Review

While there is a high uptake of BDA in the realm of supply chain management, in the view of automation supply chains and improving their value proposition by providing more accurate data for demand forecasting. There are material knowledge gaps on the SC-specific analytics applied to match demand, albeit the existing knowledge could be more amorphous. From this backdrop, the study endeavored to analyze extant literature within the ambit of BDA to unpack the current trends and possible future research directions to foster the application of BDA in SC contexts. The study adopted a systematic literature review of the extant literature published between 2014-2023. The study adopted the five-stage iterative procedure used in the systematic review methodology. The review's findings depict extensive use of big data analytics in matching demand and supply and supply chain optimization. The findings of this study adduce almost non-rebuttable evidence that big data analytics can be applied in procurement, inventory control, logistics, and order processing. Under the auspices of BDA is the SCA. Arguably, extant research has demonstrated the capability of SCA in mitigating contemporary SC risks such as mismatches between demand and supply, sub-optimal SCs, and underutilization of the supply chain infrastructures at the cusps of various echelons.

Intricate Supply Chain Demand Forecasting Based on Graph Convolution Network

Globalization has contributed to the increasing complexity of supply chain structures. In this regard, precise demand forecasting for the intricate supply chain holds paramount importance in effective supply chain management. Traditional statistical models and deep learning methods often face challenges in efficiently discerning correlations within a myriad of interconnected demands. To tackle this issue, this paper proposes an intricate supply chain demand forecasting method based on graph convolution networks adept at handling non-Euclidean data. First, the companies within the supply chain are treated as nodes in the graph structure, and the relationships between them are treated as edges, with demand data serving as the features of these edges. Then, a graph convolutional network is constructed to aggregate node and edge information. Through a multi-layer network, the relationships among nodes, edges, and historical demand are established to facilitate the prediction of supply chain demands. In this process, the graph convolutional network incorporates supply chain connectivity information into demand time series analysis. This integration of surface-level topological features and deeper latent correlation attributes across the supply chain’s nodes refines the demand forecasting precision across the entire supply chain. The validation experiment in this paper is grounded in sales data of a singular product from multiple sales nodes of an electronics company. The results demonstrate that the proposed method surpasses four other traditional demand forecasting algorithms significantly in terms of accuracy, providing substantial evidence for the superior performance of graph networks in the analysis of intricate supply chain relationships.

Optimizing Supply Chain Demand Forecasting and Inventory Management Using Large Language Models

This paper explores the potential for optimizing the Internet of Things by integrating machine learning and computer vision technologies and its implications for U.S. national security and economic competitiveness. First, the application of machine learning in IoT device optimization is introduced, emphasizing its ability to improve system intelligence and efficiency. Secondly, the critical role of computer vision technology in monitoring and reacting to changes in the physical environment is discussed, especially in security applications, such as the protection of national infrastructure and border security. Finally, the strategic significance of integrating these technologies in national security strategy and economic development is analyzed, and the direction and challenge of future research are put forward.

Artificial Intelligence in Strategic Business Decisions: Enhancing Market Competitiveness

Abstract: Integrating Artificial Intelligence (AI) into strategic decision-making is essential for enhancing market competitiveness in today's dynamic business environment. AI technologies such as machine learning, natural language processing (NLP), and predictive analytics optimize operations, personalize customer experiences, and drive product innovation. Machine learning algorithms analyze vast data to uncover patterns, aiding better decision-making. Predictive analytics forecasts market trends and consumer behaviors, allowing companies to anticipate demand and streamline supply chains, reducing risks like overproduction and stockouts. NLP-powered chatbots improve customer interactions by handling routine inquiries, freeing human agents for complex issues, and enabling personalized marketing. AI also accelerates product development by analyzing market data and consumer feedback, simulating scenarios, and predicting outcomes. Operational efficiency is enhanced through automation and optimized workflows, saving costs and increasing productivity. Despite these benefits, challenges such as data privacy, algorithmic bias, significant investment, and a shift to a data-driven culture must be managed. Effective AI integration offers significant competitive advantages, positioning companies to leverage predictive analytics, personalized customer interactions, and operational efficiency for growth and innovation.

SYSTEMATIC LITERATURE REVIEW ON ARTIFICIAL INTELLIGENCE APPLICATIONS IN SUPPLY CHAIN DEMAND FORECASTING

This systematic review investigates the applications of artificial intelligence (AI) in supply chain demand forecasting, focusing on the performance of AI-driven models compared to traditional forecasting techniques. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted, yielding a final selection of 65 peer-reviewed articles for in-depth analysis. The review explores the advantages of AI models, particularly machine learning (ML) and deep learning (DL), in improving forecasting accuracy, scalability, and responsiveness to real-time data. It also examines AI’s applications across various industries, including retail, manufacturing, e-commerce, and logistics, where AI-driven models have significantly enhanced inventory management, production scheduling, and operational efficiency. However, the review highlights challenges related to data quality, model complexity, and high implementation costs, which limit the broader adoption of AI in demand forecasting. This study provides valuable insights into the current state of AI applications in supply chain management and suggests areas for future research, particularly in improving data management and developing more interpretable AI models to facilitate wider implementation.

Research on Topology Optimization of Building Digital Supply Chain Based on Genetic Neural Network

In order to maximize the benefit of building supply chain, the topology optimization method of building digital supply chain based on genetic neural network is studied. According to the overall structural characteristics of the building digital supply chain, customer demand data, supplier sales data, manufacturer production management data, and environmental policy exception data are collected to form a building digital supply chain data set. As input data, a building digital supply chain demand prediction model based on improved genetic LSTM neural network is constructed. Capture the needs of the building digital supply chain; according to the demand of suppliers, manufacturers and customers in building digital supply chain, a nonlinear 0–1 mixed integer programming model based on the newsboy model is established. According to the various information provided by the supplier, the ant colony algorithm is used to calculate the optimal supplier. After all suppliers are identified, the topology of the digital supply chain is constructed. So far, the optimization of the supply chain has been completed. The experimental data show that this method can accurately predict the demand of construction projects, the maximum error is less than 1.5%, and can obtain the best supplier selection results. Compared with before optimization, the profit of the structural digital supply chain after topology optimization increases the most.

Implementation of industrial symbiosis under environmental tax policy

Industrial symbiosis is an important practice to achieve economic and environmental advantage. This paper explores the implementation of industrial symbiosis under environmental tax policy. Specifically, we use a game-theoretic approach to analyze the interaction between the government and the manufacturers involved in industrial symbiosis in two supply chain structures: integrated structure and decentralized structure. We find that environmental tax is an effective tool to reduce the mismatch between waste supply and demand in the symbiotic supply chain. The government is inclined to set a more stringent environmental tax in the integrated supply chain, while the tax rate that triggers the full conversion of waste in decentralized scenario is larger than that in integrated scenario. The integration of the symbiotic supply chain may promote the utilization of waste and bring a higher social welfare and better environmental performance. The profit of supply chain in the integrated structure is not always higher than that in the decentralized structure. When the market potential of by-product is not large enough and the damage of waste is moderate, it is more profitable to implement industrial symbiosis in a decentralized supply chain.

Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model

This research presents a uni-regression deep approximate forecasting model for predicting future demand in supply chains, tackling issues like complex patterns, external factors, and nonlinear relationships. It diverges from traditional models by employing a deep learning strategy through recurrent bidirectional long short-term memory (BiLSTM) and nonlinear autoregressive with exogenous inputs (NARX), focusing on regression-based approaches. The model can capture intricate dependencies and patterns that elude conventional approaches. The integration of BiLSTM and NARX provides a robust foundation for accurate demand forecasting. The novel uni-regression technique significantly improves the model’s capability to detect intricate patterns and dependencies in supply chain data, offering a new angle for demand forecasting. This approach not only broadens the scope of modeling techniques but also underlines the value of deep learning for enhanced accuracy in the fluctuating supply chain sector. The uni-regression model notably outperforms existing models in accuracy, achieving the lowest errors: mean average error (MAE) at 1.73, mean square error (MSE) at 4.14, root mean square error (RMSE) at 2.03, root mean squared scaled error (RMSSE) at 0.020, and R-squared at 0.94. This underscores its effectiveness in forecasting demand within dynamic supply chains. Practitioners and decision-makers can leverage the uni-regression model to make informed decisions, optimize inventory management, and enhance supply chain resilience. Furthermore, the findings contribute to the ongoing evolution of supply chain demand forecasting methodologies.

Pioneering food safety: Blockchain's integration in supply chain surveillance

Ensuring food safety in a world facing escalating demand and complex supply chains is a pressing challenge. Despite increasing awareness, obstacles such as information distribution, financial limitations, and insufficient infrastructure impede food safety efforts. Blockchain technology presents a promising solution by improving transparency and traceability in supply chains, which are essential for tackling food safety issues. This study explores the integration of blockchain into food safety frameworks, emphasising its compatibility and potential to transform food production and distribution. Drawing on literature, it identifies key challenges to blockchain adoption, including regulatory frameworks and interoperability issues, and proposes strategies such as government intervention and standardisation to overcome them. Ultimately, blockchain holds immense promise in revolutionizing food safety practices, ensuring safe and nutritious food for all.

Utilizing CNN2D for regressor analysis and time series forecasting & modelling of the foods demand supply chain

Utilizing CNN2D for regressor analysis and time series forecasting & modelling of the foods demand supply chain

A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting

Demand forecasting has become the most crucial part for supporting supply chain decisions. However, accurate forecasting in time series demand forecasting, particularly within supply chain operations, is challenging because of short-term data features, such as limited volume, nonlinear datasets, and near history disturbances. As one of the most promising deep learning models, long short-term memory shows superior performance in extracting implicit patterns from datasets of various areas. Thus, a novel forecasting framework, attLSTM is constructed combining enhanced bidirectional LSTM (LSTM) and self-attention mechanism. The forecasting performance of attLSTM is verified by testing six randomly selected datasets and eight additional datasets with different volumes from a given database. The proposed attLSTM is compared with seasonal autoregressive integrated moving average, support vector machine, random forest, and LSTM through two commonly applied evaluation metrics and a specially designed newsvendor cost model. Extended experiments are conducted on four benchmark datasets from other fields. These analyses demonstrate that attLSTM shows comparable performance in supporting the supply chain demand forecasting and operational decisions. The proposed framework has robust generalization capability in univariate time series demand forecasting.

A Multi-Objective Method Based on Tag Eigenvalues Is Used to Predict the Supply Chain for Online Retailers

E-commerce has grown quickly in recent years thanks to advancements in Internet and information technologies. For the majority of consumers, online shopping has emerged as a primary mode of shopping. However, it has become more challenging for businesses to satisfy consumer demand due to their increasingly individualized wants. To address the need for customized products with numerous kinds and small quantities, businesses must rebuild their supply chain systems to increase their efficiency and adaptability. The SI-LSF technique, which employs boosting learning in the target-relative feature space to lower the prediction error and enhance the algorithm's capacity to handle input-output interactions, is validated in this study using a genuine industrial dataset. The study successfully identifies the relationship between sales and sales as well as target-specific features by applying the multi-objective regression integration algorithm based on label-specific features to a real-world supply chain demand scenario.

The supply chain demand forecasting model based on LSTM and multiple clustering techniques

The supply chain demand forecasting model based on LSTM and multiple clustering techniques

Collaborative commerce model for MSMEs and creative industries in Parepare to face industry 4.0 towards industry 5.0

Collaborative commerce model for MSMEs and creative industries in Parepare to face industry 4.0 towards industry 5.0

Reviewing predictive analytics in supply chain management: Applications and benefits

Supply chain management (SCM) is a critical component of modern business operations, and the integration of predictive analytics has emerged as a transformative force in enhancing efficiency, decision-making, and overall performance. This paper presents a comprehensive review of the applications and benefits of predictive analytics in supply chain management, exploring its role in demand forecasting, inventory optimization, and supply chain visibility. The literature review provides a historical perspective on the evolution of predictive analytics in SCM, delving into key concepts and definitions. Drawing upon existing research, the paper analyzes real-world applications, case studies, and successful implementations in areas such as demand forecasting, inventory management, and supply chain visibility. Methodologically, the paper outlines the criteria for selecting relevant studies, details the search strategies employed, and highlights the sources contributing to the comprehensive understanding of predictive analytics in SCM. The exploration of applications focuses on how predictive analytics is revolutionizing demand forecasting, optimizing inventory levels, and enhancing supply chain visibility. Through case studies and examples, the paper illustrates the practical implications of implementing predictive analytics in these key areas. Real-world examples and data-driven insights underscore the transformative impact of predictive analytics on SCM processes. Despite its numerous advantages, challenges and limitations exist in the implementation of predictive analytics. This paper examines common hurdles and proposes strategies to overcome these challenges, offering a balanced perspective on the practical implications of integrating predictive analytics into supply chain management. Looking towards the future, the paper discusses emerging trends and technologies in predictive analytics, anticipating advancements that will further shape the landscape of SCM analytics. It summarizes key findings, outlines implications for practitioners and researchers, and suggests avenues for future research in the dynamic field of predictive analytics in supply chain management.