Big Data Analytics Role in Managing Complex Supplier Networks and Inventory Management

As global markets grow increasingly efficient, competition no longer takes place between individual businesses, but between entire supply chains. Collaboration can provide the competitive edge that enables all the business partners in a supply chain to prevail and grow. This paper presents a framework for customer‐supplier collaboration that facilitates effective and efficient supply chain operation. The paper reveals that the level of involvement of customers and suppliers differs across different supply chain processes and also across different sectors. While the involvement of customers is high in demand management and product development, the involvement of suppliers is high in transportation and inventory management processes. The paper also reveals that about 50 percent of the organizations surveyed indicate that suppliers and customers have little or virtually no role in the demand management, inventory management, and product development processes.

Contributors

Blockchain technology is being introduced gradually into supply chain management, addressing long-standing problems and waste-related parts of the industry. This study aims to illustrate the various ways that blockchain might improve supply chains' efficiency, traceability, security, and simplicity. Blockchain technology offers a permanent, decentralized record-keeping system that fosters honesty and trust between participants; all important data is stored in an unalterable manner. Additionally, supply chain processes like payment processing and inventory management are automated by intelligent contracts, which significantly lowers both human error and regulatory expenses. Blockchain's capacity to encrypt this data ensures a high level of security by guaranteeing information availability in the event of sophisticated cyber assaults. This increase in security makes the supply chain less vulnerable to sabotage and extortion. Through the provision of an easily accessible and verifiable record of every trade, this innovation significantly streamlines administrative compliance. Furthermore, it provides a means of confirming that the technical support standards and material sources adhere to current standards, which in turn helps to enhance customer trust in a brand and promote brand identity for the product bearing the brand mark. The study shows how blockchain technology has enhanced the capabilities of US logistics companies. It also looks at supply chain management's effectiveness and transparency in terms of blockchain technology implemented in the US.

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

Supply chain management is essential to a company's success in today's fiercely competitive business environment, regardless of the industry. Businesses are increasingly turning to big data analytics as a useful tool for supply chain improvement to meet the expanding needs of customers, save operational costs, and boost overall efficiency. In order to improve critical areas including demand forecasting, inventory management, logistics optimization, and risk reduction, research has been conducted on the collecting and use of big data in supply chain management. Big data analytics and supply chain management integration can result in more cost-effective operations, better customer service, and increased resilience to disturbances. Organisations that effectively use big data will gain a competitive advantage in the constantly changing supply chain landscape as technology and data capabilities continue to grow.

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.

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.

The context and importance of inventory management and production planning -- The importance of inventory management and production planning and scheduling -- Frameworks for inventory management and production planning and schedul-ing -- Forecasting models and techniques -- Replenishment systems for managing individual item inventories within a rm -- Order quantities with level demand -- Lot sizing with time-varying demand -- Individual items with probabilistic demand -- Special classes of items -- Managing the most important inventories -- Managing slow-moving and low-value (Class C) inventories -- Style goods and perishable items -- Managing inventory across multiple locations and multiple rms -- Coordinated replenishments at a single stocking point -- Multiechelon inventory management -- Coordinating inventory management in the supply chain -- Production management -- An overall framework for production planning & scheduling -- Medium-range aggregate production planning -- Material requirements planning and its extensions -- Just-in-time, optimized production technology and short range pro-duction scheduling -- Summary.

The evermore growing amount of available data accelerated the emergence of numerous business intelligence applications that are summarised under the term Big Data Analytics (BDA). As value networks are becoming more global and complex while the amount of data is simultaneously increasing, BDA is especially relevant to the domain of supply chain management (SCM). Its application does challenge traditional institutional arrangements as well as roles that are related to the management of data. Our empirical study addresses this challenge by an application of a Delphi survey that is embedded in organisational information processing theory. For this purpose, we integrate the assessments of experts on projections up to the year 2035 to span the future of BDA in SCM. Our study suggests that BDA will improve demand forecasts, reduce safety stocks and improve the management of supplier performance. However, supply chain processes will become increasingly automated and traditional tasks of SCM are partially substituted in consequence. Consequently, the transition of the traditional role of SCM within organisations will increase the importance of human intuition, trust and strategic decision-making.

PurposeBased on previous literature on big data analytics (BDA) and supply chain (SC) management, the purpose of this paper is to address the factors determining firms’ intention to adopt BDA in their daily operations. Specifically, this study classifies potential factors into four categories: technological, organizational, environmental factors, and SC characteristics.Design/methodology/approachDrawing on the innovation diffusion theory, a model consisted of direct technological and organizational factors as well as moderators was proposed. Subsequently, survey data was collected from 210 organizations. Then we used SPSS and SmartPLS to analyze the collected data.FindingsThe empirical results revealed that perceived benefits and top management support can significantly influence the adoption intention. And environmental factors, such as competitors’ adoption, government policy, and SC connectivity, can significantly moderate the direct relationships between driving factors and the adoption intention.Research limitations/implicationsGiven the fact that big data (BD) usage in logistics and SC management is still in the start-up stage, the interpretations toward BDA might vary from different perspectives, thus causing some ambiguity in understanding the meaning and potential BD has. In addition, we collected data through questionnaires completed by IT managers, whose viewpoint may not fully represent that of an organization.Practical implicationsThis paper tests the organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.Social implicationsThis research helps top managers assess the benefits of BDA as well as how to adjust their business strategy along the changes of environment and SC maturity.Originality/valueThis paper contributes to the literature of organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.

Supply chain management has an integrated system that can manage the entire process in preparing a product or service for all consumers. For this reason, supply chain management is one of the most important strategies in knowing the needs of customers. One example of the application in supply chain management that is currently done in Malang is a small and medium enterprise (MSME). These small and medium businesses use worm media as fertilizer. The business has two manufacturing processes namely solid fertilizer and liquid fertilizer. In the supply chain management model using 3 different models and can be known the comparison between the first SCM model to the third SCM model. The difference starts from the first SCM model that is 9:11 wherein a supply chain process from the beginning to the end it can still have an error of 2 times the error in structured management. And in the second SCM model that is 10:11 were in a supply chain process from beginning to end still has an error of 1 time in structured management. In the third SCM model 11:11 which states that in a supply chain process from beginning to end there are no errors in structured management. And in this 3rd SCM model, it can be said to be a very efficient model in the supply chain process.

The bullwhip effect is causing inefficiencies in today’s supply chains. This study deals with the potential of big data on the improvement of the various supply chain processes. The aim of this paper is to elaborate which characteristic of big data (lever) has the greatest potential to mitigate the bullwhip effect. From previous research, starting points for big data applications are derived. By using an existing system dynamics model, the big data levers ‘velocity’, ‘volume’ and ‘variety’ are transferred into a simulation. Overall, positive impacts of all the big data levers are elaborated. Findings suggest that the data property ‘velocity’ relatively bears the greatest potential to enhance performance. The results of this research will help in justifying the application of big data in supply chain management. The paper contributes to the literature by operationalising big data in the control engineering analyses.

Modern companies face numerous challenges in supply chain management, particularly due to globalization, increasing competition, and the need for quick adaptation to market changes. Inefficient supply chain management can lead to higher costs, supply delays, and reduced customer satisfaction, which directly impacts financial performance and competitiveness. The study aims to identify methods for improving supply chain management efficiency, taking into account current economic and technological conditions. Given the volatility of global markets, it is crucial to develop adaptive and resilient supply chain strategies that align with both short-term objectives and long-term sustainability goals. The study uses methods of analysis, synthesis, comparative analysis, as well as a systems approach to assess modern supply chain management strategies. The impact of cutting-edge technologies such as Big Data, artificial intelligence, the Internet of Things, and blockchain on optimizing supply chain processes was analyzed. The effectiveness of ERP systems in the context of business process integration and risk management was evaluated. The study identified key issues in supply chain management: insufficient integration between systems, low supply chain transparency, inefficient inventory management, and high logistics costs. To address these issues, the following solutions were proposed: Implementing ERP systems and enhancing their functionality for business process integration; Using Big Data analytics for demand forecasting; Developing risk management strategies, particularly through supplier and transport route diversification; Optimizing inventory and reducing logistics costs by automating and applying flexible supply models. Improving supply chain management efficiency requires active use of modern technologies and innovative optimization methods. Integration of Big Data, artificial intelligence, and the Internet of Things helps reduce costs, shorten delivery times, increase process transparency, and ensure stability in order fulfillment. The proposed solutions will contribute to enhancing the competitiveness of enterprises in dynamic economic conditions.

With the emerging importance of balancing environmental, social, and economic objectives within a company setting, growing awareness of planning of supply chains and sustainable optimal design has been brought into perspective. An immense amount of research has been devoted to this sector, in hopes of capturing these objectives to ensure long term sustainability. Among some of the approaches that have been developed, product design, inventory management, product recovery, production planning with an emphasis on remanufacturing, reverse logistics, and supply chains that are closed-looped have been given additional attention in various pieces of literature. We will review and analyze some of the research that has surfaced in regards to sustainable chemical processes along with supply chain design. Focus will be given to three main areas, (i) waste management and energy sufficient sustainable supply chains, (ii) sustainable supply chains and their effect on the environment, (iii) sustainable water management principals. The challenges that these three sectors face have been summarized, and all future opportunities have been highlighted. Environmental sustainability is an issue that receives a lot of attention in the press, journals, and media. Most of the discussions surrounding this debate focus on the environmental impacts that organizational supply chains have on the environment. The goal of this paper is to further investigate the environmental impact concerns, and how these concerns are impacting the operational and strategic management of company supply chains. The research we have complied is intended to give an overview of the practices that companies have adopted and to clarify that further research is still in the midst of being conducted.

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.