Supply chain management optimization using a two-stage business process structuring model.

Purpose: The present study is an attempt to provide a model of green supply chain management based on new product development (NPD) in the auto industry.
 Methodology: In the present study, the method of grounded theory has been used to develop a model. The required data were collected through interviews with academic experts, auto industry managers, and a review of documented meta-data and relevant reports. The sampling continued until the saturation of categories using the theoretical sampling method. The research data were analyzed simultaneously with the data collection during the three stages of open, axial, and selective coding. This process led to the development of the green model of supply chain management following NPD in the auto industry based on the grounded theory.
 Main Findings: The findings of this study suggest that managers must carefully consider all categories and subcategories identified in this study and have the necessary and sufficient information about each item to implement the green supply chain management successfully based on NPD in the automotive industry.
 Application of Study: This research indicates the need to pay attention to green supply chain management based on the development of new products in the automotive industry.
 Novelty/Originality: Development of the green model of supply chain management following NPD in the auto industry based on the grounded theory.

In the network era, the concept of supply chain management is a customer-centric. Through the analysis of the customer's actual needs and the prediction of the customers' future needs to pull the products and services. Based on such idea, many new modern supply chain management strategies constantly produced. Such as rapid response strategy, electronic ordering system and enterprise network-based supply chain system and so on. In the overall structure of the supply chain management, distribution, warehousing, transportation and other areas all have developed very well. But the supply chain management is still facing great difficulties. Although there are some difficulties in the technical and financial resources support the implementation of supply chain management. But the organization, trade partnerships, logistics costs and information-sharing and other issues will be the implementation of supply chain management. The demand has become increasingly evident in the optimization of the modern supply chain management for the virtual structure of the supply chain business contact. Through the cloud computing technology the efficiency of communications between supply chain enterprises can be maximized. The cloud computing can enhance the degree of information sharing in supply chain management.

The present study is an attempt to provide a model of green supply chain management based on new product development (NPD) in the auto industry. In the present study, the method of grounded theory has been used to develop a model. The required data were collected through interviews with academic experts, auto industry managers, and a review of documented meta-data and relevant reports. The sampling continued until the saturation of categories using theoretical sampling method. The research data were analysed simultaneously with the data collection during the three stages of open, axial and selective coding. This process led to the development of the green model of supply chain management following NPD in the auto industry based on the grounded theory. T he findings of this study suggest that managers must carefully consider all categories and subcategories identified in this study and have the necessary and sufficient information about each item to implement the green supply chain management successfully based on NPD in the automotive industry. Moreover, this research indicates the need to pay attention to green supply chain management based on the development of new products in the automotive industry.

In the era of globalization and digital transformation, Supply Chain Management (SCM) has become increasingly complex due to the exponential growth of data generated from various sources. Advanced data mining applications have emerged as pivotal tools for optimizing SCM by extracting actionable insights from vast datasets. This paper explores the integration of sophisticated data mining techniques into SCM processes to enhance decision-making, improve operational efficiency, and increase overall competitiveness. Through an extensive literature review and empirical analysis, the study highlights the significant benefits, challenges, and future directions of leveraging data mining in SCM. The findings indicate that data mining not only facilitates accurate demand forecasting and inventory optimization but also enhances supplier evaluation and logistics management, thereby contributing to a more resilient and agile supply chain.

The supply chain management (SCM) is the active management of supply chain activities to maximise customer value and achieve a sustainable competitive advantage. Horticulture sector is dominated mainly by small holdings. Horticultural markets are nearly imperfect in nature. The estimates of the marketed surplus for the year 2011–2012 indicate that the quantum of marketed surplus will be more for fruits and vegetables than for food grains and oilseeds (GoI 2007). Only 2 % of horticultural produce is processed, 0.4 % is exported and 22 % is lost or wasted in the market chain (Singh 2009). Studies on the present status of the supply chain and its management for horticultural produce suggest for more investment and extension activities. Market infrastructures in terms of storage (godown) facilities, cold storages, loading, weighing facilities, improvement in the road network and cold chain facilities constitute the basics of an efficient supply chain management. Integrating small farmers with high-value urban and export markets and provision of up-to-date market information through ICT would lead to the development and growth of the sector and can act as a model for SCM. Due to constraints in government investment, a private investment or public-private partnership (PPP) model may be encouraged. All these efforts may help to bring the horticultural sector into a systematic supply chain and logistics management for its growth and development.

Background: Information and Communication Technology (ICT) plays a crucial role in enabling the transition to a circular economy. By optimizing supply chain management and fostering innovative circular business models, ICT empowers businesses to reduce waste, conserve resources, and create more sustainable value chains. This research aims to explore the specific ways in which ICT supports the circular economy, including enhanced visibility and traceability, optimized resource allocation, improved collaboration, and the development of circular business models such as product-as-a service and sharing economy. Methods: This paper is qualitative research using Systematic Literature Review (SLR) from 275 papers that contained the relationship between ICT and circular economy as data, included 23 papers reviewed with SLR method. Findings: The result of this research is agreed that ICT has a role in circular economy. This research has explained that ICT can support circular economy through some aspects such as the advance, sustainability, efficiency through technology, etc. Conclusions: By leveraging ICT, businesses can transform their supply chains into circular systems, reducing waste, conserving resources, and creating long lasting value. Novelty/Originality of this article: Combining research from supply chain management and circular business model and the use of SLR method which is still little used.

The rising cost of quality healthcare is becoming an increasing concern. A significant part of healthcare cost is the pharmaceutical supply component. Improving healthcare supply chains is critical not only because of the financial magnitude but also because it impacts so many people. Efforts such as this project are essential in understanding the current operations of healthcare pharmacy systems and in offering decision support tools to managers struggling to make the best use of organizational resources. The purpose of this study is to address the objectives of a local hospital that exhibits typical problems in pharmacy supply chain management. We analyze the pharmacy supply network structure and the different, often conflicting goals in the decisions of the various stakeholders. We develop quantitative models useful in optimizing supply chain management and inventory management practices. We provide decision support tools that improve operational, tactical, and strategic decision making in the pharmacy supply chain and inventory management of pharmaceuticals. On one hand, advanced computerized technology that manages pharmaceutical dispensation and automates the ordering process offers considerable progress to support pharmacy product distribution. On the other hand, the available information is not utilized to help the managers in making the appropriate decisions and control the supply chain management. Quantitative methods are presented that provide simplified, practical solutions to pharmacy objectives and serve as decision support tools. For operational inventory decisions we provide the min and max par levels (reorder point and order up to level) that control the automated ordering system for pharmaceuticals. These parameters are based on two near-optimal allocation policies of cycle stock and safety stock under storage space constraint. For the tactical decision we demonstrate the influence of varying inventory holding cost rates on setting the optimal reorder point and order quantity for items. We present a strategic decision support tool to analyze the tradeoffs among the refill workload, the emergency workload, and the variety of drugs offered. We reveal the relationship of these tradeoffs to the three key performance indicators at a local care unit: the expected number of daily refills, the service level, and the storage space utilization.

A bibliometric analysis of IoT applications in logistics and supply chain management

Amidst the swift expansion of the global trade economy, enterprises are confronted with escalating competition within their industries. Enhancing market competitiveness to boost economic efficiency emerges as a pivotal strategy for ensuring corporate viability. This study employs applied mathematics to refine the optimization of supply chain management, thereby offering a novel approach to augmenting management efficacy in enterprise supply chains. Initially, the research adopts mathematical models tuned to the traditional structure of enterprise supply chains, addressing prevalent profiling issues. Subsequently, it introduces a two-tier planning model to facilitate multi-objective decision-making in supply chain management. This model is rigorously applied to resolve various decision-making scenarios. An empirical analysis is conducted using Company M as a case study. Post-optimization, Company M's inventory exhibited only one instance of material shortfall, involving 132 items. The capital tied up in inventory was significantly reduced to 214,092 yuan—a decrease of 36,319 yuan compared to the capital occupied under traditional supply chain management methods. Furthermore, the optimized supply chain management system demonstrated a notable increase in the average inventory turnover rate to 12.3507%, marking an improvement of 3.6923% over the traditional model. The findings underscore the efficacy of the proposed optimization in enhancing the economic efficiency of enterprise supply chain management. This optimization not only streamlines operations but also provides robust support for the ongoing development of enterprise supply chains.

Business Excellence model for supply chain management

15 - Modified neutrosophic fuzzy optimization model for optimal closed-loop supply chain management under uncertainty

Contributors

As public goods, hospital materials lack internal cost pressure and external efficiency motive, so there are many problems in the supply system of hospital materials - the sluggish prediction of medical material demand as well as the patient-oriented response plan, ineffective information communication and coordination between supply and demand of materials, inadequate standardization of management process, and the unreasonable external purchase, storage and distribution of materials. The innovation of supply chain management technology offers some potential benefit. From the definition of supply chain management, the connotation of hospital supply chain management system is explained; the constitution and realization model of hospital supply chain management system are analyzed; some basic problems are pointed out in hospital supply chain management system. It is concluded that, from an institutional innovation perspective, hospital resource efficiency would be improved by the model of supply chain management.

Inventory management faces increasing challenges, including data limitations and demand uncertainty. To enhance inventory forecasting and optimization in supply chain management, this study proposes a Transfer-learning Bayesian Network (TBN) framework that integrates causal inference and transfer learning. Unlike traditional inventory forecasting models that rely on historical data patterns, the proposed framework introduces a causal inference-based Bayesian network to establish explicit causal relationships between sales volume, sales revenue, and inventory levels. To address data scarcity and improve generalization, a novel transfer learning mechanism is incorporated, leveraging a balanced weight coefficient method to optimize model adaptation from a source domain to a target domain. The results indicate that the proposd approach ensures effective knowledge transfer and maintains prediction accuracy with limited training data. The TBN model consistently outperforms traditional machine learning methods and other Bayesian-based models. On the self-constructed dataset, the TBN framework achieved a mean squared error (MSE) of 4.97 and a mean absolute error (MAE) of 2.78, demonstrating superior predictive accuracy. Additionally, an analysis of the balance weight coefficient further validated its role in enhancing transfer learning efficiency and model robustness, which provides a scalable and adaptable solution for intelligent inventory management in supply chain systems.

The aim of this paper is to present a novel idea to revolutionize hospital services by integrating healthcare supply chain management principles. By using a web application which enables hospitals to establish a real-time connection and streamline patient transfers. Many sectors focus on supply chain management optimization and qualifications to construct a better network of suppliers for their end consumers. Especially in healthcare sector, not only for pharmaceutical products but also for hospital materials, supply chain management gets importance for service quality and patient satisfaction. Thus, there are many studies about supply chain management in healthcare to emphasize its importance. In this study it is aim to put forward more review of existing solution on healthcare management system and patient transfer. Keywords- Supply Chain Management, Healthcare Service Delivery, MERN stack web application, Real-time data processing, Patient Transfers, Predictive Analytics, Patient-Centric Care, Efficiency, Seamless Coordination, Fast Connectivity