Queueing network modeling and lead time compression of electronic procurement

Organizations are always looking for ways to sustain competitive advantage over others. One method to gain that advantage is to have an efficient Supply Chain Management process. Despite the efforts of implementing supply chain management, organizations find themselves with an inefficient supply chain management process and search for solutions to improve their supply chain management tasks. Previous researchers have suggested Total Quality Management as a plausible process for improving supply chain management. In addition, prior research has suggested methods of implementing Total Quality Management solutions that possibly overlap the Supply Chain management process. The overlap between the two processes could either enhance or damage an organization's current supply chain management processes that have been fulfilling organization requirements. Depending on the organization's relationships with third party logistics groups (3PL) and other supply chain partners, the implementation of total quality management could have a positive or negative impact on the supply chain management process. This study provides a conceptual framework for assessing the impact of TQM implementation in supply chain processes. Testable research hypotheses are presented for future empirical study.

Industry 4.0 technologies are causing a paradigm shift in supply chain process management. The digital transformation of the supply chains provides enormous benefits to organizations by empowering collaboration among multiple internal and external organizations and systems. This study presents a narrative review explaining the existing knowledge on digital transformation in supply chain process management using text mining. It summarizes the existing literature to explain the current state of the art in supply chain digitalization. This comprehensive review identifies the most important topics and technologies and determines the future trends in this emerging field. We investigate the articles published in Web of Science and Scopus databases and use text mining techniques (clustering and topic modeling) on the article contents. Using VOS viewer, a bibliometric analysis of 395 articles with 12,700 references is analyzed. The contents of the articles are explored using text mining approaches. The synthesized results reveal that the most important topics in digital transformation are “sustainable supply chain management” and “circular economy and industry 4.0 technologies”. The study further discovers big data, data analytics, blockchain, artificial intelligence, machine learning, and the Internet of Things as the most critical technologies for facilitating supply chain digital transformation. Finally, an overlay heatmap analysis of the research articles found that digital transformation, supply chain management, industry 4.0, decision-making, and sustainability are emerging trends in supply chain digitalization.

Objective: The objective of the paper is to investigate the influence of deployment of Information and Communication Technology (ICT) tools in a supply chain context. Methods/Analysis: The influence of deployment of ICT in Supply Chain Management (SCM) is studied using System Dynamics (SD) methodology using the tool, Causal Loop Diagram (CLD) using software Vensim. Findings: SCM discipline is grappled with dynamic and operational changes and challenges and this unpredictability can be understood better using SD. ICT capabilities fundamentally improve supply chain integration and performance. ICT adoption for SCM improves collaboration, workflow and decision-making which lead to cost savings; improved operational efficiency and inventory visibility. One process in SCM namely inventory control is described using SD. Introduction of a parameter, ICT capability dramatically changes the context and gives rise to new positive feedback loops Novelty/Improvements: The paper uses CLD for ICT influence on SCM process of inventory control. This can be extended for all supply chain management processes in both planning and execution. Case studies with the influence of specific ICT tools like RFID, Cloud, Social Media, Business Analytics, Mobile technology and Multi-agent systems are also possibilities for extending the study. Supply chain managers can deliberate on decisions, strategy, issues and policy by developing scenarios using SD tools and software packages. However the research limitation is that this is a simulation in a specific supply chain context. Generalization of this scenario may not be possibleKeywords: Causal Loop Diagram (CLD), Information and Communication Technology (ICT), Inventory, Supply Chain Management (SCM) ICT, System Dynamics (SD)

This paper delves deeper into the potential of Artificial Intelligence (AI)-enabled Supply Chain Management (SCM) as a groundbreaking technology capable of revolutionizing supply chain operations and ushering in a new era of possibilities. In today's dynamic business landscape, where agility and efficiency are paramount, AI plays a pivotal role in redefining how supply chains operate. The journey commences with an in-depth exploration of AI's fundamental concepts and its manifold applications within SCM, shedding light on its adaptability across various aspects of the supply chain, from demand forecasting to inventory optimization. Moreover, this paper illuminates the myriad benefits that AI brings to SCM practitioners. These advantages encompass heightened operational efficiency through real-time data analysis, cost reduction through predictive maintenance and optimized routing and a superior customer experience resulting from improved demand prediction and personalized service offerings. However, acknowledging the transformative power of AI in SCM, we must also acknowledge the hurdles in its implementation. This paper underscores the significant challenges that organizations may face while integrating AI into their SCM processes, ranging from data quality issues and concerns regarding privacy and security to the need for domain-specific human expertise. To address these hurdles effectively, the paper proposes a comprehensive framework. This framework encompasses a holistic strategy that aligns AI initiatives with organizational goals, governance and ethics considerations to ensure responsible AI deployment, and a clear roadmap that guides the implementation journey from inception to full integration. In conclusion, this paper offers valuable insights into the opportunities and challenges that AI-powered SCM presents in the ever-evolving business landscape. By providing practical recommendations, it equips organizations with the knowledge and tools needed to successfully harness the potential of AI in their supply chain operations, ultimately paving the way for enhanced competitiveness and sustainability in the future.

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.

LT variance or LT mean reduction in supply chain management: Which one has a higher impact on SC performance?

Increasingly, supply chain management is being recognized as the management of key business processes across the network of organizations that comprise the supply chain. While many have recognized the benefits of a process approach to managing the business and the supply chain, most are vague about what processes are to be considered, what sub-processes and activities are contained in each process, and how the processes interact with each other and with the traditional functional silos. In this paper, we provide strategic and operational descriptions of each of the eight supply chain processes identified by members of The Global Supply Chain Forum, as well as illustrations of the interfaces among the processes and an example of how a process approach can be implemented within an organization. Our aim is to provide managers with a framework to be used in implementing supply chain management, instructors with material useful in structuring a supply chain management course, and researchers with a set of opportunities for further development of the field.

This study aims to determine the effect of supply chain management and the implementation of enterprise resource planning on organizational performance. The population and sample used in this study was employees of PT. Semen Padang is involved in the supply chain management process and runs an enterprise resource planning system. Data was collected through distributing questionnaires and data processing was carried out through SPSS 16.0 software. The results of this study indicate that supply chain management has a significant positive effect on organizational performance. The results of this study indicate that the implementation of enterprise resource planning on organizational performance has a significant positive effect on organizational performance.Keywords: Enterprise resource planning, supply chain management, organizational performance

Supplier selection is a critical decision-making process in supply chain management, significantly impacting operational efficiency and cost-effectiveness. Traditional methods often struggle to handle the inherent uncertainty and vagueness involved in evaluating supplier criteria. This paper proposes a novel approach for supplier selection using Intuitionistic Trapezoidal Fuzzy Numbers (ITFNs), combined with Earth Mover's Distance (EMD) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS), to effectively model and address these uncertainties. ITFNs allow for the representation of both membership and non-membership degrees, enhancing the ability to capture imprecision in supplier evaluations. The Earth Mover's Distance, a powerful metric from transportation theory, is employed to rank the intuitionistic trapezoidal fuzzy numbers and compare suppliers based on multiple criteria using fuzzy TOPSIS. The proposed model is demonstrated through a case study, showcasing its ability to provide more flexible and accurate supplier rankings compared to conventional methods. This approach offers valuable insights for decision-makers aiming to optimize supplier selection in complex and uncertain environments, contributing to more resilient supply chains.

Inventory management and control is concerned with the acquisition and storage of materials required for supporting various business operations. Lead time reduction is another important production activity in an integrated inventory control. Lead time plays a vital role and has been a topic of interest for many authors in inventory organization. Lead time is a necessary aspect in any supply chain management and inventory management system. The time gap between placing of an order and its actual arrival in the inventory is known as lead time. In most of the literature dealing with inventory problems, either in deterministic or probabilistic model, lead time is viewed as a prescribed constant or a stochastic variable, which therefore, is not subject to control. But, in numerous sensible circumstances, lead time can he reduced by an additional crashing cost; in other words, it is controllable, so in this article, we have considered the lead time crashing cost is an exponential function of lead time. A solution procedure is developed to find the optimal solution. In this article, both lead time and the order quantity are considered as the decision variables. The behaviour of the model is presented graphically. The result is illustrated with the help of a numerical example. Finally, Graphical representation is presented to illustrate the model. The solution procedure with the help of the software Matlab 2008 is furnished to determine the optimal solution.

Abstract. Supply Chain Management (SCM) is the management of the products and goods flow from its origin point to point of consumption. During the process of SCM, information and dataset gathered for this application is massive and complex. This is due to its several processes such as procurement, product development and commercialization, physical distribution, outsourcing and partnerships. For a practical application, SCM datasets need to be managed and maintained to serve a better service to its three main categories; distributor, customer and supplier. To manage these datasets, a structure of data constellation is used to accommodate the data into the spatial database. However, the situation in geospatial database creates few problems, for example the performance of the database deteriorate especially during the query operation. We strongly believe that a more practical hierarchical tree structure is required for efficient process of SCM. Besides that, three-dimensional approach is required for the management of SCM datasets since it involve with the multi-level location such as shop lots and residential apartments. 3D R-Tree has been increasingly used for 3D geospatial database management due to its simplicity and extendibility. However, it suffers from serious overlaps between nodes. In this paper, we proposed a partition-based clustering for the construction of a hierarchical tree structure. Several datasets are tested using the proposed method and the percentage of the overlapping nodes and volume coverage are computed and compared with the original 3D R-Tree and other practical approaches. The experiments demonstrated in this paper substantiated that the hierarchical structure of the proposed partitionbased clustering is capable of preserving minimal overlap and coverage. The query performance was tested using 300,000 points of a SCM dataset and the results are presented in this paper. This paper also discusses the outlook of the structure for future reference.

Queueing Network Models (QNMs) with Finite Capacity provide powerful and realistic tools for the performance evaluation and prediction of discrete flow systems such as computer systems, communication networks and flexible manufacturing systems. Over recent years, there has been a great deal of progress towards the analysis and application of QNMs with finite capacity, and high quality research work has appeared in diverse scientific journals of learning and conference proceedings in the fields of Operations Research, Computer Science, Telecommunication Networks, Management and Industrial Engineering. However, there are still many important and interesting finite capacity queues and QNMs to be resolved, such as those involving multiple-job classes, bounds and theoretical properties, exact analysis, numerical solutions and approximate methods, as well as application studies to computer and distributed systems, high-speed networks and production systems.This volume of Annals of Operations Research on Queueing Networks with Finite Capacity presents 17 extended research papers, both from industry and academia, reflecting current original contributions in the theory and applications of finite capacity queues and networks worldwide. Earlier short versions of these papers were amongst those presented at QNets '95, The Third International Workshop on Queueing Networks with Finite Capacity, 6th-7th July, 1995, Ilkley, West Yorkshire, UK. The papers are classified into four parts covering exact analysis, approximate methods, error bounds and performance modelling studies.

Involvement of controllable lead time and variable demand for a smart manufacturing system under a supply chain management

Supply chain management is a critical aspect for every manufacturing business, in particular for safety critical systems where quality assessment and requirement satisfaction are evaluated in order to guarantee a certification of the final system. In this paper we present a blockchain-based methodology that focuses on enforcing strict data access control and that securely includes manufacturing equipment in the supply chain management process. The methodology provides cyber-security guarantees exploiting secure automated interaction between a blockchain supply chain management system and industrial manufacturing devices. A prototype of the proposed approach is implemented with state-of-the-art distributed ledger technologies integrating low-power devices hardened through the application of hardware security modules.

The integration of Artificial Intelligence (AI) into Supply-Chain Management (SCM) has revolutionized operations, offering avenues for enhanced efficiency and decision-making. AI has become pivotal in tackling various Supply-Chain Management challenges, notably enhancing demand forecasting precision and automating warehouse operations for improved efficiency and error reduction. However, a critical debate arises concerning the choice between less accurate explainable models and more accurate yet unexplainable models in Supply-Chain Management applications. This paper explores this debate within the context of various Supply-Chain Management challenges and proposes a methodology for developing models tailored to different Supply-Chain Management problems. Drawing from academic research and modelling, the paper discusses the applications of AI in demand forecasting, inventory optimization, warehouse automation, transportation management, supply chain planning, supplier management, quality control, risk management, and customer service. Additionally, it examines the trade-offs between model interpretability and accuracy, highlighting the need for a nuanced approach. The proposed methodology advocates for the development of explainable models for tasks where interpretability is crucial, such as risk management and supplier selection, while leveraging unexplainable models for tasks prioritizing accuracy, like demand forecasting and predictive maintenance. Through this approach, stakeholders gain insights into Supply-Chain Management processes, fostering better decision-making and accountability.