3D PARTITION-BASED CLUSTERING FOR SUPPLY CHAIN DATA MANAGEMENT

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.

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)

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.

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.

As a business environment changes faster, companies intend to form a supply chain to complement resources each other that are valuable when distributed unevenly. In such a situation, a supply chain manager has to divide supply chain activities to match its demand and supply and assign them to supply chain organizations properly in order to utilize resources of each organization fully. However, causal relationships of supply chain activities and performances are quite complex and ambiguous and it is not easy to establish an ante-rational plan for supply chain improvement. In this research, by viewing a supply chain as a composition of structures and operations with a hierarchical organization structure, decisions and a decision making process of supply chain management are simplified and generalized. Then, relationships between organizational capabilities of supply chain structures and operations and its performance are organized by applying quality function deployment methodology. Finally, these relationships are modeled on system dynamics, which enables quantitative simulations of supply chain performance with its parameter that represent structural and operational capabilities.

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.

The increased amount of data being generated in virtually every context provides huge potential for a variety of organisational application fields, one of them being Supply Chain Management (SCM). The possibilities and use cases of applying this new type of data, i.e. Big Data (BD), is huge and a large body of research has already been conducted in this area. The current paper aims at identifying the understanding and the applications of BD not from an academic but a practitioners’ point of view. By applying expert interviews, the main aim is to identify (i) a definition of Big Data from SCM practitioners’ point of view, (ii) current SCM activities and processes where BD is already used in practice, (iii) potential future application fields for BD as seen in SCM practice and (iv) main hinderers of BD application. The results show that Big Data is referred to as complex data sets with high volumes and a variety of sources that can't be handled with traditional approaches and require data expert knowledge and SCM domain knowledge to be used in organisational practical. Current applications include the creation of transparency in logistics and SCM, the improvement of demand planning or the support of supplier quality management. The interviewed experts coincide in the view, that BD offers huge potential in future SCM. A shared vision was the implementation of real-time transparency of Supply Chains (SC), the ability to predict the behavior of SCs based on identified data patterns and the possibility to predict the impact of decisions on SCM before they are taken.

One of the agricultural sub-sectors that occupy a strategic position in agricultural development is the horticultural sub-sector, with one of its potential commodities being avocado. Avocado is one of the export-based commodities, especially in South OKU Regency, South Sumatra Province. This study aims to obtain an overview of the Avocado Agribusiness Supply Chain management in South OKU Regency. The study was conducted in Warkuk Selatan District, South OKU Regency in February 2022. The research method used was a qualitative descriptive method with a type of data using primary data and secondary data. The data analysis method uses the Food Supply Chain Network (FSCN) method, which illustrates South Oku Regency's avocado supply chain model. The results showed that the Avocado Management Supply Chain Management model with the FSCN framework consisted of four main components: the supply chain structure, the business chain process, supply chain management, and supply chain resources. Avocado supply chain targets are still dominated to meet the domestic market and products in the form of fresh avocados for consumption. Avocado supply chain management comprises election partners, contractual agreements, transaction systems, government support, and supply chain collaboration. The avocado supply chain structure in South OKU Regency consists of farmers, collecting traders, local and non-local traders, retailers, and consumers with respective roles in the supply chain structure. Business processes in avocado supply chain management consist of procurement, replenishment, and customer order cycles. Based on marketing margin analysis, the lowest total marketing margin is found in channel IV, with a margin value of Rp 1,500 per kg. The four avocado marketing channels in South Warkuk Ranau District have Farmer’s Share ≥40%, so it is categorized as an efficient channel.

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.

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 implementation of Supply Chain Management (SCM) makes many businesses manage information, goods, services and money as good as possible so they can provide fast and high-quality services to customers. Therefore, this study aims to implement SCM in Kopi Bintang Salatiga SMEs to decrease production costs, increase revenue, improve operational efficiency and establish good relationships with customers. This study discusses the SCM process and the added value in Kopi Bintang Salatiga SMEs so in the future Kopi Bintang Salatiga SMEs can have good risk management and increase added value. The data was collected through literature study and interviewing the owner of Kopi Bintang Salatiga SMEs. This study found that weather, natural disasters, fires and coffee pests are the factors with the highest risk in the SCM process of Kopi Bintang Salatiga SMEs. The added value of farmers & post-harvest coffee processors of Kopi Bintang Salatiga Kopi Bintang Salatiga SMEs is already quite high at 99%, while at grade I coffee sales is 71% and grade II is 47% it appears that coffee with grade II has lower added value, so it is needing accuracy of coffee processing at farmers & post-harvest coffee processors so the grade I coffee product is increased.

Data-driven decision-making has become a pivotal approach to enhancing processes in supply chain management (SCM), particularly in complex supplier selection and procurement strategy (SSPS). This study develops a novel data-driven SSPS model integrating multi-objective optimisation and real-world supplier performance data to improve supply chain efficiency, sustainability, and resilience against disruptions. Unlike traditional models focusing on limited or static scenarios, this framework simultaneously considers procurement costs, delivery delays, defect rates, sustainability performance, and disruption risks in multi-product procurement planning. The model employs augmented max–min fuzzy multi-objective linear programming, leveraging historical data to balance conflicting objectives and achieve a globally optimised solution. Furthermore, an innovative supplier negotiation mechanism supported by simulation-based analyses explores capacity adjustments, enabling win-win procurement outcomes. Experimental results show that, through capacity negotiation, procurement orders could be reallocated to only three suppliers instead of four, thereby reducing supplier management complexity. Notably, when the top-performing suppliers agreed to increase capacity by 45%, the average overall procurement performance improved from 81.77% to 88.28%, enhancing all objectives without additional costs. These results also demonstrate meaningful improvements in five objectives without incurring additional procurement costs. The findings underscore the practical potential of data-driven modelling in developing intelligent, sustainable, and resilient procurement decision-making frameworks.