CLOUTIDY: A CLOUD-BASED SUPPLY CHAIN MANAGEMENT SYSTEM USING SEMAR AND BLOCKCHAIN SYSTEM

Organizations rely on various types of information systems (IS) to manage day-to-day business and make decisions such as enterprise resource planning (ERP) and supply chain management (SCM) systems. Organizations rely on ERP systems to replace their legacy systems, integrate core business processes and to help adding value and increasing visibility. Additionally, SCM systems help organizations to enhance relationships with supply chain members. It is essential for organizations to measure their business performance by taking into consideration intra-organizational and inter-organizational indicators. Therefore, the integration between ERP and SCM systems is a key to enable more business performance; that were otherwise hidden. Accordingly, the motive for this paper is to study the influence of ERP-SCM integration on enabling more business performance measures. For this reason, a business performance measures framework was constructed and then tested on two organizations using multi-case study qualitative research approach. Analysis results indicated that integrating ERP and SCM systems would render more performance measures and hence enable better and wider-scope evaluation. Consequently, managers are more informed and accordingly are able to make high quality decisions.

Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems have been growing in popularity across various segments of the electronic industry over the past few years. New initiatives in resources planning, electronic commerce and extended supply chain are fuelling the trend among corporations toward integrating strategic business applications. ERP systems assist enterprises in automating and integrating corporate cross-functions. SCM systems support information flow from various players in the supply side, reducing transaction cost and facilitating supplier-customer interactions. Given the popularity and importance of ERP and SCM systems, this research aims to examine and compare these two systems from the standpoint of organisational and technological implementation and explores the trend and opportunity of their system integration. Furthermore, the authors present and analyse two potential approaches to linking ERP and SCM systems, which indeed promote their integration. This article is concluded with the challenges and implications of the ERP and SCM system integration.

The big floods that hit Malaysia in December 2014 to January 2015 affected more than 200,000 people and caused 21 fatalities. Shortage of food supply, clean water, and problems caused by unreliable communication continued to affect flood victims even after the flood water had receded. As aid is crucial, the correct management of the supply chain is important to allow a correct and relevant assistance to the needy population. Thus, the Supply Chain Management (SCM) system, which includes inventory management system, automatic matching of supply and demand, consumption trend analysis and data visualization is critical so that the delivery of humanitarian aid can be executed on time, which in turn reduces the impact of the disaster. Lack of either one of these components can lead to problems of forecasting the needs of the flood victims and causes unnecessary delays in decision making. The main objective of this paper is to propose a framework for SCM to support flood disaster relief operation and describe the different phases within the supply chain of food aid in a humanitarian crisis. This proposed framework is to enhance the existing SCM system so that the three phases involved in a flood disaster, which are pre-disaster, during disaster and post-disaster can be managed in a more systematic way. Based on the proposed framework, a supply chain management system can be developed to include inventory management system for monitoring, managing and distributing goods; matching of supply and demand for overcoming demand uncertainty; and data visualization for quick decision making.

Employees’ Reactions to IT-enabled Process Innovations in the Age of Data Analytics in Healthcare

Due to the intensely increasing competition and rapidly changing demand, Mass Customization(MC) manufacture has been the new trend for modern enterprises to respond quickly to the variational and individual demand in global market. To satisfy the operation of MC manufacture, its supply chain must be effective and flexible enough. Findings of researches and practices have shown that some intelligent activities, such as negotiation, decision and collaboration, have become the “bottleneck” to improve the performance of supply chain. This paper applied the Autonomous Intelligent System(AIS) to deal with the intelligent activities intelligently and automatically, and thereof provided a new method for the Supply Chain Management(SCM) on MC manufacture. First, a new supply chain model based on E-HUB was presented according to the requirements of MC manufacture, and then the structure and operation of AIS were designed to support that SCM. Finally, the development technology of AIS was discussed.

We need the ability to quickly adapt to new supply chain operations in order to keep up with the ever-evolving nature of today’s business climate. Supply chain management activities are one area where software agents have been widely implemented and used with great effectiveness. Agents’ actions are defined by their intended function, and their utility is evaluated in light of their design goals. Managing intricate supply networks now requires the use of decision-support tools. By quickly identifying and fixing bottlenecks and capitalising on market opportunities, logistics diagnostics helps freight transport businesses thrive for the long haul. The management system takes a logistical approach in order to prioritise value. Values and needs of consumers are always changing, and so are the goods and services that meet their needs. used to create those values are the foundation of the logistic method. The research’s ultimate goal is to formulate a systematic means of creating a supply chain management system and performance index. This study employed the research approaches of system analysis, logical-structural procedures, categorization, and grouping. Incorporating big data analytics with the ability to apply autonomous corrective control actions, this article creates a multi-agent-based supply chain management system with an enhanced performance index. The implications of the system on the flexibility of the supply chain are examined.

The Radio Frequency Identification has opened a new phase in Supply Chain Management that must be considered by software reuse. The System of Systems presents in Supply Chain Management make use of very reusable information through of repeated scenarios and actors with the same function. However, do not found a specific domain analysis process based on a well defined set of guidelines and steps to the supply chain domain. This paper present an approach of Domain Analysis to developing Radio Frequency Indentification Systems in Supply Chain Management.

In the new dynamic economic environment where supply chains increasingly face constant change and instability, the better supply chain planning and management enabled by advanced data management systems provides enhanced value proposition for customers resulting in improved profitability for firms along the supply chain. However, achieving such high quality level of supply chain visibility is not an easy task requiring technological capability, organizational willingness and data quality management intensively demanding attention from both managers and scholars. Therefore, the chapter begins with an overview of the role of information systems in supply chain management followed by a discussion regarding the role of information quality in successful supply chain interactions. Data quality management in terms of strategy and data governance is then reviewed. Finally, data quality tools complementing strategy dimension of data quality management are studied.

Modern supply chain is a complex system and plays an important role for different sectors under the globalization economic integration background. Supply chain management system is proposed to handle the increasing complexity and improve the efficiency of flows of goods. It is also useful to prevent potential frauds and guarantee trade compliance. Currently, most companies maintain their own IT system for supply chain management. However, this approach has some limitations that prevent one to get most of the supply chain information. Using emerging decentralized ledger technology to build supply chain management system is a promising direction. However, decentralized ledger usually suffers from low performance and lack of capability to protect information stored on the ledger. To overcome these challenges, we propose CoC, a novel supply chain management system based on hybrid decentralized ledger. We develop an efficient block construction method with the model and security mechanism to prevent unauthorized access to data stored on the ledger.

Background. Information is one of the key elements for effective supply chain (SC) management. Availability of timely information ensures the continuous movement of commodity flows, minimizes the company's losses associated with the lack of goods or excess stocks, allows you to plan production and financial flows, take into account changes in consumer interests. The systems of information exchange between the participants of the SC are used. Predicting future sales is necessary to control the flow of goods, so firms do consumer demand forecasting.Objective. Since the explicit information has different usefulness for forecasting and decision-making for different SC participants, the problem arises of assessing the priority of various types of information in the SC management system in order to obtain a more accurate forecast of consumer demand. The aim of the work is to develop an algorithm for calculating the values of the relative importance or priority of various types of information in the SC based on a hierarchical model of criteria using the opinions of the SC participants.Methods. The task of systemic assessment of the priority of information needs in the SC management system is formulated as a task of multi-criteria decision support. There are several types of information that can be used to predict consumer demand, these are alternative solutions. A hierarchical model of SC has been built, which includes criteria for the cost of obtaining information from other participants of the SC, accuracy and reliability of the source of information, degree of its use and other criteria affecting the efficiency of using information for forecasting demand.Results. An algorithm for analyzing the SC management system based on a decision support hierarchical model is proposed. The functional capabilities of the developed decision support system for solving multiple criteria decision support problems are shown, when the input information are evaluations of the SC subjects.Conclusions. The developed algorithm is used for a wider class of decision support problems, in particular when part of the expert assessments is not sufficiently consistent. In this case, the most inconsistent assessments are searched without the participation of an expert, the set of expert evaluations is adjusted by various methods depending on the properties of the corresponding pairwise comparison matrices. The proposed algorithm includes several methods for calculating the local weights of the elements of decision support hierarchical model and various methods of aggregating these weights, which increase the reliability of the obtained results.

The paper presents possible solutions to small farmers in the agricultural sector in a small island setting grappling with challenges of enhancing their local industry and import substitution through a supply-demand chain framework. In 2005, Hawaiian farms produced approximately 363,200 kg of avocados. However, Hawaiian demand for these fruits required an additional 908,000 kg of avocados that were supplied through imports. At the same time, it is worth noting; almost 49% of locally produced avocados did not reach the market. These facts clearly suggest inefficiencies in the local avocado supply chain. Review of the literature on supply chain management suggests a sustainable avocado industry requires an integrated supply and demand chain management system. Hence, a modified conceptual model of supply-chain management called the Supply Demand Integrated Management (SDIM) model is proposed in this paper. Traditional supply chains contain three functional components: supply coordination, physical production and logistic management. In the new model, we propose adding R4D (Research for Development), a public good provided by the government to agricultural industries as an integral part of the supply-demand chain system. It articulates the significance of research and supply chain partnerships between small growers, industry groups, and public agencies. A well-integrated demand/supply chain in the avocado industry allows sharing of vital market information which can contribute to long-term strategic decisions that help local avocado growers increase their competitiveness against foreign imports. It also provides metrics in terms of consumer satisfaction, profitability, competitiveness, self-sufficiency, import substitution and sustainability. The SDIM model as applied to the avocado industry suggests two hypotheses relevant to the current situation. The hypotheses are: (1) There is a lack of coordination between what the farmers are growing and the chefs' (a high-end customer/buyer) preferences; (2) Forming an avocado farmer cooperative will enhance the profitability of local farmers. Survey data was collected from Hawaiian chefs and avocado farmers. Data was analysed using a Duncan multi-range test and a mathematical model, respectively, to test the hypotheses. The test revealed a lack of coordination between what the farmers are growing and buyer preferences. The results from the mathematical model suggested that formation of an avocado marketing cooperative would enhance the profitability of local farmers. Thus, the SDIM framework expands existing body of knowledge about supply chain management and provides useful information to local avocado growers that can help strengthen their supply and demand chain management.

Through the analysis of the feasibility of using network technology and network technology in supply chain management system, the paper puts forward the design framework of supply chain management system based on the Internet of things, describes the key technology of the system. The application of Internet of things in the supply chain management system, that will realize the effective management of the whole supply chain; enhance the competitiveness of enterprises and the whole supply chain.

Most of the traditional approaches for gathering data in the information sharing platforms of manufacturing supply chains are outdated and can negatively impact the speed of real-time sharing of information. In this technology-driven world, the supply chain management operations based on the Internet of Things (IoT) are bringing day after day a great business potential. Therefore, connecting physical objects together in support of intelligent decision making, consists an enabler of real-time quality management and control in the supply chain. The connected objects should meet market demands correctly, rapidly, and profitably. The supply chains should maintain and sustain technology-based and quality-driven capabilities in order to decrease network wide costs, reduce lead duration and transit duration and improve user experience. This work tackles the implications of the roll-out of the internet of things in supply chain management tasks. We focus on the quality of experience and the quality assurance arising from data consumption as a result of connecting objects to the Internet and how this can be translated into traces and evidences for some selected properties. The proposed model will be illustrated with a test scenario to verify the selected requirements.

Multi-criteria decision-making techniques have been used to solve a range of real world problems in management science and specific supply chain management (SCM) problems (e.g. supply chain design and reconfiguration, purchasing, scheduling, supplier selection). The results obtained are encouraging. Nevertheless, robust approaches for solving multi-criteria supply chain problems are still in progress, and more research is needed before an effective and operational framework can be developed. The proposed approach introduces a two-phase hierarchical approach to solve a multi-criteria SCM problem integrating both strategic and tactical decisions where the supply chain is evaluated based on the supply chain operations reference model. The latter considers various metrics such as delivery reliability, flexibility, responsiveness and cost. The first phase evaluates different supply chains configurations using analytic hierarchy process. The second phase solves the network for the optimal safety stock placement using dynamic programming. The output from this two-phase process is a supply chain network configuration that has the right amount of safety stocks at the right place to absorb variability in demand.

The integration of machine learning (ML) in supply chain management (SCM) is revolutionizing the way financial transactions are processed, monitored, and optimized. Traditional financial transaction systems within supply chains often suffer from inefficiencies, latency, fraud vulnerabilities, and limited scalability. This paper explores various machine learning approaches that enhance financial transactions in SCM by improving accuracy, speed, security, and decision-making. Specifically, the study examines supervised learning techniques such as regression and classification for credit risk assessment and payment forecasting, as well as unsupervised learning for anomaly detection and fraud prevention in transactional data. Reinforcement learning is also discussed in the context of dynamic pricing, payment scheduling, and financial risk optimization across supplier networks. Deep learning models, particularly those using neural networks, are highlighted for their capabilities in real-time financial transaction processing and predictive analytics in complex, multi-tiered supply chains. Case studies from leading industries demonstrate the effectiveness of ML in enhancing transparency, reducing transaction costs, and increasing trust between supply chain partners. Furthermore, the paper addresses challenges such as data quality, interpretability of models, regulatory compliance, and the need for robust infrastructure to support ML implementation. The research also outlines best practices for integrating ML into existing financial systems and supply chain platforms, emphasizing the importance of cross-functional collaboration among IT, finance, and operations teams. The study concludes that the adoption of ML-driven solutions can significantly elevate the performance of financial transaction systems in SCM, resulting in improved cash flow management, better supplier relationships, and more resilient supply networks. These findings offer valuable insights for businesses seeking to leverage machine learning for strategic financial and supply chain innovation. Keywords: Machine Learning, Financial Transactions, Supply Chain Management, Predictive Analytics, Anomaly Detection, Reinforcement Learning, Credit Risk Assessment, Fraud Prevention, Dynamic Pricing, Transaction Optimization.