SUPPLY CHAIN MANAGEMENT BASED ON HIERARCHICAL DECISION SUPPORT MODEL

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.

Supply chain management in today's dynamic and complex business environment demands innovative approaches to decision support. This research introduces a novel hybrid framework that combines grid partition, rough set methods, and fuzzy logic to generate adaptive fuzzy rules tailored to supply chain data. By integrating these techniques, the study provides a comprehensive decision support system capable of addressing the intricacies and uncertainties prevalent in supply chain operations. A numerical example illustrates the practical application of this framework in optimizing inventory management within an e-commerce supply chain. The results showcase the effectiveness of the adaptive fuzzy rules in minimizing stockouts, reducing excess inventory, and optimizing inventory costs. Additionally, the study emphasizes the importance of balancing rule quality and complexity using a tunable parameter, offering flexibility for rule customization. The interpretability of the generated fuzzy rules further enhances their practical utility, enabling domain experts to comprehend and adjust decision criteria. This research not only contributes to advancing decision support systems in supply chain management but also lays the groundwork for future exploration of real-world data integration, adaptability to dynamic environments, and scalability challenges, thus promising significant enhancements in supply chain performance and resilience.

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.

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.

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.

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.

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.

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

Supply chain management (SCM) system is an essential requirement for companies and manufacturers to collaborate in doing business. There are many techniques to manage supply chains, such as using Excel sheets and web-based applications. However, these techniques are ineffective, insecure, and prone to human error. In this paper, we propose CLOUTIDY, a cloud-based SCM system using SEMAR (Service Market) and Blockchain system. We modify JUGO architecture to develop SEMAR as a broker between users and cloud service providers. Also, we apply the Blockchain concept to store the activity log of the SCM system in a decentralized database. CLOUTIDY system can solve several common cases: service selection, resource provisioning, authentication and access control. Also, it improves the security of data by storing each activity log of the supply chain management system in the Blockchain system.

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 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.

The challenges presented by recent changes in the business environment have sharpened the focus on the need for robust approaches to supply chain improvement. For most companies around the world, resources have been reduced, inventory drained, technology spending curtailed, and core processes scaled back in order to adjust to the current reality of reduced demand. So, this paper mainly concentrates on giving some tips based on recession recovery strategies. Firstly, this paper discussed the supply chain management trends in and up to 2015, then general concept of recession and the relation between recession and supply chain management system, then some fatal mistakes of recession recovery according to the supply chain experts and recession and recovery related literature reviews are summarized to identify ultimately the recession recovery strategies for the manufacturer. The paper ended with some supply chain management related predictions, tips for reducing supply chain barriers. Moreover, a brief section of this paper also here highlights the supply-chain infrastructure helpful for recession recovery that is one of the important factors for the reason of cost-reduction, resource planning and maintenance etc. in today's supply chain management and related environment.

The challenges presented by recent changes in the business environment have sharpened the focus on the need for robust approaches to supply chain improvement. For most companies around the world, resources have been reduced, inventory drained, technology spending curtailed, and core processes scaled back in order to adjust to the current reality of reduced demand. So, this paper mainly concentrates on giving some tips based on recession recovery strategies. Firstly, this paper discussed the supply chain management trends in and up to 2015, then general concept of recession and the relation between recession and supply chain management system, then some fatal mistakes of recession recovery according to the supply chain experts and recession and recovery related literature reviews are summarized to identify ultimately the recession recovery strategies for the manufacturer. The paper ended with some supply chain management related predictions, tips for reducing supply chain barriers. Moreover, a brief section of this paper also here highlights the supply-chain infrastructure helpful for recession recovery that is one of the important factors for the reason of cost-reduction, resource planning and maintenance etc. in today's supply chain management and related environment.

Farmers are inhibited from Agri supply chains due to upfront transaction costs. There is ample evidence that farmers' participation in supply chains produce benefits for farmers. This paper analyses how blockchain might support farmers to reduce transaction costs and improve their involvement in supply chains by reviewing the literature. The study contributes some theoretical insights to our knowledge on how transaction costs can be reduced by implementing supply chain collaboration with blockchain technology. It further presents some design implications for a blockchain-enabled system that would also support collective marketing. Practitioners in the Agri domain can use this analysis as a support for developing a system that improves farmers' participation in Agri supply chains.