Efficient Supply Chain Research Articles

Effectiveness of Halal Traceability and Self-Declared Certification on Indonesian MSMEs Performance

This study aims to examine in depth the effectiveness of halal tracea-bility in Indonesia and identify the impact of self-declared halal certifi-cation on the performance of small and medium-sized enterprises (SMEs) in the country. The research method used is qualitative, which is reinforced by statistical analysis through survey data from the Popu-lix application as well as a comprehensive literature review. Data anal-ysis was conducted through various techniques, including regression tests, correlation tests, chi-square tests, and content analysis. The re-search findings confirm that the effectiveness of the halal tracking sys-tem significantly contributes to improving the resilience and competi-tiveness of MSMEs. Moreover, self-declared halal certification has been proven to increase consumer confidence in halal products and their integrity. This research makes an important contribution by high-lighting the urgency of integrating efficient halal supply chain man-agement practices, including reliable tracking systems and credible cer-tification. Furthermore, this research underscores the necessity of en-hancing consumer awareness of halal products, thereby facilitating in-formed purchasing decisions and fostering the growth of a more resili-ent halal ecosystem in Indonesia.

Optimizing smart grid performance: A stochastic approach to renewable energy integration

Smart grids offer numerous possibilities for developing and executing sustainable and efficient electricity supply chains that exhibit increased resilience to disturbances. The exploration of the Electric Supply Chain Network (ESCND) design problem using smart grids has been limited. This article aims to tackle this challenge through the application of a robust multi-objective optimization approach, encompassing three economic goals (maximizing profit), environmental goals (minimizing greenhouse gas emissions), and resilience goals (maximizing network resilience). Additionally, the optimization process takes into account the dual objectives of maximizing efficiency and minimizing energy loss by incorporating relevant cost considerations. The proposed methodology incorporates distinctive features of smart grids, such as demandside management programs, microgrid structures, bidirectional distribution lines, and consumer–supplier nodes. It addresses various interconnected decisions, including location placement, capacity expansion, load allocation, and pricing. To solve the formulated model, a potent combination of multi-objective optimization methods based on cutting-plane algorithms and AUGMECON2 is introduced. Subsequently, the proposed model and solution approach are applied to a practical case study, and the obtained results are comprehensively examined. The findings suggest that, despite potential contradictions between economic and environmental objectives, the implementation of smart grids can concurrently improve environmental performance and network flexibility.

Supply Chain Management Enhance NG Efficiency and Collaboration Industry Company

Purpose Collaboration may be the most important core competency and has been described as the engine of efficient supply chain management. There is a common perception, nevertheless, that not many businesses have fully realised its potential. In order to determine best practices and evaluate the state of supply chain collaboration, a research was conducted. Design, procedure, and strategy CEOs of supply chains offered advice on working together. A conceptual model characterising the behaviour, culture, and interpersonal relationships linked to productive collaboration was created using survey data, in-person interviews, and an analysis of the literature on collaboration Results. The increase of efficiency, effectiveness, and market positioning for the enterprises of the responders are positive results associated to collaboration. Research constraints and implications Although the limited sample size is a drawback, it is counterbalanced by the calibre of the respondent pool and their level of training and experience. Getting feedback from only one partner in the cooperative partnerships is another drawback. It would be possible to ascertain how collaboration-related interactions and variables evolve over time by creating a longitudinal research. Consequences for practice A "blurring of lines" between organisations was highlighted by a number of respondents in contrast to a "us vs. them" mentality. This was conveyed in several ways, including by recognising the agreements as co-owned, treating them as a single unit, and reorienting the attention to the greatest possible compromise. Even though many of the respondents said that benefits are not given out equally, they acknowledged that they benefit enough from the cooperative agreements to make them worthwhile. Uniqueness and worth Many of today's leading firms are mentioned in real-world practical experiences.

Supply chain network design based on Big Data Analytics: heuristic-simulation method in a pharmaceutical case study

Digital technology is a pivotal aspect of the ongoing digital transformation upon which supply chain networks (SCNs) heavily rely. Through the utilization of Big Data Analytics (BDA), organizations can gain insights into sales, production and formulate decision support systems. The pharmaceutical supply chain emerges as a crucial player in producing and distributing medications vital for treating COVID-19 patients. In this study, we have innovatively designed a multi-echelon Pharmaceutical Supply Chain Network (PSCN) using BDA during the COVID-19 pandemic. We have developed a novel Mixed-Integer Non-Linear Programming model addressing allocation, production planning, and inventory control decisions, known as the LAPI problem. A transformation method is employed to convert the non-linear model into a linear one to enhance computational efficiency. The primary objective of our mathematical model is to minimize the total cost and CO2 emissions of the transportation system and established centres while maximizing the shelf life of medicines. Our experiments reveal that medium- and large-scale problems, characterized by the three main attributes of BDA – variety, velocity and volume (3 V‟s), pose challenges for optimal solutions. We present a case study of Iran to validate the mathematical model for large-scale problems. Employing a simulation approach, we estimate the demand for required pharmaceuticals, underscoring our proposed model’s real-world applicability and effectiveness. This research contributes to the growing necessity of eco-friendly and efficient supply chain strategies, especially in the critical domain of pharmaceuticals during pandemic situations.

Consumer service level-oriented resilience optimisation for e-commerce supply chain considering hybrid strategies with blockchain adoption

ABSTRACT The booming development of customised e-commerce makes e-commerce supply chains experience a severe test of consumer service level (CSL). An efficient e-commerce supply chain resilience optimisation method addressing economic performance and service performance is proposed to improve customised services under disruption risks. The proposed method includes a hybrid strategy considering both a resistance strategy with blockchain (BCT) adoption and time-dependent recovery strategies simultaneously. A two-stage multi-period multi-product stochastic programming with BCT adoption is then proposed, which (1) implements BCT as the resistance strategy in the pre-disruption stage; (2) collaborates four recovery strategies in the post-disruption stage; (3) supports the e-tailer in making decisions and optimises his profit and order fulfillment time while taking product priorities into account. Using the actual data of Chinese e-commerce during the public health emergency in 2020, it is demonstrated that (1) the applicability of the model with BCT adoption in both supply chain resilience and CSL improvement; (2) the performance of the hybrid strategy in long-term disruption management; (3) the efficiency and robustness of the proposed solution approach for multi-objective high-dimensional stochastic programming problems. E-tailers can optimise decision-making under long-term disruptions by the proposed methodology in practices.

Supply Chain Optimization in the Package Industry through Machine Learning Analysis

The package industry relies heavily on efficient supply chain management to meet customer demands and maintain profitability. However, managing complex supply chains involving multiple suppliers, transportation networks, and distribution channels poses significant challenges. This research proposes a machine learning-based approach to optimize supply chain operations in the package industry. By analysing historical data on supply chain activities, including procurement, inventory management, and distribution, our system aims to identify patterns and trends to improve decision-making processes. Machine learning algorithms such as support vector machine, naive Bayes, and logistic regression are utilized to forecast demand, optimize inventory levels, and streamline logistics operations. Experimental results demonstrate the effectiveness of the proposed approach in enhancing supply chain efficiency and reducing operational costs in the package industry.

Agriyug: A Python-Based Approach to ECommerce in Agriculture

This research explores the evolving landscape of e-commerce, particularly in the context of agricultural product transactions. With a growing trend in online shopping, users increasingly seek a seamless and enjoyable experience, specifically in the domain of purchasing agricultural goods. This study delves into the transformative role of Python in shaping the infrastructure of e-commerce for agricultural products. The research initiative, labelled as "AgriYug," aims to provide valuable insights into the changing dynamics and prospects of agricultural e-commerce. The focus is on enhancing the online shopping experience for farm products by leveraging the capabilities of Python technology. This research outlines the development of an e-commerce platform specifically designed to empower Indian farmers by enabling direct sales to consumers without intermediary intervention. Aligned with the national sentiment recognizing farmers as the backbone of the country, the platform addresses the economic challenges faced by farmers due to middlemen in the supply chain. The objective is to enhance farmers' economic autonomy and provide consumers with affordable, fresh produce. This initiative contributes to the broader goal of optimizing the agricultural market by eliminating excessive intermediary fees, ensuring fair compensation for farmers, and creating a more efficient supply chain.

Biodiesel supply chain network design: a comprehensive review with qualitative and quantitative insights.

The global community is actively pursuing alternative energy sources to mitigate environmental concerns and decrease dependence on fossil fuels. Biodiesel, recognized as a clean and eco-friendly fuel with advantages over petroleum-based alternatives, has been identified as a viable substitute. However, its commercialization encounters challenges due to costly production processes. Establishing a more efficient supply chain for mass production and distribution could surmount these obstacles, rendering biodiesel a cost-effective solution. Despite numerous review articles across various renewable energy supply chain domains, there remains a gap in the literature specifically addressing the biodiesel supply chain network design. This research entails a comprehensive systematic literature review (SLR) focusing on the design of biodiesel supply chain networks. The primary objective is to formulate an economically, environmentally, and socially optimized supply chain framework. The review also seeks to offer a holistic overview of pertinent technical terms and key activities involved in these supply chains. Through this SLR, a thorough examination and synthesis of existing literature will yield valuable insights into the design and optimization of biodiesel supply chains. Additionally, it will identify critical research gaps in the field, proposing the exploration of fourth-generation feedstocks, integration of multi-channel chains, and the incorporation of sustainability and resilience aspects into the supply chain network design. These proposed areas aim to address existing knowledge gaps and enhance the overall effectiveness of biodiesel supply chain networks.

Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization

Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. Traditional optimization methods often struggle to adapt to the complex and dynamic nature of these systems. This paper presents a novel blockchain-based zero-trust supply chain security framework integrated with deep reinforcement learning (SAC-rainbow) to address these challenges. The SAC-rainbow framework leverages the Soft Actor–Critic (SAC) algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC-rainbow algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The SAC-rainbow framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles. This research paves the way for developing secure, transparent, and efficient supply chain management systems in the face of growing complexity and security risks.

“E-Commerce Supply Chain Efficiency: A Case Study of Amazon Ecommerce Company”

This research is on supply chain efficiency of e-commerce industry. this research is on specific company for get idea about all e-commerce platform. Amazon company is one of the largest ecommerce companies in the world with extensive market capitalization. We are going to know that how amazon company maintain its supply and orders with their demands. There are mainly four key elements for efficient supply chain is integration, operation, purchasing and distribution. With supply chain efficiency, amazon will pick up inventory from manufacturing facilities around the world, ship it across borders, handle customs clearance and ground transportation, store inventory in bulk, manage replenishment across amazon and other sales channels, and driver directly to customer. All without seller having to worry about managing their supply chain. The new solution allows sellers to spend more time building great products, delighting customers, and growing their business, while amazon handles the logistics, improves delivery speed and reduce costs for sellers

Wholesale Price Discrimination and Contract Unobservability

This study examines how wholesale price discrimination (WD) by a supplier affects different parties in a supply chain involving a common supplier distributing homogeneous products through two competing retailers with different costs under wholesale price contracts. Especially, we allow for contract unobservability, where the contract terms between the supplier and each retailer are secret to the rival retailer. Intuitively, given the downstream asymmetry, WD should be more advantageous than the uniform wholesale price (UW) scheme for the supplier. This is true under observable contracts, as we show that WD benefits the supplier and less efficient retailer but hurts the more efficient retailer, supply chain, and consumers. Under unobservability, however, we find that the supplier may be better off by committing to UW. The intuition is that contract unobservability induces the supplier to set lower discretionary wholesale prices, which can outweigh the benefits of pricing flexibility. Consequently, a lack of commitment to UW can benefit both retailers, improve supply chain efficiency, and increase consumer surplus. Our findings suggest that policymakers should be cautious about imposing restrictions on WD. We also consider three extensions for robustness and offer new insights.

A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach

Annually, different regions of the world are affected by natural disasters such as floods and earthquakes, resulting in significant loss of lives and financial resources. These events necessitate rescue operations, including the provision and distribution of relief items like food and clothing. One of the most critical challenges in such crises is meeting the blood requirement, as an efficient and reliable blood supply chain is indispensable. The perishable nature of blood precludes the establishment of a reserve stock, making it essential to minimize shortages through effective approaches and designs. In this study, we develop a mathematical programming model to optimize supply chains in post-crisis scenarios using multiple objectives. Presented model allocates blood to various demand facilities based on their quantity and location, considering potential situations. We employ real data from a case study in Iran and a robust optimization approach to address the issue. The study identifies blood donation centers and medical facilities, as well as the number and locations of new facilities needed. We also conduct scenario analysis to enhance the realism of presented approach. Presented research demonstrates that with proper management, crises of this nature can be handled with minimal expense and deficiency.

An integrated framework for assessing renewable-energy supply chains using multicriteria decision-making: a study on Bangladesh

Abstract The depletion in non-renewable energy sources and a fast-growing population in Bangladesh are exacerbating the already existing energy scarcity, highlighting the need for an efficient and robust renewable-energy supply chain. The primary goal of this study is to evaluate the most optimized renewable-energy supply chain based on natural resource availability and government policies of Bangladesh. In the present study, four renewable energy resources, including solar, biomass, wind and hydropower, are studied and nine subcriteria are defined under four primary criteria for each supply chain. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) are multicriteria decision-making approaches used in this study to compare and choose the best renewable-energy supply chain. The relative significance of four supply-chain criteria for primary renewable energy in this study, namely energy procurement, production, operations and maintenance costs, and social and environmental impact, is gathered via a survey. The results of this research, supported by a comprehensive sensitivity analysis, indicate that hydropower is the best renewable-energy supply chain, followed by wind as a compromise solution, biomass and solar. The study also demonstrates that no energy source can satisfy all supply-chain criteria alone; each resource is better for a specific criterion—solar is better for procurement, hydropower is significant for production and wind is remarkable for operations and social impact. Therefore, to maximize output, renewable energy sources must be integrated. From Bangladesh’s perspective, for the first time, by using TOPSIS and VIKOR together, this study offers significant insights to establish an efficient and sustainable renewable-energy supply chain for practitioners, academics and policymakers.

Relationship between Socially Sustainable Supply Chains and United Nations Sustainable Development Goals: A Perspective from the Indian Auto Ancillary Industry

This article aims to understand the level of awareness about the United Nations sustainable development goals among supply chain leaders. Further, it explores how organizations’ social sustainability policies can help achieve sustainable development goals. The multi-stage process involves a structured survey of supply chain management leaders from the auto ancillary industry of the National Capital Region of India, and data were analyzed using econometric tools for meaningful insights. The significant findings from the study highlight that there is a considerable lack of awareness among supply chain leaders about the sustainable development goals defined by the United Nations. However, supply chain leaders make policies that influence social sustainability positively; they do not link it with sustainable development goals. The study reveals that leaders were unanimous on improving and adopting socially sustainable measures for creating efficient supply chains. This is one of the first studies that investigated the relationships between sustainable supply chain management and sustainable development goals laid down by the United Nations.

A Decision Support Model for Barriers and Optimal Strategy Design in Sustainable Humanitarian Supply Chain Management

Sustainable humanitarian supply chain (SHSC) management enables effective and efficient responses to natural and human-made disasters. Existing literature falls short of offering decision support (DS) models to address the barriers and strategies to designing SHSC. To this end, this study develops a DS model that identifies and prioritizes the barriers to SHSC and determines optimal strategies for mitigating those barriers. This study adopted both qualitative and quantitative approaches. As part of the qualitative approach, a field study was applied using in-depth interviews to determine the barriers and corresponding strategies, while under the quantitative approach, a quality function deployment (QFD) integrated optimization technique was used to prioritize barriers and determine optimal strategies to mitigate the SHSC barriers. The study found that a lack of contingency planning, the prevalence of corruption and political interference, and a lack of social and environmental awareness are the most important barriers, while logistics outsourcing, supply chain (SC) performance management, and SC flexibility are the most essential strategies. We also found that our DS model is highly flexible and can be adapted under different scenarios, which makes the model applicable to different contexts. This study has a significant contribution to literature and practice. We developed a novel decision model that captured cost savings and leveraged both cost and time savings from interrelated strategies to determine the best optimal strategy while applying QFD-integrated optimization modeling. The paper’s findings will assist humanitarian SC managers in designing an effective, efficient, and sustainable humanitarian SC.

Analysis of Business Intelligence Technology in the Big Data Era A Case Study of Alibaba Group

In the age of big data, business intelligence technology is pivotal in enhancing user experiences and driving innovation across industries. This paper focuses on Alibaba Group, a trailblazer in e-commerce, to examine the transformative role of business intelligence. This paper investigates Alibabas cutting-edge application of business intelligence technology, focusing on intelligent recommendation systems, personalized marketing strategies, and efficient supply chain management. The recommendation system harnesses data analysis to provide tailored product suggestions, boosting user satisfaction and sales. Data-driven marketing strategies enable Alibaba to create personalized promotions and coupons, enhancing user experiences and building loyalty. Intelligent supply chain management employs real-time monitoring, optimized transportation, and data-driven decisions to ensure timely deliveries and cost efficiency. A case study of Alibaba's "Singles Day Global Shopping Festival" illustrates how business intelligence technology creates a dynamic, data-powered shopping event. Every decision during this event is informed by real-time analysis and AI insights, enabling swift responses to evolving consumer needs. In summary, business intelligence, a driving force in the age of big data, is at the heart of Alibabas success. Alibaba has tapped the potential of business intelligence by enhancing the user experience and facilitating data-driven decision-making.

A digital twin-enabled digital spare parts supply chain

An efficient and effective spare parts supply chain management is crucial to ensure highly available production systems. However, achieving such a spare parts supply chain management is challenging due to the intrinsic characteristics of spare parts. Traditional spare parts supply chains (TSPSCs) adopt large spare parts inventories to tackle such intrinsic characteristics, with disadvantages in terms of high inventory costs. Recently, Additive Manufacturing (AM) has shown breakthrough potential, allowing the transition from TSPSCs to digital spare parts supply chains (DSPSCs), where spare parts are no longer stored in large amounts but are stored digitally and produced only when needed. However, despite DSPSCs benefits, a complete transition from TSPSCs to DSPSCs is still far from becoming reality. Indeed, hindering such a transition is a lack of expertise in three main areas, i.e. how to design, operate, and control and monitor a DSPSC. With this position paper, we envision how a digital twin (DT) could overcome these issues, enabling a full transition to a DSPSC. Moreover, this work further concretely suggests how such a DT could be developed, providing the basis for developing both the theoretical and practical knowledge necessary to render DSPSCs a reality.

Städte als Knotenpunkte von Konflikten. Die Rolle transnationaler Konzerne in städtischen Konflikten um Lieferketten und Logistik

AbstractMuch conflict studies scholarship on cities has focused on post-war urban reconstruction. However, high rates of violence and perceptions of urban insecurity, particularly in Latin America, have diluted the clear distinction between armed conflict and peace. From a different perspective, logistics studies have pointed out the conflictive impacts of goods movement on urban communities where efficient supply chains are the ultimate priority. This contribution offers an explorative reading of the 2021 protests in Colombia (‘paro urbano’) as the manifestation of a conflict in supply chain urbanism, and foregrounds the role of TNCs (transnational corporations) in escalating the conflict. Particularly in the city of Cali, protesters blocked roads and access for businesses for weeks. While contesting the national government and highlighting the extreme urban inequality exacerbated by policies primarily catered to TNCs and direct investments, they employed the role of the city as an essential logistics node as leverage in the conflict.

Smart Cities and Smart Supply Chain: Integration for Sustainable Urban Development.

As urbanization accelerates globally, the concept of smart cities emerges as a promising solution to address the multifaceted challenges of sustainable urban development. Smart cities leverage advanced technologies to enhance efficiency, sustainability, and quality of life for their residents. Concurrently, the evolution of supply chain management towards smart supply chains has gained traction, driven by the imperative of optimizing logistics, minimizing waste, and enhancing resilience. This paper explores the integration of smart cities and smart supply chains as a holistic approach to foster sustainable urban development. The integration of smart cities and smart supply chains presents a symbiotic relationship wherein advancements in one domain catalyze improvements in the other. Smart cities rely on seamless and efficient supply chain operations to ensure the smooth flow of goods and services, essential for meeting the needs of urban inhabitants. [1],[2] Conversely, smart supply chains benefit from the data-rich environment and infrastructure of smart cities to optimize routing, inventory management, and last-mile delivery, thereby reducing environmental impact and enhancing overall efficiency. Key components of this integration include the utilization of Internet of Things (IoT) devices, big data analytics, artificial intelligence (AI), and blockchain technology to enable real-time monitoring, predictive analytics, and transparent, secure transactions throughout the urban supply chain. By harnessing these technologies, cities can achieve significant improvements in resource utilization, energy efficiency, and carbon footprint reduction. Furthermore, the paper examines the role of public-private partnerships (PPPs) and regulatory frameworks in facilitating the integration of smart cities and smart supply chains. Collaborative efforts between government entities, industry stakeholders, and technology providers are essential to overcome barriers such as data privacy concerns, interoperability challenges, and investment constraints. In conclusion, the integration of smart cities and smart supply chains represents a promising pathway towards achieving the goals of sustainability, resilience, and prosperity in urban environments. By embracing innovative technologies and fostering collaboration, cities can pave the way for a more efficient, equitable, and environmentally conscious future.

Factor analysis of environmental effects in circular closed‐loop supply chain network design and modelling under uncertainty in the manufacturing industry

AbstractSustainable development and competitive advantage are impacted by strategic choices that maximize resource value and reduce waste. Numerous instances of thriving OEM remanufacturing can be observed, predominantly in the business‐to‐business domain. The significance of Closed‐Loop Supply Chain (CLSC) in generating value and managing the recovery process is widely acknowledged within the supply chain industry. Manufacturing companies now have to come up with green supply chain and process design strategies in response to recent changes in environmental regulations. This study designates the specific features of circular closed‐loop supply‐chain design considering end‐of‐life products. Uncertainty in various aspects, such as acquisition, processing, and market stages, is impeding the progress of circular economies and also sustainable development in closed‐loop supply chains (CLSCs). This has led to increased complexity in remanufacturing processes and decreased efficiency. To address this issue, the study proposes a comprehensive, integrative approach for establishing a sustainable CLSC network that adapts to fluctuating demand through a questionnaire analysis. Moreover, the study introduces a multi‐objective optimization model for a dual‐channel supply chain network, aiming to enhance the flow. This model considers both economic and environmental objectives to achieve a sustainable and efficient supply chain system. To determine the ideal circular closed‐loop supply chain (CLSC) network design, the research proposes linear programming model with mixed integer, where recovery can take place in one of three ways: through material recovery, component recovery, or product recovery. Numerous findings from a thorough analysis by data backup that inform CLSC managers of ways to improve product returns in terms of quantity and quality. To address the uncertainty problem, the research also developed the fuzzy credibility constraint technique with a Simulated Annealing algorithm. Using cutting‐edge methods, it is explored and compared how sensitivity analysis results, the impact of altering the problem parameters, and the performance of the suggested model respectively.