Efficient Supply Chain Research Articles

Labor-Saving in Order Picking- A Real Time Study Advancing Lean Operations

Purpose: Efficient warehouse operations rely heavily on optimizing order picking processes, which are known to constitute a significant portion of overall operational costs. The aim is to enhance operational efficiency and cost-effectiveness by minimizing non-value-added tasks in warehouse operations. Materials and Methods: This study investigates the transition from an Assisted-Picker model to a Solo-Picker model at a prominent 3PL service provider in India. The analysis compares two approaches for picking orders—pairing laborers versus employing a Solo-Picker—using metrics such as travel time and picking time. Findings: Results reveal that while the Assisted-Picker model may initially show faster completion times for individual orders, the Solo-Picker model proves more efficient overall. This is attributed to the elimination of idle time associated with helpers and enhanced labor utilization efficiency. Real-time productivity data collected during the study period demonstrate a remarkable 44% increase in productivity with the Solo-Picker model, resulting in substantial Labor-Savings and significant reductions in operational costs. Implications to Theory, Practice and Policy: This study highlights the critical importance of reevaluating traditional labor allocation strategies in warehouse environments. By identifying and eliminating non-value-added work, warehouses can move closer to lean and efficient supply chain management practices, ultimately enhancing overall productivity and cost-efficiency.

Impact of resource reconfiguration on the dairy supply chain resilience

Investment in food supply chain resilience, as a critical infrastructure, has become necessary for all governments. Disruption of food supply chains can lead to significant economic challenges. Building a resilient supply chain requires resources; however, it is difficult for firms to allocate resources to various resilience strategies. This study allocates the budget to resilient capacity, that is, absorptive, adaptive, and restorative capacity, to minimize supply chain costs and maximize service levels. We developed a novel multi-objective mixed-integer nonlinear programming method for problem formulation. The developed model was converted into an equivalent linear model. We used the Monte Carlo approach to generate the scenarios and the average sample approximation to determine the required scenarios. Finally, the Lexicographic max-min approach solves the model using actual data from a dairy supply chain. The analysis revealed that allocating 50% of the budget to restorative capacity and the remaining to adaptive and absorptive capacity optimizes supply chain performance. This study provides insights for managers to make better decisions with a knowledge-based background, allocate resources to various resilient strategies, and build a more resilient and efficient supply chain.

A nutrient circular economy framework for wastewater treatment plants

Given the criticality of phosphorous and the harm eutrophication causes, wastewater treatment plants (WWTP) are undergoing circular economy (CE) conversions into becoming centres for resource recovery. This framework paper identifies the environmental, societal, political, commercial, economic, consumer, regulatory, legal, infrastructural, technological, international compliance, academic, agricultural, and plant-operator factors affecting the micro, macro and meso success and failure of nutrient CE WWTP across scope classifications. From this, nutrient CE framework is not as simplistic as it looks, given the multitude of impingers that need to be considered. The lack of technological readiness and vast selection methodologies for some nutrient recovery technologies, low private-sector investment, divisive consumer perceptions, unique economic situations, localised regulatory specifications, threats of inefficient technology lock-ins, poor acceptance among farmers and WWTP operators due to infrastructural incompatibilities, lack of efficient CE supply chains, legacy infrastructure, poor digitised solutions for nutrient CE management, low training and awareness, and integration of nutrient CE WWTPs with broader society are barriers. On the contrary, nutrient CE WWTP will mainly be driven by regulations, subsidies and consumer-WWTP-farmer nutrient CE acceptances. This framework aims to tie in and provide solutions to overcoming barriers to nutrient CE WWTPs that is universally applicable. Consequently, the framework provides a holistic structure for policy, legislative, academic, agricultural, plant operators, commercial and consumer stakeholders a roadmap for the success and difficulties of nutrient CE WWTP adoption.

Carbon finance, green supply chain management, and firms’ environmental innovation: the moderating role of financing structure

Many countries have introduced policies on carbon finance to promote the green development of firms and achieve a balance between the economy and the environment. This study uses institutional theory to construct a relationship model between carbon finance, green supply chain management, financing structure, and firms’ environmental innovation. It uses multiple regression analysis to examine the impact of carbon finance on firms’ environmental innovation, the mediating role of green supply chain management, and the moderating role of financing structure by selecting 1529 A-share listed companies in Shanghai and Shenzhen from 2012 to 2022 as its sample. The results show that carbon finance has a positive influence on firms’ environmental innovation, with green supply chain management playing a mediating role in this relationship. Additionally, financing structure weakens the positive effect of carbon finance on firms’ environmental innovation. Based on these findings, suggestions are made to improve the carbon trading market and trading system, build an efficient green supply chain management and supervision system, and encourage firms to optimise the financing structure.

Technology Focus: Offshore Facilities (September 2024)

Offshore wind power involves generating electricity from wind farms located at sea. Because wind speeds are generally higher offshore than on land, these farms produce more electricity for the same amount of installed capacity. Both wind and solar energy are renewable and do not emit carbon, which helps reduce dependence on fossil fuels such as oil and gas. The papers synopsized in this year’s Offshore Facilities feature cover a breadth of related subjects that will be critical to the future of the industry. Automation in the offshore oil and gas industry makes production safer, faster, and more efficient. It reduces human error, improves quality control, and allows real-time monitoring of operations. Machine learning improves offshore industries by enhancing safety, efficiency, and sustainability It tracks and predicts environmental changes, such as oil spills or marine life patterns, aiding in better environmental management and response. Remotely operated vehicles are used in offshore operations to inspect, maintain, and repair underwater infrastructure; the offshore industry uses 3D video imaging technology to provide detailed real-time images of the underwater environment. Corrosion in offshore environments occurs because of factors such as seawater, sea salts, temperature, salinity, and moisture. It is crucial to monitor corrosion because it can lead to major structural failures. Hydrogen can be produced offshore using renewable energy sources such as wind and solar, which do not emit greenhouse gases and help combat climate change. Supply-chain logistics boost efficiency by cutting costs, removing unnecessary steps, and speeding up product delivery. An efficient supply chain can lead to higher revenue, lower operational costs, and better overall performance. Decommissioning is the final stage of an oil or gas project and involves significant technical, economic, social, and environmental challenges. It is a lengthy, costly, and complex process that must be carefully planned and managed to avoid serious problems. Several ideas have come to the surface that show innovative ways to repurpose a decommissioned platform or ways to cut the cost of decommissioning. Recommended additional reading at OnePetro: www.onepetro.org. SPE 217706 Dynamic Simulation of Riser-Gas Handling With a Fixed-Choke Constant Outflow by Zhaoguang Yuan, SLB, et al. OTC 35092 Reducing Carbon Footprint by Using Fiber-Reinforced Polymers in FPSOs by S. Eves, NOV, et al. OTC 35393 Challenges and Approaches to Solutions for Green Decommissioning and Recycling of Offshore Facilities by Carlos Alberto Machado, American Bureau of Shipping, et al.

A Blockchain-Based Access Control System for Secure and Efficient Hazardous Material Supply Chains

A Blockchain-Based Access Control System for Secure and Efficient Hazardous Material Supply Chains

Closed-loop supply chain network design with price-greenness-sensitive demand: A distributionally robust chance-constrained optimization approach

In response to the government’s heightened focus on recycling and remanufacturing, as well as the growing awareness among consumers about environmental security, manufacturing companies are currently required to establish efficient closed-loop supply chain networks in order to improve their socialreputation and competitive advantage. This study investigates the optimization of a Closed-Loop Supply Chain (CLSC) network that involves multiple products, multiple periods, and uncertain returns, which also considers the influence of many factors, such as carbon cap-and-trade policy, raw part procurement discounts, and facility capacity constraints, on the supply chain. Simultaneously, customer demand is sensitive to both product pricing and product greenness, and product greenness can be improved by investing in emission reduction technologies. To address the uncertainty in the returns, we propose a two-stage distributionally robust chance-constrained optimization model, which is transformed into a mixed integer linear programming model. To efficiently address the complex problem, we designan improved Benders decomposition (IBD) algorithm. The experimental results confirm that the IBD algorithm has significant advantages when compared to the Benders decomposition algorithm. Additionally, this study conducted a sensitivity analysis on key parameters and proposed operation suggestions of practical importance.

Utilizing the Internet of Things (IoT), Artificial Intelligence, Machine Learning, and Vehicle Telematics for Sustainable Growth in Small and Medium Firms (SMEs)

New technologies like the Internet of Things (IoT), artificial intelligence, machine learning, and vehicle telematics have tremendous potential to improve SMEs business processes, increase efficiency, and reduce costs to obtain a competitive advantage. However, the application of these technologies is also associated with certain difficulties for SMEs to adopt and incorporate them in their business processes due to limited resources, knowledge and funds. The advancement in technologies such as IoT and the digitization and datafication of physical infrastructure and processes are causing massive shifts across fields. While an increasing number of devices are being connected to the internet and are capturing large volumes of information about operations, users and the physical environment, new opportunities are arising to leverage that big data for better analytics and automation. The purpose of this paper is to assess how SMEs can apply IoT, AI, machine learning and vehicle telematics for sustainable development by enhancing business processes, data analysis, predictive maintenance and efficient supply chain and transportation.

Designing a centralized storage hydrogen supply chain network with multi-period and bi-objective optimization

This study introduces a multi-period centralized storage optimization model aimed at designing an efficient hydrogen supply chain system, considering cost and emissions as dual objectives. It integrates multiple energy sources, production and storage methods, transport combinations, demand scenarios, and carbon capture systems, offering a comprehensive decision-making approach for hydrogen network design. Employing the mixed-integer linear programming methodology, the proposed model resolves these complexities. The research applies this model to a case study in France, generating six unique scenarios for 10 and 15 cities, and compares them against two distinct decentralized models. The findings consistently highlight the centralized storage model’s cost benefits across various demand scenarios, including cases of unrestricted emissions as well as cases with limited emission targets. The cost-effectiveness of this proposed model enhances its feasibility within the current context of decarbonization.

The Influence of Governance on Cyber Supply Chain Performance with Mediating Effect from Cyber Supply Chain Visibility

This research aims to analyze the influence of governance on Cyber Supply Chain (CSC) Performance, which is mediated by Cyber Supply Chain (CSC) Visibility to achieve effective and efficient cyber supply chain risk management (CSCRM) practices for the supply chain management (SCM) process. The population of this research is all manufacturing companies on the island of Java, Indonesia, that have used computerized systems in their supply chain processes with qualifications or certifications ISO 27001, ISO 28000, ISO 28001, or other certifications that are management information system standards. This research will reveal the strengths or weaknesses of the mediating role of CSC Visibility on CSC Performance in manufacturing companies. This study also found empirical evidence that having a dedicated governance team consisting of technical and non-technical personnel is important in CSCRM in the manufacturing industry, especially Java Island in Indonesia.

Integrating Blockchain and Deep Reinforcement Learning for Secure and Efficient Supply Chain Management in Tertiary Institutions

Integrating Blockchain and Deep Reinforcement Learning for Secure and Efficient Supply Chain Management in Tertiary Institutions

Performance linkage in renewable energy supply chain: A comparative analysis with coal power and the entire industry

Establishing an efficient renewable energy supply chain is crucial for mitigating climate change, contingent upon policy influence and market complementarity. Accordingly, this study 1) Draws on the characteristics of “market mechanism” and “price regulation” within China's renewable energy industry to compare the supply chain with the entire industry and the coal power industry, laying the groundwork for an analytical framework to assess performance linkage. 2) Examines the role of markets and policies in moderating performance linkage within the renewable energy supply chain based on cash flow inflow and final commodity pricing. Empirical findings indicate that: 1) China's renewable energy supply chain exhibits weaker market circulation mechanisms than the overall industry, but it has not yet shown zero-sum game characteristics akin to the coal power supply chain. 2) Cash flow inflow and final commodity pricing are identified as pivotal factors enabling China's renewable energy industry to circumvent zero-sum games while still lacking a robust positive cycle. This research aids policymakers in dynamically assessing market-based circulation levels within the renewable energy supply chain and facilitates quantifying price regulation and market funding roles in updating industrial policies.

The Role of Logistics and Infrastructure in Promoting International Trade

This study aims to explore the role of logistics and infrastructure in promoting international trade. Through systematic analysis of relevant theories and practical impacts, the research finds that logistics and infrastructure play key roles in reducing transaction costs, enhancing trade speed and reliability, and promoting market connectivity and integration. Specifically, an efficient logistics system can optimize transportation, warehousing, distribution, and inventory management, thereby improving the overall efficiency of international trade. Meanwhile, modern transportation, information and communication, and energy infrastructure provide robust support for international trade, facilitating efficient global supply chain operations and economic growth. However, the logistics and infrastructure sectors also face challenges such as infrastructure aging, cross-border logistics barriers, environmental issues, and sustainability concerns. In the future, smart logistics, green logistics, public-private partnerships, and international cooperation will become important development trends. Based on these findings, this paper proposes that governments should formulate supportive policies and improve infrastructure investment mechanisms, while enterprises should enhance logistics optimization and technological innovation. By conducting an in-depth study of the role of logistics and infrastructure in international trade, this research aims to provide theoretical support and practical guidance for policymakers and enterprises, promoting sustainable development in international trade.

Bibliometric Insights into Balancing Efficiency and Security in Urban Supply Chains

This study examines the balance between urban transportation security and supply chain efficiency amid global disruptions like the COVID-19 pandemic, attacks on maritime lanes, and geopolitical conflicts. The authors combine bibliometric network analysis and natural language processing (NLP) to review scholarly literature from 2017 to 2023. This approach identifies and visualizes evolving thematic trends. Findings reveal a shift towards integrating advanced technologies, such as blockchain, AI, and IoT, to enhance security without compromising efficiency. This study highlights the significance of integrating urban planning principles and sustainable practices to enhance transportation security and supply chain efficiency within urban environments. Actionable insights for policymakers and supply chain managers emphasize the adoption of integrated, technology-driven security solutions to foster resilient and sustainable supply chains. This research contributes to academic discourse by refining the understanding of evolving priorities in transportation security and supply chain management, offering both theoretical and practical implications for secure and efficient global supply chains.

Leveraging advanced technologies in Supply Chain Risk Management (SCRM) to mitigate healthcare disruptions: A comprehensive review

The Healthcare sector is undeniably one of the most critical sectors in any given economy and disruptions in this industry have dire consequences that affect patient care, the efficiency of healthcare systems and by far public health. This publication seeks to analyze and discuss the vital role of supply chain risk management in addressing disruptions, becoming resilient and ensuring that healthcare services function effectively. The pandemic (COVID-19) brought to fore just how critical it is to have a robust and agile system for supply chain risk management in healthcare, the sudden and unprecedented surge in demand for essential medical equipment like personal protective equipment (PPE) left millions of people at risk thereby jeopardizing their safety and health. This crisis echoed the urgent need for efficient and effective supply chain risk management strategies to mitigate such risks and ensure the availability of vital medical supplies. This review paper will be sharing different strategies and actionable tips to develop contingency plans, identify, reduce potential disruptions and recommended practices for risk management in the healthcare supply chain. This will highlight key areas such as inventory management and stockpiling, which focuses on maintaining safety stock levels to buffer against sudden disruptions, using advanced analytics to forecast demand and manage inventory levels. It will also examine risk assessment and identification by pinpointing potential attacks, threats, and vulnerabilities within the healthcare supply chain through thorough risk assessments. Additionally, it will cover supplier base diversification, emphasizing the importance of engaging multiple suppliers to reduce dependency on a single source and enhance supply chain resilience by balancing global and local suppliers to mitigate geographic disruption risks. Furthermore, it will explore the integration of technology, leveraging innovative solutions like blockchain for enhanced transparency, security, and traceability of supply chain transactions.

TransTLA: A Transfer Learning Approach with TCN-LSTM-Attention for Household Appliance Sales Forecasting in Small Towns

Deep learning (DL) has been widely applied to forecast the sales volume of household appliances with high accuracy. Unfortunately, in small towns, due to the limited amount of historical sales data, it is difficult to forecast household appliance sales accurately. To overcome the above-mentioned challenge, we propose a novel household appliance sales forecasting algorithm based on transfer learning, temporal convolutional network (TCN), long short-term memory (LSTM), and attention mechanism (called “TransTLA”). Firstly, we combine TCN and LSTM to exploit the spatiotemporal correlation of sales data. Secondly, we utilize the attention mechanism to make full use of the features of sales data. Finally, in order to mitigate the impact of data scarcity and regional differences, a transfer learning technique is used to improve the predictive performance in small towns, with the help of the learning experience from the megacity. The experimental outcomes reveal that the proposed TransTLA model significantly outperforms traditional forecasting methods in predicting small town household appliance sales volumes. Specifically, TransTLA achieves an average mean absolute error (MAE) improvement of 27.60% over LSTM, 9.23% over convolutional neural networks (CNN), and 11.00% over the CNN-LSTM-Attention model across one to four step-ahead predictions. This study addresses the data scarcity problem in small town sales forecasting, helping businesses improve inventory management, enhance customer satisfaction, and contribute to a more efficient supply chain, benefiting the overall economy.

E-procurement optimization in supply chain: A dynamic approach using evolutionary algorithms

The increasing dynamism of global markets, coupled with the occurrence of unpredictable events, has introduced substantial challenges in formulating efficient supply chain strategies. The inherent dynamic nature of logistic networks necessitates a departure from traditional supply chain methodologies. This study proposes an advanced solution for dynamic e-procurement utilizing evolutionary algorithms (EAs). In conventional supply chains involving buyers and suppliers, a critical challenge is identifying cost-efficient suppliers capable of fulfilling consumer demands amidst fluctuating prices and quantities. Traditional optimization techniques often fail to perform effectively under these dynamic conditions. Moreover, detecting changes during the optimization process is an additional hurdle in dynamic optimization problems. Recent advancements have demonstrated the efficacy of EAs in solving a variety of real-world dynamic optimization issues.This research introduces a novel evolutionary algorithmic framework that integrates the Hybrid Multipopulational Reinitialization Strategy (HMRS), with a proposed hybrid change detection mechanism (named Smirnov-based Multi-sensor Detection Mechanism (SMDM)) to address the dynamic e-procurement problems. The proposed framework enhances the algorithm’s adaptability and responsiveness to real-time changes within the e-procurement environment. By effectively detecting and responding to these variations, the framework aims to optimize procurement processes, ensuring efficiency and robustness in managing fluctuating requirements and conditions inherent to dynamic e-procurement scenarios. The empirical analysis presented underscores the superiority of Differential Evolution (DE) variants over Genetic Algorithm (GA) variants within the procurement context. The detailed empirical study validates the effectiveness of the proposed dynamic approach in addressing the challenges associated with dynamic e-procurement. Considering real-world parameter fluctuations, the proposed approach demonstrates significant resilience, positioning it as a robust and efficient solution for optimizing the e-procurement process and adeptly managing the complexities of the supply chain environment.

Utilizing the Internet of Things (IoT), Artificial Intelligence, Machine Learning, and Vehicle Telematics for Sustainable Growth in Small and Medium Firms (SMEs)

New technologies like the Internet of Things (IoT), artificial intelligence, machine learning, and vehicle telematics have tremendous potential to improve SMEs business processes, increase efficiency, and reduce costs to obtain a competitive advantage. However, the application of these technologies is also associated with certain difficulties for SMEs to adopt and incorporate them in their business processes due to limited resources, knowledge, and funds. The advancement in technologies such as IoT and the digitization and datafication of physical infrastructure and processes are causing massive shifts across fields. While an increasing number of devices are being connected to the internet and are capturing large volumes of information about operations, users, and the physical environment, new opportunities are arising to leverage that big data for better analytics and automation. The purpose of this paper is to assess how SMEs can apply IoT, AI, machine learning, and vehicle telematics for sustainable development by enhancing business processes, data analysis, predictive maintenance, and efficient supply chain and transportation.

Analyzing the Negative Impact of Supply Chain Regionalization

In response to increasing global trade, IKEA implemented a more efficient supply chain strategy and introduced a new policy in 2011 to localize its supply chain. The objective of this inquiry was to examine the alterations in supply chain management. Following the transition from centralized to regionalized supply chains, corporations faced adverse consequences such as decreased operational efficiency, increased operating expenses, and reduced adaptability in meeting market needs. These implications include increased complexity in supply chain management, decentralized operating models that result in repeated investments in fixed assets, and trade restrictions that create challenges in integrating information. The article proposes implementing efficient management systems, enhancing collaboration with local suppliers, and utilizing artificial intelligence to optimize the structure of the supply chain based on the observed impacts and outcomes. The objective is to offer risk management guidance for firms that have reached maximum production capacity and require a shift in their supply chain management methods.

Analyzing the Negative Impact of Supply Chain Regionalization

In response to increasing global trade, IKEA implemented a more efficient supply chain strategy and introduced a new policy in 2011 to localize its supply chain. The objective of this inquiry was to examine the alterations in supply chain management. Following the transition from centralized to regionalized supply chains, corporations faced adverse consequences such as decreased operational efficiency, increased operating expenses, and reduced adaptability in meeting market needs. These implications include increased complexity in supply chain management, decentralized operating models that result in repeated investments in fixed assets, and trade restrictions that create challenges in integrating information. The article proposes implementing efficient management systems, enhancing collaboration with local suppliers, and utilizing artificial intelligence to optimize the structure of the supply chain based on the observed impacts and outcomes. The objective is to offer risk management guidance for firms that have reached maximum production capacity and require a shift in their supply chain management methods.