Analyzing Key Challenges for Implementing Circular Supply Chains in The Indian Electronics Sector: A Study Using AHP-Entropy Methodology

The Circular Economy (CE) prioritizes sustainable practices over conventional ones that deplete resources and generate waste, which is crucial for the electronics industry due to valuable metals and harmful compounds in E-waste. This study aims to identify core obstacles to establishing Circular Supply Chains (CSC) in India's electronic sector. This article delineates twenty-seven challenges encountered by the Indian electronic manufacturing Supply Chain (SC) through a comprehensive literature review and insights from industry experts from prominent electronic multinational companies. These challenges were subsequently categorized into five distinct groups. To examine the prioritization of critical obstacles, we used the Analytical Hierarchy Process (AHP) technique in conjunction with Shannon's Entropy approach. The model robustness was investigated by performing sensitivity analysis. The investigation uncovers the cost of sustainable materials, restricted financial capacity for implementing CSCM initiatives, absence of fiscal incentives for advancing CSC, focus on immediate economic gains, and lack of matured technology resources as core obstacles. These study outcomes will help decision-makers understand the main challenges in migrating to a CSC, and accordingly, they may pick out a successful navigation path. The scientific procedure of modeling obstacles' criticality prioritization will assist them in enhanced decision-making and focused efforts for incorporating circularity in SC

Optimization Design of Fresh Cold Chain Logistics Network for Carbon Footprint

With the upgrading of consumption, the public demand for cold chain products and quality requirements is getting higher and higher, which promotes the rapid development of China’s cold chain logistics industry. As a transportation business with high energy consumption and high carbon emissions, cold chain logistics makes enterprises face high-cost pressure. Also, it contradicts the current low-carbon economy advocated by society. In order to balance the economic benefits brought by the rapid development of cold chain logistics and the negative impact on the environment, this paper constructs a multi-objective model with the lowest network construction cost and carbon emission as the objective function based on the customer demand in the region and designs a genetic algorithm with local search to solve the model. Simultaneously optimizing the distribution center location and distribution plan, the construction cost and carbon emission of the cold chain logistics network can be reduced to save costs and improve economic benefits. At the same time, the economic and social benefits are taken into account, which can provide a scientific decision-making basis for the network construction and operation of enterprises with cold chain logistics demands and contribute to the sustainable development of cold chain logistics

Intelligent Collaborative Sustainable Supply Chain Optimization: An Evolutionary Transfer Learning Framework

A multi-objective, multi-product, and cross-network sustainable supply chain network design problem is considered. We propose a mixed-integer linear programming model for cross-network collaboration, considering carbon trading, and design an evolutionary transfer learning algorithm to solve the model. The proposed model incorporates economic and environmental objectives, integrating carbon pricing, partner sharing, and logistics collaboration to account for carbon emissions within economic costs, thus achieving multi-objective optimization. The evolutionary transfer learning algorithm incorporates evolutionary procedures and a Markov decision-making process to solve the model efficiently. Extensive experiments based on real-world data are constructed, and the results demonstrate that the proposed method enhances problem-solving efficiency and accuracy across various scenarios while enhancing its stability and robustness. Additionally, case studies of different scales are demonstrated to verify the strong transferability of the proposed method

The Impact of Gauge Cluster Displays on Driver Attention and Mental Workload: An Eye-Tracking Study

With the increasing digitalization of automotive interfaces, optimizing gauge cluster design is crucial for minimizing driver cognitive load and distraction. Gauge clusters contain critical driving-related information linked directly to safety, such as speed, fuel levels, and warning indicators, which drivers must quickly and accurately perceive. This study uses eye tracking to examine the impact of analog and digital gauge clusters on driver attention. Thirty participants viewed driving videos to search for information while their eye movements were recorded. The first experiment compared analog and digital clusters, revealing that digital displays allowed faster visual searches and reduced cognitive effort. The second experiment analyzed digital clusters with various dash types and colors. The results indicate that the dual-dash digital display with a white background was the most effective in the cluster design. This research provides practical guidelines for the cluster design to increase driving engagement and decrease the cognitive workload on drivers. Therefore, the results will encourage automotive interface designers to consider ergonomic directions for gauge clusters from a user-centered perspective

Model-Driven Transformation of Digital Business Processes

Digitalization through technologies such as sensors, information, and communication technologies creates enormous volumes of data, promotes increased internal and external interactions, and opens new opportunities, particularly in maintenance. One of the major impediments to enterprise transformation is the complexity of business processes. This complexity, in turn, manifests itself through stochastic dynamics to degrade performance. Therefore, we propose a framework for business process transformation. The key model for this framework is based on the business process complexity model with a stochastic dynamic (BPCSD) using Markov chains, which estimates the expectation and variance of the processing steps and flow time. We also propose mapping key elements of the widely used Business Process Modeling Notation (BPMN) to BPCSD and validate these in a maintenance operation. In the case study, we present a method to integrate these models and derive options for business process transformation. We identify tacit processes from a communication log and find that including the tacit processes in the BPSCD model improves its predicted expectation and variance of flow times by 2% and 28%, respectively. We also show the application of the BPCSD as a tool for business process transformation. Using the BPCSD, we identify the efficient options that can reduce the flow time and the standard deviation by 20~ 29%. The business processes presented in the case study are pervasive, and the associated digital infrastructure is widely used in a variety of enterprises, which makes the study relevant to maintenance management in many digital enterprises

An Effective Hybrid Novel Genetic and Adaptive Artificial Bee Colony (NG-AABC) Metaheuristic Algorithm for Transforming Concurrent Scheduling Problems

In Industry 4.0, Automated Guided Vehicles (AGVs) enhance material handling efficiency and cost reduction. However, research on multi-objective scheduling of jobs, tools, automated storage, and AGVs in Flexible Manufacturing Systems (FMS) is limited. This study introduces the Novel Genetic and Adaptive Artificial Bee Colony Algorithm (NG-AABCA) to minimize the makespan, total tardiness, and penalty costs. NG-AABCA integrates cognitive (ε1) and social (ε2) learning factors, often overlooked, to achieve optimal solutions by leveraging external sources like the global optimal solution. This approach expedites convergence and avoids local optima by adjusting parameters iteratively. The Genetic Algorithm component employs elitism and Random-Restart Hill-Climbing to balance solution quality and diversity. Compared to other algorithms, NG-AABCA reduces makespan by 5.3% and tardiness by 8.7%, promising increased productivity and efficient resource use. This robust method aims to transform manufacturing optimization in Industry 4.0, addressing complex scheduling challenges in FMSs effectively

Managing The Online Channel by Coordinating A Third-Party Logistics and Service Provider Along with A Dual-Channel Retailer

This paper considers traditional and online stores under the context of a dual-channel retailing system. Fully refunded returns are permissible in both forms: same-channel and cross-channel. We examined three different coordination strategies that may form between the retailer and a third-party logistics and service provider. The provider was tasked to manage the online store’s orders through transaction-based fees, flat-based fees, and gain-sharing contracts. For each of those strategies, we found the online store’s optimal pricing policy and the seasonal fee, if applicable. The performance ratings of the partners under the different strategies were compared, and the managerial insights were provided using analytical as well as numerical analysis. It was found that the retailer is always more profitable under the flat-based fee strategy compared to the gain-sharing strategy, while the provider was almost always more profitable under the latter strategy. Moreover, a low rate of return encouraged the retailer to have more independence by implementing the transaction-based fee strategy, while a high rate pushed the retailer to have more logistical involvement and support through the implementation of either the flat-based fee or gain-sharing strategies

Adjusting The Factors Affecting The Internal Rate of Return on Investment in Production-Sharing Oil Contracts to Stabilize The Interests of The Investor

The financial system of production-sharing contracts has more complications in comparison with other oil contracts, especially production participation contracts. The financial system and the conditions governing the payment and repayment of production-sharing contracts can be modeled at various times during the implementation of these contracts, and by using this modeling, the profitability of the plan for the parties of the contract can be evaluated, and an optimal decision can be made based on the results of the modeling In the research literature, the economic analysis of oil production-sharing contracts in the middle stages of contract implementation has not been done or very little has been done from the perspective of the investor. In this paper, economic mathematical modeling of oil production-sharing contracts with the aim of maximizing the investor's internal rate of return is presented. In this model, the data is derived from a real contract that has been delayed for approximately 30 months, as well as payment and repayment forecasts for the next 56 months. To optimize the model, we have simulated monthly payments and repayments were generated by observing their minimum and maximum values in 2000 times and each time, the rate of return was calculated, and the optimal payment and repayment amount was determined. Results specify that if the contract has been delayed, the investor can improve the internal rate of return by managing the timing of payments

A Systematic Analysis of Supply Chain Risk Management Literature: 2012-2021

In order to secure supply chains (SCs), researchers and policy makers need to be abreast of developments in supply chain risk management (SCRM). This study selected frequently cited papers of WoS from the last 10 years, performing quantitative visualization analysis to establish the current trends within the field. Further attention was paid to those studies that focused on the impact of COVID-19. This study used a keyword timeline and clustering analysis map to establish the main research directions between 2012 to 2018, as well as the perspectives from which SC optimization was studied from 2018 to 2021. The key journals and research institutions for SCRM are established, as well as the key categories that the published literature falls under. Cluster analysis shows which areas in the published literature have the most references. Finally, the study establishes the direction of current trends within SCRM, as well as its understudied areas

Retail or Commissioned Live-Streaming? Mode Choice of A Platform Supply Chain Considering Consumers’ Preferences

''live-streaming + e-commerce'' mode emerged as time required. In this paper, we aim to investigate the manufacturer’s pricing and mode choice of cooperation with a live streamer in a dual-channel live-streaming supply chain consisting of a single manufacturer, a KOL(Key Opinion Leader) live streamer, and a live-streaming platform, considering different consumers’ preferences. We depict two scenarios for the KOL streamer, retail live-streaming and commissioned live-streaming modes, in the presence of a manufacturer's self-live-streaming and investigate the optimal mode choice with the Stackelberg game. The paper discovers that under the commissioned live-streaming mode, the price of KOL live-streaming is positively (negatively) correlated with the commission ratio (consumers' preferences for the manufacturer’s self-live-streaming) and lower than that under manufacturer self-live-streaming under a low commission ratio (a high consumers' preferences for manufacturer’s self-live-streaming). In both scenarios, the KOL live-streaming’s sales effort is consistently lower than that of the manufacturer's self-live-streaming channel. Additionally, the consumer's sensitivity coefficient, the trust degree, the impact of KOL streamers, and the proportion of impulsive consumers are positively correlated with both channels' price, sales effort, and profit