Showing papers in "International Journal of Information Systems and Supply Chain Management in 2023"
TL;DR: In this article , the authors proposed a bi-objective approach to reduce both performance indicators by adopting the genetic algorithm, which can reduce the bullwhip effect highly if specific configurations are selected from the Pareto frontier.
Abstract: Downstream demand inference (DDI) emerged in the supply chain theory, allowing an upstream actor to infer the demand occurring at his formal downstream actor without need of information sharing. Literature showed that simultaneously minimizing the average inventory level and the bullwhip effect isn't possible. In this paper, the authors show that demand inference is not only possible between direct supply chain links, but also at any downstream level. The authors propose a bi-objective approach to reduce both performance indicators by adopting the genetic algorithm. Simulation results show that bullwhip effect can be reduced highly if specific configurations are selected from the Pareto frontier. Numerical results show that demand's time-series structure, lead-times, holding and shortage costs, don't affect the behaviour of the bullwhip effect indicator. Moreover, the sensitivity analysis show that the optimization approach is robust when faced to varied initializations. Finally, the authors conclude the paper with managerial implications in multi-level supply chains.
3 citations
TL;DR: In this paper , the authors proposed strategic partnerships with main suppliers (SPWMS), information sharing with supply chain partners (ISWSCP), internal supply chain integration (ISCI), and supply chain innovation (SCI) in new normal and SMEs performance.
Abstract: This study proposes strategic partnerships with main suppliers (SPWMS), strategic partnerships with target customers (SPWTC), information sharing with supply chain partners (ISWSCP), internal supply chain integration (ISCI), and supply chain innovation (SCI) in new normal and SMEs performance. The result showed that: (1) SPWMS was a significant predictor of SMEs performance, (2) SCI in new normal was not a significant moderator of the relationship between SPWMS and SMEs performance, (3) SPWTC was a significant predictor of SMEs performance, (4) SCI in new normal did not significantly moderate the relationship between SPWTC and SMEs performance. (5) ISWSCP was a significant predictor of SMEs performance, (6) SCI in new normal was not a significant moderator of the relationship between ISWSCP and SMEs performance, (7) ISCI was the significant predictor of SMEs performance, (8) SCI in new normal was not a significant moderator of the relationship between ISCI and SMEs performance.
TL;DR: In this article , the authors identified the risk factors in smart supply chains based on an extensive literature review and interviews with professionals, and proposed a new framework for assessing risks using a quantitative approach.
Abstract: This research provides a framework for assessing risks in smart supply chains using a quantitative approach. This study identifies the risk factors in smart supply chains based on an extensive literature review and interviews with professionals. By analyzing different concepts of the previous frameworks, a new one is proposed for the smart supply chain. This new framework is applied to the data collected from a survey of Canadian supply chain professionals (n = 56). The authors conducted an exploratory factor analysis to examine the construct validity of the survey results. After evaluating and assessing risks for different smart supply chain risk factors, some constructs were developed. The survey's results point to the most important risk factors for the smart supply chain, prioritized based on their high probabilities and impacts. These include risk of complexity, web application failure, talent shortage, and high-cost risk. The results also show that the most commonly implemented smart technologies in the supply chain sector are bar codes and social media.
TL;DR: In this paper , the authors proposed an option-based hedging mechanism for airlines in a parallel alliance to transfer bumped passengers to their alliance partner's flight to maximize alliance-wide revenue.
Abstract: The extant literature proposes an option-based hedging mechanism for airlines in a parallel alliance to transfer bumped passengers to their alliance partner's flight. This paper extends this literature by conducting strategic analyses and developing a two-stage simulation-based algorithm to identify the best strategy for applying the hedging mechanism. Specifically, the best strategy refers to the best number of options for the allied carriers to transact. The authors show that there exists a robust result of the best number of options, and it is obtained under the objective of maximizing alliance-wide revenue. The result of this paper can provide direct guidance to the management of airlines on the best practice of hedging ex-post overbooking risks and matching supply with demand.
TL;DR: In this paper , the authors report work-life balance pre-and during the COVID-19 pandemic by bibliometric analysis, and show that COVID19 significantly impacted work life balance and related research.
Abstract: Work-life balance helps to maintain an attractive organizational culture and remove work-life conflicts and show the path to employees of how to be more efficient in different work roles. This balanced practice is giving a care and feeling of protection to the employees. It motivates better performance that contributes to employee engagement indices. The main purpose of this study is to report work-life balance pre- and during the COVID-19 pandemic by bibliometric analysis. This study analyzed 4,030 “work-life balance” studies published between January 1, 2010 and December 31, 2019, from the pre-pandemic era, and 1,143 studies published during the pandemic (between January 1, 2020-March 24, 2021). The data were extracted from the Scopus database using keywords “work-life balance” and keywords in titles (items) analyzed using VOSviewer software. Co-occurrence connection between keywords in titles and density visualization based on the total link strength clearly shows that COVID-19 significantly impacted work-life balance and related research.
TL;DR: Zhang et al. as mentioned in this paper proposed an integrated deep reinforcement learning-based logistics management model (DELLMM) to increase and optimize the logistic distribution, which can be used in inventors and price control applications.
Abstract: Resource balance is one of the most critical concerns in the existing logistic domain within dynamic transport networks. Modern solutions are used to maximize demand and supply prediction in collaboration with these problems. However, the great difficulty of transportation networks, profound uncertainties of potential demand and availability, and non-convex market limits make conventional resource management main paths. Hence, this paper proposes an integrated deep reinforcement learning-based logistics management model (DELLMM) to increase and optimize the logistic distribution. An optimization approach can be used in inventors and price control applications. This research methodology gives the fundamentals of information retrieval and the scope of blockchain integration. The conceptual framework of use cases for an efficient logistic management system with blockchain has been discussed. This research designs the deep reinforcement learning system that can boost optimization and other business operations due to impressive improvements in generic self-learning algorithms for optimal management. Thus, the experimental results show that DELLMM improves logistics management and optimized distribution compared to other methods with the highest operability of 94.35%, latency reduction of 97.12%, efficiency of 98.01%, trust enhancement of 96.37%, and sustainability of 97.80%.
TL;DR: In this paper , the authors proposed an intelligent time scheduling management model (ITSMM) based on cloud computing and a web-based platform for cold-chain logistics and distribution systems, which establishes a time scheduling model to reduce the overall order operation cost, diminish the variance among the expected and actual time of finalizing the service orders, and improve useful logistics service providers' satisfaction.
Abstract: The cold chain maintains and transports fresh food in the correct temperature range for slow biological decay processes and delivers safe, high-quality food to customers. Ensuring that quality and efficiency are not affected by the supply chain of cold chain products is a goal. Therefore, this paper proposes the intelligent time scheduling management model (ITSMM) based on cloud computing and a web-based platform for cold-chain logistics and distribution systems. This paper establishes a time scheduling model to reduce the overall order operation cost, diminish the variance among the expected and actual time of finalizing the service orders, and improve useful logistics service providers' satisfaction. Data, including all cold chain phases (distributors, industry, consumers, and retailers), have been gathered. This paper examines the distribution cost and time refrigerated vehicles, thus instituting a cold chain distribution vehicle path optimization.