Blood supply chain network design: a systematic review of literature and implications for future research

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-02-20 DOI:10.1108/jm2-05-2022-0132

G. Ağaç, Birdogan Baki, I. Ar

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引用次数: 1

Abstract

Purpose The purpose of this study is to systematically review the existing literature on the blood supply chain (BSC) from a network design perspective and highlight the research gaps in this area. Moreover, it also aims to pinpoint new research opportunities based on the recent innovative technologies for the BSC network design. Design/methodology/approach The study gives a comprehensive systematic review of the BSC network design studies until October 2021. This review was carried out in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA). In the literature review, a total of 87 studies were analyzed under six main categories as model structure, application model, solution approach, problem type, the parties of the supply chain and innovative technologies. Findings The results of the study present the researchers’ tendencies and preferences when designing their BSC network models. Research limitations/implications The study presents a guide for researchers and practitioners on BSC from the point of view of network design and encourages adopting innovative technologies in their BSC network designs. Originality/value The study provides a comprehensive systematic review of related studies from the BSC network design perspective and explores research gaps in the collection and distribution processes. Furthermore, it addresses innovative research opportunities by using innovative technologies in the area of BSC network design.

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血液供应链网络设计:对文献的系统回顾和对未来研究的启示

本研究的目的是从网络设计的角度系统回顾现有关于血液供应链(BSC)的文献，并突出该领域的研究空白。此外，它还旨在根据BSC网络设计的最新创新技术确定新的研究机会。该研究对平衡计分卡网络设计研究进行了全面系统的回顾，直至2021年10月。本综述按照系统评价和荟萃分析的首选报告项目(PRISMA)进行。在文献综述中，共对87项研究进行了模型结构、应用模型、解决方法、问题类型、供应链各方和创新技术6个主要类别的分析。研究结果显示了研究人员在设计平衡记分卡网络模型时的倾向和偏好。本研究从网络设计的角度为平衡记分卡的研究人员和实践者提供了指南，并鼓励他们在平衡记分卡网络设计中采用创新技术。本研究从平衡记分卡网络设计的角度对相关研究进行了全面系统的回顾，并探讨了收集和分配过程中的研究空白。此外，它通过在平衡计分卡网络设计领域使用创新技术来解决创新研究机会。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.