Measuring economic resilience of manufacturing organization leveraging integrated data envelopment analysis (DEA)-machine learning approach

IF 3 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Wassiuzzaman Khan, Md. Saiful Islam, Niamat Ullah Ibne Hossain, Steven Fazio
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引用次数: 0

Abstract

ABSTRACTThis study aims to assess the economic resilience of manufacturing firms through a combination of output-oriented data envelopment analysis (DEA) and machine learning techniques. The research draws on economic resilience factors identified in the literature and focuses on three categories: economic-related factors (financial flexibility, microeconomic market, macroeconomic stability), production-related factors (restoration of production, backup inventories, resource pooling/sharing), and management-related factors (diversification of activities, good governance (management), relocation). Using DEA, a mathematical approach, the study computes and analyzes the contributions of various components to economic resilience. The results of DEA normalization indicate that the highest weighted criteria are financial flexibility, good governance (management), and resource pooling (sharing). To gain a deeper understanding of the data structure, the K-means algorithm is employed for clustering and analysis. K-means clustering is a popular exploratory data analysis technique that aims to group samples into clusters of equal variances by minimizing inertia or the sum of squares within each cluster. The combination of these techniques with sensitivity analysis provides a novel analytical approach for policy formulation and decision-making. The findings have implications for practitioners and domain experts, offering valuable insights into enhancing economic resilience in the manufacturing sector.JEL CLASSIFICATION: PRISMAKEYWORDS: Economic resiliencedata envelopment analysis (DEA)machine learning approachdecision-making unit (DMU)K-means clustering Disclosure statementNo potential conflict of interest was reported by the author(s).
利用集成数据包络分析(DEA)-机器学习方法测量制造组织的经济弹性
摘要本研究旨在结合产出导向数据包络分析(DEA)和机器学习技术,评估制造业企业的经济弹性。该研究借鉴了文献中确定的经济弹性因素,并将重点放在三类:经济相关因素(金融灵活性、微观经济市场、宏观经济稳定性)、生产相关因素(生产恢复、备用库存、资源池/共享)和管理相关因素(活动多样化、良好治理(管理)、搬迁)。使用DEA,一种数学方法,该研究计算和分析了各种组成部分对经济弹性的贡献。DEA归一化的结果表明,权重最高的标准是财务灵活性、良好治理(管理)和资源池(共享)。为了更深入地理解数据结构,采用K-means算法进行聚类和分析。K-means聚类是一种流行的探索性数据分析技术,旨在通过最小化每个簇内的惯性或平方和,将样本分组到方差相等的簇中。这些技术与敏感性分析相结合,为政策制定和决策提供了一种新的分析方法。研究结果对从业人员和领域专家具有启示意义,为提高制造业的经济弹性提供了有价值的见解。JEL分类:prismakkey关键词:经济弹性数据包络分析(DEA)机器学习方法决策单元(DMU) k均值聚类披露声明作者未报告潜在利益冲突。
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来源期刊
CiteScore
8.50
自引率
33.30%
发文量
40
期刊介绍: International Journal of Management Science and Engineering Management (IJMSEM) is a peer-reviewed quarterly journal that provides an international forum for researchers and practitioners of management science and engineering management. The journal focuses on identifying problems in the field, and using innovative management theories and new management methods to provide solutions. IJMSEM is committed to providing a platform for researchers and practitioners of management science and engineering management to share experiences and communicate ideas. Articles published in IJMSEM contain fresh information and approaches. They provide key information that will contribute to new scientific inquiries and improve competency, efficiency, and productivity in the field. IJMSEM focuses on the following: 1. identifying Management Science problems in engineering; 2. using management theory and methods to solve above problems innovatively and effectively; 3. developing new management theory and method to the newly emerged management issues in engineering; IJMSEM prefers papers with practical background, clear problem description, understandable physical and mathematical model, physical model with practical significance and theoretical framework, operable algorithm and successful practical applications. IJMSEM also takes into account management papers of original contributions in one or several aspects of these elements.
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