Purpose

The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.

Design/methodology/approach

The study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.

Findings

The study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.

Practical implications

This study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.

Originality/value

The novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.

中文翻译：

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后 COVID-19 大流行时代可持续制造运营大数据分析投资壁垒强度分析

目的

该研究提出了采用大数据分析 (BDA) 应对冠状病毒后疾病 (COVID-19) 流行病的可持续制造运营 (SMO) 的各种障碍。在这项研究中，通过广泛的文献回顾和专家的意见，确定了投资 BDA 实施的 17 个障碍。进行基于问卷的调查以收集专家的答复。在因子分析的帮助下，已识别的障碍分为三类。这些是组织障碍、数据管理障碍和人为障碍。对于障碍的量化，应用了图论矩阵方法（GTMA）。

设计/方法/方法

该研究提出了在 COVID-19 大流行后为 SMO 采用 BDA 的各种障碍。在这项研究中，通过广泛的文献回顾和专家的意见，确定了投资 BDA 实施的 17 个障碍。进行基于问卷的调查以收集专家的答复。在因子分析的帮助下，已识别的障碍分为三类。这些是组织障碍、数据管理障碍和人为障碍。对于障碍的量化，应用了 GTMA。

发现

该研究确定了投资 BDA 实施的障碍。它根据因子分析对障碍进行分类，并计算 SMO 投资 BDA 的每一类障碍的强度。据观察，组织障碍的强度最高，而人的障碍强度最小。

实际影响

这项研究可以帮助组织做出战略决策，投资 BDA 应用程序以实现可持续发展目标之一。组织应优先考虑他们的努力，以应对组织障碍类别下的障碍，然后是数据管理障碍和人为障碍。

原创性/价值

本文的新颖之处在于分析了印度制造组织背景下 SMO 投资 BDA 的障碍。研究结果将有助于专业人士和从业人员根据障碍的实际性质和强度制定政策。