Big data analytics application for sustainable manufacturing operations: analysis of strategic factors

Abstract

In the present era of Industry 4.0, organizations are transforming from traditional production systems to digital production systems. This transformation is in terms of additional deployment of technologies that lead to digitization and integration of products and services, business processes and customers, etc. A high volume of unstructured data is being created across different processes due to digitization. The digitization captures the data that includes text, images, multimedia, etc., due to multiplicity of platforms, e.g., machine-to-machine communications, sensors networks, cyber-physical systems, and Internet of Things. Managing this huge data generated from different sources has become a challenging task. Big data analytics (BDA) may be helpful in managing this unstructured data for effective decision making and sustainable operations. Many organizations are struggling to integrate BDA with their manufacturing processes for sustainable operations. The application of BDA from a sustainability perspective is not extensively researched in the current literature. Therefore, firstly this study explores the contribution of BDA in sustainable manufacturing operations. It further identifies strategic factors for the successful application of BDA in manufacturing for sustainable operations. For a detailed analysis of strategic factors in manufacturing, a hybrid approach comprising the analytic hierarchy process, fuzzy TOPSIS and DEMATEL is used. Results revealed that development of contract agreement among all stakeholders, engagement of top management, capability to handle big data, availability of quality and reliable data, developing team of knowledgeable, and capable decision-makers have emerged as major strategic factors for the application of BDA in the manufacturing sector for sustainable operations. Major contribution of this study is in analyzing BDA benefits for manufacturing sector, identifying major strategic factors in implementation and categorization of these factors into cause and effect group. These findings may be used by managers as guidelines for successful implementation of BDA across different functions in their respective organization to achieve sustainable operations goal. The results of this study will also motivate industry professionals to integrate BDA with their manufacturing functions for effective decision making and sustainable operations.

Investigation of Big Data Analytics for Sustainable Smart City Development: An Emerging Country

“…They also identified the various challenges related to big data analytics. A similar application of big data analytics could be also seen in Kumar et al, (2021) and Bajic et al, (2021) in the manufacturing sector. In addition, Kusi-Sarpong et al, (2021) provide a framework for the risks that organizations could face while using big data analytics in sustainable supply chains, as well as strategies for overcoming them.…”

Technology-Driven Responsiveness in Times of COVID-19: A Fuzzy Delphi and Fuzzy AHP-Based Approach

“…The smart warehousing (V2) enabler ranks third amongst the list of enablers in the technology enablers of supply chain responsiveness in the food sector. The very distinction between the traditional and smart warehouse is that of efficiency with which the smart warehouse operates, unlike their counterparts which are inefficient due to manual handling of tasks (Kumar et al, 2021a(Kumar et al, , 2021b. Smart warehouse pride on minimal manual handling and integration of best practices resulting in maximum efficiency (Jabbar et al, 2018).…”

“…The findings of the study are in line with the extant literature. For example, authors have argued that the solutions from big data also increase the real-time end-to-end visibility in the supply chain, thereby enhancing its responsiveness (Kumar et al, 2021a(Kumar et al, , 2021b. Further, the analytics provided by big data drive time compression, which is the essential characteristic of a responsive supply chain to react efficiently in the wake of external environmental shocks (Kache & Seuring, 2017).…”

Analysis of barriers intensity for investment in big data analytics for sustainable manufacturing operations in post-COVID-19 pandemic era

“…Through this strategy, the organization can meet the overall development plans which include a reduction in resource utilization and pollution. This further promotes the organizations to save for future generations and elevates the energy usage while minimizing the cost at the same time (Roy and Singh, 2017; Kumar et al , 2021a, b).…”

“…In spite of the significant application of BDA for sustainable manufacturing operations, organizations hesitate in making investments in emerging technologies such as BDA (Kumar et al , 2019). Limited research has been done on identifying major challenges in making investments for BDA applications in the context of sustainable manufacturing (Kumar et al ., 2021a). There have been many studies that have investigated various issues of sustainable operations.…”