Proceedings of Conference on Technologies for Future Cities ISBN-978-93-5565-236-2 148 Applicability of Big Data Analytics in Sustainable Supply Chain Management: A Proposed Framework Prashant Jain Department of Mechanical Engineering, Pillai College of Engineering, Navi Mumbai, India pjain68@gmail.com Dhanraj P. Tambuskar Department of Mechanical Engineering, Pillai College of Engineering, Navi Mumbai, India dhanrajt@mes.ac.in Vaibhav S. Narwane Department of Mechanical Engineering, K.J Somaiya College of Engineering, Mumbai, India vsnarwane@somaiya.ed Abstract— The extensive use of digital devices in business operations continually generates voluminous and varied data sets that are called big data (BD). The BD has got great potential to turnaround a business by means of big data analytics (BDA). The aim of this study is to explore the factors affecting the applicability of BDA in sustainable supply chain management (SSCM). Through an extensive literature survey a total of ten factors have been identified as per PESTEL framework which covers political, economic, social, technological, environmental and legal categories of factors. The identified factors are environmental policy and regulations, sustainable performance, competitive advantage, managerial and leadership commitment, stakeholders involvement and capabilities, technology resources and readiness, technology integration, lean and green practices, improvement in environmental performance, compliance with state regulations. The BDA is the moderating factor between these ten factors and SSCM. A research model has been proposed with pertinent hypotheses development for structural equation modelling. The outcome of the suggested execution of the model is expected to help managers in overcoming their lacunae and setting priorities regarding investment in BDA technologies for competitive advantage. Keywords— Big data analytics, Sustainable supply chain management, PESTEL framework, Structural equation modelling I. INTRODUCTION Big data analytics (BDA) is made up of two parts – big data and data analytics [1]. The first part ‘big data’ (BD) refers to the massive amount of data that is generated in business operations due to the extensive use of sophisticated technologies such as barcodes, sensors, RFID and Internet of Things etc. [2]. The second part ‘data analytics’ refers to the use of advanced technologies for the analysis of these voluminous and complex data sets. These technologies include data mining, statistics, predictive and descriptive methods, artificial intelligence etc. [3]. BDA has a special role to play in the present era of highly dynamic and unstable business environment wherein the managers have realized the importance of data driven decision making rather than depending on the intuitions [4]. The rapidly growing manufacturing sector also calls for the adoption of BDA capabilities for supporting substantial business advantage and improved organizational performance [5]. When we talk about performance and competitiveness of a business, the term operations’ sustainability automatically comes in picture. BDA plays a crucial role in operations’ sustainability. Companies widely accept this as BDA contributes to the strategic planning of the organizations for improved organizational performance and enhanced sustainability of the supply chain [6]. This is in line with the United Nations’ agenda for sustainable development, which recommends the collection, processing, and dissemination of all the varieties of big data for effective policy making, monitoring, and progress evaluation [7]. Even though the BD is recognized to be a latest worldwide sensation and this new way of business offers new opportunities, there are unanticipated new challenges also for generating new business models and fine tuning the existing operations for creating space for business gains [8]. The advent of BDA has not spread to business operations in the same proportion. Many organizations are seen to be reluctant to the implementation of BDA due to psychological and organizational issues and lack of understanding of the its benefits [9]. Manufacturing sector of developing economies is facing challenges in the implementation of BDA systems in their operations [10]. The objective of this paper is to address this issue. It explores the possibilities of employing BDA for quality improvement of business decisions. The research objectives are articulated as follows: RO1: Identification of the PESTEL factors for adoption of BDA technologies for SSCM. RO2: To develop a conceptual framework for the above mentioned purpose. The article is organized in four sections. Section 2 elucidates the literature on BDA, SSCM, PESTEL analysis, and the research gaps. Section 3 gives an account of the various PESTEL factors that are crucial to the application of BDA in SSCM. Section 4 and 5 present the conceptual model and the methodology respectively. Section 6 discusses the conclusion of the study. II. LITERATURE REVIEW A. BDA in Manufacturing and Supply Chain Operations In the current times the manufacturing firms have to operate in increasingly complex and uncertain environment with the processes involving complicated operations and constraints [11]. In this scenario the BDA is receiving increasing attention as an enabler for improved performance of manufacturing processes. The widespread use of distributed control and state of the art ICT technologies has led to considerable evolution of the production processes [12]. In the same way BDA has become a significant enabler for SC operations too. SCM may be defined as planning, organizing and directing of all the functions involved in the transfer of raw material from supplier to the manufacturer, distribution of final product to the customer, and relationship management with all the stakeholders of supply chain [13]. Sharing of information in the SC has a positive effect on the performance of large as well as small scale organizations [14,15]. Obviously, BDA plays an important role in SC and operations management [16]. Application of BDA in the manufacturing supply chain has been increasing significantly both in the developed and developing countries. BDA