International Journal of Computer Applications (0975 – 8887) Volume 181 – No. 34, December 2018 12 Studying the Inter-Relationship amongst the Barriers to Implementation of Analytics in Manufacturing Supply Chains Bhoomica Aggarwal HCL Technologies Private Limited, Sector 126, Noida, India Remica Aggarwal School of Business, University of Petroleum & Energy Studies, Dehradun, India S. P. Singh Department of Management Studies, IIT, Delhi, India ABSTRACT With every economy becoming globalized , operations of global manufacturing and logistics teams are becoming complex and challenging . Delayed shipments, inefficient plants, inconsistent suppliers can stall and delay the shipments thereby increasing the company’s supply chain costs. Managing demand volatility and cost fluctuations in supply chain and making it visible globally are some of the challenges which supply chain managers are facing. As per Accenture report , only up to 17 % of the supply chain managers are comfortable implementing analytics to supply chain functions which means despite being a need for these supply chain managers and despite being the fact that analytics can serve as their problem solver , it cannot , and still has a long way to go to prove itself in this domain . The required foundation is still in its nascent stage . This research work thus focuses on studying and exploring the barriers to implementation of analytics or big data analytics to manufacturing supply chains . After exploring , it further study the interrelationship amongst them with the help of Interpretive Structural Modelling (ISM) methodology . Keywords Manufacturing ; supply chain operations ; supply chain analytics ; real time decision making 1. INTRODUCTION Supply chain usually comprise of an integrated system of organizations, people, activities, information and resources so as to deliver the semi-finished or finished product or service from supplier or manufacturer to customer 1 . With every economy becoming globalized and companies increasing their presence across countries, operations of global manufacturing and logistics teams are becoming complex and challenging. Delayed shipments, inefficient plants, inconsistent suppliers can stall and delay the shipments thereby increasing the company’s supply chain costs. Some of the major challenges that supply chain executives are facing today is to manage demand volatility and cost fluctuations in supply chain and to make the global supply chain and logistic processes visible in the global environment 2 . Thus, the inclination of present day industry towards using analytics cannot be ignored . Analytics over time has evolved from being just descriptive to an advanced level of predictive and prescriptive states leading to optimized proactive decision making. As per the report by Markets and Markets, the global supply chain analytics market is expected to grow from USD 2.5 billion in 2014 to USD 4.8 billion by 2019, at 14.6% CAGR 2 . Thus , there is a great scope of using big data analytics (BDA) by manufacturing companies for achieving business success in the global market. In addition, due to advances in information and communication technology (ICT) such as Web 2.0 and the internet of things (IoT), amount of data has also increased considerably [1-4]. Due to these advancements, there are many opportunities to develop BDA tools and apply big data techniques to manufacturing supply chains. Though the potential is huge, the widespread adoption of analytics have been curtailed by several barriers such as poor quality and unavailability of data, functional silos, unclear strategic fit, and rudimentary IT infrastructure to name a few 3 . The paper focuses on establishing the interrelationship amongst the various barriers to successful implementation of big data analytics to manufacturing supply chains using the Interpretive Structural Modelling methodology (ISM) . The paper is arranged as follows : Section 2 presents the literature review in two sections . Section 2.1 presents the literature review on analytics and its applications particularly in supply chain. Section 2.2 presents the literature review on recognition of barriers to implementation of analytics to supply chains. Section 3 presents the interpretive structural modelling methodology . A Mic-mac analysis is conducted and an ISM model is prepared in section 4 . Conclusions and future directions are presented in section 5 . 2. LITERATURE REVIEW 2.1 Literature review on conceptual analysis of big data analytics in manufacturing supply chains Big data analytics was conceptualize by internet corporations like Google, Yahoo, Amazon and Netflix. The consumer activity data was analyzed by these corporations in their decision-making processes [5-6]. Conceptual analysis of BDA and its use [7] has been performed by various authors and its applications has been explored in various fields such as IoT environment [7]; finance economics and health care [8],[4] ; telecommunications [10]; supply chain management [11], [12], [13] , [14] . Manufacturing supply chain related problems has been solved considering data quality and data availability issues [3] as well as forecasting techniques [15] . A conceptual framework has been developed by [14] to observe current trends in supply chain management using twitter . [16] investigated the potential scope of using big data to manage product lifecycles. [5] showed how big data predictive analytics helps to measure the sustainability of supply chains. [17] determined a relationship between sustainable supply chain management and big data predictive analytics. Research paper by [18] gives a detailed content analysis of big data related supply chain applications identified in Scopus . Around 35 articles to cover the last 5-10 years have been explored . These include researches highlighting the application of big data analytics in supply chain and logistics [2,3]; data reuse and data resell in