Framework for implementing big data analytics in Indian manufacturing: ISM-MICMAC and Fuzzy-AHP approach

摘要

Manufacturing firms generate a massive amount of data points because of higher than ever connected devices and sensor technology adoption. These data points could be from varied sources, ranging from flow time and cycle time through different machines in an assembly line to shop floor data collected from sensors viz. temperature, stress capability, pressure, etc. Analysis of this data can help manufacturers in many ways, viz. predict breakdown-reduction in downtime and waste, optimal inventory level-resource optimization, etc. The data may be highly voluminous, highly unstructured, coming from varied sources at a higher speed. Thus, big data analytics has become more critical than ever for the manufacturing industry to have the capability of effectively deriving business value from the vast amount of generated data. Manufacturing firms face hindrances and failures in the implementation of big data analytics. It is, therefore, necessary for the companies in the Indian manufacturing sector to identify and examine the reason and nature of barriers resisting the implementation of Big Data Analytics (BDA) to their organization. This paper explores the existing literature available to identify the barriers, categorized based on different functions of an organization. A total of 16 barriers are determined from the rigorous review of existing research. A survey is conducted on the industry experts from automobile, steel, automotive parts manufacturer, and electrical equipment industries to obtain a contextual relationship between the barriers. Interpretive Structural Modeling and MICMAC (Cross-impact matrix multiplication applied to classification) are the analytical techniques used in this research to classify the barriers into different impact levels and importance. Independent factors (barriers) have high driving power and are the key factors that were further analyzed using Fuzzy AHP to determine their comparative priority/importance. The result of this research shows that barriers related to Management and Infrastructure & Technology are the main hurdles in the implementation of big data analytics in the manufacturing industry. Six critical barriers (based on high driving power) are; lack of long-term vision, lack of commitment from top management, lack of infrastructure facility, lack of funding, lack of availability of specific data tools, and lack of training facility. Lack of commitment from top management is the most critical barrier. Research focuses on a comprehensive analysis of the barriers in implementing big data analytics (BDA) in manufacturing firms. The novelty lies in (a) finding an extensive list of barriers, (b) application domain and geography, and (c) the multi-criteria decision making technique used for finding the critical barriers to the implementation of big data analytics. The findings of this research will help industry leaders to formulate a better plan before the application of BDA in their organizations.