Leeway of Lean Concept to Optimize Big Data in Manufacturing Industry: An Exploratory Review

Implementation of lean concept is most recent trend in manufacturing industries. Enterprise resource planning (ERP) solutions such as SAP, Oracle, and BAAN IV are used to carry out day-to-day life activities for operational convenience. All these activities recorded and generated very big data, and the managers or strategic decision-makers heavily rely on them for decision-making. The challenging task here is tomanage such type of big data ofmanufacturing company using lean concept. Present study comprises two: lean principle and data optimization concept of manufacturing activities. Here, we focus to integrate lean principles for the optimization of big data for efficient and effective decision-making, and also attempt to summarize the experience gain from the study. Big data generally stands for datasets that may be recorded and analyzed computationally to generate trends and pattern. These data quantities stored are indeed required large space and have a valuable cost in present era of development. For manufacturing unit, the big data can help to improve the product quality and impart lucidity in the work practices, which are having an ability to untangle uncertainty such as inconsistence availability and performance of machines and assembly shop as a system. Desirable transparency and predictive manufacturing as application approach required large amount of data and advanced tool for prediction to use this big data as useful information. Lean term is applicable to minimize the waste generated from the big data collection. Application of lean principles for managing big data of manufacturing process is a kind of minimizing the GIGO (garbage in garbage out) to reduce the data cost and also reduce the time of data processing for the decision-making of the managers. Present study gives new approach to manage big data very accurately using lean principles.