IMPLEMENTATION OF LEAN PRODUCTION SYSTEM IN A MANUFACTURING ENVIRONMENT

Abstract

Organizations are facing stiff competitions domestically as well as globally due to the impact of liberalization and rapid development of technologies. To achieve a competitive advantage, managers attempt to transform their organization by implementing successful management philosophies proposed by Japanese and western management experts, such as Just in Time (JIT), Total Quality Management (TQM), Total Productive Maintenance (TPM), Six Sigma (SS), Lean Manufacturing Systems (LMS) etc. But the challenge is to make a decision of implementing a management based and people oriented philosophy and practice like Lean Manufacturing System (LMS) or a technically sophisticated system Flexible Manufacturing System (FMS) or Computer Integrated Manufacturing System (CIMS). Implementing such massive change management programs involves huge investment and creates a longstanding impact on various resources. Traditional techniques cannot be applied as they do not account for intangible factors for decision making, which necessitate the use of Multi Criteria Decision- Making models (MCDM). In this research work an attempt has been made to implement the Lean production System in manufacturing industries. In the process of implementation of LPS, the application of Multi Criteria Decision Making (MCDM) models, like Analytical Hierarchy Process (AHP), Analytical Network Process (ANP) and Artificial Neural Network, are used to analyze and select the alternative based on the impact of various factors contributing to the performance measures of the manufacturing process of the organization. The selection of a manufacturing method for developing new products with optimal quality, minimal cost in the shortest time possible is an important phase of the lean production system. Hence Artificial Neural Network (ANN), a computational model based on the structure and functions of biological neural networks are considered. Nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are