Implementation of Lean Production System in A Manufacturing Environment

Abstract

Organizations are facing stiff competitions domestically as well as newlineglobally due to the impact of liberalization and rapid development of newlinetechnologies. To achieve a competitive advantage, managers attempt to newlinetransform their organization by implementing successful management newlinephilosophies proposed by Japanese and western management experts, such as newlineJust in Time (JIT), Total Quality Management (TQM), Total Productive newlineMaintenance (TPM), Six Sigma (SS), Lean Manufacturing Systems (LMS) newlineetc. But the challenge is to make a decision of implementing a management newlinebased and people oriented philosophy and practice like Lean Manufacturing newlineSystem (LMS) or a technically sophisticated system Flexible Manufacturing newlineSystem (FMS) or Computer Integrated Manufacturing System (CIMS). newlineImplementing such massive change management programs involves huge newlineinvestment and creates a longstanding impact on various resources. newlineTraditional techniques cannot be applied as they do not account for intangible newlinefactors for decision making, which necessitate the use of Multi Criteria newlineDecision- Making models (MCDM). In this research work an attempt has newlinebeen made to implement the Lean production System in manufacturing newlineindustries. In the process of implementation of LPS, the application of newlineMulti Criteria Decision Making (MCDM) models, like Analytical Hierarchy newlineProcess (AHP), Analytical Network Process (ANP) and Artificial Neural newlineNetwork, are used to analyze and select the alternative based on the impact of newlinevarious factors contributing to the performance measures of the newlinemanufacturing process of the organization. The selection of a manufacturing newlinemethod for developing new products with optimal quality, minimal cost in the newlineshortest time possible is an important phase of the lean production system. newlineHence Artificial Neural Network (ANN), a computational model based on the newlinestructure and functions of biological neural networks are considered. newlineNonlinear statistical data modeling tools where the complex relationships newlinebetween inputs and outputs are modeled which is used to facilitate for product newlinemanufacturing method selection. Initially, general sorting is employed to newlineselect an initial product platform. Then using repertory grids method, newlinedesigners contribute importance ratings to the design options. These ratings newlineare employed to reduce the number of the derived from design options, and newlinethereby used as input data to a neural network. The neural network is then newlinetrained by using Levenberg- Marquart Algorithm in Mat lab software. The newlinetrained neural network is applied to classify the set of options into different newlinepatterns. The classification results can subsequently served as base for the newlinescreening of preferred manufacturing options. newlineIn the process of implementation of lean production system, the factors newlinecontributing for the effective implementation of lean production system was newlinealso identified. The need for methods of quick improvement of quality, newlinestandardization of work, cost effective product development cycle, improved newlineproduction facilities, coordination with supplier, coordinating with supporting newlinesoftware including design, leanness, shop floor control processes and newlinesatisfaction of customer is becoming important. newlineIn order to understand the factors that affect the process of newlineimplementing lean production system and the effective management of these newlinefactors that affect the process of implementing lean production system which newlineis critical for successful implementation, a more detailed description of the newlinefactors that affect lean production implementation was identified. As the newlinefactors are likely to be intertwined, it is necessary to understand the dynamics newlineand intensity of their relationships as this can provide a broader understanding newlineon why companies are successful or not in implementing lean; hence this newlinework focuses towards the application of the mathematical model advanced newlinemultivariate factor analysis. After performing factor analysis the total number newlineof 25 factors in the study reduced to 2 significant factors from the remaining newline22 insignificant factors based on certain criterion. The 2 significant factors newlinetechno environment and ergonomics a work method factor, were considered newlinefor LPS implementation in other manufacturing industries which shows that newlinea significant increase in overall working condition of the industry which in newlineturn led to the increase in the productivity Ultimately it is suggested to newlineconsider these few critical factors for effective implementation. newline