IDENTIFICATION OF MAJOR LEAN WASTE AND ITS CONTRIBUTING FACTORS USING THE FUZZY

IDENTIFICATION OF MAJOR LEAN WASTE AND ITS CONTRIBUTING FACTORS USING THE FUZZY ANALYTICAL HIERARCHY PROCESS P. Arunagiri1 and A. Gnanavelbabu2 Department of Industrial Engineering, Anna University, Chennai, India E-mail: arunn79@yahoo.com; agbabu@annauniv.edu Received September 2015, Accepted April 2016 No. 15-CSME-99, E.I.C. Accession Number 3864 ABSTRACT Lean refers to the reduction of non-value added activities in industries. It focuses on seven types of lean waste. The significant challenge is to identify and reduce the major lean waste. With this objective, a survey was conducted in an international exhibition in India using a questionnaire. The collected data were analyzed using Analytic Hierarchy Process (AHP) software template to check consistency. Finding consistent results obtained in AHP satisfactory, ranking was carried out to find the major lean waste using fuzzy AHP. After the identification of the major lean waste, the major contributing factors for the waste were ranked using the Binary Logistic Regression (BLR). These contributing factors were further investigated for the waste elimination in the automobile component manufacturing industries. Keywords: lean systems; analytic hierarchy process; fuzzy AHP; binary logistic regression. IDENTIFICATION DES PRINCIPALES SOURCES DE GASPILLAGE ET SES FACTEURS CONTRIBUTIFS UTILISANT LE PROCÉDÉ DE L’ANALYSE HIÉRARCHIQUE FLOUE RÉSUMÉ La production au plus juste (lean) réfère à la réduction des activités à non valeurs ajoutées dans les industries. Elle se concentre sur sept types de gaspillage. Le défiimportant est d’identifier et de réduire les principales sources de gaspillage. Avec cet objectif en tête, on a mené un sondage par questionnaire dans une exposition internationale en Inde. Les données colligées ont été analysées au moyen du logiciel de méthodologie du procédé d’analyse hiérarchique (PAH) pour vérifier la consistance. Les résultats obtenus étant conformes au procédé d’analyse hiérarchique, le classement a été réalisé dans le but de trouver les principales sources de gaspillage en utilisant le procédé de l’analyse hiérarchique floue. Suite à l’identification des principales sources de gaspillage, les facteurs contributifs principaux ont été classés en utilisant la régression logis- tique binaire. Ces facteurs ont été étudiés plus en profondeur pour éliminer le gaspillage dans les industries manufacturières de composants automobiles. Mots-clés : système d’élimination de gaspillage (lean); procédé d’analyse hiérarchique; procédé d’analyse hiérarchique floue; régression logistique binaire. Transactions of the Canadian Society for Mechanical Engineering, Vol. 40, No. 3, 2016 371 1. INTRODUCTION Lean production is a method for the elimination of waste occurring in the manufacturing process. Lean means creation of value addition for customers with limited resources. Lean organization understands the customer value and focuses its key process for continuous increase in value. Lean transformation is used to characterize a company mainly from traditional thinking to lean thinking. Lean refers to the process that eliminates non-value added activities. The ultimate goal is to provide perfect value to the customer to enable production process with zero waste. Khalil et al. [1] started to analyze the existing situation of waste elimination through Wastes Relations Matrix (WRM). Elimination of lean waste helps reduction of manual effort, minimizes space requirements, reduces capital and production time and eventually proves cost effective. Most of the previous researchers focus on lean tools rather than on lean waste. This has given rise to the need for finding the major lean waste out of seven types of waste.The primary objective of this work is to identify the major lean waste in automobile component manufacturing industries using Fuzzy AHP. Saaty’s [2] theory states fuzzy AHP showing relatively adequate description compared to the traditional AHP methods, wherein fuzziness and vagueness existing in many decision-making problems contribute to imprecise judgments of decision makers as stated in [3]. The secondary objective of this work is to determine the major contributing factors for the major lean waste using BLR. Basu et al. [4] used a constrained form of BLR for diagnosis of such problems. These two objectives are investigated to identify and suggest the major lean waste using Fuzzy AHP and also to determine the major contributing factors for the lean waste using BLR. 2. LITERATURE REVIEW Lean manufacturing is a management philosophy parented by Toyota Motor Company. The main principles of lean manufacturing have been derived from the Toyota Production System (TPS). The aim of lean pro- duction is to reduce the seven cardinal lean wastes. Ahmed and Hoda [5] have carried out a case study in which bottlenecks were identified. The Lean Kaizen technique was used to remove the bottlenecks through reduction in cycle time, increasing productivity and eliminating lean waste. Koukoulaki [6] has stated that lean production can have combined effects based on the management style of the firms. Fullerton et al. [7] state that lean production is conceptually multifaceted, with its philosophical characteristics that are often difficult to measure directly. Yanga et al. [8] state waiting as the most common non-value adding activity. Demeter and Matyusz [9] have concentrated on improvement of inventory turnover performance through the use of lean practices. Firms making extensive application of lean practices had higher inventory turnover. Arunagiri and Gnanavelbabu [10] focus mainly on the various process methodologies that could reduce delay in the manufacturing process leading to productivity improvement. Powell et al. [11] have focussed on production section and created a state map for the future to suggest ways to reduce lead-time and increase productivity. Hofer et al. [12] stated that the effect of lean production on financial perfor- mance is partial mediation through inventory leanness. There is strong evidence for lean practices yielding larger performance benefits. Ringena et al. [13] demonstrated the Value Stream Mapping (VSM) technique and discussed the application of lean systems initiative on a product as VSM is involved in all the process steps. This visual tool helps identification of the hidden waste and sources of waste. Natasya et al. [14] state that the conceptual model for leanness measurement had been developed and designed at two levels namely, the dimensions and the factors. The model also indicates the relationship between lean dimensions in the manufacturing systems and eight types of waste. Krishnan and Parveen [15] studied and compared the various lean tools used and adopted in the manufacturing and the service sectors. Bruun and Mefford [16] provided a background in lean manufacturing and presented an overview of manufacturing wastes. The introduction of lean tools and techniques is useful in transforming a company into a high performing lean enterprise. Arunagiri and Gnanavelbabu [17] dealt with the identification of major lean production waste in 372 Transactions of the Canadian Society for Mechanical Engineering, Vol. 40, No. 3, 2016 Table 1. Seven types of waste and its examples. Type of waste Definition Examples Overproduction (OPN) Parts are manufactured without any new order or demand from cus- tomer [18]. OPN leads to excessive work in process stocks [19]. Large batch size, unstable schedule, unbalanced cells, inaccurate infor- mation on demand. Defects (DES) Production with incorrect specifica- tions, physical defects leading to in- crease in cost [18]. Inadequate training, skill shortage, operator error, excessive stock. Inventory (INY) Storage of products with no orders on hand [18]. Excess inventory, large batch size, long change over time. Transportation (TPN) Movement of materials that do not add any value to the product [18]. Poor layout, large batch size, multi- ple storage locations. Waiting (WTG) Idle time for machines or workers due to bottlenecks or ill planned production flow [18]. Long changeover, unreliable pro- cess, time required to perform re- work. Motion (MON) Unnecessary motions of workers, which divert them from actual pro- cessing work [18]. Motion in- volves poor ergonomics of produc- tion [20]. Poor layout, poor method design, large batch size, poor workplace or- ganization. Overprocessing (OPG) Unintentionally conduct of more processing work than warranted by customer requirement [18]. No standardization of ideal tech- niques, unclear specification or quality acceptance standards. automobile industries, using the weighted average method. Most of the previous researchers focus on lean kaizen, VSM, lean tools, effects of lean, inventory leanness and delay analysis. There is no study available in the prioritization of lean waste using the Multi criteria decision making process. The major lean waste is determined for filling this gap using Fuzzy AHP. The major contributing factors are identified using BLR. 3. MAJOR LEAN WASTE AND EXAMPLES The seven types of lean waste have been identified as part of the Toyota production System. The list has since been modified and expanded with the addition of various practices of lean system. Each lean waste is defined with the relevant examples as stated in Table 1. 4. METHODOLOGY The step by step methodology followed for identification of the major waste and its contributing factors are shown in Fig. 1. The tools used for this research are AHP, Fuzzy AHP and BLR. 4.1. Conventional AHP AHP is one of the methods for ranking alternatives and selecting the best one when the decision maker has multiple options. In AHP, preferences between alternatives are determined through making pairwise com- parisons as stated in [21]. The crux of AHP is the determination of the relative weights to rank the decision alternatives. Assuming n criteria at a given hierarchy, the procedure uploads/Industriel/ 2016-csme-at-identification-of-major-leanwaste-and-its-contributing-factors.pdf

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