IJSTE - International Journal of Science Technology & Engineering Volume 3 Issue 01 July 2016 ISSN (online): 2349-784X Methodology of Evaluating the overall Equipment Effectiveness in Machine Shop Manufacturing Industry through Total Productive Maintenance Meghraj Scholar Department of Mechanical Engineering Chhatrapati Shivaji Institute of Technology, Durg, Chhattisgarh, India Dr. Sridhar K Professor Department of Mechanical Engineering Chhatrapati Shivaji Institute of Technology, Durg, Chhattisgarh, India Abstract To implement the TPM tools and techniques a particular unit of machine shop in the Manufacturing Industry is considered in order to evaluate the contributions of total productive maintenance (TPM) initiatives to improve manufacturing performance in machine shop. Due to frequent failures and breakdowns occurred in machine shop, it requires more effort on maintenance due to which increased in maintenance cost and this results into different losses at work place which cause low availability, poor performance and low quality of the output ultimately cause low overall equipment effectiveness (OEE) of the machine. The correlations between various TPM implementation dimensions and manufacturing performance improvements have been evaluated and validated by employing OEE in machine shop. The purpose of this paper is to evaluate the contributions of TPM initiatives through Work sampling to achieve the World class OEE which raise the Quality excellence. Hence in sampling study has been conducted to compute the downtime and utilization of the machines and to improve OEE. The study establishes that focused TPM implementation over a reasonable time period can strategically contribute towards utilization of significant manufacturing performance enhancements. Keywords: Total Productive Maintenance, Overall Equipment Effectiveness, Work Sampling and Utilization I. INTRO DUCTIO N Total Productive Maintenance play an important role due to it control over the various factors Overall equipment effectiveness such as performance, quality, and availability of the machine in the industries. In today s situation, the importance of the keeping good condition of equipment has its own importance since the condition and performance of the equipment have large role in the quality and availability of the products. Many developing countries have liberalized their economies by adopting the TPM and its pillars to remain in the competition but due to the complexity and rapid demand make the organization difficult to follow the TPM. If we focuses of the present status of Indian industries through the literature survey no one industries has OEE equal to the world class OEE, it just about average of 50%-70% of OEE which is comparatively low as world class OEE. Some of the industries trying to achieve the OEE to make the plant in good condition or it also take the place in the market as a leader or competitor. Continuous analysis of the plant by the help of TPM pillar can reduce the trouble up to some extent. II. CO NTRIBUTIO NS TOWARDS TPM PRACTICES IMPROVING MANUFACTURING PERFO RMANCE The advancement in technology had lead to the industrial revolution and higher level competition for survival. PDCA approach is one of the TPM tool which help to plan the activity within the organization [1]. The author (Abhishek Jain et al. [2]) explained that by adopting many improvement strategies to enhance their performance as TPM, Kaizen or Continuous improveme nt (CI), and pillar of TPM model, is a good concept for small continuous improvements. S.Nakajima (Author) has described about challenging limits for TPM, method for calculation of OEE & its comparison with world class manufacturing, possible areas of wastage of resources which may occur losses [3]. The objective of TPM is maximizing of equipment effectiveness by maximization of machine utilization and machine availability. As one of the pillars of TPM activities, Kaizen pursues efficie nt equipment, operator and material and energy utilization that is extremes of productivity and aims at achieving substantial effects. Kaizen activities try to thoroughly eliminate losses in which six major losses that were identified [4]. Frequent machine breakdowns, low plant availability, increased rejection are a great threat to increase operating cost and lower productivity. The company has to suffer due to lower availability of machines and performance rate also decreases as a result of breakdowns which may leads to possess extra cost during the production [5]. The author (Paropate and Sambhe [6]) explained that world-class manufacturing which has been recognized as one of the significant operation strategy to regain the production losses due to equipment inefficiency. Fishbone diagram is one of the seven basic Quality Control tools used for All rights reserved by www.ijste.org 231
identifying the root causes of problems. Fishbone diagrams have been constructed mostly based on the categories of man, machine, method and material. If root causes are eliminated, breakdowns of equipment would reduce which would reduce the downtime of machine and ultimately increase the OEE [7]. The sequencing of jobs plays an important role in saving time and raise the per unit production. The sequencing of jobs depends not only on the assembly line of the industry but also on the customers [8]. The author (Barelwala and Varotaria [9]) state that Total Productive Maintenance (TPM) has recognized as one o f the significant operation strategy to regain the production losses due to equipmen t inefficiency. Many organizations have implemented TPM to improve their equipment efficiency and to obtain the competitive advantage in the global market in terms of cost and quality. III. OVERALL EQ UIPMENT EFFECTIVENESS The OEE measure is central dimension for the formulation and execution of a TPM improvement strategy. In which TPM has the standards of 90 percent availability, 95 percent performance efficiency and with 99.9 percent rate of quality. An overall 85 percent benchmark OEE is considered as world-class performance for the organizations 10. Overall equipment effectiveness (OEE) methodology incorporates metrics from all equipment manufacturing states guidelines into a measurement system that helps manufacturing and operations teams improve equipment performance and, therefore, reducing equipment cost of ownership (COO) [11]. OEE = Availability X Performance Rate X Quality Rate IV. MO DEL FO R THE TPM IMPLEMENTATIO N Fig. 1: Proposed Model of TPM Implementation for the enhancement of the Machine Shop of manufacturing industry All rights reserved by www.ijste.org 232
The above proposed model as shown in Figure 1 is useful for the implementation of TPM pillars in the industry in which they have to follows certain result and analysis. Ultimately improve the result of OEE of the machine shop of manufacturing industry. A. Case Study Data collected and analyzed using quantitative methods. Data collected from various resources likes observation of machines, historical data recorded register, interview and discussion session. The findings and way forward will be discussed in the following section. Overall Equipment Effectiveness (OEE) is a total measure of performance that relates the availability of t he process to the performance and quality. Due to the continuous running or working at from large period machine requires planned maintenance or necessary maintenance which make the good health condition of the machine so that it can produce maximum as per machine capacity and reduced the unplanned breakdown time. To justifying the research, the results of OEE calculation of different rating factors were observed for Vertical boring machine, Horizontal boring machine, Gear cutting machine and Horizontal lathe machine as shown in the below sections. B. Work Sampling According to L.H.C. Tippett Work sampling is defined as: A technique in which a statistically number of instantaneous observations are taken, over a period of time, of a group of machines, processes or workers. Each observation recorded for a particular activity or delay is a measure of the percentage of time observed by the occurrence. Work sampling technique will obtain a maximum of information in a minimum of time, which is important, both from the standpoint of the more efficient utilization of the time-study man's time and the cost to the plant [12]. Work sampling is a technique of industrial engineering used to measure the productivity potential of men, machines or workplaces through random observations and also used t o calculate the standard time [13]. Procedure for Conducting a Work sampling Study: 1) Decide on the objective of the study. 2) Obtain the approval of the supervisor of the department in which work study is to be conducted. 3) Decide upon work and delay elements. 4) Decide upon the duration of the study. 5) Determine the desired accuracy of results. 6) Make a preliminary estimate of the percentage occurrence of the activity or delay to be measured. 7) Design the study 8) Make the observation according to the plan, analyze and summarize the data. 9) Check the accuracy of the data at the end of the study 10) Prepare the report. Table 1 Work sampling observations for Vertical boring machine Dept.-Machine Shop Machine name: M1045 Operation: Boring Date:21.09.2015 S. No. Time Observation Remarks 1 09.00 am W Setting 2 09.12 am W Setting 3 09.17 am W Setting 4 09.25 am I Idle 5 09.30 am W Setting 6 09.36 am W Inspecting the dimensions 7 09.40 am W Setting 8 09.45 am W Setting 9 09.50 am W Machining 10 09.55 am W Machining 11 09.58 am W Machining 12 10.03 am W Machining 13 10.08 am I Idle 14 10.15 am I Idle 15 10.19 am W Machining 16 10.23 am W Machining Date:23.09.2015 17 09.03 am W Machining 18 09.11 am W Machining 19 09.18 am W Machining 20 09.23 am I Went to supervisor room 21 09.28 am I Idle 22 09.34 am W Machining 23 09.39 am W Machining All rights reserved by www.ijste.org 233
24 09.44 am W Machining 25 09.50 am W Setting 26 09.56 am W Setting 27 10.04 am W Inspecting the dimensions 28 10.10 am W Inspecting the dimensions 29 10.15 am W Machining 30 10.19 am W Machining 31 10.24 am W Machining 32 10.29 am W Machining Date: 24.09.2015 33 09.00 am W Machining 34 09.06 am W Machining 35 09.10am W Machining 36 09.14 am W Setting 37 09.17 am W Machining 38 09.21am W Setting 39 09.26 am I Idle 40 09.31 am W Setting 41 09.36 am W Setting 42 09.39 am W Machining 43 09.44 am W Machining 44 09.58 am W Machining setup 45 10.03 am W Machining 46 10.09 am W Machining 47 10.15 am W Machining 48 10.18 am W Inspecting the dimensions Date: 26.09.2015 49 09.10 am W Setting 50 09.14 am I Went to supervisor room 51 09.18 am W Setting 52 09.23 am I Idle 53 09.28 am W Setting 54 09.35 am W Machining 55 09.41 am W Machining 56 09.48 am W Machining 57 09.55 am W Machining 58 10.00 am W Machining 59 10.04 am W Machining 60 10.08 am W Inspecting the dimensions 61 10.13 am I Idle 62 10.18 am I Went to supervisor room 63 10.24 am I Idle 64 10.31 am W Setting Date:28.09.2015 65 09.01 am W Setting 66 09.06 am W Idle 67 09.11 am W Setting 68 09.16 am W Machining 69 09.22 am W Machining 70 09.26 am W Machining 71 09.31 am W Inspecting the dimensions 72 09.37 am W Machining 73 09.43 am W Machining 74 09.48 am W Machining 75 10.03 am W Setting 76 10.07 am W Machining 77 10.12 am W Machining 78 10.16 am W Machining 79 10.22 am W Machining 80 10.28 am W Machining To find no. of observations required we had taken 80 observation in the preliminary study. To get a confidence level of 95% and accuracy limit ± 10% the total observation required is completed from the preliminary study as follows: P=11/80 =0.1375 All rights reserved by www.ijste.org 234
Idle observations = 11 Working observations =69 To get no. of observations required we used the formula P(1 P) S P = n Where P = 11/80 = 0.1375 N = no. of observations required = 2500 Many operations or activities which are impractical or costly to measure by time study can be measured by Work sampling. It usually requires lesser man-hours and costs less to make a Work sampling instead of making a continuous time study. Work sampling is performed to measure the activities and delays of workers or machine as shown in Table 1. Utilization of Vertical Boring machine and the operator observed through work sampling as shown in Figure 2. Table - 2 Results through Work Sampling after Implementation of TPM (March 2015) Machine 1 2 3 4 Scheduled Run Time(hrs) 80 58 189.38 65 Unplanned Stoppage(hrs) 12.33 36.08 20.44 1 Actual Run Time(hrs) 67.67 21.92 168.94 64 Targeted no. of items 3 12 10 2 No. of item yield 2 11 7 1 Reject/Defect items 0 0 0 0 Items Allow 2 11 7 1 Availability 84.587 37.793 98.458 98.461 Performance Rate 66.667 91.667 70 50 Quality Rate 100 100 100 100 OEE 56.392 34.644 68.921 49.23 Machine 1: Vertical Boring Machine, Machine 2: Horizontal Boring Machine, Machine 3: Gear cutting Machine, Machine 4: Horizontal Lathe Machine Table - 3 Results through Work Sampling after Implementation of TPM (June 2015) Machine 1 2 3 4 Scheduled Run Time(hrs) 150 31 190 10 Unplanned Stoppage(hrs) 21 13.25 25.65 1 Actual Run Time(hrs) 129 17.75 164.35 9 Targeted no. of items 8 12 6 2 No. of item yield 6 6 4 2 Reject/Defect items 0 0 0 0 Items Allow 6 6 4 2 Availability 86 57.258 86.5 90 Performance Rate 75 50 66.667 100 Quality Rate 100 100 100 100 OEE 64.5 46.847 57.667 90 Fig. 2: Utilization of Vertical Boring machine and the operator observed through work sampling To evaluate effectiveness of TPM implementation steps, OEE value was calculated (as shown in table 2). It is observed that work sampling is giving results on par with continuous observation. TPM has been implemented in the firm in stages and All rights reserved by www.ijste.org 235
significant improvements have been observed. After implementing Statistical Process Control, significant improvement in the quality has been observed and these improvements shown in the Table 3. The pie diagram figure 2 shows significant observations of the Vertical Boring machine through work sampling study. V. CO NCLUSIONS A machine shop of manufacturing industry has been studied and analyzed to identify the TPM implementation issues and related factors to achieve goal from OEE as a result of TPM implementation. The required numbers of observations are collected to perform the Work sampling study to arrive the availability and utilization of the machines. To eliminate the machine idleness due to operator and to increase the productivity, work sampling technique can be used to identify the productive potential of men/machines/workplaces. It can be seen that OEE on machine shop has shown a progressive growth, which is an indication of increase in equipment availability, increase in rate of performance and quality rating. As a result overall productivity of industry also increased. OEE value is encouraging and with the passage of time results will be quite good and may reach a world class OEE value of 85%. Through TPM process focus, the cost and quality were improved significantly by reducing and minimizing equipment losses and failures. Thus, from the study of work sampling the overall effectiveness of equipment also improved significantly. REFERENCES [1] P. K. Suresh, TPM Implementation in a Food Industry-A PDCA Approach, International Journal of Scientific and Research Publicatio ns, Vo lum e 0 2, Issue 11, Pp. 1-9, November 2012, ISSN: 2250-3153. [2] Abhishek Jain, Harwinder Singh and Rajbir Bhatti, Performance Improvement of a medium size organization by using Continuous Improvement Strategy through OEE Enhancement: A Case Study, International Journal of Darshan Institute on Engineering Research & Emerging Technologies, Vo lum e 0 4, Issue No. 02, Pp. 45-57, 2015, ISSN: 2320-7590. [3] S. Nakajima, Introduction to Total Productive Maintenance, Productivity press, Cambridge, MA, 1988. [4] J. Venkatesh, An Introduction to Total Productive Maintenance, The Plant Maintenance Resource Centre, April 16, 2007, Available at: www.plantmaintenance.com/articles/tpm_intro.pdf. [5] Binoy Boban and Jenson Joseph E, Enhancing Overall Equipment Effectiveness for a Manufacturing Firm through To tal P roductiv e Mainten an ce, International Journal of Emerging Technology and Advanced Engineering, Volume 03, Issue No. 08, Pp.435-429, August 2013, ISSN: 2250-2459. [6] Ravikant V. Paropate and Dr. Rajeshkumar U. Sambhe, The Implementation and Evaluation of Total Productive Maintenance A Case Study of midsized Indian Enterprise, International Journal of Application or Innovation in Engineering & Management, Volume 02, Issue No. 10, Pp. 1 20-1 25, Oct ober 2013, ISSN 2319 4847. [7] Papari Das and Thuleswar Nath, Root Cause Analysis of the Major Equipment Breakdown Problems of the Tube Section of a FMCG Co m p any as an Approach to Improve OEE, International Journal of Engineering Trends and Technology, Volume 27, Issue No. 04, Pp.207-213, September 2015, ISSN: 2231-5381. [8] Prof Pradeep Kumar, Dr. K. V. M. Varambally and Dr. Lewlyn L.R. Rodrigues, A Methodology for Implementing Total Product ive M aintenance in Manufacturing Industries A Case Study, International Journal of Engineering Research and Development, Volume 05, Issue No. 02, Pp. 32-39, December 2012, ISSN: 2278-067X. [9] Prof. Chintan Barelwala and Jagdish Varotaria, Barriers and Solutions of TPM Implementation In PNG Distribution Company, In ternational Jo urnal o f Science, Engineering and Technology Research, Volume 03, Issue No. 07, Pp. 2048-2053, July 2014, ISSN: 2278 7798. [10] Albert H.C. Tsang & P.K. Chan, TPM implementation in China: a case study, International Journal of Quality & Reliability Management, Vo lum e 1 7, Issue No. 02, 2000. [11] Pradeep Kumar, Raviraj Shetty and Lewlyn L.R. Rodrigues, Overall Equipment Efficiency and Productiv-ity of a News Paper Printing Machine of a Daily News Paper Company - A Case Study, International Journal of Engineering Practical Research Volume 03 Issue No. 01, February 2014. [12] M. E. Gregory, W. M. Roberts, and J. Hader, Work-Sampling as a Technique in Determining Labor Utilization of Dairy Plants, Journal of Dairy Science, Pp. 1820-1822. [13] Suman Balany, Determination of Productive Potential of Bottleneck Machines through Work Sampling, International Journal of Innovative Research in Science, Engineering and Technology, Volume 05, Issue No. 02, February 2016, ISSN: 2347-6710. All rights reserved by www.ijste.org 236