reduction of operator's loading and unloading time using lean systems

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 10, October 2017, pp. 207–216, Article ID: IJMET_08_10_025 Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=10 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication

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REDUCTION OF OPERATOR’S LOADING AND UNLOADING TIME USING LEAN SYSTEMS FOR PRODUCTIVITY IMPROVEMENT A. Gnanavelbabu Department of Industrial Engineering, CEG Campus, Anna University, Chennai, Tamilnadu, India P. Arunagiri, G. Bharathiraja, V. Jayakumar and V. Velmurugan Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha University, Chennai, Tamilnadu, India ABSTRACT The objective of this paper is to implement the concepts of lean production in terms of waste elimination, in the matter of identification of waste time, factors leading to it and eventually reduce\eliminate the same to provide guidance to the industry. The research describes an approach to wait time waste of operator manual loading and unloading time measurement. It is uniformly applied and creates results that can be compared from one production line to another. The wait time waste elimination method is a common approach for gathering and combining data to support identification and elimination of time waste in the industrial lifecycle of a product unit. The lean production system is to determine an efficient production process through the removal of waste and to implement a flow. Wastes of various types are seen in the manufacturing area. One among them is the time taken for loading and unloading of work piece. Reduction in time waste will improve the each production line and in an overall improvement in the number of components produced per shift. Industrial data analysis has been carried out for calculating the current time taken and improving the production process by setting the optimal time to get the required output in terms of increase in the number of components produced per shift. Keywords: Waiting time, Lean production, Optimised time, Allocated time, Actual time. Cite this Article: A. Gnanavelbabu, P. Arunagiri, G. Bharathiraja, V. Jayakumar and V. Velmurugan, Reduction of Operator’s Loading and Unloading Time using Lean Systems for Productivity Improvement, International Journal of Mechanical Engineering and Technology 8(10), 2017, pp. 207–216. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=10

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Reduction of Operator’s Loading and Unloading Time using Lean Systems for Productivity Improvement

1. INTRODUCTION Lean systems are used in industries on the basis of the requirement of the relevant lean tools and techniques. Every firm uses specific lean tools to reduce the waste and increase the number of components produced over a period of time. Roberto Panizzolo (1998) has worked on lean production model by 27 excellent firms. Their study suggests that the implementation of lean production principles and the perspective analysis shifted from operations management to relationships management. Suggestions for future research have been indicated through the empirical findings. Tu et al (2006) stated that the review of absorptive capacity and develop the reliable instrument to measure the impact of organization. Fawaz Abdulmalek & Jayant Rajgopal (2007) have made a study o the impact of lean principles adopted in a large steel mill. The VSM tool was used to solve the problems in the firm. Moreover a new simulation model has been developed for lead time and inventory reduction. Richard Lindeke et al. (2009) state that the Temporal Think Tank (TTT) brings round all to work as a team to develop their decision making skills to incubate new process or products and after some time the individual are return to original position to implement the learned procedure. Tarcisio Abreu Saurin & Cleber Fabricio Ferreira (2009) has made an assessment of the impact of LPS (Lean Production System) on working conditions in a harvester assembly line of an American-owned plant in Brazil. They have grouped the data collected as work content, work organization, continuous improvement and health and safety. The working conditions were fairly good and had improved after the introduction of LPS. Krisztina Demeter & Zsolt Matyusz (2011) have focused on improvement of the inventory turnover performance using lean systems. Firms that widely apply lean systems have high inventory turnover than those that do not rely on lean systems. The data were analysed using International Manufacturing Strategy Survey (IMSS). Kuhlang et al. (2011) have suggested a methodical approach connecting the VSM and Method Time Measurement (MTM) and offers new advantages to reduce the lead time and increase productivity. Arnout Pool et al. (2011) affirm that the cyclic schedules fit in a lean improvement approach for the semi-process industry. The cyclic schedules help to improve production quality and supply-chain coordination and discrete event simulation. It is useful tool in facilitating a participative design of a cyclic schedule. Noor Azlina et al. (2012) found a company that had implemented green lean total quality information management have seen good financial outcomes in Malaysian automotive industries. Stewart Robinson et al. (2012) have used discrete event simulation and lean care approaches for the improvement of process and service delivery in health care systems. Richard turner & Ju Ann Lane (2013) list the specific applications of the concepts to support coordination of systems engineering activities with large scale system acquisition, development and evolution. Sharma Neha et al. (2013) have studied application of the lean systems leading to the continuous production process with a focus of the steel industry. Sukhwinder Singh Jolly (2013) have reviewed the role of lean manufacturing for various organization, lean manufacturing techniques and the benefits achieved through lean systems. Matt & Rauch (2013) have analyzed the suitability of existing lean methods in small size enterprises in Italy. Through a case study the difficulties in the implementation stage and how to eradicate the same were discussed. Keitany & Riwo Abudho (2014) have identified the impact of lean towards tools, quality, employee perceptions, and challenges. Findings suggest that lean can improve using latest technology, customer involvement, reduce resistance, challenges faced while adopting lean in Kenyan industries. Daryl Powell et al. (2014) focus on the production line of main leaf spring. Analysis of the current map with the experts and a future map is designed to propose a different way to reduce lead-time and to increase productivity/ output. Michael Dwianto Nirwan & Wawan Dhewanto (2015) support focus of lean startup methodology on agile testing and learning cycle for validating

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hypotheses. The various barriers for implementation of lean in Indonesian environment and the success rate of lean in United States environment has been discussed.

2. METHODOLOGY The purpose of the methodology is to achieve the objective of the work. It is a guideline to analyze wait time using the eight step Practical Problem Solving method. The step by step process as elaborated below was used to find out the operators manual wait time reduction. The 8 step problem-solving methodology was used for crank case cell in Greaves Cotton (P) Ltd, Ranipet, India. The step by step process involved in the eight step PPS methodology is listed below. xt into it.

A) Clarify the Problem In the current situation, the number of components produced per shift was focused for the entire sections crank case cell. The number of the components manufactured in ideal condition was determined. There was a wide gap between the allocated time and actual time.

B) Break down the Problem The process involved in the operators manual loading and unloading operation time was carefully studied. Duration of inactivity or time wasted on unnecessary efforts was identified.

C) Set the Target The current level of production was observed and production level considered ideal for the optimal time was set. The aim of the target setting was to increase the output from current level to targeted level.

D) Analyze the Root Cause Fish bone diagram was used to identify the various causes for the reduction of loading and unloading time during the production process as shown in Figure 1 below. Calculate the loading and unloading time for each operation.

Mapping the current production process.

Setting the optimised time for the loading and unloading .

Calculate the optimized time for each loading and unloading operation.

Reduction of loading and unloading time during production process environment.

Increase the number of components produced per shift .

Figure 1 Fish bone diagram to find loading and unloading time

E) Develop Countermeasures 1. The operator’s loading and unloading time for each process was tabulated. 2. The total operator allocated and actual time for each level has been calculated. 3. Graphs were plotted between the number of operations Vs Operators Allocated time, Actual time and optimized time of crank case cell. 4. The actual processing time per shift in crank case cell were calculated. 5. The optimized operators loading and unloading time per shift in crank case cell were calculated. http://www.iaeme.com/IJMET/index.asp

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F) Implementation of the Countermeasures The various activities carried out in various sections were mapped. The section mapping gave a clear idea of material movement from the first machine to the last machine till it was converted into the finished product. The implementation of the reduction in operators loading and unloading time was been carried out in the various sections. The results were monitored and increase in the number of components per shift was calculated.

3. PROCESS MAPPING OF PRODUCTION AREA FOR CRANK CASE CELL Mapping the production process in Greaves cotton (P) Ltd for different sections of the production system was done. The various areas in the production system are crank case cell was first taken up and mapping was done mapped based on the various operations carried out in this section and the movement of the material from machine 1 to machine 18 was carefully studied. The movement of material is shown in Figure 2 below.

Machine

1

Machine 2

Machine 3

Machine 4

Machine 5

Machine 6

Machine 7

Machine 8

Machine 9

Machine 10

Machine 11

Machine 12

Machine 13

Machine 14

Machine 15

Machine 16

Machine 17

Machine 18

Figure 2 Process mapping of crank case cell

A) Operators Loading and Unloading Time MUDA Analysis in the Crank Case Cell For each set of machines from machine 1 to machine 16, the total allocated time, and actual time were calculated and the optimized time was set to find the ideal output. The graphs were plotted for the allocated, actual and the optimized manual operators loading and unloading time for each operation as shown in the Figure 3 below.

Figure 3 Number of operation carried out Vs allocated time, actual time and optimized time for crank case cell

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B) Monitoring the Process and Results for Operator’s Allocated, Actual and Optimized Time in Crank Case Cell The time calculation has been carried out for allocated processing time per shift in crank cell. The actual and optimized processing time can be calculated accordingly. The allocated time for loading and unloading was 30 seconds. During the actual production, the time taken was very much less when compared to the allocated time. The operators loading and unloading the time taken was approximately 20 – 25 seconds. To achieve a optimum output, an optimized time of 20 seconds was set for loading and unloading and output parameters were calculated with reference to this time. The total time saved per shift was 39 minutes. The average time for producing one component was 3 minutes and the number of components produced in the optimized time was 161 per shift and 483 components per day. The current performance, modified performance and the time were noted after re-modification of the system calculated which indicated the improvement in the productivity. The simulated results using the Pro model software also indicates that there is improvement in the number of components after modifying the optimized operator manual loading and unloading time.

C) Simulation Carried out for Allocated Time Using Pro model Software in Crank Case Cell Single capacity locations are those that have the capacity for handling one job at a time. Multi-capacity locations are those that involve handling more than one job at a time. Here, in the case, taken up single capacity locations were the machines on the shop floor and multicapacity locations were the pallets, inspection deck, rack etc. The first set of machines include a Vertical Machine Centre (VMC)1-VMC 8, a total of 8 machines and the second set included VMC 9- VMC 16. The VMC was simulated as Lathe due to non-availability of options in the Pro model software for all the simulated results.

Figure 4 Location utilization for allocated time in the crank case cell

For the first set of machines, the location utilization was 100% from start to end of the schedule. For the second set, only 33% was utilized at the start of the operation. Starting hour 1, there was an increase in the utilization rate from 33% to 64%. This gradually decreased during hour 2 to 54% and in hour 3 to 52 %.The utilization remained constant at hour 3 and hour 4. At hour 5 there was an increase in utilization to 62% which was constant at hour 6 also. At the end of the operation at hour 7 the utilization decreased from 62 % to 56% which ended the operation schedule as shown in Figure 4 above.

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Figure 5 Simulated results for allocated time in the crank case cell

Figure 5 above indicates the various factors that were considered by the software during the simulation. First consideration of the entity states graph, indicated the current status of the jobs under process. In other words, it can also be called as work in process status. As mentioned earlier, the researcher has considered one type of job for crank case cell. Here, product 1 was completed by 23%, of effort. Similarly, the total output from the system was 104 from type 1. The average time in the system was 3.81 hours for one product and average was 0.88 hours for the specific operation. In multi-capacity location state the pallets for part storage were considered. Here 100% utilization was seen for the pallets and emptiness is nil.

Figure 6 Machine utilization for allocated time in the crank case cell

The machines processing the jobs were under the single capacity location. The first set of machines included VMC 1-VMC 8 utilized fully 100% during the 8-hour schedule. The second set of machines included VMC 9-VMC 16 which utilized only 55% during the operation and 45% of the machines remained idle as shown in Figure 6 above.

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D) Simulation Carried out for Actual Time Using Promodel Software in Crank Case Cell Here in the case, single capacity locations were the machines on the shop floor and multicapacity locations were the pallets, inspection deck, rack etc. The first set of machines included VMC 1-VMC 8, of 8 machines in all were there in the first set. The second set included VMC 9- VMC 16 as shown in Figure 7 below.

Figure 7 Location utilization for actual time in the crank case cell

For the first set of machines, the location utilization was 100% from start to end for the schedule. For the second set, at only 31% was utilized at the start of the operation. From hour 1 there was an increase in the utilization rate from 31% to 64%. This gradually decreased during hour 2 to 58% and in hour 3 to 56 %.The utilization remained constant at hour 3 and hour 4. At hour 5, there was an increase in utilization to 58% and the utilization for hour 6 was 64%. This remains constant at hour 7 also.

Figure 8 Simulated results for actual time in the crank case cell

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Figure 8 indicates the various factors that were considered by the software during the simulation. To start with, consideration of the entity states graph, indicates the current status of the jobs being processed. In other words, it can also be called as work in process status. As mentioned earlier, it was considered as one type of job for crank case cell. Presently Product 1 is completed by 23%, similarly, the total number of product from the system were 112 products for type 1. The average time in the system was 4.01 hours for one product and the average time of operation was 0.87 hours. In the multi-capacity location state, the pallets for part storage were considered. Here 100% utilization was seen for the pallets and emptiness is nil.

Figure 9 Machine utilization for actual time in the crank case cell

The machines processing the jobs come under the single capacity location. The first set of machines included VMC1-VMC 8 which was utilized 100% during 8-hour schedule as shown in Figure 9 above. The second set of machines included VMC 9-VMC 16 which was utilized only at 55 % during the operation. 45% of the machines remained idle. The Pro model software simulated results indicated an increase in the number of components. Both the calculated and simulated results show positive with the outputs

E) Standardize and Share Success Various steps were taken over a period of time for monitoring the operator manual loading and unloading time. Material movements were carefully monitored. Man and material movement considered unnecessary were avoided by setting the optimized time for the loading and unloading. The setting of the optimized time periodically increased the daily output. The output from the crank case cell showed a marginal change per shift.

4. CONCLUSIONS Today lean faces a major challenge for implementation in industries. But many industrial organizations have started adopting various techniques for ensuring productivity improvement and to stay in the market. Process mapping has been done for the crank case cell and individual process mapping has been done for all the above sections. The problem was solved using the 8 step PPS methodology. In crank case cell , the number of output per shift before optimization was 148 whereas the number of output per shift after optimization was 161 components per shift. The number of components increased from 444 to 483 components in crank case cell. The output increase in the crank case cell was found. The types of lean waste analyzed were waiting time reduction, motion, transportation and over processing. The lean tools used are concepts of Process mapping, 8 step problem-solving methodologies,

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elimination of waste, Set up time reduction. Here increase in the output in the industrial data analysis results after setting the optimized loading and unloading time increases the number of components per shift. The results indicate that there is impact of lean systems for the productivity improvement in automobile industries.

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